CN118043690A - Apparatus and method for diagnosing battery cells - Google Patents

Abstract

A battery cell diagnosis apparatus according to an embodiment of the present invention includes: a current measurement unit configured to measure a current of a battery cell; a voltage sensing unit configured to sense a cell voltage of the battery cell; and a first control unit configured to transmit first information of the battery cell including data acquired from the current measurement unit and the voltage sensing unit to an external device, and to receive second information including diagnostic information of the battery cell acquired based on the first information from the external device, and to diagnose an abnormal state of the battery cell based on the first information and the second information.

Description

Device and method for diagnosing battery cells

Technical Field

This application claims priority to Korean Patent Application Nos. 10-2022-0065020, 10-2022-0065021, and 10-2022-0065022 filed in Korea on May 26, 2022, the disclosures of which are incorporated herein by reference.

The present disclosure relates to a battery cell diagnosis device and method, and more particularly to a battery cell diagnosis device and method for diagnosing the state of a battery cell.

Background Art

Recently, the demand for portable electronic products such as laptop computers, cameras, and mobile phones has increased dramatically, and with the vigorous development of electric vehicles, accumulators for energy storage, robots, and satellites, a lot of research is being conducted on high-performance batteries that can be repeatedly charged.

At present, commercially available batteries include nickel-cadmium batteries, nickel-metal hydride batteries, nickel-zinc batteries, lithium batteries, etc. Among them, lithium batteries have little or no memory effect. Therefore, compared with nickel-based batteries, lithium batteries have the advantages of free charge and discharge, extremely low self-discharge rate and high energy density, and are receiving more and more attention.

Recently, as applications requiring high voltages (e.g., electric vehicles, energy storage systems) have become popular, batteries used in electric vehicles or energy storage systems (ESS) may catch fire during use in some cases.

Therefore, there is an increasing need for a diagnostic technology that can accurately detect abnormalities in multiple battery cells connected in a battery pack.

Summary of the invention

Technical issues

The present disclosure is intended to solve the problems of the related art, and thus the present disclosure is intended to provide an apparatus and method for effectively diagnosing an abnormal state of a battery cell by linking an on-board diagnostic device and a non-on-board diagnostic device.

These and other objects and advantages of the present disclosure can be understood from the following detailed description and will become more apparent from the exemplary embodiments of the present disclosure. Moreover, it will be easily understood that the objects and advantages of the present disclosure can be achieved by the means shown in the appended claims and their combinations.

Technical Solution

A battery cell diagnostic device according to an embodiment of the present disclosure includes: the battery cell diagnostic device includes: a current measuring unit, which is configured to measure the current of the battery cell; a voltage sensing unit, which is configured to sense the cell voltage of the battery cell; and a first control unit, which is configured to send first information of the battery cell including data obtained from the current measuring unit and the voltage sensing unit to an external device, receive second information from the external device including diagnostic information of the battery cell obtained based on the first information, and diagnose an abnormal state of the battery cell based on the first information and the second information.

The diagnostic information of the battery cell may include at least one of lithium plating diagnosis of the battery cell, abnormal parallel connection of the battery cell, and internal short circuit of the battery cell.

The first control unit may be configured to display information about an abnormal state of the battery cell on a display unit based on the diagnosis information of the battery cell included in the second information.

The first control unit may be configured to: detect at least one of a voltage abnormality of the battery cell and a behavior abnormality of the battery cell based on the first information; and diagnose an abnormal state of the battery cell based on at least one of the voltage abnormality, the behavior abnormality and the second information.

The first control unit may be configured to generate third information indicating whether the battery cell is in the abnormal state based on at least one of the voltage abnormality, the behavior abnormality, and the second information.

The first control unit may be configured to display the third information on a display unit.

The first control unit may be configured to send the third information to a second control unit of a device equipped with the battery cell.

The first control unit can be configured to: generate time series data representing the history of the battery cell voltage included in the first information over time; determine a first average battery cell voltage and a second average battery cell voltage for each battery cell based on the time series data, the first average battery cell voltage being a short-term moving average and the second average battery cell voltage being a long-term moving average; and detect voltage abnormality of the battery cell based on the difference between the first average battery cell voltage and the second average battery cell voltage.

The battery cell diagnostic device may be configured to diagnose a plurality of battery cells.

The first control unit can be configured to: determine, for each battery cell among the multiple battery cells, a long-term and short-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage; determine an average value of the long-term and short-term average differences of the multiple battery cells; determine, for each battery cell among the multiple battery cells, a cell diagnostic deviation corresponding to a deviation between the average value of the long-term and short-term average differences and the long-term and short-term average differences; and detect a battery cell that satisfies the condition that the cell diagnostic deviation exceeds a diagnostic threshold as a cell with abnormal voltage.

In another aspect of the present disclosure, the battery cell diagnostic apparatus may be configured to diagnose a plurality of battery cells.

The first control unit can be configured to: determine, for each of the multiple battery cells, a long-term and short-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage; determine the average of the long-term and short-term average differences of the multiple battery cells; determine, for each of the multiple battery cells, a cell diagnostic deviation corresponding to the deviation between the average of the long-term and short-term average differences and the long-term and short-term average differences; determine a statistical variable threshold value that depends on the standard deviation of the cell diagnostic deviation of the multiple battery cells; filter the time series data based on the statistical variable threshold value to generate filtered time series data; and detect voltage abnormality of the battery cell based on the time or number of data when the filtered time series data exceeds the diagnostic threshold.

The first control unit may be configured to: determine, for each of the plurality of battery cells, a long-term and short-term average difference corresponding to the difference between the first average cell voltage and the second average cell voltage; determine a normalized value corresponding to the average of the long-term and short-term average differences of the plurality of battery cells; normalize, for each of the plurality of battery cells, the long-term and short-term average differences according to the normalized value; determine a statistical variable threshold value depending on a standard deviation of the normalized cell diagnostic deviations of the plurality of battery cells; filter, for each of the plurality of battery cells, the normalized long-term and short-term average differences of each battery cell based on the statistical variable threshold value to generate filtered time series data; and detect voltage abnormality of the battery cell based on the time or number of data at which the filtered time series data exceeds a diagnostic threshold.

The first control unit can be configured to: determine multiple sub-voltage curves by applying a moving window of a first time length to the time series of the battery cell voltage included in the first information; determine the long-term average voltage value of each sub-voltage curve using a first averaging filter of the first time length; determine the short-term average voltage value of each sub-voltage curve using a second averaging filter of a second time length shorter than the first time length; determine a voltage deviation corresponding to the difference between the long-term average voltage value and the short-term average voltage value of each sub-voltage curve; and compare each of the multiple voltage deviations determined for the multiple sub-voltage curves with at least one of a first threshold deviation and a second threshold deviation to detect abnormal behavior of the battery cell.

The first control unit may be configured to detect the behavioral abnormality corresponding to two voltage deviations among the plurality of voltage deviations that respectively satisfy a first condition, a second condition, and a third condition.

The first condition may be satisfied when a first voltage deviation of the two voltage deviations is equal to or greater than the first threshold deviation.

The second condition may be satisfied when a second voltage deviation of the two voltage deviations is equal to or smaller than the second threshold deviation.

The third condition may be satisfied when the time interval between the two voltage deviations is equal to or less than a threshold time.

The second information may indicate whether a cumulative capacity difference change amount is greater than or equal to a threshold, and the cumulative capacity difference change amount is a sum of capacity difference change amounts.

Each of the capacity difference changes may be a difference between the capacity difference of the battery cell in a kth charge and discharge cycle and the capacity difference of the battery cell in a k-1th charge and discharge cycle, and k may be a natural number greater than or equal to 2.

The capacity difference of each charge and discharge cycle may correspond to a difference between a charge capacity of the battery cell during a charge process of the charge and discharge cycle and a discharge capacity of the battery cell during a discharge process of the charge and discharge cycle.

Each of the charge capacity and the discharge capacity may be derivable from data acquired from the current measurement unit and included in the first information.

The second information may represent a capacity difference variation between consecutive charge and discharge cycles of the battery cell.

The capacity difference of each charge and discharge cycle of the battery cell may be a difference between (i) a charge capacity of the battery cell during the charge and discharge cycle of the battery cell and (ii) a discharge capacity of the battery cell during the discharge cycle of the battery cell.

The second information may indicate whether parallel connection of a plurality of unit cells included in the battery cell is abnormal based on a result of monitoring, by the external device, a change in the estimated capacity value over time.

The estimated capacity value may represent a full charge capacity of the battery cell based on charge and discharge data.

The charge and discharge data may include a voltage time series indicating that the voltage of the battery cell varies with time and a current time series indicating that the charge and discharge current of the battery cell varies with time.

The second information may indicate whether the battery cell has an internal short circuit based on a first SOC change of the battery cell and a standard factor.

The standard factor may be determined by applying a statistical algorithm to the first SOC variation of at least two battery cells of a plurality of battery cells.

The first SOC change may be a difference between a first SOC at a first charging time point and a second SOC at a second charging time point of each battery cell.

The first SOC may be estimated by applying a SOC estimation algorithm to the state parameters of the battery cell at the first charging time point.

The second SOC may be estimated by applying the SOC estimation algorithm to the state parameters of the battery cell at the second charging time point.

The state parameter may be acquired based on the first information.

In another aspect of the present disclosure, a battery cell diagnosis system is further provided, which includes the battery cell diagnosis device according to one aspect of the present disclosure.

The external device may be configured to derive the second information based on at least a portion of the first information.

In another aspect of the present disclosure, a battery cell diagnosis method is provided, comprising: acquiring, with the aid of a control unit, data including at least one of a charging current and a discharging current of a battery cell and a cell voltage of the battery cell; sending, with the aid of the control unit, first information including the acquired data of the battery cell to an external device; receiving, with the aid of the control unit, second information from the external device, the second information including diagnostic information of the battery cell acquired based on the first information; and diagnosing, with the aid of the control unit, an abnormal state of the battery cell based on the first information and the second information.

In another aspect of the present disclosure, the battery cell diagnosis method may further include: detecting at least one of voltage abnormality and behavior abnormality of the battery cell with the help of the control unit based on the first information of the battery cell; and diagnosing the abnormal state of the battery cell based on the voltage abnormality of the battery cell, the behavior abnormality of the battery cell and at least one of the second information with the help of the control unit.

Beneficial Effects

According to at least one embodiment of the present disclosure, by linking an on-board device and an off-board device, an abnormal state of a battery cell may be effectively diagnosed.

According to at least one embodiment of the present disclosure, software resources and time required to diagnose abnormalities in each battery cell can be saved by linking an on-board device and an off-board device, and the possibility of misdiagnosis due to an increase in the number of abnormal battery cells in multiple battery cells can be reduced.

According to at least one embodiment of the present disclosure, both the long-term trend and the short-term trend of the cell voltage of each battery cell may be considered, so that an abnormal change of the corresponding battery cell may be accurately detected.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate preferred embodiments of the present disclosure and, together with the foregoing disclosure, are used to provide a further understanding of the technical features of the present disclosure, and thus the present disclosure is not to be construed as being limited to the accompanying drawings.

FIG. 1 is an exemplary diagram illustrating a system including a battery cell diagnosis apparatus according to an embodiment of the present disclosure.

FIG. 2 is a block diagram schematically showing a functional configuration of a battery cell diagnostic apparatus according to an embodiment of the present disclosure.

FIG. 3 is an exemplary diagram conceptually illustrating a configuration of an electric vehicle according to an embodiment of the present disclosure.

4 a to 4 h are graphs exemplarily illustrating a process of detecting voltage abnormality of each battery cell based on time series data indicating a cell voltage variation of each of the plurality of battery cells shown in FIG. 3 .

FIG. 5 is a graph exemplarily showing a voltage curve of a battery cell mentioned in various embodiments of the present disclosure corresponding to an original time series of an actual voltage value of the cell voltage.

FIG. 6 is a graph exemplarily showing a measured voltage curve obtained by integrating measurement noise with an original time series corresponding to the voltage curve of FIG. 5 .

FIG. 7 is a graph exemplarily showing a first moving average curve obtained by applying a first averaging filter to the voltage curve of FIG. 6 .

FIG. 8 is a graph exemplarily showing a second moving average curve obtained by applying a second averaging filter to the voltage curve of FIG. 6 .

FIG. 9 is a graph exemplarily showing a voltage deviation curve as a difference between the first moving average curve of FIG. 7 and the second moving average curve of FIG. 8 .

FIG. 10 is a block diagram illustrating a schematic configuration of an external device according to an embodiment of the present disclosure.

FIG. 11 is a diagram exemplarily showing a schematic configuration of a battery cell mentioned in various embodiments of the present disclosure.

FIG. 12 is a diagram for illustrating a first capacity abnormality (incomplete disconnection fault) of a battery cell mentioned in various embodiments of the present disclosure.

FIG. 13 is a diagram for illustrating a second capacity abnormality (complete disconnection fault) of a battery cell mentioned in various embodiments of the present disclosure.

FIG. 14 is an exemplary diagram for illustrating a relationship between a capacity abnormality and a full charge capacity of a battery cell mentioned in various embodiments of the present disclosure.

FIG. 15 is a reference diagram for illustrating an exemplary equivalent circuit of a battery cell mentioned in various embodiments of the present disclosure.

FIG. 16 is an exemplary graph for comparing SOC changes of battery cells according to the presence or absence of an internal short circuit abnormality mentioned in various embodiments of the present disclosure.

FIG. 17 is another exemplary graph for comparing SOC changes of battery cells according to the presence or absence of the internal short circuit abnormality mentioned in various embodiments of the present disclosure.

FIG. 18 is yet another exemplary graph for comparing SOC changes of battery cells according to the presence or absence of the internal short circuit abnormality mentioned in various embodiments of the present disclosure.

FIG. 19 is a flowchart of the battery cell diagnostic apparatus diagnosing an abnormal state of a battery cell using an external device according to an embodiment of the present disclosure.

FIG. 20 is a flow chart exemplarily illustrating a voltage anomaly detection method according to an embodiment of the present disclosure.

FIG. 21 is another flowchart exemplarily illustrating a voltage abnormality detection method according to an embodiment of the present disclosure.

FIG. 22 is another flowchart exemplarily illustrating a voltage abnormality detection method according to an embodiment of the present disclosure.

FIG. 23 is another flowchart exemplarily illustrating a voltage abnormality detection method according to an embodiment of the present disclosure.

FIG. 24 is another flowchart exemplarily illustrating a voltage abnormality detection method according to an embodiment of the present disclosure.

FIG. 25 is a flow chart illustrating a method for detecting abnormal behavior according to an embodiment of the present disclosure.

FIG. 26 is another flowchart schematically illustrating a behavior anomaly detection method according to an embodiment of the present disclosure.

FIG. 27 is a flow chart exemplarily illustrating a method for detecting lithium plating anomalies according to an embodiment of the present disclosure.

FIG. 28 is another flow chart exemplarily illustrating a method for detecting lithium plating anomalies according to an embodiment of the present disclosure.

FIG. 29 is another flowchart exemplarily illustrating a method for detecting lithium plating anomalies according to an embodiment of the present disclosure.

FIG. 30 is another flowchart exemplarily illustrating a method for detecting lithium plating anomalies according to an embodiment of the present disclosure.

FIG. 31 is a graph showing changes in data measured in an experimental example to which the method for an external device to detect whether lithium plating occurs according to an embodiment of the present disclosure is applied.

FIG. 32 is a graph showing changes in data measured in another experimental example in which the lithium plating abnormality detection method according to an embodiment of the present disclosure is applied.

33 is a flowchart of the battery cell diagnostic apparatus diagnosing an abnormal state of a battery cell using an external device according to an embodiment of the present disclosure.

FIG. 34 is a flowchart exemplarily illustrating a battery diagnosis method according to an embodiment of the present disclosure.

FIG. 35 is a flowchart of the battery cell diagnostic apparatus diagnosing an abnormal state of a battery cell using an external device according to an embodiment of the present disclosure.

FIG. 36 is a flow chart exemplarily illustrating a battery management method according to an embodiment of the present disclosure.

FIG. 37 is another flow chart exemplarily illustrating a battery management method according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Before the description, it should be understood that the terms used in the specification and the appended claims should not be interpreted as limited to the general and dictionary meanings, but are interpreted based on the meanings and concepts corresponding to the technical aspects of the present disclosure based on the principle of allowing the inventor to appropriately define the terms for the best interpretation.

Therefore, the descriptions presented herein are merely preferred embodiments for illustrative purposes, and are not intended to limit the scope of the present disclosure, and it should be appreciated that other equivalents and modifications may be made to the present disclosure without departing from the scope of the present disclosure.

FIG. 1 is a diagram showing an example of a battery cell diagnosis system 1 including a battery cell diagnosis apparatus 1000 .

1 , the battery cell diagnostic system 1 may be configured to include a battery cell diagnostic device 1000 and an external device 2000. However, this is merely a preferred embodiment for implementing the present disclosure, and some components may be added or deleted as needed. It should be noted that the components of the battery cell diagnostic system 1 shown in FIG. 1 represent functionally different functional elements, and a plurality of components may be implemented to be integrated with each other in an actual physical environment.

In the battery cell diagnostic system 1, the battery cell diagnostic device 1000 is a computing device that diagnoses an abnormal state of a battery cell and provides a diagnostic result to a user. The battery cell diagnostic device 1000 may refer to an on-board computing device included in a BMS (battery management system). For example, the battery cell diagnostic device 1000 may be a computing device included in a BMS provided in an electric vehicle of a user. This is an embodiment, and the present disclosure is not limited thereto, but may include various devices equipped with computing functions and communication functions.

The battery cell diagnostic device 1000 may acquire data including at least one of a charge current and a discharge current of a battery cell and a cell voltage as a voltage across the battery cell. For example, the battery cell diagnostic device 1000 may acquire data regarding at least one of a charge current and a discharge current of a battery cell provided in an electric vehicle and a cell voltage as a voltage across the battery cell.

According to an embodiment of the present disclosure, the battery cell diagnostic apparatus 1000 may generate first information of the battery cell. The first information may include data related to at least one of a charge current and a discharge current of the battery cell and a cell voltage as a voltage across the battery cell.

The battery cell diagnostic apparatus 1000 may transmit the first information to the external device 2000. The external device 2000 may receive the first information and derive second information about the battery cell based on at least a portion of the received first information. The external device 2000 may transmit the second information to the battery cell diagnostic apparatus 1000. The battery cell diagnostic apparatus 1000 may diagnose an abnormal state of the battery cell based on the first information and the second information.

In the battery cell diagnostic system 1, the external device 2000 may refer to an off-board computing device that provides the second information generated using the first information to the battery cell diagnostic device 1000. To this end, the external device 2000 may receive information about the voltage, current or temperature of the battery cell from various electric vehicles, and store at least one algorithm that can diagnose the battery cell based on the received information. In addition, the external device 2000 may store the information about the voltage, current or temperature of the battery cell received from various electric vehicles as big data, and store at least one artificial intelligence model that can diagnose the battery cell based on the big data. The computing device may be a notebook, a desktop computer, a laptop computer, etc., but is not limited thereto, and may include any type of device equipped with a computing function and a communication function. However, if the second information is provided to a plurality of devices 1000 for diagnosing battery cells, the external device 2000 may preferably be implemented as a server computing device.

The components of the battery cell diagnosis system 1 can communicate via a network. Here, the network can be implemented as all types of wired/wireless networks, such as a local area network (LAN), a wide area network (WAN), a mobile radio communication network, Wibro (wireless broadband Internet), etc.

Hereinafter, the battery cell diagnosis system 1 according to the embodiment of the present disclosure is described with reference to Fig. 1. Hereinafter, the battery cell diagnosis apparatus 1000 according to the embodiment of the present disclosure will be described in detail with reference to Fig. 2.

2 is a block diagram schematically illustrating a functional configuration of a battery cell diagnostic apparatus 1000 according to an embodiment of the present disclosure. Referring to FIG. 2 , the battery cell diagnostic apparatus 1000 may include a current measuring unit 100 , a voltage sensing unit 200 , a data acquiring unit 300 , a first control unit 400 , and a display unit 500 .

The current measuring unit 100 may measure the current of the battery cell. Here, the current may be at least one of a charging current and a discharging current of the battery cell.

Preferably, the current measuring unit 100 can measure the charging current when the battery cell is charged. In addition, the current measuring unit 100 can measure the discharging current when the battery cell is discharged.

The voltage sensing unit 200 may be configured to sense a cell voltage of a battery cell. For example, the voltage sensing unit 200 may sense a voltage signal representing a voltage across the battery cell. This will be described in detail later with reference to FIG. 3 .

The data acquiring unit 300 may periodically acquire data from the current measuring unit 100 and the voltage sensing unit 200 .

In one embodiment, the first control unit 400 may generate first information of the battery cell based on the data acquired by the data acquisition unit 300. For example, the first information may include current information about at least one of a charging current and a discharging current of the battery cell and voltage information about a cell voltage of the battery cell.

In another embodiment, the first control unit 400 may directly acquire the current information about the battery cell from the current measuring unit 100 , and directly acquire the voltage information about the battery cell from the voltage sensing unit 200 .

The first control unit 400 may be configured to transmit the generated first information to the external device 2000. In addition, the first control unit 400 may receive second information from the external device 2000, the second information including diagnostic information of the battery cell acquired based on the first information.

Specifically, the second information is battery cell diagnostic information generated by the external device 2000 based on the first information. For example, the battery cell diagnostic information may include at least one of battery cell lithium plating diagnosis, battery cell parallel connection abnormality, or battery cell internal short circuit.

The first control unit 400 may be configured to diagnose an abnormal state of the battery cell based on the first information and the second information.

Specifically, the first control unit 400 can detect at least one of the voltage abnormality of the battery cell and the behavior abnormality of the battery cell based on the first information. In addition, the first control unit 400 can be configured to diagnose the abnormal state of the battery cell based on at least one of the voltage abnormality, the behavior abnormality and the second information. Therefore, the present disclosure is characterized in that the battery cell diagnostic device 1000 and the external device 2000 diagnose different diagnostic items instead of diagnosing the same diagnostic items. For example, the battery cell diagnostic device 1000 can diagnose at least one of the voltage abnormality and the behavior abnormality of the battery cell, and the external device 2000 can diagnose the lithium plating of the battery cell, the parallel connection abnormality of the battery cell and the internal short circuit of the battery cell.

Features of the first control unit 400 detecting the voltage abnormality or the behavior abnormality of the battery cell based on the first information will be described later.

The display unit 500 may include at least one display. The display unit 500 may display information about an abnormal state of the battery cell on the included display.

Here, the display unit 500 may be electrically connected to the first control unit 400 and may be included in a load device that receives power from the cell group CG. When the load device is an electric vehicle, a hybrid vehicle, a plug-in hybrid vehicle, etc., the diagnostic result information may be output via an integrated information display of the vehicle.

For example, the first control unit 400 may be configured to display information about an abnormal state of the battery cell on the display unit 500 based on the diagnosis information of the battery cell included in the second information.

As another embodiment, the first control unit 400 may generate third information indicating whether the battery cell is in an abnormal state based on at least one of the voltage abnormality, the behavior abnormality, and the second information. In addition, the first control unit 400 may display the third information using a display included in the display unit 500. By displaying the third information by the first control unit 400 using the display included in the display unit 500, the abnormality of the battery cell may be specifically provided to the user.

In addition, the first control unit 400 may transmit the third information to the second control unit of the device equipped with the battery cell.

Here, the second control unit may be configured to perform a function of controlling a device equipped with a battery cell. For example, the device equipped with a battery cell may be an electric vehicle. In this case, the second control unit may be an ECU (electronic control unit) configured to control the electric vehicle. The first control unit 400 may send the third information to the second control unit of the electric vehicle equipped with the battery cell.

3 is an exemplary diagram conceptually showing a configuration of an electric vehicle according to an embodiment of the present disclosure. Referring to FIG. 3 , the electric vehicle includes a battery pack 10 , an inverter INV, an electric motor M, and a second control unit 600 .

The battery pack 10 may include a cell group CG, a switch S, and a battery cell diagnostic apparatus 1000 .

The cell group CG can be connected to the inverter INV by means of a pair of power terminals provided in the battery pack 10. The cell group CG includes a plurality of battery cells _BC1 to _BCN connected in series (where N is a natural number equal to or greater than 2). The type of each battery cell _BCi is not particularly limited as long as it can be recharged like a lithium-ion battery cell. i is an index of the cell identification. i is a natural number from 1 to N.

The switch S is connected in series to the cell group CG. The switch S is installed on a current path for charging and discharging the cell group CG. The switch S is controlled to be turned on/off in response to a switching signal from the battery cell diagnostic device 1000. Preferably, the working state of the switch S can be controlled by the first control unit 400 to be an on state or an off state.

For example, the switch S may be a mechanical relay turned on/off by the magnetic force of a coil. As another embodiment, the switch S may be a semiconductor switch such as a field effect transistor (FET) or a metal oxide semiconductor field effect transistor (MOSFET).

The inverter INV is configured to convert the DC current from the cell group CG into AC current in response to a command from the battery cell diagnostic device 1000 .

The electric motor M may be, for example, a three-phase AC motor. The electric motor M is driven using AC power from an inverter INV.

The battery cell diagnostic device 1000 is configured to be responsible for overall control related to charging and discharging of the cell group CG.

The battery cell diagnosis apparatus 1000 may further include at least one of a temperature sensor T and an interface unit I/F.

The voltage sensing unit 200 is connected to the positive and negative electrodes of each of the plurality of battery cells _BC1 to BC _N via a plurality of voltage sensing lines. The voltage sensing unit 200 is configured to measure a cell voltage across each battery cell BC _i and generate a voltage signal representing the measured cell voltage.

The current measuring unit 100 is connected in series to the cell group CG via a current path. The current measuring unit 100 is configured to detect a battery current flowing through the cell group CG and generate a current signal representing the detected battery current (which may also be referred to as a "charge and discharge current"). Since the plurality of battery cells _BC1 to _BCN are connected in series, the battery current flowing through any one of the plurality of battery cells _BC1 to _BCN may be the same as the battery current flowing through the other battery cells. The current measuring unit 100 may be implemented using one or a combination of two or more of known current detection elements such as a shunt resistor and a Hall effect element.

The temperature sensor T is configured to detect the temperature of the cell group CG and generate a temperature signal indicating the detected temperature. For example, the temperature sensor T may measure the temperature of the cell group CG, or may individually measure the temperature of each battery cell BC _i included in the cell group CG.

The first control unit 400 may be operably coupled to the voltage sensing unit 200, the temperature sensor T, the current measuring unit 100, the interface unit I/F, and/or the switch S. The first control unit 400 may collect sensing signals from the voltage sensing unit 200, the current measuring unit 100, and the temperature sensor T. The sensing signal refers to a synchronously detected voltage signal, current signal, and/or temperature signal.

The interface unit I/F may include a communication circuit configured to support wired communication or wireless communication between the first control unit 400 and the second control unit 600. For example, the wired communication may be CAN (Controller Area Network) and/or CAN-FD (Controller Area Network with Flexible Data Rate) communication, and the wireless communication may be ZigBee or Bluetooth communication. Of course, as long as the wired/wireless communication between the first control unit 400 and the second control unit 600 is supported, the type of communication protocol is not particularly limited.

The interface unit I/F may be coupled with an output device (eg, a display, a speaker) that provides information received from the second control unit 600 and/or the first control unit 400 in a user-recognizable form.

The second control unit 600 may control the inverter INV based on battery information (eg, voltage, current, temperature, SOC) collected through communication with the battery cell diagnostic apparatus 1000 .

When the switch S is turned on when the electric load and/or charger operates, the battery cells _BC1 to BC _N included in the battery pack 10 can be charged or discharged. When the switch S is turned off when the battery cells _BC1 to BC _N are charged or discharged, the battery cells _BC1 to BC _N can be switched to an idle state.

The first control unit 400 may turn on the switch S in response to the on signal. The first control unit 400 may turn off the switch S in response to the off signal. The on signal is a signal requesting a transition from an idle state to a charge or discharge state. The off signal is a signal requesting a transition from a charge or discharge state to an idle state. Alternatively, the on/off control of the switch S may be performed by the second control unit 600 instead of the first control unit 400.

In FIG3 , the battery cell diagnostic device 1000 is shown as being included in the battery pack 10 of the electric vehicle, but this should be understood as an embodiment. For example, the battery cell diagnostic device 1000 may be included in a test system for selecting abnormally behaving battery cells in the manufacturing process of the battery cells BC ₁ to BC _N. As another embodiment, the battery cell diagnostic device 1000 may also be included in an energy storage system (ESS) including the battery cells BC ₁ to BC _N.

The first control unit 400 may detect voltage abnormality and behavior abnormality of the battery cell by using the first information. First, a method in which the first control unit 400 detects voltage abnormality of the battery cell using the first information will be described in detail with reference to FIG.

FIGS. 4 a to 4 h are graphs exemplarily showing a process of detecting voltage abnormality of each battery cell based on time series data indicating a cell voltage variation of each of the plurality of battery cells BC ₁ to BC _N shown in FIG. 3 .

FIG4a shows a voltage curve of each of a plurality of battery cells BC ₁ to BC _N. The number of battery cells shown in FIG4a is 14. The first control unit 400 collects a voltage signal from the voltage sensing unit 200 every unit time and records the voltage value of the cell voltage of each battery cell BC _i in the first information. The unit time may be an integer multiple of the voltage measurement period of the voltage sensing unit 200.

The first control unit 400 may be configured to generate time series data representing a history of the cell voltage included in the first information over time.

Specifically, the first control unit 400 may generate cell voltage time series data representing the history of the cell voltage of each battery cell over time based on the voltage value of the cell voltage of each battery cell BC _i included in the first information. The number of cell voltage time series data increases by 1 each time the cell voltage is measured.

The plurality of voltage curves shown in Fig. 4a are associated one-to-one with the plurality of battery cells _BC1 to BC _N. Therefore, each voltage curve represents a change history of the cell voltage of any one of the battery cells BC associated therewith.

The first control unit 400 may be configured to determine a first average cell voltage and a second average cell voltage of each battery cell based on the time series data. Here, the first average cell voltage may be a short-term moving average, and the second average cell voltage may be a long-term moving average.

Specifically, the first control unit 400 may determine a moving average value for each unit time of each of the plurality of battery cells _BC1 to BC _N by using one moving window or two moving windows. When two moving windows are used, a time length of one moving window is different from a time length of the other moving window.

Here, the time length of each moving window is an integer multiple of the unit time, the end point of each moving window is the current time point, and the starting point of each moving window is a point of a predetermined time length before the current time point.

Hereinafter, for convenience of description, of the two moving windows, the one associated with the shorter time length will be referred to as a first moving window, and the one associated with the longer time length will be referred to as a second moving window.

The first control unit 400 may diagnose the voltage abnormality of each battery cell BC _i using only the first moving window or using both the first moving window and the second moving window.

The first control unit 400 may compare short-term and long-term variation trends of the cell voltage of the ith battery cell BC _i based on the cell voltage of the ith battery cell BC _i collected for each unit time.

The first control unit 400 may determine a first average cell voltage for each unit time, which is a moving average of the i-th battery cell BC _i by means of a first moving window, by using the following Equation 1 or Equation 2. That is, the first control unit 400 may determine the first average cell voltage of each battery cell by using the first moving window.

Equation 1 is a moving average calculation formula using the arithmetic average method, and Equation 2 is a moving average calculation formula using the weighted average method.

<Equation 1>

<Equation 2>

In Equation 1 and Equation 2, k is a time index indicating a current time point, SMA _i [k] is a first average cell voltage of an i-th battery cell BC _i at the current time, S is a time length of a first moving window divided by a unit time, and _Vi [k] is a cell voltage of an i-th battery cell BC _i at the current time point. For example, if the unit time is 1 second and the time length of the first moving window is 10 seconds, S is 10. When x is a natural number less than or equal to k, _Vi [kx] and SMA _i [kx] represent a cell voltage and a first average cell voltage of an i-th battery cell BC _i when the time index is kx, respectively. For reference, the first control unit 400 may be set to increase the time index by 1 for each unit time.

The first control unit 400 may determine a second average cell voltage per unit time, which is a moving average of the i-th battery cell BC _i by means of a second moving window, by using the following Equation 3 or Equation 4. That is, the second control unit 400 may determine the second average cell voltage by using the second moving window.

Equation 3 is a moving average calculation formula using an arithmetic average method, and Equation 4 is a moving average calculation formula using a weighted average method.

<Equation 3>

<Equation 4>

In Equation 3 and Equation 4, k is a time index indicating a current time point, LMA _i [k] is a second average cell voltage of an i-th battery cell BC _i at the current time, L is a time length of a second moving window divided by a unit time, and V _i [k] is a cell voltage of an i-th battery cell BC _i at the current time point. For example, if the unit time is 1 second and the time length of the second moving window is 100 seconds, L is 100. When x is a natural number less than or equal to k, LMA _i [kx] represents a second average cell voltage when the time index is kx.

In one embodiment, as _Vi [k] of Equations 1 to 4, the first control unit 400 may input the difference between the standard cell voltage of the cell group CG at the current time point and the cell voltage of the battery cell BC _i , instead of inputting the cell voltage of each battery cell BC _i at the current time point.

The standard cell voltage of the cell group CG at the current time point is an average value of a plurality of cell voltages at the current time point determined based on the plurality of battery cells _BC1 to BC _N. In a modified example, the average value of the plurality of cell voltages may be replaced by a median value.

Specifically, the first control unit 400 may set VD _i [k] of the following Equation 5 to V _i [k] of Equations 1 to 4.

<Equation 5>

VD _i [k] = Vav [k] – V _i [k]

In Equation 5, Vav[k] is an average value of multiple cell voltages, which is used as the standard cell voltage of the cell group CG at the current time point.

When the time length of the first moving window is shorter than the time length of the second moving window, the first average cell voltage may be referred to as a “short-term moving average” of the cell voltage, and the second average cell voltage may be referred to as a “long-term moving average” of the cell voltage.

The first control unit may detect voltage abnormality of the battery cell based on a difference between the first average cell voltage and the second average cell voltage. This will be described in detail with reference to FIG. 4b.

The first control unit may determine, for each of the plurality of battery cells, long-term and short-term average differences corresponding to a difference between a first average cell voltage and a second average cell voltage of the battery cells.

Fig. 4b shows the short-term moving average and the long-term moving average of the cell voltage of the i-th battery cell BC _i determined according to the multiple voltage curves shown in Fig. 4a. In Fig. 4b, the horizontal axis represents time and the vertical axis represents the moving average of the cell voltage.

4b, a plurality of moving average lines Si indicated by dotted lines are one-to-one related to a plurality of battery cells _BC1 to BC _N , and represent a change history of a first average cell voltage (SMA _i [k]) of each battery cell BC according to time. In addition, a plurality of moving average lines Li indicated by solid lines are one-to-one related to a plurality of battery cells _BC1 to BC _N , and represent a change history of a second average cell voltage (LMA _i [k]) of each battery cell BC according to time.

The dashed line graph and the solid line graph are obtained using Equation 2 and Equation 4, respectively. In addition, VD _i [k] of Equation 5 is used as V _i [k] of Equation 2 and Equation 4, and Vav [k] is set to the average value of the plurality of cell voltages. The time length of the first moving window is 10 seconds, and the time length of the second moving window is 100 seconds.

The first control unit 400 may determine an average value of the determined long-term average differences and short-term average differences of the plurality of battery cells.

In addition, for each of the plurality of battery cells, the first control unit 400 may determine a battery cell diagnostic deviation corresponding to a deviation between an average of the long-term and short-term average differences of all the battery cells and the long-term and short-term average difference of the battery cell. For example, the first control unit 400 may determine the battery cell diagnostic deviation of each battery cell by calculating a deviation between the long-term and short-term average difference of each battery cell and the long-term and short-term average difference.

Fig. 4c shows the change of the long-term and short-term average differences (absolute values) corresponding to the difference between the first average cell voltage (SMA _i [k]) and the second average cell voltage (LMA _i [k]) of each battery cell shown in Fig. 4b according to time. In Fig. 4c, the horizontal axis represents time, and the vertical axis represents the long-term and short-term average differences of each battery cell BC _i .

The long-term and short-term average difference of each battery cell BC _i is the difference between the first average cell voltage SMA _i and the second average cell voltage LMA _i of each battery cell BC _i for each unit time. As an example, the long-term and short-term average difference of the i-th battery cell BC _i may be the same as a value obtained by subtracting one (e.g., the larger one) of SMA _i [k] and LMA _i [k] from the other (e.g., the smaller one).

The long-term and short-term average differences of the ith battery cell BC _i depend on the short-term and long-term variations of the cell voltage of the ith battery cell BC _i .

The temperature or SOH of the i-th battery cell BC _i continuously affects the cell voltage of the i-th battery cell BC _i in the short term and in the long term. Therefore, if there is no voltage abnormality of the i-th battery cell BC _i , the long-term and short-term average differences of the i-th battery cell BC _i are not significantly different from those of other battery cells.

On the other hand, the voltage abnormality suddenly generated due to the internal short circuit and/or the external short circuit in the i-th battery cell BCi affects the first average cell voltage ( _SMAi [k]) to a greater extent than the second average cell voltage ( _LMAi [k]). As a result, the long-term and short-term average differences of the i-th battery cell _BCi have a large deviation from the long-term and short-term average differences of the remaining battery cells without voltage abnormality.

The first control unit 400 may determine the long-term and short-term average differences (|SMA _i [k]-LMA _i [k]|) of each battery cell BC _i for each unit time. In addition, the first control unit 400 may determine the average value of the long-term and short-term average differences (|SMA _i [k]-LMA _i [k]|). Hereinafter, the average value is expressed as |SMA _i [k]-LMA _i [k]|av. The first control unit 400 may also determine the deviation of the long-term and short-term average differences (|SMAi[k]-LMAi[k]|) from the average value of the long-term and short-term average differences (|SMAi[k]-LMAi[k]|av) as a cell diagnostic deviation (Ddiag, i[k]). In addition, the first control unit 400 may detect the voltage abnormality of each battery cell BC _i based on the cell diagnostic deviation (Ddiag, i[k]).

In an embodiment, for at least one battery cell among the plurality of battery cells, the first control unit 400 may detect a battery cell that satisfies a condition that the battery cell diagnosis deviation exceeds a diagnosis threshold as a battery cell with abnormal voltage. For example, when the battery cell diagnosis deviation (Ddiag, i[k]) of the i-th battery cell BCi exceeds a preset diagnosis threshold (e.g., 0.015), the first control unit 400 may determine that a voltage abnormality has occurred in the corresponding i-th battery cell _BCi , and detect the voltage abnormality of the battery cell.

In another embodiment, the first control unit 400 may determine a statistical variable threshold value based on a standard deviation of the cell diagnostic deviations of the plurality of battery cells. In addition, the first control unit 400 may filter the time series data based on the statistical variable threshold value to generate filtered time series data. Finally, the first control unit 400 may be configured to detect a voltage anomaly of the battery cell based on a time or a number of data of the filtered time series data exceeding a diagnostic threshold value for at least one of the plurality of battery cells. Here, for ease of explanation, the features of detecting a voltage anomaly of a battery cell using a statistical variable threshold value will be described in detail later by considering the following embodiments of normalized cell diagnostic deviations.

Meanwhile, the first control unit 400 may determine a normalization value corresponding to the determined average of the long-term and short-term average differences of the plurality of battery cells. In addition, the first control unit 400 may normalize the long-term and short-term average differences according to the normalization value for each of the plurality of battery cells.

For example, the first control unit 400 may use the normalized standard value to normalize the long-term and short-term average differences (|SMAi[k]-LMAi[k]|) of each battery cell BC _i in order to detect voltage abnormality. Preferably, the normalized standard value is the average value of the long-term and short-term average differences (|SMAi[k]-LMAi[k]|av).

Specifically, the first control unit 400 may set the average value (|SMA _i [k]-LMA _i [k]|av) of the long-term and short-term average differences of the first to Nth battery cells BC _i to BC _N as a normalized standard value. The first control unit 400 may also divide the long-term and short-term average differences (|SMA _i [k]-LMA _i [k]|) of each battery cell BC _i by the normalized standard value to normalize the long-term and short-term average differences (|SMA _i [k]-LMA _i [k]|).

The following equation 6 represents an equation for normalizing the long-term and short-term average differences (|SMA _i [k]-LMA _i [k]|) of each battery cell BC _i . In an embodiment, the value calculated by equation 6 may be referred to as a normalized cell diagnostic deviation (D*diag,i[k]).

<Equation 6>

D*diag, i[k]＝(|SMA _i [k]-LMA _i [k]|)÷(|SMA _i [k]-LMA _i [k]|av)

In Equation 6, |SMA _i [k]-LMA _i [k]| is the long-term and short-term average differences of the i-th battery cell BC _i at the current time point, |SMA _i [k]-LMA _i [k]|av is the average value (normalized standard value) of the long-term and short-term average differences of all battery cells, and D*diag,i[k] is the normalized cell diagnostic deviation of the i-th battery cell BC _i at the current time point. The symbol “*” indicates that the parameter is normalized.

The long-term and short-term average differences (|SMA _i [k]-LMA _i [k]|) of each battery cell BC _i may also be normalized by the logarithmic operation of the following equation 7. In an embodiment, the value calculated by equation 7 may also be referred to as a normalized cell diagnostic deviation (D*diag,i[k]).

<Equation 7>

D*diag,i[k]=Log|SMA _i [k]-LMA _i [k]|

FIG4d shows the change of the normalized cell diagnostic deviation (D*diag, i[k]) of each battery cell BC _i over time. The cell diagnostic deviation (D*diag, i[k]) is calculated using Equation 6. In FIG4d, the horizontal axis represents time, and the vertical axis represents the cell diagnostic deviation (D*diag, i[k]) of each battery cell BC _i .

4d, as the long-term and short-term average differences (|SMA _i [k]-LMA _i [k]|) of each battery cell BC _i are normalized, it can be seen that the change in the long-term and short-term average differences of each battery cell BC _i is amplified based on the average value. Therefore, the voltage abnormality of the battery cell can be detected more accurately.

The first control unit 400 may determine the statistical variable threshold value according to the standard deviation of the normalized cell diagnostic deviations of the plurality of battery cells.

For example, the first control unit 400 may detect a voltage abnormality in each battery cell BC _i by comparing the normalized cell diagnostic deviation (D*diag,i[k]) of each battery cell BC _i with a statistical variable threshold value (Dthreshold[k]).

For example, the first control unit 400 may determine the statistical variable threshold value (Dthreshold[k]) for each unit time using Equation 8 below.

<Equation 8>

Dthreshold[k]＝β*Sigma(D*diag,i[k])

In Equation 8, Sigma is a function that calculates the standard deviation of the normalized cell diagnostic deviation (D*diag, i[k]) of all battery cells BC at time index k. β is a factor that determines the diagnostic sensitivity. β can be appropriately determined by trial and error so that when the present disclosure is applied to a cell group containing battery cells with actual voltage anomalies, the corresponding battery cells can be detected as voltage abnormal cells. In one embodiment, β can be set to 5 or greater, or 6 or greater, or 7 or greater, or 8 or greater, or 9 or greater. Since the Dthreshold[k] generated by Equation 8 is multiple, they constitute time series data.

On the other hand, in a battery cell with abnormal voltage, the normalized cell diagnostic deviation (D*diag, i[k]) is relatively greater than that of a normal battery cell. Therefore, when calculating Sigma(D*diag, i[k]) at time index k to improve detection accuracy and reliability, it is desirable to exclude max(D*diag, i[k]) corresponding to the maximum value. Here, max is a function that returns the maximum value of multiple input variables, and the input variable is the normalized cell diagnostic deviation (D*diag, i[k]) of all battery cells.

In FIG. 4 d , time series data representing temporal changes in the threshold value (Dthreshold[k]) of a statistical variable corresponds to the distribution indicated in the darkest color among all the distributions.

The first control unit 400 may be configured to filter the normalized long-term and short-term average differences of each battery cell based on a statistical variable threshold value to generate filtered time series data for each battery cell of the plurality of battery cells.

Specifically, the first control unit 400 may generate the time series data of the filtered diagnosis value by filtering the time series data of the cell diagnosis deviation of each battery cell based on the statistical variable threshold.

For example, the first control unit 400 may determine a statistical variable threshold value (Dthreshold[k]) at time index k, and then determine a filtered diagnostic value (Dfilter,i[k]) by filtering the normalized cell diagnostic deviation (D*diag,i[k]) of each battery cell BC _i using Equation 9 below.

<Equation 9>

Dfilter, i[k]=D*diag, i[k]-Dthreshold[k] (IF D*diag, i[k]>Dthreshold[k])

Dfilter, i[k]＝0(IF D*diag, i[k]≤Dthreshold[k])

Two values may be assigned to the filter diagnostic value (Dfilter, i[k]) for each battery cell BCi. That is, if the cell diagnostic deviation (D*diag, i[k]) is greater than the statistical variable threshold value (Dthreshold[k]), the difference between the cell diagnostic deviation (D*diag, i[k]) and the statistical variable threshold value (Dthreshold[k]) is assigned to the filter diagnostic value (Dfilter, i[k]). On the other hand, if the cell diagnostic deviation (D*diag, i[k]) is less than or equal to the statistical variable threshold value (Dthreshold[k]), 0 is assigned to the filter diagnostic value (Dfilter, i[k]).

The first control unit 400 may be configured to detect voltage abnormality of the battery cell based on a time or a data number at which the filtered time series data exceeds a diagnosis threshold for at least one battery cell among the plurality of battery cells.

Specifically, the first control unit 400 may detect the voltage abnormality of the battery cell according to the time when the filtered diagnostic value exceeds the diagnostic threshold or the number of data of the filtered diagnostic value exceeding the diagnostic threshold.

FIG. 4 e is a diagram showing time series data of filtered diagnostic values (D filter, i[k]) obtained by filtering the cell diagnostic deviation (D*diag, i[k]) at time index k.

4e, an irregular pattern having a positive value of about 3000 seconds is found in the filtered diagnostic value (Dfilter,i[k]) of the specific battery cell. For reference, the specific battery cell having the irregular pattern is the battery cell having the time series data indicated by A in FIG4d.

In one embodiment, the first control unit 400 may integrate the time _region in which the filtered diagnostic value (Dfilter, i[k]) is greater than a diagnostic threshold value (e.g., 0) in the time series data of the filtered diagnostic value (Dfilter, i[k]) for each battery cell BC i, and detect the battery cell for which the condition that the integration time is greater than a preset standard time is met as a battery cell with abnormal voltage.

Preferably, the first control unit 400 may integrate the time region that continuously satisfies the condition that the filter diagnosis value (Dfilter, i[k]) is greater than the diagnosis threshold. If there are multiple corresponding time intervals, the first control unit 400 may independently calculate the integration time for each time interval.

The first control unit 400 may detect the voltage abnormality of the battery cell according to the time for which the filtered diagnostic value exceeds the diagnostic threshold or the number of data of the filtered diagnostic value exceeding the diagnostic threshold.

For example, the first control unit 400 can integrate the number _{of data} included in the time region in which the filtered diagnostic value (Dfilter, i[k]) is greater than the diagnostic threshold (e.g., 0) in the time series data of the filtered diagnostic value (Dfilter, i[k]) for each battery cell BC i, and detect the battery cell for which the condition that the data integration value is greater than a preset standard count is met as a battery cell with abnormal voltage.

Preferably, the first control unit 400 may integrate only the amount of data included in the time region that continuously satisfies the condition that the filter diagnosis value (Dfilter, i[k]) is greater than the diagnosis threshold. If there are multiple corresponding time regions, the first control unit 400 may independently integrate the amount of data in each time region.

At the same time, the first control unit 400 may replace V _i [k] in equations 1 to 5 with the normalized cell diagnostic deviation (D*diag, i[k]) of each battery cell BC _i shown in FIG. 4d. In addition, at time index k, the first control unit 400 may calculate the average value of the long-term and short-term average differences (|SMA _i [k]-LMA i [k]|) of the cell diagnostic deviation (D*diag, i[k]), calculate the average value of the long-term and short-term average differences (|SMA _i [k]-LMA _i [k]|) of the cell diagnostic deviation (D*diag, i[k]), calculate the cell diagnostic deviation (Ddiag, _i [k]) corresponding to the difference between the long-term and short-term average differences (|SMA _i [k]-LMA _i [k]|) compared with the average value, and calculate the long-term and short-term average differences (|SMA _i [k]-LMA i [k]| ₎ using equation 6. [k]|), determine the statistical variable threshold (Dthreshold[k]) of the normalized cell diagnostic deviation (D*diag, i[k]) using Equation 8, determine the filtered diagnostic value (Dfilter, i[k]) by filtering the cell diagnostic deviation (D*diag, i[k]) using Equation 9, and recursively use the time series data to detect voltage anomalies of the battery cells.

FIG4f is a graph showing changes in the long-term and short-term average differences (|SMA _i [k]-LMA i [k]|) of the time series data ( FIG4d ) of the normalized cell diagnostic deviation (D*diag, _i [k]) according to time. In Equations 2, 4, and 5 for calculating the long-term and short-term average differences (|SMA _i [k]-LMA _i [k]|), V _i [k] may be replaced with D*diag i[k], and Vav[k] may be replaced with the average value of D*diag i[k].

4g is a graph showing time series data of normalized cell diagnostic deviation (D*diag,i[k]) calculated using Equation 6. In FIG4g , time series data of the statistical variable threshold value (Dthreshold[k]) corresponds to the distribution indicated in the darkest color.

FIG. 4h is a graph showing the distribution of time series data of filtered diagnostic values (Dfilter, i[k]) obtained by filtering the time series data of cell diagnostic deviations (D*diag, i[k]) using Equation 9. FIG.

In one embodiment, the first control unit 400 may integrate the time _region in which the filtered diagnostic value (Dfilter, i[k]) is greater than a diagnostic threshold value (e.g., 0) in the time series data of the filtered diagnostic value (Dfilter, i[k]) for each battery cell BC i, and detect the battery cell for which the condition that the integration time is greater than a preset standard time is met as a battery cell with abnormal voltage.

Preferably, the first control unit 400 may integrate the time region that continuously satisfies the condition that the filter diagnosis value (Dfilter, i[k]) is greater than the diagnosis threshold. If there are multiple corresponding time regions, the first control unit 400 may independently calculate the integration time for each time region.

In another embodiment, the first control unit 400 may integrate the number of data _included in the time region in which the filtered diagnostic value (Dfilter, i[k]) is greater than a diagnostic threshold value (e.g., 0) in the time series data of the filtered diagnostic value (Dfilter, i[k]) for each battery cell BC i, and detect the battery cell for which the condition that the data integration value is greater than a preset standard count is met as a battery cell with abnormal voltage.

Preferably, the first control unit 400 may integrate only the amount of data included in the time region that continuously satisfies the filter diagnosis value (Dfilter, i[k]) greater than the diagnosis threshold. If there are multiple corresponding time regions, the first control unit 400 may independently integrate the amount of data in each time region.

The first control unit 400 may additionally repeat the above recursive operation process multiple times according to a standard number. That is, the first control unit 400 may replace the voltage time series data shown in FIG. 4a with the time series data of the normalized cell diagnostic deviation (D*diag, i[k]) (e.g., the data of FIG. 4g). In addition, at time index k, the first control unit 400 may calculate the long-term and short-term average differences (|SMA _i [k]-LMA _i [k]|), calculate the average value of the long-term and short-term average differences (|SMA _i [k]-LMA _i [k]|), calculate the cell diagnostic deviation (Ddiag, i[k]) corresponding to the difference between the long-term and short-term average differences (|SMA _i [k]-LMA _i [k]|) and the average value, calculate the normalized cell diagnostic deviation (D*diag, i[k]) of the long-term and short-term average differences (|SMA _i [k]-LMA _i [k]|), and calculate the long-term and short-term average differences (|SMA _i [k]-LMA _{i [k]|)} using Equation 6. [k]|), determine a statistical variable threshold value (Dthreshold[k]) of the cell diagnostic deviation (D*diag, i[k]) using Equation 8, determine a filtered diagnostic value Dfilter, i[k] by filtering the cell diagnostic deviation (D*diag, i[k]) using Equation 9, and recursively use time series data of the filtered diagnostic value Dfilter, i[k] to detect voltage abnormality of the battery cell.

If the recursive operation process as described above is repeated, the voltage abnormality of the battery cell can be diagnosed more accurately. That is, referring to FIG4e, a positive distribution pattern is observed only in two time regions in the time series data of the filtered diagnostic value (Dfilter, i[k]) of the battery cell with voltage abnormality. However, referring to FIG4h, in the time series data of the filtered diagnostic value (Dfilter, i[k]) of the battery cell with voltage abnormality, a positive distribution pattern is observed in more time regions than in FIG4e. Therefore, if the recursive operation process is repeated, the time point at which the voltage abnormality occurs in the battery cell can be detected more accurately.

So far, a detailed method for detecting voltage abnormality using the first information by the first control unit 400 is described. Hereinafter, a method for detecting behavior abnormality using the first information by the first control unit 400 will be described in detail with reference to FIGS.

FIG. 5 is a graph exemplarily showing a voltage curve corresponding to an original time series of an actual voltage value of a cell voltage of a battery cell mentioned in various embodiments of the present disclosure, and FIG. 6 is a graph exemplarily showing a measured voltage curve C2 obtained by integrating measurement noise with an original time series corresponding to the voltage curve C1 of FIG. 5 . FIG. 7 is a graph exemplarily showing a first moving average curve AC1 obtained by applying a first averaging filter to the voltage curve C2 of FIG. 6 , and FIG. 8 is a graph exemplarily showing a second moving average curve AC2 obtained by applying a second averaging filter to the voltage curve C2 of FIG. 6 . FIG. 9 is a graph exemplarily showing a voltage deviation curve VC which is a difference between the first moving average curve AC1 of FIG. 7 and the second moving average curve AC2 of FIG. 8 .

5, the voltage curve C1 is an example of an original time series of actual voltage values of the cell voltage of the battery cell BC during charging including a predetermined period t1 to tM. For better understanding, the cell voltage increases linearly, and illustrations of actual voltage values for periods before t1 and after tM are omitted.

If the battery cell BC is normal, the cell voltage continues to gradually rise during charging. On the other hand, if the battery cell BC has abnormal behavior with any fault condition (e.g., micro short circuit, part of the electrode joint is torn), abnormal behavior in which the cell voltage temporarily drops or rises even during charging may be observed irregularly.

The voltage curve C1 of FIG5 is related to an embodiment in which the battery cell BC has a behavioral anomaly, in which region X represents a time range in which the cell voltage drops sharply as a behavioral anomaly, and region Y represents a time range in which the cell voltage rises rapidly as a behavioral anomaly. FIG5 shows the cell voltage during charging, but the cell voltage of the battery cell with abnormal behavior may change even during discharge or storage as a behavioral anomaly. For example, during discharge, the cell voltage of a normal battery cell drops continuously and slowly, while the cell voltage of the battery cell with abnormal behavior may temporarily rise or fall sharply.

Next, referring to Fig. 6, voltage curve C2 represents the result of integrating measurement noise with the actual cell voltage of voltage curve C1 of Fig. 5. That is, voltage curve C2 of Fig. 6 represents a time series in which voltage values representing measured cell voltages are arranged in time order.

When M is a natural number indicating a predetermined total number of sampling times (e.g., 300) and K is a natural number less than or equal to M, tK is a measurement timing (Kth measurement timing tK) of a Kth voltage value (Vm[K]) in chronological order among a total number of M voltage values included in the voltage curve C2, and a time interval between two adjacent measurement timings is separated by a predetermined sampling time (e.g., 0.1 seconds). The voltage value (Vm[K]) is a data point indexed to the measurement timing tK among a total number of M voltage values included in the voltage curve C2.

Due to internal and external factors of the voltage sensing unit 200 (e.g., temperature, sampling rate, electromagnetic waves, etc. of the voltage measuring device), measurement noise may be irregularly generated over time. The current curve C3 is a time series of current values of the battery current measured within a predetermined period (t1 to tM). For ease of explanation, the battery current is illustrated as being constant during the predetermined period (t1 to tM).

If the voltage curve C2 of Figure 6 is compared with the voltage curve C1 of Figure 5, the abnormal behavior (X, Y) can be easily identified from the voltage curve C1 of Figure 5 without measurement noise, but there is a problem that it is difficult to identify the abnormal behavior (X, Y) from the voltage curve C2 mixed with measurement noise throughout the predetermined period (t1 to tM).

The inventors of the present disclosure have confirmed that the above-mentioned problem can be solved by applying a first averaging filter and a second averaging filter to a time series of voltage values (measured values) including measurement noise generated in the measurement timing of the cell voltage. The time series of voltage values of the battery cell BC to be detected acquired within a predetermined period of time in the past can be referred to as a "standard voltage curve", and the time series of current values can be referred to as a "standard current curve". Hereinafter, the voltage curve C2 and the current curve C3 of FIG. 6 will be described as being assumed to be the standard voltage curve C2 and the standard current curve C3, respectively.

The first control unit 400 may be configured to determine a plurality of sub-voltage curves by applying a moving window of a first time length to a time series of cell voltages included in the first information.

Specifically, the first control unit 400 can determine multiple sub-voltage curves by applying a moving window of the first time length to the standard voltage curve C2. In addition, the first control unit 400 can determine multiple sub-current curves that are one-to-one associated with the multiple sub-voltage curves by applying a moving window of the first time length to the standard current curve C3.

When K is a natural number less than or equal to M, a total of M sub-voltage curves (i.e., first to Mth sub-voltage curves) can be determined according to the standard voltage curve C2. The standard voltage curve C2 includes a total of M voltage values (i.e., first to Mth voltage values) measured sequentially for each sampling time W.

The sub-voltage curve SK is a subset of the standard voltage curve C2 and includes (A/W+1) voltage values that are continuous in time sequence. For example, when the sampling time W=0.1 seconds and the first time length A=10 seconds, the sub-voltage curve SK is a time sequence of a total of 101 voltage values, i.e., from the (K-P)th voltage value to the (K+P)th voltage value. P=A/2W=50.

In Fig. 6, RK is a sub-current curve associated with the sub-voltage curve SK. Therefore, the sub-current curve RK may also include (A/W+1) data points (current values) that are continuous in time sequence.

Since the battery current fluctuates greatly, the cell voltage also fluctuates greatly. The sudden fluctuation of the cell voltage caused by the battery current may prevent the abnormal behavior of the cell voltage from being identified from the standard voltage curve C2.

The first control unit 400 may determine a current variation of the sub-current curve RK, which is a difference between a maximum current value and a minimum current value of the sub-current curve RK. The first control unit 400 may detect abnormal behavior of a battery cell by using the sub-current curve RK based on a time series of cell voltages measured when a change in battery current is small, such as constant current charging or shelving.

The first control unit 400 may determine a long-term average voltage value and a short-term average voltage value of the sub-voltage curve SK associated with the sub-current curve RK when the current variation is less than the threshold variation.

The first control unit 400 may perform a calculation process described below with respect to the sub-voltage curve SK when the current change amount of the sub-current curve RK is equal to or less than the threshold change amount.

The first control unit 400 may determine a long-term average voltage value of each sub-voltage curve SK by using a first average filter of a first time length.

7 , the first average voltage curve AC1 may be acquired by applying a first average filter of a first time length A to the standard voltage curve C2. The first average filter is a low-pass filter and may be a centered moving average whose subset size (A/W+1) corresponds to the first time length A. For example, the first control unit 400 determines a long-term average voltage value (Vav1[K]) indexed to the measurement timing tK by averaging the (A/W+1)th voltage values (i.e., the (K-P)th voltage value to the (K-1)th voltage value, the Kth voltage value, and the (K+1)th voltage value to the (K+P)th voltage value) included in the sub-voltage curve SK. The following equation 10 shows the first average filter.

<Equation 10>

In equation 10, Vm[i] is the i-th voltage value included in the standard voltage curve C2, A is the first time length, W is the sampling time, P=A/2W, and Vav1[K] is the long-term average voltage value at the measurement timing tK. The first control unit 400 can determine the first average voltage curve AC1 of FIG. 7 by replacing K of equation 10 with 1 to M. The first time length A is predetermined to be an integer multiple of the sampling time W. Therefore, the first time length A indicates the size of the subset (A/W+1) used to obtain the long-term average voltage value (Vav1[K]).

In addition, the first control unit 400 may determine the short-term average voltage value of the sub-voltage curve by using a second average filter having a second time length shorter than the first time length.

8 , the second average voltage curve AC2 is obtained by applying a second average filter of a second time length B shorter than the first time length A to the standard voltage curve C2. The second average filter is a low-pass filter and may be a centered moving average, with a subset size (B/W+1) corresponding to the second time length B.

As an embodiment, the first control unit 400 determines a short-term average voltage value (Vav2[K]) indexed to the measurement timing tK by averaging the (B/W+1)th voltage values included in the sub-voltage curve SK (i.e., the (K-Q)th voltage value to the (K-1)th voltage value, the Kth voltage value, and the (K+1)th voltage value to the (K+Q)th voltage value). Q=B/2W. The short-term average voltage value (Vav2[K]) is the average value of a subset UK of the sub-voltage curve SK. The subset UK is located within the time range (tK-P to tK+P) of the sub-voltage curve SK, and is a voltage curve having a time range (tK-Q to tK+Q) with the same measurement timing tK as the time range (tK-P to tK+P). The following equation 11 shows the second averaging filter.

<Equation 11>

In equation 11, Vm[i] is the i-th voltage value included in the standard voltage curve C2, B is the second time length, W is the sampling time, Q=B/2W, and Vav2[K] is the short-term average voltage value at the measurement timing tK. The first control unit 400 can determine the second average voltage curve AC2 of FIG. 8 by substituting 1 to M for K of equation 11 one by one. The second time length B is predetermined to be an integer multiple of the sampling time W. Therefore, the second time length B indicates the size of the subset (B/W+1) used to obtain the short-term average voltage value (Vav2[K]).

The first time length A>the second time length B, each data point of the first average voltage curve AC1 (i.e., the long-term average voltage value) can be called a "long-term average value", and each data point of the second average voltage curve AC2 (i.e., the short-term average voltage value) can be called a "short-term average value". For example, A can be ten times B.

The first control unit 400 may determine a voltage deviation corresponding to a difference between a long-term average voltage value and a short-term average voltage value of the sub voltage curve.

For example, the first control unit 400 may determine the voltage deviation associated with the sub-voltage curve by subtracting one of the long-term average voltage value and the short-term average voltage value of each sub-voltage curve from the other.

9, the voltage deviation curve VC is the result of subtracting one of the first average voltage curve AC1 and the second average voltage curve AC2 from the other. That is, the voltage deviation curve VC is a time series of a total of M voltage deviations for a predetermined period (t1 to tM). The voltage deviation (ΔV[K]) associated with the sub-voltage curve SK is a value obtained by subtracting one of the long-term average voltage value (Vav1[K]) and the short-term average voltage value (Vav2[K]) from the other. For example, ΔV[K]=Vav2[K]-Vav1[K].

As described above, the long-term average voltage value (Vav1[K]) is the long-term average cell voltage of the first time length A centered at the measurement timing tK, and the short-term average voltage value (Vav2[K]) is the short-term average cell voltage of the second time length B centered at the measurement timing tK. Therefore, by subtracting one of the long-term average voltage value (Vav1[K]) and the short-term average voltage value (Vav2[K]) from the other to obtain the voltage deviation (ΔV[K]), there is an effect of effectively removing the measurement noise generated in the predetermined period before and after the measurement timing tK.

There is an advantage in that measurement noise generated in a specific period before and after the measurement timing tK is offset to a considerable extent by subtracting the long-term average voltage value (Vav1[K]) and the short-term average voltage value (Vav2[K]) from the other.

The first control unit 400 may compare each of the plurality of voltage deviations (ΔV[K]) determined for the plurality of sub-voltage curves with at least one of the first threshold deviation TH1 and the second threshold deviation TH2 in order to detect abnormal behavior of the battery cell.

For example, the first control unit 400 may compare the voltage deviation (ΔV[K]) with a first threshold deviation TH1 and a second threshold deviation TH2. The first threshold deviation TH1 may be a predetermined positive number (e.g., +0.001V), and the second threshold deviation TH2 may be a predetermined negative number having the same absolute value as the first threshold deviation TH1 (e.g., -0.001V).

When a predetermined number (eg, 10) of voltage deviations among all voltage deviations included in the voltage deviation curve VC are equal to or greater than the first threshold deviation TH1 or equal to or less than the second threshold deviation TH2, the first control unit 400 may detect that the battery cell BC has abnormal behavior.

The first control unit 400 may be configured to detect the behavior abnormality in response to any two voltage deviations among the plurality of voltage deviations satisfying a first condition, a second condition, and a third condition, respectively.

Specifically, the first control unit 400 may be configured to determine that the battery cell has abnormal behavior when any two voltage deviations among the plurality of voltage deviations determined for the plurality of sub-voltage curves satisfy the first condition, the second condition, and the third condition.

For example, when any two voltage deviations satisfy the first condition, the second condition, and the third condition among the total number of M voltage deviations included in the voltage deviation curve VC, the first control unit 400 may determine that the battery cell BC has abnormal behavior. The first condition is satisfied when one of the two voltage deviations is greater than or equal to the first threshold deviation TH1. The second condition is satisfied when the other of the two voltage deviations is less than or equal to the second threshold deviation TH2. The third condition is satisfied when the time interval between the two voltage deviations is equal to or less than the threshold time. The threshold time may be predetermined to be less than the first time length A. Referring to FIG. 9 , the voltage deviation (ΔV[a]) is less than or equal to the second threshold deviation TH2 (satisfying the second condition), and the voltage deviation (ΔV[b]) is greater than or equal to the first threshold deviation TH1 (satisfying the first condition). Therefore, when the time interval (Δt=tb-ta) between the two voltage deviations (ΔV[a], ΔV[b]) is less than or equal to the threshold time, the battery cell BC may be detected as having abnormal behavior.

Hereinafter, the external device 2000 according to an embodiment of the present disclosure will be described in detail with reference to FIG. 10 .

10 is a block diagram showing a schematic configuration of an external device 2000 according to an embodiment of the present disclosure. The external device 2000 may be a dedicated device for diagnosing a battery cell. The external device 2000 may include a storage unit 2100 and a third control unit 2200.

The storage unit 2100 may collect the charge and discharge data included in the first information and store the collected charge and discharge data.

The type of the storage unit 2100 is not particularly limited as long as it can record and erase data and/or information. As an embodiment, the storage unit 2100 may be a RAM, a ROM, a register, a flash memory, a hard disk, or a magnetic recording medium.

The storage unit 2100 may be electrically connected to the third control unit 2200 via a data bus or the like, and thus may be accessed by the third control unit 2200 .

The storage unit 2100 stores and/or updates and/or erases and/or sends programs including various control logics executed by the third control unit 2200 and/or data and/or preset data, parameters, lookup information/tables, etc. generated when the control logics are executed.

First, an embodiment in which the diagnosis information of the battery cell included in the second information is information on lithium plating diagnosis of the battery cell will be described.

In one embodiment, the second information may indicate whether the accumulated capacity difference change is greater than or equal to a threshold value. Here, the accumulated capacity difference change may be the sum of the capacity difference changes. That is, the sum of multiple capacity difference changes may be calculated as the accumulated capacity difference change. Here, each of the capacity difference changes is the difference between the capacity difference of the kth charge and discharge cycle of the battery cell and the capacity difference of the k-1th charge and discharge cycle of the battery cell (k is a natural number greater than or equal to 2). Here, the capacity difference of each charge and discharge cycle corresponds to the difference between the charge capacity of the battery cell during the charging process of the charge and discharge cycle and the discharge capacity of the battery cell during the discharge process of the charge and discharge cycle. Here, each of the charge capacity and the discharge capacity can be derived from the data obtained from the current measurement unit and included in the first information.

The third control unit 2200 may generate second information including diagnostic information related to lithium plating diagnosis of the battery cell by using the first information. The number of charge and discharge cycles required to generate the second information may be set in advance. In one embodiment, the number of charge and discharge cycles required to generate the second information may be 20.

For example, a charge and discharge cycle may include a charge cycle and a discharge cycle. A charge cycle may refer to charging a battery from a lower limit of a preset charge voltage region to an upper limit and stopping charging, while maintaining a constant battery cell temperature. A discharge cycle may refer to stabilizing the battery for a predetermined time after the charge cycle is completed, and then discharging the battery from an upper limit of a preset discharge voltage region to a lower limit and stopping discharging, while maintaining the battery cell temperature the same as the charge cycle. The charge voltage region and the discharge voltage region may be the same or different. However, when performing multiple charge and discharge cycles, preferably, the charge voltage region of the charge cycle is the same, and the discharge voltage region of the discharge cycle is also the same.

In another embodiment, the charging cycle refers to charging the battery from the lower limit of the preset charging voltage range to the upper limit and stopping the charging, while keeping the battery cell temperature constant. The discharging cycle starts discharging from the upper limit of the preset discharge voltage range, and stops discharging when the cumulative current value reaches the preset discharge capacity by integrating the discharge current. When performing multiple charge and discharge cycles, preferably, the charging voltage range of the charging cycle is the same and the discharge capacity of the discharge cycle is the same.

The third control unit 2200 may derive the charge capacity and discharge capacity of the battery cell from the data included in the first information. Specifically, the third control unit 2200 may receive the current measurement value included in the first information in the Kth (k is a natural number greater than or equal to 2) charge and discharge cycle by using the information about the charge current or discharge current included in the first information, and calculate the charge capacity (ChgAh[k]) and the discharge capacity (DchgAh[k]).

For example, the third control unit 2200 may initialize the charge-discharge cycle index k to 1, and respectively set the first capacity difference change amount ΔdAh[1] and the first cumulative capacity difference change amount Initialized to 0.

The third control unit 2200 may start a first charge and discharge cycle of the battery. In this specification, when the external device 2000 starts a charge and discharge cycle, it means that the data corresponding to the charge and discharge cycle is acquired using the first information.

The third control unit 2200 may calculate the charging capacity (ChgAh[1]) and the discharging capacity (DchgAh[1]) using the current measurement value included in the first information during the first charge and discharge cycle.

The first information may include information on a charge cycle performed in a preset charge voltage region and a discharge cycle performed in a preset discharge voltage region.

The charging voltage region and the discharging voltage region may be the same or different. Preferably, after the charging cycle ends, the discharging cycle begins after the voltage of the battery cell stabilizes. In addition, the discharging cycle may end when the voltage of the battery cell reaches a preset discharge end voltage or when the integrated value of the discharge current reaches a preset discharge capacity. When the start and end of the charging cycle and the discharging cycle are controlled based on the voltage value, the external device 2000 may refer to the voltage measurement value included in the first information. The voltage measurement value included in the first information may be a value measured by the voltage sensing unit 200.

The third control unit 2200 may continue to perform the charge-discharge cycle until the index k for the charge-discharge cycle is equal to n. n is a preset natural number, which is the total number of charge-discharge cycles that can be continued to detect the lithium deposition anomaly. In one embodiment, n may be 20.

The third control unit 2200 may determine a capacity difference (dAh[k]) corresponding to the difference between the charge capacity (ChgAh[k]) and the discharge capacity (DchgAh[k]). That is, the capacity difference (dAh[k]) may be calculated as the difference between the charge capacity (ChgAh[k]) and the discharge capacity (DchgAh[k]).

For example, the first charge and discharge cycle (e.g., k=1) will be described. The third control unit 2200 may record the determined capacity difference (dAh[1]) in the storage unit 2100 together with a timestamp. In one embodiment, the capacity difference (dAh[1]) may be determined by subtracting the discharge capacity (DchgAh[1]) from the charge capacity (ChgAh[1]). The third control unit 2200 may determine the capacity difference (dAh[1]) corresponding to the difference between the charge capacity (ChgAh[1]) and the discharge capacity (DchgAh[1]), and record the determined capacity difference (dAh[1]) in the storage unit 2100 together with a timestamp. In one embodiment, the capacity difference (dAh[1]) may be determined by subtracting the discharge capacity (DchgAh[1]) from the charge capacity (ChgAh[1]).

The third control unit 2200 may determine the Kth capacity difference variation (ΔdAh[k]) by subtracting the capacity difference (dAh[k]) of the Kth charge and discharge cycle from the capacity difference (dAh[k-1]) of the k-1th charge and discharge cycle. That is, the capacity difference variation (ΔdAh[k]) may be calculated as the difference between the capacity difference (dAh[k]) of the Kth charge and discharge cycle and the capacity difference (dAh[k-1]) of the k-1th charge and discharge cycle.

For example, the third control unit 2200 may determine the second capacity difference variation amount (ΔdAh[2]) by subtracting the capacity difference (dAh[2]) of the second charge and discharge cycle from the capacity difference (dAh[1]) of the first charge and discharge cycle.

If the Kth capacity difference variation (ΔdAh[k]) is greater than the standard value, the third control unit 2200 may update the cumulative capacity difference variation by adding the Kth capacity difference variation (ΔdAh[k]) to the cumulative capacity difference variation. Here, the cumulative capacity difference variation may be the sum of a plurality of calculated capacity difference variations.

For example, the third control unit 2200 calculates the second capacity difference change amount (ΔdAh[2]) and the first cumulative capacity difference change amount (ΔdAh[2]). The cumulative capacity difference change is updated by adding and the second cumulative capacity difference change is determined. As a reference, the first cumulative capacity difference change It is 0, which is the initialization value.

If the updated cumulative capacity difference variation is greater than or equal to the threshold, the third control unit 2200 may detect the presence of lithium deposition abnormality and generate second information.

The threshold value may refer to a value suitable for detecting lithium plating abnormality. For example, the threshold value may be 0.1% of the battery capacity. The threshold value may be a value preset in the external device 2000 or a value included in the first information.

If the updated cumulative capacity difference change is greater than or equal to the threshold, the third control unit 2200 may determine that lithium deposition abnormality occurs inside the battery and generate second information. That is, the second information may indicate whether the cumulative capacity difference change is greater than or equal to the threshold.

In another embodiment, the second information may also represent the capacity difference change between consecutive charge and discharge cycles of the battery cell. Here, the capacity difference of each charge and discharge cycle of the battery cell may be the difference between (i) the charge capacity of the battery cell during the charging process of the charge and discharge cycle of the battery cell and (ii) the discharge capacity of the battery cell during the discharge process of the charge and discharge cycle of the battery cell. In this case, the first control unit 400 may calculate the cumulative capacity difference change by calculating the sum of the capacity difference changes based on the second information. In addition, the first control unit 400 may determine whether the cumulative capacity difference change is greater than or equal to a threshold value.

Next, an embodiment will be described in which the diagnosis information of the battery cells included in the second information is information on abnormality in parallel connection of the battery cells.

The second information may indicate that a plurality of unit cells included in the battery cell are abnormally connected in parallel based on a result of monitoring the estimated capacity value over time by the external device 2000. Here, the estimated capacity value may indicate a full charge capacity of the battery cell based on the charge and discharge data. Here, the charge and discharge data may include a voltage time series indicating the voltage of the battery cell over time and a current time series indicating the charge and discharge current of the battery cell over time.

The storage unit 2100 can collect charge and discharge data including a voltage time series representing the voltage across the battery cell varying over time and a current time series representing the charge and discharge current flowing through the battery cell varying over time by using the information about the charge current or discharge current included in the first information, and store the collected charge and discharge data.

The third control unit 2200 may determine an estimated capacity value representing the full charge capacity of the battery cell based on the charge and discharge data. In addition, the third control unit 2200 may detect a parallel connection abnormality by monitoring a change in the determined estimated capacity value over time. The diagnostic information of the battery cell corresponding to the parallel connection abnormality may be included in the second information.

A method in which the external device 2000 detects a parallel connection abnormality will be described in detail with reference to FIGS. 11 to 14 .

Figure 11 is a diagram exemplarily showing the schematic configuration of the battery cell shown in Figure 3, Figure 12 is a diagram for showing the first capacity abnormality (incomplete disconnection fault) of the battery cell, and Figure 13 is a diagram for showing the second capacity abnormality (complete disconnection fault) of the battery cell.

11 , the battery B includes an electrode assembly B200 , a positive electrode lead B210 , a negative electrode lead B220 , and a housing B230 .

The electrode assembly B200 is an embodiment of a plurality of unit cells BUC1 to UCM connected in parallel (M is a natural number of 2 or more). The unit cell BUC includes a separator B203, a positive electrode plate B201, and a negative electrode plate B202 insulated from the positive electrode plate B201 by the separator B203.

The positive electrode plate B201 has a positive electrode tab B205 as a portion connected to one end of a positive electrode lead B210, and the negative electrode plate B202 has a negative electrode tab B206 as a portion connected to one end of a negative electrode lead B220.

The electrode assembly B200 is accommodated in the housing B230 together with the electrolyte in a state where the positive electrode connector B205 and the negative electrode connector B206 of the plurality of unit cells UC1 to UCM are respectively connected to one end of the positive electrode lead B210 and the negative electrode lead B220. The other ends of the positive electrode lead B210 and the negative electrode lead B220 exposed to the outside of the housing B230 are respectively set as the positive terminal and the negative terminal of the battery B.

Referring to Figure 12, the first capacity abnormality of the electrode assembly B200 may refer to a state in which the contact resistances R1, R2 between the unit cells UC1, UC2 and the electrode leads B210, B220 change significantly and irregularly due to minor damage and/or incomplete disconnection failures in the electrode connectors B205, B206 of some unit cells UC1, UC2 among the multiple unit cells UC1 to UCM.

The incomplete disconnection failure may mean that the disconnected portions of the electrode tabs B205, B206 do not maintain a state of being separated from each other, the disconnected portions are flexibly connected and separated according to the contraction and expansion of the battery B, and the contact area during connection is also variable.

When the contact resistance in the unit cells UC1 and UC2 remains small, all the unit cells UC1 to UCM are charged and discharged almost in the same manner, and as the contact resistance R1 and R2 increase, the unit cells UC1 and UC2 immediately enter a state of separation (disconnection) from the remaining unit cells UC3 to UCM, so the capacity of the battery B shows an irregular behavior of rapid increase or decrease, and this irregular behavior depends largely on the contact resistance R1 and R2 of the unit cells UC1 and UC2. For example, when a large tension acts between the electrode terminals B205 and B206 of the unit cells UC1 and UC2 and the electrode leads B210 and B220 due to the expansion of the battery B, the contact resistance R1 and R2 of the unit cells UC1 and UC2 increase, and conversely, as the tension gradually decreases, the contact resistance R1 and R2 of the unit cells UC1 and UC2 decrease.

Referring to Figure 13, the second capacity abnormality of the electrode assembly B200 is equivalent to the state that some unit cells UC1, UC2 among the multiple unit cells UC1 to UCM are irreversibly broken, that is, the charging and discharging current paths between the unit cells UC1, UC2 and the electrode leads B210, B220 are irreversibly lost due to the complete disconnection failure of the unit cells UC1, UC2.

The complete disconnection fault is a state in which the electrode terminals B205, B206 or the electrode plates B201, B202 of the unit cells UC1, UC2 are cut into a plurality of parts separated and cannot be reconnected, and is distinguished from the above-mentioned incomplete disconnection fault.

The occurrence of the second capacity abnormality caused by the unit cells UC1 and UC2 at a specific time during the manufacture or use of the battery B may mean that the unit cells UC1 and UC2 are irreversibly removed from the electrode leads B210 and B220. Therefore, from the specific time when the second capacity abnormality occurs, the unit cells UC1 and UC2 do not contribute to the charge and discharge of the battery B at all, and therefore the capacity of the battery B depends only on the capacity of the remaining unit cells UC3 to UCM except the unit cells UC1 and UC2.

The external device 2000 may periodically or non-periodically repeat a process for determining an estimated capacity value representing the full charge capacity (FCC) of the battery cell by applying the capacity estimation model to the charge and discharge data. The external device 2000 may monitor changes in the estimated capacity value over time. The capacity estimation model is an algorithm that receives the charge and discharge data and provides an estimated capacity value as a corresponding output, and is a combination of several functions.

Specifically, the capacity estimation model may include: (i) a first function that calculates the current integral value of the charge and discharge current of battery B in a certain period or variable period in the past according to the current time series of battery B; (ii) a second function that calculates the OCV (open circuit voltage) of battery B in a certain period or variable period in the past according to the voltage time series and/or current time series of battery B; (iii) a third function that calculates the SOC (state of charge) of battery B according to the OCV of battery B using a given OCV-SOC relationship table; and (iv) a fourth function that calculates the estimated result of the full charge capacity of battery B, i.e., the estimated capacity value, according to the ratio of the current integral value and the SOC change value calculated for the common period. The following equation 12 is an embodiment of the fourth function.

<Equation 12>

In the above equation, ΔAht1 _-t2 is a current integral value of the charge and discharge current repeatedly measured within the time range between t1 and t2, ΔSOC _t1-t2 is a SOC change value within the time range between t1 and t2, and FCC _t2 is an estimated capacity value representing the full charge capacity at time t2. Time t1 may be the latest time before time t2 and satisfying that the absolute value of ΔAh _t1-t2 is greater than or equal to the standard integral value and/or the absolute value of ΔSOC _t1-t2 is greater than or equal to the standard change value. The standard integral value and the standard change value may be predetermined to prevent the accuracy of FCC _t2 from being deteriorated due to very small absolute values of ΔAh _t1-t2 and/or ΔSOC _t1-t2 .

When calculating the current integral value, the charging current can be set as a positive number, and the discharging current can be set as a negative number. Time t2 is the calculation timing of the full charge capacity. If the full charge capacity is calculated repeatedly at every first time interval, it will be easily understood by those skilled in the art that time t2 is shifted to the first time interval.

For example, when the current integral value and the SOC change value in the past common period are +100Ah [ampere-hour] and +80%, respectively, the estimated capacity value of the full charge capacity is 125Ah. As another embodiment, if the current integral value and the SOC change value in the past common period are -75Ah [ampere-hour] and -60%, respectively, the estimated capacity value of the full charge capacity is also 125Ah.

The full charge capacity indicates the maximum storage capacity of the battery B, that is, the remaining capacity of the battery B at SOC 100%. Normally, the full charge capacity decreases slowly as the battery B degrades. Therefore, when the amount by which the full charge capacity decreases with respect to a short time interval exceeds a certain level, it indicates that the first capacity abnormality or the second capacity abnormality occurs.

FIG. 14 is an exemplary diagram for illustrating the relationship between the capacity abnormality of a battery cell and the full charge capacity.

14 , a curve C21 shows the change of the full charge capacity of a normal battery cell over time. For better understanding, the curve C21 is simplified to show that the full charge capacity of a normal battery cell decreases linearly over time.

Curve C22 illustrates the change of the full charge capacity of battery B over time when the first capacity abnormality and the second capacity abnormality occur sequentially. As shown in FIG12, curve C22 represents the full charge capacity of battery B in which the first capacity abnormality occurs due to minor damage and/or incomplete disconnection failure of unit cells UC1, UC2. In curve C22, the full charge capacity gradually decreases from time ta (e.g., the release time of the battery cell) to time tb, then decreases rapidly from time tb to time tc, and then increases rapidly from time tc to time td. That is, most of the reduction in the full charge capacity between time tb and time tc is recovered between time tc and time td. As described above with reference to FIG12, this is because the contact resistances R1, R2 of the unit cells UC1, UC2 increase rapidly between time tb and time tc, and then return to normal levels between time tc and time td.

This means that if the first capacity anomaly persists for a long time, it may develop (intensify) into a second capacity anomaly. Referring to curve C22, after time td, from time te to time tf, similar to the previous period from time tb to time tc, the full charge capacity decreases rapidly. However, in contrast to the behavior from time tc to td, even after time tf, when the rapid decrease in full charge capacity stops, curve C22 has a slope similar to curve C21 in a state where the full charge capacity has not returned to a normal level. As described above with reference to FIG. 13, this is because a complete disconnection failure of unit cells UC1, UC2, i.e., a second capacity anomaly, occurs near time te.

The external device 2000 determines whether the first capacity abnormality and/or the second capacity abnormality of the parallel connection B200 occurs by monitoring the full charge capacity according to the curve C22 (ie, the change in the estimated capacity value over time (time series)).

Specifically, the external device 2000 determines whether the first capacity abnormality and/or the second capacity abnormality of the parallel connection B200 occurs by applying the diagnostic logic to two estimated capacity values of the first time and the second time shifted to the first time interval, wherein the second time interval is equal to or greater than the first time interval. The second time is the time after the first time passes the second time interval, and the first time and the second time can be set by the external device 2000 to increase one first time interval for each first time interval. The first time interval can be equal to the collection period of the charge and discharge data (or the calculation period of the estimated capacity value), and the second time interval can be an integer multiple (e.g., 10 times) of the first time interval.

The diagnostic logic may include (i) a first routine that determines that the threshold capacity value at the second time is less than the estimated capacity value at the first time, and (ii) a second routine that compares the estimated capacity value at the second time to the threshold capacity value at the second time.

In the first routine, the threshold capacity value at the second time may be equal to the result of subtracting the standard capacity value from the estimated capacity value at the first time or the result of multiplying the estimated capacity value at the first time by a standard factor less than 1. The standard capacity value may be recorded in the memory 120 as a predetermined value in consideration of the total number M of the plurality of unit cells UC1 to UCM included in the battery B and the design capacity of the battery B (full charge capacity in a new state). The standard factor may be recorded in the memory 120 as a predetermined value (e.g., (M-1)/M, (M-2)/M) in consideration of the total number M of the plurality of unit cells UC1 to UCM included in the battery B. Curve C23 of FIG. 14 shows the change over time of the threshold capacity value calculated by applying the first routine to curve C22.

If the estimated capacity value at the second time is less than the threshold capacity value at the second time, the external device 2000 may determine that at least one of the first capacity abnormality and the second capacity abnormality has occurred in the parallel connection B200. In addition, the external device 2000 may generate second information indicating whether there is a parallel connection abnormality.

Specifically, whenever the estimated capacity value at the second time is less than the threshold capacity value at the second time, the external device 2000 may increase the diagnostic count by 1. Whenever the estimated capacity value at the second time is greater than or equal to the threshold capacity value at the second time, the calculation circuit 130 may reset the diagnostic count to an initial value (e.g., 0) or reduce the diagnostic count by 1. In response to the estimated capacity value at the second time being restored to the threshold capacity value at the second time or higher before the diagnostic count reaches the threshold count, the external device 2000 may determine that the abnormality type of the parallel connection B200 is the first capacity abnormality. In response to the diagnostic count reaching the threshold count (e.g., 5), the external device 2000 may determine that the second capacity abnormality of the parallel connection B200 has occurred.

In FIG. 14 , time ta+, tb+, tc+, td+, te+, and tf+ are times shifted in the positive direction from time ta, tb, tc, td, te, and tf, respectively, at the second time interval. In the time range between time tx and time ty, curve C22 is located below curve C22. Therefore, from time tx to time ty, the diagnostic count for each first time interval increases by 1. The external device 2000 may activate a predetermined protection function associated with the second capacity abnormality of battery B in response to the diagnostic count reaching the threshold count before time ty.

Finally, an embodiment will be described in which the diagnosis information of the battery cell included in the second information is information on the internal short circuit of the battery cell.

The second information may indicate whether the battery cell has an internal short circuit based on the first SOC change and the standard factor of the battery cell. Here, the standard factor may be determined by applying a statistical algorithm to the first SOC change of at least two battery cells among the plurality of battery cells. Here, the first SOC change may be the difference between the first SOC of each battery cell at a first charging time point and the second SOC at a second charging time point. Here, the first SOC may be estimated by applying an SOC estimation algorithm to the state parameters of the battery cell at the first charging time point. Here, the second SOC may be estimated by applying an SOC estimation algorithm to the state parameters of the battery cell at the second charging time point. Here, the state parameters may be acquired based on the first information.

The storage unit 2100 may acquire a state parameter of each of the plurality of battery cells by using the information included in the first information. The state parameter may include a voltage, a current, and/or a temperature of the battery cell BC.

The third control unit 2200 can monitor the SOC change of the battery cell BC during the charging period, the discharging period and/or the idle period of the battery pack 10 by applying the SOC estimation algorithm to the state parameters of the battery cell BC. For example, as the SOC estimation algorithm, an OCV-SOC relationship diagram or a Kalman filter can be used. The OCV-SOC relationship diagram and the Kalman filter are widely used SOC estimation techniques, and therefore will not be described in detail.

The third control unit 2200 can determine a first SOC variation of each battery cell BC, which is a difference between a first SOC at _{a first} charging time point and a second SOC at a second charging time point, by applying an SOC estimation algorithm to a state parameter of each of the plurality of battery cells BC1 to BC _N acquired during charging. The first charging time point and the second charging time point are not particularly limited as long as they are two different time points within a recent charging period.

The third control unit 2200 may determine the standard factor by applying a statistical algorithm to the first SOC changes of at least two battery cells among the plurality of battery cells. The standard factor may be a representative value of the first SOC changes of at least two battery cells among the plurality of battery cells BC ₁ to BC _N. For example, the standard factor may be an average value or a median value of the first SOC changes of at least two battery cells among the plurality of battery cells.

The third control unit 2200 may detect an internal short circuit abnormality of each battery cell based on the first SOC variation of each battery cell and the standard factor. In addition, diagnosis information of the battery cell corresponding to whether the battery cell has an internal short circuit may be included in the second information.

Hereinafter, a method of detecting an internal short circuit abnormality by the external device 2000 will be described in detail with reference to FIGS. 15 to 18 .

15 is a diagram for illustrating an exemplary equivalent circuit of a battery cell. In this specification, a normal battery cell refers to a battery cell without an internal short circuit abnormality among a plurality of battery cells _BC1 to _BCN , and an abnormal battery cell refers to a battery cell with an internal short circuit abnormality among a plurality of battery cells _BC1 to _BCN .

15 , a normal battery cell may be equivalent to a series circuit of a DC voltage source (V _DC ), an internal resistance component (R ₀ ), and an RC pair (R ₁ , C). In contrast, an abnormal battery cell may be equivalent to a battery cell in which an additional resistance component (R _{ISC ) is connected between both ends of the series circuit corresponding to the normal battery cell. The additional resistance component (R ISC )} serves as a path for a leakage current (I _ISC ).

When the abnormal battery cell is charged, some of the charging power is consumed as leakage current (I _ISC ) without being stored in the abnormal battery cell. In addition, when the abnormal battery cell is discharged, some of the discharge power is consumed as leakage current (I _ISC ) without being supplied to the electrical load. For reference, when the abnormal battery cell is idle, the energy stored in the abnormal battery cell is consumed as leakage current (I _ISC ), similar to discharge. The decrease in the resistance value of the resistor (R _ISC ) means that the internal short circuit abnormality is enhanced, and as the internal short circuit abnormality worsens, the amount of power consumed as leakage current (I _ISC ) may increase.

Therefore, during charging, the voltage change of the abnormal battery cell (i.e., the increase in SOC) is smaller than that of the normal battery cell. Meanwhile, during discharging, the voltage change of the abnormal battery cell (i.e., the decrease in SOC) is greater than that of the normal battery cell.

16 to 18 are exemplary graphs for comparing changes in the SOC of a battery cell according to the presence or absence of an internal short circuit abnormality. FIGS. 16 to 18 respectively show changes in charge and discharge current, voltage of a battery cell BC, and SOC of a battery cell BC in the same period.

Referring to FIG. 16 , time point t0 and time point t4 represent time points when the idle state is switched to the charging state, time point t1 and time point t5 represent time points when the charging state is switched to the idle state, time point t2 represents time points when the idle state is switched to the discharging state, and time point t3 represents time points when the discharging state is switched to the idle state. That is, in FIG. 16 , the period from time point t0 to time point t1 and the period from time point t4 to time point t5 are charging periods, the period from time point t2 to time point t3 is a discharging period, and the remaining period is a standby period. For ease of explanation, in FIG. 16 , a positive value is assigned to the charging current of each charging period, a negative value is assigned to the discharging current of the discharging period, and the current in each period is shown as constant.

In FIG17 , curve VC2 represents a voltage curve of a normal battery cell corresponding to the current curve shown in FIG16 , and curve VC3 represents a voltage curve of an abnormal battery cell corresponding to the current curve shown in FIG16 . Curve VC2 may be considered as a time series of average voltages of the plurality of battery cells _BC1 to BC _N. The external device 2000 may periodically or non-periodically acquire a state parameter of each of the plurality of battery cells _BC1 to BC _N , and record the time series of the state parameters in the storage unit 2100.

17 , during charging, the voltages of both the normal battery cells and the abnormal battery cells gradually increase. However, since the abnormal battery cells have a lower charging power capacity than the normal battery cells, the voltage increase of the abnormal battery cells is smaller than the voltage increase of the normal battery cells.

During discharge, the voltages of both the normal battery cells and the abnormal battery cells gradually decrease. However, in the abnormal battery cells, in addition to the discharge power of the normal battery cells, additional power is consumed due to the leakage current (IISC), so the voltage decrease amount of the abnormal battery cells is greater than the voltage decrease amount of the normal battery cells.

In Fig. 18, curve VC4 represents the SOC curve of a normal battery cell corresponding to the voltage curve VC2 shown in Fig. 17, and curve VC5 represents the SOC curve of an abnormal battery cell corresponding to the voltage curve VC3 shown in Fig. 17. Curve VC4 can also be regarded as a time series of the average SOC of a plurality of battery cells _BC1 to BC _N.

The external device 2000 can monitor the SOC change of the battery cell BC during the charging period, the discharging period and/or the idle period of the battery pack 10 by applying the SOC estimation algorithm to the state parameters of the battery cell BC. For example, regarding the SOC estimation algorithm, an OCV-SOC relationship diagram or a Kalman filter can be used. The OCV-SOC relationship diagram and the Kalman filter are widely used SOC estimation techniques, and therefore will not be described in detail.

Referring to FIG. 18 , in the charging period, the abnormal battery cell has a smaller SOC increase rate and increase amount than the normal battery cell. In the discharging period, the abnormal battery cell has a higher SOC decrease rate and decrease amount than the normal battery cell. In addition, in the idle period, the SOC of the normal battery cell is generally constant, while the SOC of the abnormal battery cell gradually decreases even when the charge and discharge current does not flow.

Whenever the battery pack 10 is charged, the external device 2000 may perform a diagnostic process for detecting an internal short circuit abnormality of the battery cell BC based on a change in SOC of all the battery cells among the plurality of battery cells BC ₁ to _BC N in a recent charging period. For example, when the external device 2000 switches from charging to idle at time point t1, an internal short circuit abnormality of the battery cell BC may be detected based on a change in SOC of all the battery cells among the plurality of battery cells BC ₁ to BC _N acquired during the charging period (t0 to t1).

As another embodiment, when the external device 2000 switches from charging to idling at time point t5, an internal short circuit abnormality may be detected based on SOC changes of all battery cells of the plurality of battery cells _BC1 to _BCN acquired in the most recent charging period (t4 to t5).

Alternatively, whenever the battery pack 10 is charged or discharged, the external device 2000 may perform a diagnostic process for detecting an internal short circuit abnormality of a battery cell _BC based on SOC changes of all battery cells among the plurality of battery cells BC ₁ to BC _N in a recent charging period and SOC changes of all battery cells among the plurality of battery cells BC 1 to BC _N in a recent discharging period.

For example, when the external device 2000 switches from a discharging state to an idle state at time point t3, the external device 2000 can detect an internal short circuit abnormality of the battery cell BC based on the SOC changes of all battery cells among the multiple battery cells BC ₁ to BC _N obtained in the most recent charging period (t0 to t1) and based on the SOC changes of all battery cells among the multiple battery cells BC ₁ to BC _N obtained in the most recent discharging period (t2 to t3).

As another embodiment, when switching from a charging state to an idle state at time point t5, the external device 2000 can detect an internal short circuit abnormality of the battery cell BC based on the SOC changes of all battery cells among the multiple battery cells BC ₁ to BC _N obtained in the most recent discharge period (t2 to t3) and the SOC changes of all battery cells among the multiple battery cells BC ₁ to BC _N obtained in the most recent charging period (t4 to t5).

In Figures 16 to 18, the idle mode is located between the charging period and the discharging period, but this is only one embodiment. For example, it is possible to switch from the charging state to the discharging state without the idle state, or from the discharging state to the charging state without the idle state.

Hereinafter, the battery cell abnormal state diagnosis method using the battery cell diagnosis apparatus 1000 and the external device 2000 of the present disclosure will be described in detail. The operation of the control circuit 220 will be described in more detail in various embodiments of the battery diagnosis method.

In this specification, the function performed by the external device 2000 may include the function performed by the third control unit 2200 .

Hereinafter, a battery cell diagnosis method abnormal state using the battery cell diagnosis apparatus 1000 of the present disclosure described above and the external device 2000 will be described in detail. The operation of the control circuit 220 will be described in more detail in various embodiments of the battery diagnosis method.

FIG. 19 is a flowchart of the battery cell diagnostic apparatus 1000 diagnosing an abnormal state of a cell using an external device 2000 according to an embodiment of the present disclosure.

In step S1000, the battery cell diagnostic device 1000 may acquire data. For example, the battery cell diagnostic device 1000 may acquire data measured by the current measuring unit 100 or the voltage sensing unit 200 using the data acquisition unit 300. Specifically, the battery cell diagnostic device 1000 may acquire data about at least one of the charging current, discharging current, and voltage signal measured by the current measuring unit 100 or the voltage sensing unit 200 by using the data acquisition unit 300. In addition, the battery cell diagnostic device 1000 may acquire data about the temperature signal detected by the temperature sensor T using the data acquisition unit 300.

In step S2000, the battery cell diagnostic device 1000 may generate first information. For example, the battery cell diagnostic device 1000 may generate first information of the battery cell based on the data acquired in step S1000. The first information may include data on at least one of a charging current, a discharging current, and a voltage signal of the battery cell as an abnormality judgment target.

In step S3000 , the battery cell diagnosis apparatus 1000 may transmit the generated first information to the external device 2000 .

In step S4000, the battery cell diagnosis apparatus 1000 may detect voltage abnormality. This will be described in detail with reference to FIGS. 20 to 24.

In step S5000, the battery cell diagnosis apparatus 1000 may detect a behavior abnormality. This will be described in detail with reference to FIGS. 25 to 26.

In step S7000, the external device 2000 may detect lithium deposition abnormality. This will be described in detail with reference to FIGS. 27 to 32.

In step S8000, the external device 2000 may generate second information. The second information may include whether lithium plating abnormality is detected.

In step S9000 , the external device 2000 may transmit the second information to the battery cell diagnosis apparatus 1000 .

In step S6000, the battery cell diagnosis apparatus 1000 may diagnose the abnormal state of the battery cell. The battery cell diagnosis apparatus 1000 may diagnose the abnormal state of the battery cell based on the voltage abnormality, the behavior abnormality and the second information.

20 to 24 are flowcharts illustrating in detail a process of detecting voltage abnormality by the battery cell diagnosis apparatus 1000 according to an embodiment of the present disclosure.

Fig. 20 is a flowchart exemplarily illustrating a voltage abnormality detection method according to an embodiment of the present disclosure. The method of Fig. 20 may be periodically performed by the first control unit 400 per unit time.

In step S4310, the first control unit 400 may collect a voltage signal representing a cell voltage of each of the plurality of battery cells _BC1 to _BCN included in the first information, and generate time series data of the cell voltage of each battery cell BC (see FIG. 4a). In the time series data of the cell voltage, the number of data may increase by 1 for each unit time.

For example, _Vi [k] or _VDi [k] of Equation 5 may be used as the cell voltage.

In step S4320, the first control unit 400 _may determine a first average cell voltage (S4MA _i [k], see Equation 1 and Equation 11) and a second average cell voltage (LMA _i [k], see Equation 3 and Equation 4) of each battery cell BC i based on the time series data of the cell voltage of each battery cell BC _i (see FIG. 4 b ).

The first average cell voltage (S4MA _i [k]) may refer to a short-term moving average value of the cell voltage of each battery cell BC _i within a first moving window having a first time length. The second average cell voltage (LMA _i [k]) may refer to a long-term moving average value of the cell voltage of each battery cell BC _i within a second moving window having a second time length. When calculating the first average cell voltage (S4MA _i [k]) and the second average cell voltage (LMA _i [k]), Vi _[ k] or VD _i [k] may be used.

In step S4330, the first control unit 400 may determine the long-term and short-term average differences (|S4MA _i [k]-LMA _i [k]|) of each battery cell BC _i (see FIG. 4 c ).

In step S4340, the first control unit 400 may determine a cell diagnostic deviation (Ddiag, i[k]) of each battery cell BC _i . The cell diagnostic deviation (Ddiag, i[k]) may refer to a deviation between an average value of the long-term and short-term average differences of all battery cells (|S4MA _i [k]-LMA _i [k]|av) and the long-term and short-term average difference (|S4MA _i [k]-LMA _i [k]|) of the i-th battery cell BC _i .

In step S4350, the first control unit 400 may determine whether the diagnosis time has passed. The diagnosis time may be preset. If the determination of step S4350 is yes, step S4360 is continued, and if the determination of step S4350 is no, steps S4310 to S4340 are repeated again.

In step S4360, the first control unit 400 may generate time series data regarding the cell diagnosis deviation (Ddiag,i[k]) of each battery cell BC _i collected during the diagnosis time.

In step S4370, the first control unit 400 may detect the voltage abnormality of each battery cell BC _i by analyzing the time series data regarding the cell diagnosis deviation (Ddiag,i[k]).

In one embodiment, the first control unit 400 may integrate the time region in which the cell diagnostic deviation (Ddiag, i[k]) is greater than a diagnostic threshold (e.g., 0.015) in the time series data of the cell diagnostic deviation (Ddiag, i[k]) of each battery cell BCi, and detect the cell for which the condition that the integration time is greater than a preset standard time is met as a cell with abnormal voltage.

For example, the first control unit 400 may integrate only the time region that continuously satisfies the condition that the cell diagnosis deviation (Ddiag, i[k]) is greater than the diagnosis threshold. If there are multiple corresponding time regions, the first control unit 400 may calculate the integration time independently for each time region.

In another embodiment, the first control unit 400 may integrate the number of _data in which the cell diagnostic deviation (Ddiag, i[k]) is greater than a diagnostic threshold (e.g., 0.015) in the time series data of the cell diagnostic deviation (Ddiag, i[k]) of each battery cell BC i, and detect the battery cell for which the condition that the data integral value is greater than a preset standard count is met as a battery cell with abnormal voltage.

The first control unit 400 may integrate only the amount of data included in the time zone that continuously satisfies the condition that the cell diagnosis deviation (Ddiag, i[k]) is greater than the diagnosis threshold. If there are multiple corresponding time zones, the first control unit 400 may independently integrate the amount of data in each time zone.

Fig. 21 is another flowchart exemplarily illustrating a voltage abnormality detection method according to an embodiment of the present disclosure. The method of Fig. 21 may be periodically performed by the first control unit 400 every unit time.

In the method for detecting voltage abnormality of Fig. 21, steps S4310 to S4360 are substantially the same as those of the embodiment of Fig. 20, and thus description thereof will be omitted. After step S4360, step S4380 is continued.

In step S4380, the first control unit 400 may generate time series data of a statistical variable threshold value (Dthreshold[k]) using Equation 8. The input of the Sigma function of Equation 8 is the time series data of the cell diagnostic deviation (Ddiag, i[k]) of all battery cells generated in step S4360. Preferably, the maximum value of the cell diagnostic deviation (Ddiag, i[k]) may be excluded from the input value of the Sigma function. The cell diagnostic deviation (Ddiag, i[k]) is the deviation from the average of the long-term and short-term mean differences (|SMA _i [k]-LMA _i [k]|).

In step S4390, the first control unit 400 may generate time series data of a filtered diagnosis value (Dfilter,i[k]) by filtering the cell diagnosis deviation (Ddiag,i[k]) of each battery cell BC _i using Equation 9.

When using Equation 9, D*diag,i[k] can be replaced by Ddiag,i[k].

In step S4400 , the first control unit 400 may determine whether the voltage of each battery cell BC _i is abnormal by analyzing the time series data of the filtered diagnosis value (Dfilter, i[k]).

In one embodiment, the first control unit 400 can integrate the time _region in which the filtered diagnostic value (Dfilter, i[k]) is greater than a diagnostic threshold (e.g., 0) in the time series data of the filtered diagnostic value (Dfilter, i[k]) of each battery cell BC i, and judge the battery cell for which the condition that the integration time is greater than a preset standard time is met as a battery cell with abnormal voltage.

Preferably, the first control unit 400 may integrate only the time regions that continuously satisfy the condition that the filter diagnosis value (Dfilter, i[k]) is greater than the diagnosis threshold. If there are multiple corresponding time regions, the first control unit 400 may independently calculate the integration time of each time region.

In another embodiment, the first control unit 400 may _integrate the number of data included in the time region in which the filtered diagnostic value (Dfilter, i[k]) is greater than a diagnostic threshold value (e.g., 0) in the time series data of the filtered diagnostic value (Dfilter, i[k]) of each battery cell BC i, and detect the battery cell for which the condition that the data integral value is greater than a preset standard count is met as a battery cell with abnormal voltage.

Preferably, the first control unit 400 may integrate only the amount of data included in the time region that continuously satisfies the filter diagnosis value (Dfilter, i[k]) greater than the diagnosis threshold. If there are multiple corresponding time regions, the first control unit 400 may independently integrate the amount of data in each time region.

Fig. 22 is another flowchart exemplarily illustrating a voltage abnormality detection method according to an embodiment of the present disclosure. The method of Fig. 22 may be periodically executed by the first control unit 400 every unit time.

The battery diagnosis method according to the third embodiment is substantially the same as the first embodiment except that steps S4340, S4360, and S4370 are changed to steps S4341, S4361, and S4371, respectively. Therefore, only the configuration having a difference will be described.

In step S4341, the first control unit 400 may determine a normalized cell diagnostic deviation (D*diag,i[k]) of the long-term and short-term average _differences (|SMA _i [k]-LMA _i [k]|) of each battery cell BC i using Equation 6. The normalized standard value is the average value of the long-term and short-term average differences (|SMA _i [k]-LMA _i [k]|). Equation 6 may be replaced by Equation 7.

In step S4361, the first control unit 400 may generate time series data of a normalized cell diagnostic deviation (D*diag,i[k]) of each battery cell BC _i collected during the diagnosis time (see FIG. 4d).

In step S4371, the first control unit 400 may detect the voltage abnormality of each battery cell BC _i by analyzing the time series data of the normalized cell diagnostic deviation (D*diag,i[k]).

In one embodiment, the first control unit 400 may integrate the time region in which the cell diagnostic deviation (D*diag, i[k] ₎ is greater than a diagnostic threshold (e.g., 4) in the time series data of the normalized cell diagnostic deviation (D*diag, i[k]) of each battery cell BC i, and detect the battery cell for which the condition that the integration time is greater than a preset standard time is met as a battery cell with abnormal voltage.

The first control unit 400 may integrate only the time regions that continuously satisfy the condition that the normalized cell diagnostic deviation (D*diag, i[k]) is greater than the diagnostic threshold. If there are multiple corresponding time regions, the first control unit 400 may independently calculate the integration time for each time region.

In another embodiment, the first control unit 400 may integrate the number of data in which the cell diagnostic deviation is greater than a diagnostic threshold (e.g., 4 ₎ in the time series data of the normalized cell diagnostic deviation (D*diag, i[k]) of each battery cell BC i, and detect the battery cell for which the condition that the data integral value is greater than a preset standard count is met as a battery cell with abnormal voltage.

The first control unit 400 may integrate only the amount of data included in the time region that continuously satisfies the normalized cell diagnostic deviation (D*diag, i[k]) greater than the diagnostic threshold. If there are multiple corresponding time regions, the first control unit 400 may independently integrate the amount of data in each time region.

Fig. 23 is another flowchart exemplarily showing a voltage abnormality detection method according to an embodiment of the present disclosure. The method of Fig. 23 may be periodically performed by the first control unit 400 every unit time.

The battery diagnosis method according to FIG. 23 is substantially the same as the battery diagnosis method of FIG. 21 except that steps S4340, S4360, S4380, S4390, and S4400 are respectively changed to steps S4341, S4361, S4381, S4391, and S4401, and the remaining configurations are substantially the same. Therefore, only the configuration different from FIG. 21 will be described with respect to the embodiment of FIG. 23.

In step S4341, the first control unit 400 may determine a normalized cell diagnostic deviation (D*diag,i[k]) of the long-term and short-term average _differences (|SMA _i [k]-LMA _i [k]|) of each battery cell BC i using Equation 6. The normalized standard value may refer to an average value of the long-term and short-term average differences (|SMA _i [k]-LMA _i [k]|). Equation 6 may be replaced by Equation 7.

In step S4361, the first control unit 400 may generate time series data of a normalized cell diagnostic deviation (D*diag,i[k]) of each battery cell BC _i collected during the diagnosis time (see FIG. 4d).

In step S4381, the first control unit 400 may generate time series data of a statistical variable threshold value (Dthreshold[k]) using equation 8. The input of the Sigma function of equation 8 is the time series data of the normalized cell diagnostic deviation (D*diag, i[k]) of all battery cells generated in step S4361. According to an embodiment, at each time index, the maximum value of the cell diagnostic deviation (D*diag, i[k]) may be excluded from the input value of the Sigma function.

In step S4391, the first control unit 400 may generate time series data of a diagnosis value (Dfilter, i[k]) by filtering the cell diagnosis deviation (D*diag, i[k]) of each battery cell BC _i based on the statistical variable threshold (Dthreshold[k]) using Equation 9.

In step S4401 , the first control unit 400 may detect voltage abnormality of each battery cell BC _i by analyzing time series data of the filtered diagnosis value (Dfilter,i[k]).

In one embodiment, the first control unit 400 may integrate the time _region in which the filtered diagnostic value (Dfilter, i[k]) is greater than a diagnostic threshold value (e.g., 0) in the time series data of the filtered diagnostic value (Dfilter, i[k]) of each battery cell BC i, and detect the battery cell for which the condition that the integration time is greater than a preset standard time is met as a battery cell with abnormal voltage.

For example, the first control unit 400 may integrate the time region that continuously satisfies the condition that the filter diagnosis value (Dfilter, i[k]) is greater than the diagnosis threshold. If there are multiple corresponding time regions, the first control unit 400 may independently calculate the integration time of each time region.

In another embodiment, the first control unit 400 may _integrate the number of data included in the time region in which the filtered diagnostic value (Dfilter, i[k]) is greater than a diagnostic threshold value (e.g., 0) in the time series data of the filtered diagnostic value (Dfilter, i[k]) of each battery cell BC i, and detect the battery cell for which the condition that the data integral value is greater than a preset standard count is met as a battery cell with abnormal voltage.

The first control unit 400 may integrate only the amount of data included in the time region that continuously satisfies the condition that the filter diagnosis value (Dfilter, i[k]) is greater than the diagnosis threshold. If there are multiple corresponding time regions, the first control unit 400 may independently integrate the amount of data in each time region.

FIG. 24 is a flowchart illustrating another method for detecting voltage abnormality according to an embodiment of the present disclosure.

In Fig. 24, steps S4310 to S4361 are substantially the same as those in Fig. 23. Therefore, only the configuration different from Fig. 23 will be described.

In step S4410, the first control unit 400 may use the time series data of the normalized cell diagnostic deviation (D*diag, i[k]) of each battery cell BC _i (see FIG. 4f ) to generate time series data of a first moving average value (SMA _i [k]) and a second moving average value (LMA _i [k]) of the cell diagnostic deviation (D*diag, i[k]).

In step S4420, the first control unit 400 can use Equation 6 (see Figure 4g ₎ to generate time series data of normalized cell diagnostic deviation (D*diag, i[k]) for time series data of first moving average value (SMA _i [k]) and time series data of second moving average value (LMA _i [k]) for each battery cell BC i.

In step S4430, the first control unit 400 may generate time series data of a statistical variable threshold value (Dthreshold[k]) using Equation 8 (see FIG. 4g).

In step S4440, the first control unit 400 may generate time series data of a filtered diagnosis value (Dfilter,i[k]) of each battery cell BC _i based on a statistical variable threshold value (Dthreshold[k]) using Equation 9 (see FIG. 4h).

In step S4450 , the first control unit 400 may detect the voltage abnormality of each battery cell BC _i by analyzing the time series data of the filtered diagnosis value (Dfilter,i[k]) of each battery cell BC _i .

In one embodiment, the first control unit 400 may integrate the time _region in which the filtered diagnostic value (Dfilter, i[k]) is greater than a diagnostic threshold (e.g., 0) in the time series data of the filtered diagnostic value (Dfilter, i[k]) of each battery cell BC i, and detect the battery cell for which the condition that the integration time is greater than a preset standard time is met as a battery cell with abnormal voltage.

The first control unit 400 may integrate the time region that continuously satisfies the condition that the filter diagnosis value (Dfilter, i[k]) is greater than the diagnosis threshold. If there are multiple corresponding time regions, the first control unit 400 may independently calculate the integration time of each time region.

The first control unit 400 can integrate the number of data _included in the time region where the filtered diagnostic value (Dfilter, i[k]) is greater than the diagnostic threshold (for example, 0) in the time series data of the filtered diagnostic value (Dfilter, i[k]) of each battery cell BC i, and detect the battery cell whose data integration value is greater than a preset standard count as a battery cell with abnormal voltage.

The first control unit 400 may integrate only the amount of data included in the time region that continuously satisfies the condition that the filter diagnosis value (Dfilter, i[k]) is greater than the diagnosis threshold. If there are multiple corresponding time regions, the first control unit 400 may independently integrate the amount of data in each time region.

24, the first control unit 400 may recursively perform step S4410 and step S4420 two or more times. The first control unit 400 may regenerate the time series data of the first moving average (SMA i [k]) and the second moving average (LMA _i [k]) of the cell diagnostic deviation (D*diag, i[k]) in step S4410 by using the time series data of the normalized cell diagnostic deviation (D*diag, _i [k]) generated in step S4420.

In step S4420, the first control unit 400 may generate time series data of the normalized cell diagnostic _deviation (D*diag, _i [k]) again based on Equation 6 by using the time series data of the first moving average value (SMA i[k]) and the time series data of the second moving average value (LMA _i [k]) of each battery cell BC i. This recursive algorithm may be repeated a predetermined number of times.

When steps S4410 and S4420 are performed according to the recursive algorithm, steps S4430 to S4450 may be implemented using the time series data of the cell diagnostic deviation (D*diag, i[k]) finally calculated by the recursive algorithm.

In an embodiment of the present disclosure, the first control unit 400 may detect voltage abnormalities of all battery cells, and then, when voltage abnormalities are detected in specific battery cells, the first control unit 400 may generate third information including detection result information. In addition, the first control unit 400 may record the identification information ID of the battery cell diagnosed with voltage abnormality, the time point at which the voltage abnormality is detected, and the detection flag in a storage unit (not shown).

The third information may include a message indicating that a cell with abnormal voltage exists in the cell group CG. Alternatively, the third information may include a warning message indicating that a detailed inspection of the battery cells _BC1 to BC _N is required.

According to the above embodiment, for each unit time, two moving average values of the cell voltage of each battery cell are determined for two different time lengths, and based on the difference between the two moving average values of each of the multiple battery cells, the voltage abnormality of each battery cell can be detected efficiently and accurately.

According to another aspect, voltage abnormality of each battery cell may be accurately detected by applying advanced techniques such as normalization and/or statistical variable thresholds when analyzing the difference in the variation trends of two moving average values of each battery cell.

According to yet another aspect, a time region where a voltage abnormality occurs for each battery cell and/or a voltage abnormality detection count may be accurately detected by analyzing time series data of a filtered diagnosis value determined based on a statistical variable threshold.

As described above, the method of detecting voltage abnormality by the first control unit 400 included in the battery cell diagnosis apparatus 1000 has been reviewed. Hereinafter, the method of detecting behavior abnormality by the first control unit 400 included in the battery cell diagnosis apparatus 1000 will be reviewed.

25 and 26 are flowcharts showing in detail the process of detecting abnormal behavior by the battery cell diagnosis apparatus 1000 according to an embodiment of the present disclosure. FIG25 is a flowchart exemplarily showing a method for detecting abnormal behavior according to an embodiment of the present disclosure.

In step S5710, the first control unit 400 determines a plurality of sub-voltage curves by applying a moving window of a first time length A to the standard voltage curve C2. The standard voltage curve C2 is a time series representing a plurality of voltage values of the cell voltage of the battery cell BC measured at each sampling time of a predetermined period (t1 to tM).

In step S5720, the first control unit 400 determines a voltage deviation (ΔV[K]) associated with each sub-voltage curve SK of the plurality of sub-voltage curves. Step S5720 may include steps S5722, S5724, and S5726 as sub-steps.

In step S5722, the first control unit 400 may determine a long-term average voltage value (Vav1[K]) of the sub-voltage curve SK by using a first averaging filter of a first time length A (see Equation 10).

In step S5724, the first control unit 400 may determine a short-term average voltage value (Vav2[K]) of the sub-voltage curve SK by using a second averaging filter of a second time length B (see Equation 11).

In step S5726 , the first control unit 400 may determine the voltage deviation (ΔV[K]) by subtracting one of the long-term average voltage value ( Vav1 [K]) and the short-term average voltage value ( Vav2 [K]) from the other.

In step S5730, the first control unit 400 may determine whether the battery cell BC has a behavioral abnormality by comparing each of the plurality of voltage deviations determined for the plurality of sub-voltage curves with at least one of the first threshold deviation and the second threshold deviation. When the value of step S5730 is "yes", the process may proceed to step S5740.

In step S5740 , the first control unit 400 may detect abnormal behavior of the battery cell BC.

FIG. 26 is another flowchart exemplarily illustrating a behavior anomaly detection method according to an embodiment of the present disclosure.

In step S5800, the first control unit 400 may determine a plurality of sub-current curves by applying a moving window of a first time length A to the standard current curve C3. The standard current curve C3 is a time series of a plurality of current values representing the battery current of the battery cell BC measured at each sampling time of a predetermined period (t1 to tM).

In step S5810, the first control unit 400 may determine a plurality of sub-voltage curves by applying a moving window of a first time length A to the standard voltage curve C2. Step S5810 is the same as step S5710.

In step S5812, the first control unit 400 may determine a current change amount of each sub-current curve RK among the plurality of sub-current curves.

In step S5820, the first control unit 400 may determine a voltage deviation (ΔV[K]) of each sub-voltage curve SK associated with each sub-current curve RK in which the current variation is equal to or less than the threshold variation among the plurality of sub-voltage curves. Step S5820 may include steps S5722, S5724, and S5726 of FIG. 25 .

In step S5830, the first control unit 400 may determine whether the battery cell BC has a behavioral abnormality by comparing each voltage deviation determined in step S5820 with at least one of the first threshold deviation and the second threshold deviation. If the value of step S5830 is "yes", the process proceeds to step S5840.

In step S5840 , the first control unit 400 may detect abnormal behavior of the battery cell BC.

27 to 30 are flowcharts illustrating in detail a process in which the external device 2000 detects lithium plating abnormality while repeatedly performing charge and discharge cycles using first information according to an embodiment of the present disclosure.

The external device 2000 may detect lithium plating abnormality according to an embodiment of the present disclosure as shown in the flowcharts of FIGS. 27 to 30 , and generate second information including the detection result.

FIG. 27 is a flow chart exemplarily illustrating a method for detecting lithium plating anomalies according to an embodiment of the present disclosure.

First, the external device 2000 initializes the charge-discharge cycle index k to 1 in step S7010, and calculates the first capacity difference change amount (ΔdAh[1]) and the first cumulative capacity difference change amount (ΔdAh[1]) in step S7020. Initialized to 0 respectively.

Subsequently, the external device 2000 may start a first charge and discharge cycle of the battery in step S7030. In this specification, when the external device 2000 starts the charge and discharge cycle, it may mean that data corresponding to the charge and discharge cycle is acquired using the first information.

Subsequently, the external device 2000 may calculate the charge capacity (ChgAh[1]) and the discharge capacity (DchgAh[1]) using the current measurement value included in the first information during the first charge and discharge cycle in step S7040.

The first information may include information on a charge cycle performed in a preset charge voltage region and a discharge cycle performed in a preset discharge voltage region.

The charging voltage region and the discharging voltage region may be the same or different. Preferably, after the charging cycle is completed, the discharging cycle is started after the battery cell voltage is stabilized. In addition, the discharging cycle may end when the battery cell voltage reaches a preset discharge end voltage or when the integrated value of the discharge current reaches a preset discharge capacity. When the start and end of the charging cycle and the discharging cycle are controlled based on the voltage value, the external device 2000 may refer to the voltage measurement value included in the first information. The voltage measurement value included in the first information may be a value measured by the voltage sensing unit 200.

In step S7050 , the external device 2000 may determine a capacity difference (dAh[ 1 ]) corresponding to a difference between a charge capacity (ChgAh[ 1 ]) and a discharge capacity (DchgAh[ 1 ]).

The external device 2000 may record the determined capacity difference (dAh[1]) together with a time stamp in the storage unit 2100. In one embodiment, the capacity difference (dAh[1]) may be determined by subtracting the discharge capacity (DchgAh[1]) from the charge capacity (ChgAh[1]).

In step S7060, the external device 2000 may determine whether the index k of the charge-discharge cycle is equal to n, where n is a preset natural number, which may be the total number of charge-discharge cycles continued to detect lithium deposition anomalies. In one embodiment, n may be 20.

If the determination of step S7060 is yes, the external device 2000 may terminate the process for detecting lithium deposition abnormality. On the other hand, if the determination of step S7060 is no, the external device 2000 may move the process to S7070.

In step S7070, the external device 2000 may start a second charge and discharge cycle. The conditions of the second charge and discharge cycle may be substantially the same as the conditions of the first charge and discharge cycle.

Subsequently, the external device 2000 may determine the charge capacity (Chg Ah[2]) and the discharge capacity (Dchg Ah[2]) of the second charge and discharge cycle of the battery in step S7080, and determine the capacity difference (dAh[2]) corresponding to the difference between the charge capacity (Chg Ah[2]) and the discharge capacity (Dchg Ah[2]) in step S7090.

Subsequently, the external device 2000 may determine a second capacity difference change amount (ΔdAh[2]) by subtracting the capacity difference (dAh[2]) of the second charge and discharge cycle from the capacity difference (dAh[1]) of the first charge and discharge cycle in step S7100. After step S7100, step S7110 of FIG. 28 may be performed.

FIG. 28 is another flow chart exemplarily illustrating a method for detecting lithium plating anomalies according to an embodiment of the present disclosure.

The external device 2000 may determine whether the second capacity difference variation (ΔdAh[2]) is greater than a standard value in step S7110. The standard value may be zero.

If the determination in step S7110 is yes, the external device 2000 may, in step S7120, compare the second capacity difference change amount (ΔdAh[2]) with the first cumulative capacity difference change amount (ΔdAh[2]). The cumulative capacity difference change is updated by adding, and the updated value is determined as the second cumulative capacity difference change First cumulative capacity difference change amount Can be 0 as an initialization value.

On the other hand, if the determination in step S7110 is negative, an initial value of 0 may be assigned to the second cumulative capacity difference change amount. Instead of comparing the second capacity difference change (ΔdAh[2]) with the first cumulative capacity difference change Add.

The external device 2000 may determine the second cumulative capacity difference change amount in step S7140. Whether it is greater than or equal to a threshold value. The threshold value may refer to a value suitable for detecting lithium plating abnormality. For example, the threshold value may be 0.1% of the battery capacity. The threshold value may be a value preset in the external device 2000 or a value included in the first information.

If the determination in step S7140 is yes, the external device 2000 may detect lithium deposition abnormality in step S7150.

If the determination in step S7140 is negative, that is, if the second cumulative capacity difference change amount If the value is less than the threshold value (or equal to 0), the external device 2000 can determine in step S7160 whether the index k of the charge-discharge cycle is equal to n. Here, n is the total number of charge-discharge cycles that can be used to detect lithium deposition anomalies.

If the determination in step S7160 is yes, the external device 2000 may finally determine that no lithium deposition abnormality has occurred in the battery, and terminate the process because the charge and discharge cycle for detecting lithium deposition is completed.

The external device 2000 may output that no lithium deposition abnormality has been detected through the second information. For example, the second information may include a message indicating that no lithium deposition abnormality has occurred.

On the other hand, if the determination in step S7160 is negative, the external device 2000 may further continue the charge-discharge cycle to detect the lithium deposition abnormality. After step S7160, step S7180 of FIG. 29 is performed.

FIG. 29 is a flowchart exemplarily illustrating another method for detecting lithium plating anomaly according to an embodiment of the present invention.

In step S7180, the external device 2000 starts the third charge and discharge cycle. The conditions of the third charge and discharge cycle may be substantially the same as the conditions of the first charge and discharge cycle.

The external device 2000 may determine the charge capacity (Chg Ah[3]) and the discharge capacity (Dchg Ah[3]) of the battery during the third charge and discharge cycle in step S7190, and determine the capacity difference (dAh[3]) corresponding to the difference between the charge capacity (Chg Ah[3]) and the discharge capacity (Dchg Ah[3]) in step S7200.

The external device 2000 may determine a third capacity difference change amount (ΔdAh[3]) by subtracting the capacity difference (dAh[3]) of the third charge and discharge cycle from the capacity difference (dAh[2]) of the second charge and discharge cycle in step S7210 .

The external device 2000 may determine whether the third capacity difference variation (ΔdAh[3]) is greater than a standard value in step S7220. For example, the standard value may be zero.

If the determination in step S7220 is yes, the external device 2000 may, in step S7230, compare the third capacity difference change amount (ΔdAh[3]) with the second cumulative capacity difference change amount (ΔdAh[3]). The cumulative capacity difference change amount is updated by adding, and the updated value is determined as the third cumulative capacity difference change amount

On the other hand, if the determination in step S7220 is negative, then in step S7240, the external device 2000 may assign an initial value of 0 to the third accumulated capacity difference change amount. Instead of comparing the third capacity difference change (ΔdAh[3]) with the second cumulative capacity difference change Add.

After step S7230 and step S7240, step S7250 may be performed.

In step S7250, the external device 2000 may determine the third cumulative capacity difference change amount. Is it greater than or equal to the threshold?

If the determination in step S7250 is yes, the external device 2000 may detect abnormal lithium deposition inside the battery in step S7260.

The external device 2000 may terminate the process after detecting the lithium deposition abnormality in step S7260.

If the determination in step S7250 is negative, that is, if the third cumulative capacity difference change amount If the value is less than the threshold value (or 0), the external device 2000 can determine in step S7270 whether the index k of the charge-discharge cycle is equal to n. Here, n is the total number of charge-discharge cycles that can be used to detect whether lithium plating has occurred inside the battery.

If the judgment in step S7270 is yes, the charge and discharge cycle for detecting lithium deposition abnormality has been completed, so it is judged that no lithium deposition abnormality has occurred in the battery, and the process can be terminated.

The external device 2000 may generate the second information after the process is terminated. The external device 2000 may generate a warning message indicating that lithium plating has been detected in the second information. Alternatively, the second information may include a message indicating that no lithium plating abnormality has been detected.

On the other hand, if the determination in step S7270 is negative, the external device 2000 may further continue the charge and discharge cycle to detect lithium plating abnormality.

The detection logic of lithium plating anomaly continued by the external device 2000 in the fourth charge-discharge cycle and subsequent charge-discharge cycles is substantially the same as described above.

30 is another flowchart exemplarily illustrating a lithium plating abnormality detection method according to an embodiment of the present disclosure. Hereinafter, the process performed by the external device 2000 in the fourth to nth charge and discharge cycles will be summarized and described with reference to FIG.

In step S7280, the external device 2000 starts the Kth charge-discharge cycle (k is a natural number from 4 to n). The conditions of the Kth charge-discharge cycle are substantially the same as those of the first charge-discharge cycle.

Subsequently, the external device 2000 determines the charge capacity (ChgAh[k]) and the discharge capacity (DchgAh[k]) of the battery during the Kth charge and discharge cycle in step S7290, and determines the capacity difference (dAh[k]) corresponding to the difference between the charge capacity (ChgAh[k]) and the discharge capacity (DchgAh[k]).

Subsequently, the external device 2000 determines a Kth capacity difference variation amount (ΔdAh[k]) by subtracting the capacity difference (dAh[k]) of the Kth charge and discharge cycle from the capacity difference (dAh[k]) of the k-1th charge and discharge cycle in step S7310 .

Then, the external device 2000 determines whether the Kth capacity difference variation (ΔdAh[k]) is greater than a standard value in step S7320. Preferably, the standard value is zero.

If the determination in step S7320 is yes, the external device 2000 may, in step S7330, compare the Kth capacity difference change amount (ΔdAh[k]) with the k-1th cumulative capacity difference change amount (ΔdAh[k]). The cumulative capacity difference change is updated by adding, and the updated value is determined as the Kth cumulative capacity difference change

On the other hand, if the determination in step S7320 is negative, then in step S7340, the external device 2000 may assign an initial value of 0 to the Kth cumulative capacity difference change amount. Instead of comparing the Kth capacity difference change (ΔdAh[k]) with the k-1th cumulative capacity difference change Add.

After step S7330 and step S7340, proceed to step S7350.

In step S7350, the external device 2000 determines the Kth cumulative capacity difference change amount. Is it greater than or equal to the threshold?

If the determination in step S7350 is yes, the external device 2000 may determine in step S7360 that lithium deposition abnormality is detected in the battery and terminate the process.

If the judgment of step S7350 is no, that is, if the Kth cumulative capacity difference change amount If the value is less than the threshold value (or 0), the external device 2000 may determine in step S7370 whether the index k of the charge-discharge cycle is equal to n. Here, n is the total number of charge-discharge cycles that can be used to detect whether lithium plating has occurred inside the battery.

If the judgment in step S7370 is yes, the charge and discharge cycle for detecting lithium plating is completed, thereby ultimately determining that no lithium plating abnormality has occurred in the battery, and the process can be terminated.

On the other hand, if the determination of step S7370 is negative, the external device 2000 returns the process to step S7280 to further continue the charge-discharge cycle to detect the lithium deposition anomaly. Therefore, steps S7280 to S7370 may be repeated periodically until the index k of the charge-discharge cycle becomes n.

According to an embodiment of the present disclosure, if the capacity difference change amount calculated in the current charge and discharge cycle is equal to or less than the standard value, the cumulative capacity difference change amount calculated until the previous cycle may be initialized to 0. In addition, if the capacity difference change amount calculated in the current charge and discharge cycle is greater than the standard value, the current capacity difference change amount may be added to the previous cumulative capacity difference change amount. Thus, the cumulative capacity difference change amount increases. The previous cumulative capacity difference change amount has a value of 0 or a positive value. If it has a positive value, the capacity difference change amount calculated in the continuous charge and discharge cycle that exceeds the standard value may be integrated.

In addition, when the capacity difference variation is integrated and the capacity difference variation decreases to a standard value or less in a specific charge and discharge cycle, the cumulative capacity difference variation can be initialized to 0. By applying this logic, the cumulative capacity difference variation corresponds to a quantitative indicator for measuring a lithium plating anomaly. That is, if the capacity difference variation is greater than the standard value, it may mean that there is a possibility of lithium plating.

In addition, if the condition that the capacity difference change exceeds the standard value in multiple consecutive charge and discharge cycles in a time series is successively satisfied, and the cumulative capacity difference change increases to a threshold value or more, it may mean that the possibility of lithium precipitation is high. The technical significance of the present disclosure is to use the factor of the cumulative capacity difference change to quantify the possibility of lithium precipitation.

FIG. 31 is a graph showing changes in data measured in an experimental example in which a method for an external device 2000 to detect whether lithium plating occurs according to an embodiment of the present disclosure is applied.

In this experimental example, a pouch-type lithium polymer battery was used. The lithium polymer battery selected for the experiment was degraded and was therefore in a state where lithium deposition on the negative electrode had begun. The current capacity of the lithium polymer battery, which reflects the degree of degradation, is about 50Ah. The charging condition of the charging cycle is CC-CV charging. When the CC charging target voltage is reached, the CC charging is terminated and converted to CV charging, and when the CV charging current reaches the target current, the charging is terminated. The discharge condition of the discharge cycle is CC discharge, and the discharge is terminated when the discharge is up to a given discharge capacity. The temperature condition for the charging cycle and the discharge cycle is 45°C. The standard value for determining whether to integrate the capacity difference change is set to 0, and the threshold for diagnosing lithium deposition anomalies is set to 0.06Ah.

Graph ① is a graph showing the measurement results of the charge capacity (ChgAh[k]) and the discharge capacity (DchgAh[k]) for each charge and discharge cycle. The charge capacity (ChgAh[k]) and the discharge capacity (DchgAh[k]) are calculated by integrating the current value measured via the sense resistor. Due to the error in the discharge current measurement value, the discharge capacity is greater than the charge capacity from the fourth discharge cycle.

Graph ② is a graph showing the capacity difference (dAh[k]) for each charge and discharge cycle. Referring to the graph, since the discharge capacity is greater than the charge capacity from the fourth charge and discharge cycle, the capacity difference (dAh[k]) becomes negative from the fourth cycle.

Graph ③ is a graph showing the capacity difference change (ΔdAh[k]) for each charge and discharge cycle. The indexes of the charge and discharge cycles in which the capacity difference change (ΔdAh[k]) is positive are 2 to 13, 17, 18, and 20. The indexes of the charge and discharge cycles in which the capacity difference change (ΔdAh[k]) is negative are 14 to 16 and 19.

Graph ④ shows the cumulative capacity difference change for each charge and discharge cycle. The indexes of the charge and discharge cycles in which the capacity difference change (ΔdAh[k]) is positive are 2 to 13. Therefore, as the capacity difference change (ΔdAh[k]) of the second to 13th charge and discharge cycles are integrated, the accumulated capacity difference change In addition, when the capacity difference change amount of the 13th charge and discharge cycle is accumulated, the accumulated capacity difference change amount The threshold value exceeds 0.06Ah. Therefore, the external device 2000 continues to perform the charge and discharge cycle until the 13th cycle, and then determines that abnormal lithium deposition occurs inside the battery, outputs the abnormal lithium deposition detection result through the second information, and terminates the detection process. Since lithium is deposited at the negative electrode of the lithium polymer battery used in this experiment, it can be seen that the detection accuracy of the present disclosure is high.

FIG. 32 is a graph showing changes in data measured in another experimental example in which the lithium plating abnormality detection method according to an embodiment of the present disclosure is applied.

In FIG. 32 , graph ①′ is the same as graph ① of the above-mentioned experimental example. Graph ①′ is a graph showing the measurement results of the charge capacity (ChgAh[k]) and the discharge capacity (DchgAh[k]) when a current measuring device having a current measurement value error different from that of the above-mentioned experimental example is used. In this experimental example, the error of the discharge current measurement value is larger than that of the above-mentioned experimental example. Therefore, the discharge capacity (DchgAh[k]) graph is shifted upward compared with the above-mentioned experimental example.

Graphs ② and ②' are graphs showing the capacity difference (dAh[k]) per charge and discharge cycle, graphs ③ and ③' are graphs showing the capacity difference change (ΔdAh[k]) per charge and discharge cycle, and graphs ④ and ④' are graphs showing the cumulative capacity difference change per charge and discharge cycle. of charts.

Graphs ②, ③, and ④ are calculated using the data of graph ①, and graphs ②’, ③’, and ④’ are calculated using the data of graph ①’.

As shown in Figure 32, charts ②, ③ and ④ and charts ②', ③' and ④' are basically the same. Therefore, even if the discharge current value includes a measurement error, regardless of the size of the error, the external device 2000 continues to the 13th charge and discharge cycle, and then determines that lithium deposition abnormality occurs inside the battery, outputs the lithium deposition abnormality detection result via the second information, and terminates the detection process. It can be seen from the experimental results that regardless of the error of the current measurement value, the present disclosure can reliably detect lithium deposition abnormality.

33 is a flowchart of the battery cell diagnostic apparatus 1000 according to an embodiment of the present disclosure diagnosing an abnormal state of a battery cell using an external device 2000. The same features as the previous embodiment will not be described in detail.

In step S7000 of Fig. 33, the external device 2000 may detect whether there is a parallel connection abnormality. This will be described in detail with reference to Fig. 34.

Fig. 34 is a flow chart exemplarily illustrating a battery diagnosis method according to an embodiment of the present disclosure. The method of Fig. 34 may be repeatedly performed at a first time interval.

34 , in step S7610 , the external device 2000 may collect the charge and discharge data of the battery B included in the first information.

In step S7620, the external device 2000 may determine an estimated capacity value representing the full charge capacity of the battery B. Step S7620 may include steps S7622 and S7624. In step S7622, the external device 2000 may determine the current integral value and the SOC change value of the battery B by inputting the charge and discharge data into the capacity estimation model. In step S7624, the external device 2000 may determine the estimated capacity value representing the full charge capacity of the battery B based on the ratio between the current integral value and the SOC change value of the battery B. The external device 2000 may store a time series of the estimated capacity values.

In step S7630, the external device 2000 can detect an abnormality in the parallel connection B200 by monitoring the change of the estimated capacity value over time. Step S7630 may include steps S7632, S7634, S7636, S7638, and S7639. In step S7632, the external device 2000 may determine that the threshold capacity value of the second time is less than the estimated capacity value of the first time. For example, the second time may be the timing of calculating the current estimated capacity value, and the first time may be the timing of calculating the estimated capacity value 10 times before. In step S7634, the external device 2000 may compare the estimated capacity value of the second time with the threshold capacity value of the second time. If the estimated capacity value of the second time is less than the threshold capacity value of the second time, this indicates that at least one of the first capacity abnormality and the second capacity abnormality has occurred in the parallel connection B200. If the value of step S7634 is "yes", the process proceeds to step S7636. Otherwise, the process may proceed to step S7638. In step S7636, the external device 2000 may increase the diagnostic count by 1. In step S7638, the external device 2000 may reset the diagnostic count. In step S7639, the external device 2000 may determine whether the diagnostic count reaches a threshold count. If the value of step S7639 is "yes", this indicates that at least one unit cell UC of the parallel connection B200 is detected to have a second capacity abnormality, which is a complete disconnection fault.

In step S7640, the external device 2000 may detect a parallel connection abnormality. The external device 2000 may determine the number of unit cells in which the parallel connection abnormality is detected. The external device 2000 may determine the number of unit cells having a complete disconnection fault among the plurality of unit cells UC1 to UCM based on two estimated capacity values at two past times (e.g., te, tf, at which the maximum decrease value of the estimated capacity value occurs) with a second time interval or smaller.

The external device 2000 can determine the number of unit cells having a complete disconnection fault among the plurality of unit cells UC1 to UCM. The number of abnormal unit cells can be determined to be equal to the maximum integer not greater than ΔAhmax/(Ahp/M). Ahp is the estimated capacity value at the earlier time (te) of two times (e.g., te, tf). ΔAhmax is the maximum reduction in full charge capacity within two times (e.g., te, tf) before the timing of detecting the abnormality of the parallel connection 200, and is the result obtained by subtracting the estimated capacity value at the later time (tf) from the estimated capacity value at the previous time (te). For example, when Ahp=122Ah, ΔAhmax=27Ah, and M=10, since 2≤27Ah/(122Ah/10)＜3, the number of abnormal unit cells can be determined to be 2.

35 is a flowchart of the battery cell diagnostic apparatus 1000 according to an embodiment of the present disclosure diagnosing an abnormal state of a cell using an external device 2000. The same features as the previous embodiment will not be described in detail.

In step S7000 of Fig. 35, the external device 2000 may detect whether there is an internal short circuit abnormality. This will be described in detail with reference to Figs. 36 and 37.

36 and 37 are flowcharts specifically illustrating a process of detecting an internal short circuit abnormality when the external device 2000 repeats a charge and discharge cycle using first information according to an embodiment of the present disclosure.

According to the flowcharts shown in FIGS. 36 and 37 , the external device 2000 may detect an internal short circuit abnormality according to an embodiment of the present disclosure, and generate second information including a detection result.

FIG36 is a flow chart exemplarily showing a battery management method according to an embodiment of the present disclosure. The method of FIG36 is used to detect an internal short circuit abnormality of a battery cell BC based on the SOC trends of all the plurality of battery cells _BC1 to _BCN monitored in a recent charging period. For ease of explanation, it is assumed that the recent charging period is from time point t4 to time point t5.

36, in step S7610, the external device 2000 may determine a first SOC variation, which is a difference between a first SOC at a first charging time point and a second SOC at a second charging time point, by applying an SOC estimation algorithm to each state parameter of each battery cell BC1 to _BCN acquired using the first information for each battery cell _BC during charging of the battery pack 10. The first charging time point and the second charging time point are not particularly limited as long as they are two different time points within a recent charging period.

For example, the first charging time point may be the start time point t4 of the most recent charging period, and the second charging time point may be the end time point t5 of the most recent charging period. Since the method of FIG. 36 involves charging, the first SOC change represents an increase in SOC from the first charging time point to the second charging time point. For example, referring to FIG. 18, the first SOC change of the abnormal battery cell is the difference between the first SOC VC54 and the second SOC VC55.

In step S7620, the external device 2000 determines a standard factor by applying a statistical algorithm to the first SOC variation of at least two battery cells among the plurality of battery cells BC ₁ to BC _N. The standard factor may be equal to an average value or a median value of the first SOC variation of at least two battery cells among the plurality of battery cells BC ₁ to BC _N. For example, referring to FIG. 18 , when curve VC4 is an average value of the first SOC variation, the standard factor is a difference between SOC VC44 and SOC VC45.

In step S7630, the external device 2000 detects the internal short circuit abnormality by comparing the first SOC variation with a standard factor of each battery cell BC. In detecting the internal short circuit abnormality, one or a combination of two or more of the following detection conditions may be used.

[Condition #1: The first SOC change must be smaller than the standard factor by a threshold value TH1 or more]

[Condition #2: The ratio of the first SOC change to the standard factor must be equal to or less than the standard value TH2, where TH2 is 0 to 1]

[Condition #3: The ratio of the first SOC change to the standard factor must be smaller than the previous ratio by a threshold value TH3 or more]

In condition #3, the previous ratio is the ratio of the first SOC change to the standard factor in the charging period before the most recent charging period (t0 to t1 in FIG. 17 ).

The thresholds TH1, TH2, TH3 may be predetermined fixed values. Alternatively, the external device 2000 may determine at least one of the thresholds TH1, TH2, TH3 based on an integrated value of the charging current measured during a period from the first charging time point to the second charging time point.

Whenever the charging mode of the battery pack 10 is restarted, at least one of the threshold values TH1, TH2, TH3 may be re-updated. For example, the external device 2000 may obtain a target value of the SOC change (e.g., 60%) by dividing the integrated value of the charging current (e.g., 3Ah [ampere-hour]) by the design capacity of the battery cell BC (e.g., 5Ah), and determine at least one of the threshold values TH1, TH2, TH3 by multiplying the ratio of the standard factor to the target value by a predetermined scaling constant (which is a positive value). The scaling constant used to determine any one of the threshold values TH1, TH2, TH3 may be different from the scaling constant used to determine the other of the threshold values TH1, TH2, TH3. The target value may be determined during at least one of steps S7610, S7620, and S7630. At least one of the threshold values TH1, TH2, TH3 may be determined during at least one of steps S7620 and S7630.

When all of the plurality of battery cells _BC1 to BC _N are normal, the target value and the standard factor may be substantially equal to each other. Meanwhile, as the number of battery cells having an internal short circuit abnormality among the plurality of battery cells _BC1 to BC _N increases, the standard factor is greatly reduced from the target value. Therefore, by determining at least one of the threshold values TH1, TH2, TH3 according to the above method, the accuracy of detecting the internal short circuit abnormality may be improved.

Meanwhile, after the target value is determined before step S7620, in step S7620, the standard factor may be determined using only the first SOC changes that are less than or equal to _{the target value among all the first SOC changes of the plurality of battery cells BC 1 to} BC _N. In this case, when determining the standard factor, the first SOC change that exceeds the target value is excluded among all the first SOC changes of the plurality of battery cells BC 1 to BC _N , so that the battery cell BC having a relatively severe internal short circuit abnormality can be preferentially detected from among the plurality of battery cells BC ₁ to BC _N.

FIG37 is another flowchart exemplarily illustrating a battery management method according to an embodiment of the present disclosure. The method of FIG37 is used to detect an internal short circuit abnormality of a battery cell BC based on the SOC trends of all of the plurality of battery cells _BC1 to _BCN monitored in the most recent discharge period and the most recent charge period, respectively. For ease of explanation, it is assumed that the most recent charge period is from time point t4 to time point t5, and the most recent discharge period is from time point t6 to time point t7.

37, in step S7710, the external device 2000 may determine a first SOC change, which is a difference between a first SOC at a first charging time point and a second SOC at a second charging time point, by applying an SOC estimation algorithm to each state parameter of each of the plurality of battery cells _BC1 to _BCN acquired during charging of the battery pack 10 using first information for each battery cell BC. The first charging time point and the second charging time point are not particularly limited as long as they are two different time points within a recent charging period. For example, the first charging time point may be a start time point t4 of the recent charging period, and the second charging time point may be an end time point t5 of the recent charging period.

In step S7720, the external device 2000 may determine a second SOC change, which is a difference between a third SOC at a first discharge time point and a fourth SOC at a second discharge time point, by applying an SOC estimation algorithm to a state parameter of each of the plurality of battery cells _BC1 to _BCN acquired during discharge of the battery pack 10 for each battery cell BC. The first discharge time point and the second discharge time point are not particularly limited as long as they are two different time points within a recent discharge period. For example, the first discharge time point may be a start time point t6 of a recent charge period, and the second discharge time point may be an end time point t7 of a recent charge period.

18 , in the abnormal battery cell, the first SOC variation is a difference between a first SOC VC54 and a second SOC VC55 , and the second SOC variation is a difference between a third SOC VC56 and a fourth SOC VC57 .

In FIG. 37 , step S7710 is before step S7720, but this should be understood as an embodiment. For example, if the most recent charging period is before the most recent discharging period, step S7720 may be before step S7710. As another embodiment, after both the most recent charging period and the most recent discharging period have ended, step S7710 and step S7720 may be performed simultaneously.

In step S7730, the external device 2000 may determine the abnormal factor by dividing the first SOC variation by the second SOC variation for each battery cell BC. That is, the abnormal factor may be determined according to the formula of "abnormal factor = (first SOC variation) ÷ (second SOC variation)".

For example, referring to FIG. 18 , the abnormal factor of the abnormal battery cell may be determined according to the formula “{SOC(VC55)-SOC(VC54)}÷{SOC(VC56)-SOC(VC57)}”. The abnormal factor may also be referred to as coulombic efficiency.

In step S7740 , the external device 2000 may determine a standard factor by applying a statistical algorithm to abnormal factors of at least two battery cells among the plurality of battery cells BC ₁ to BC _N.

The standard factor may be equal to an average value or a median value of abnormal factors of at least two battery cells among the plurality of battery cells BC ₁ to BC _N. For example, referring to FIG. 18 , when curve VC4 is an average SOC of the plurality of battery cells BC ₁ to BC _N , the standard factor may be determined according to the formula “{SOC(VC45)-SOC(VC44)}÷{SOC(VC46)-SOC(VC47)}”.

In step S7750, the external device 2000 may detect the internal short circuit abnormality of the battery cell BC by comparing the abnormal factor with the standard factor for each battery cell BC. In detecting the internal short circuit abnormality, one or a combination of two or more of the following detection conditions may be used.

[Condition #1: The abnormal factor must be smaller than the standard factor by a threshold of TH11 or more]

[Condition #2: Relative Coulombic efficiency must be equal to or less than threshold TH12, where TH12 is 0 to 1]

[Condition #3: The ratio of the abnormal factor to the standard factor must be smaller than the previous ratio by a threshold value of TH13 or more]

In condition #2, the relative coulombic efficiency can be the ratio of the anomalous factor to the standard factor, ie, "abnormal factor ÷ standard factor".

In condition #3, the previous ratio is a ratio of an abnormal factor to a standard factor based on a first SOC in a charging period (t4 to t5 in FIG. 17) and a second SOC in a discharging period (t2 to t3 in FIG. 17) before the most recent discharging period (t6 to t7).

The thresholds TH11, TH12, TH13 may be predetermined values. As an embodiment, the thresholds TH11, TH12, TH13 may be the same as the predetermined thresholds TH1, TH2, TH3 described above with reference to FIG. 36 .

The external device 2000 may determine at least one of the thresholds TH11, TH12, TH13 based on an integrated value of a charging current measured during a period from a first charging time point to a second charging time point and an integrated value of a discharging current measured during a period from a first discharging time point to a second discharging time point.

Whenever the charging mode or the discharging mode of the battery pack 10 is restarted, at least one of the threshold values TH11, TH12, and TH13 may be re-updated. For example, the external device 2000 may obtain the target value by dividing the integral value of the charging current by the integral value of the discharging current. The external device 2000 may determine at least one of the threshold values TH11, TH12, and TH13 by multiplying the ratio of the standard factor to the target value by a predetermined scaling constant (which is a positive value). The scaling constant used to determine any one of the threshold values TH11, TH12, and TH13 may be different from the scaling constant used to determine the other of the threshold values TH11, TH12, and TH13. The target value may be determined during at least one of steps S7710, S7720, S7730, and S7740. At least one of the threshold values TH1, TH2, and TH3 may be determined during at least one of steps S7730 and S7740.

When all of the plurality of battery cells _BC1 to BC _N are normal, the target value and the standard factor may be substantially equal to each other. Meanwhile, as the number of battery cells having an internal short circuit abnormality among the plurality of battery cells _BC1 to BC _N increases, the standard factor is greatly reduced from the target value. Therefore, by determining at least one of the threshold values TH11, TH12, TH13 according to the above method, the accuracy of detecting the internal short circuit abnormality may be improved.

Meanwhile, after the target value is determined before step S7740, in step S7740, the standard factor may be determined using only the abnormal factors less than or equal to the target value among all the abnormal factors of the plurality of battery cells BC ₁ to BC _N. In this case, when determining the standard factor, since the abnormal factors exceeding the target value are excluded from all the abnormal factors of the plurality of battery cells BC ₁ to BC _N , the battery cell BC having a relatively severe internal short circuit abnormality may be preferentially detected from among the plurality of battery cells BC ₁ to BC _N.

In each embodiment, when the internal short circuit abnormality is detected in a predetermined number or more of the plurality of battery cells _BC1 to BC _N , the external device 2000 may generate second information indicating that the internal short circuit abnormality is detected.

In each embodiment, when an internal short circuit abnormality is detected in a predetermined number or more of the plurality of battery cells _BC1 to BC _N , the external device 2000 may reduce the allowable range of the charge and discharge current. For example, the upper limit (positive value) of the allowable range may be reduced, or the lower limit (negative value) of the allowable range may be increased in proportion to the number of abnormal battery cells.

For example, the external device 2000 according to an embodiment of the present disclosure may be included in a diagnostic system for diagnosing an abnormality of a battery cell. The diagnostic system may be operated in an electric vehicle repair shop, a battery manufacturer, or a battery maintenance company. For example, the diagnostic system may be used to diagnose an abnormality of a battery cell loaded in an electric vehicle or an energy storage system, or may be used to diagnose an abnormality in a newly developed model of battery produced by a battery manufacturer. In particular, in the latter case, before the newly developed model of battery is commercialized, the state of the battery may be checked by using the external device 2000.

As another embodiment, the external device 2000 may be included in a control element of a system equipped with a battery.

In one embodiment, the external device 2000 may be included in the control system of the electric vehicle. In this case, the external device 2000 may collect data on the charge capacity and discharge capacity of the battery cell during the process of charging and discharging the battery installed in the electric vehicle, diagnose the state of the battery cell using the collected data, and output the diagnosis result to the integrated control display of the electric vehicle.

In another embodiment, the external device 2000 may be included in the control system of the energy storage system. In this case, the external device 2000 may collect data on the charge capacity and discharge capacity of the battery cells during the charge and discharge of the energy storage system, diagnose the state of the battery cells using the collected data, and output the diagnosis results via a display of an integrated management computer accessible to an operator.

When the diagnostic result about lithium plating anomaly is output via the display, the user of the electric vehicle or the operator of the energy storage system can take appropriate safety measures. In one embodiment, the user of the electric vehicle can visit a repair shop and undergo an inspection. In another embodiment, the operator of the energy storage system can replace the corresponding battery with a new battery.

In the present disclosure, the external device 2000 may optionally include a processor, an application specific integrated circuit (ASIC), other chipsets, logic circuits, registers, communication modems, data processing devices, etc. known in the art to perform the above-mentioned various control logics. In addition, when the control logic is implemented in software, the external device 2000 can be implemented as a group of program modules. In this case, the program modules can be stored in a memory and executed by a processor. The memory can be arranged inside or outside the processor and can be connected to the processor by means of various known computer components. In addition, the memory can be included in the storage unit 2100 of the present disclosure. In addition, the memory refers to a device in which information is stored regardless of the type of the device, not a specific storage device.

At least one or more of the various control logics of the external device 2000 can be combined, and the combined control logic can be written in a computer-readable code scheme and recorded in a computer-readable recording medium. The type of recording medium is not particularly limited as long as it can be accessed by a processor included in the computer. As an embodiment, the recording medium includes at least one selected from the group consisting of ROM, RAM, registers, CD-ROM, magnetic tape, hard disk, floppy disk and optical data recording device. In addition, the code scheme can be distributed, stored and executed on a networked computer. In addition, the functional program, code and code segment for implementing the combined control logic can be easily inferred by a programmer in the field to which the present disclosure belongs.

The present disclosure has been described in detail. However, it should be understood that the detailed description and specific examples, while indicating preferred embodiments of the present disclosure, are given by way of illustration only, since various changes and modifications within the scope of the present disclosure will become apparent to those skilled in the art from this detailed description.

(Explanation of Reference Numerals)

1: Battery cell diagnostic system

1000: Battery cell diagnostic equipment

2000: External Devices

100: Current measurement unit

200: Voltage sensing unit

300: Data acquisition unit

400: First control unit

500: Display unit

600: Second control unit

10: Battery Pack

Claims (20)

1. A battery cell diagnostic device, the battery cell diagnostic device comprising: a current measuring unit configured to measure a current of a battery cell; a voltage sensing unit configured to sense a cell voltage of the battery cell; and a first control unit, the first control unit being configured to send first information of the battery cell including data acquired from the current measuring unit and the voltage sensing unit to an external device, receive second information including diagnostic information of the battery cell acquired based on the first information from the external device, and diagnose an abnormal state of the battery cell based on the first information and the second information. 2. The battery cell diagnostic device according to claim 1, The diagnostic information of the battery cell includes at least one of lithium plating diagnosis of the battery cell, abnormal parallel connection of the battery cell and internal short circuit of the battery cell. 3. The battery cell diagnostic device according to claim 1, Wherein, the first control unit is configured to display information about an abnormal state of the battery cell on a display unit based on the diagnostic information of the battery cell included in the second information. 4. The battery cell diagnostic device according to claim 1, Wherein, the first control unit is configured as: detecting at least one of a voltage abnormality of the battery cell and a behavior abnormality of the battery cell based on the first information; and An abnormal state of the battery cell is diagnosed based on at least one of the voltage abnormality, the behavior abnormality, and the second information. 5. The battery cell diagnostic device according to claim 4, The first control unit is configured to generate third information indicating whether the battery cell is in the abnormal state based on at least one of the voltage abnormality, the behavior abnormality and the second information. 6. The battery cell diagnostic device according to claim 5, Wherein, the first control unit is configured to display the third information on a display unit. 7. The battery cell diagnostic device according to claim 5, The first control unit is configured to send the third information to a second control unit of a device equipped with the battery cell. 8. The battery cell diagnostic device according to claim 1, Wherein, the first control unit is configured as: generating time series data representing a history of the cell voltage included in the first information over time; Determining a first average cell voltage and a second average cell voltage for each battery cell based on the time series data, the first average cell voltage being a short-term moving average and the second average cell voltage being a long-term moving average; and A voltage abnormality of the battery cell is detected based on a difference between the first average cell voltage and the second average cell voltage. 9. The battery cell diagnostic device according to claim 8, Wherein, the battery cell diagnostic device is configured to diagnose a plurality of battery cells, and Wherein, the first control unit is configured as: determining, for each battery cell of the plurality of battery cells, long-term and short-term average differences corresponding to a difference between the first average cell voltage and the second average cell voltage; determining an average of the long-term and short-term average differences for the plurality of battery cells; determining, for each battery cell of the plurality of battery cells, a cell diagnostic deviation corresponding to a deviation between an average of the long-term and short-term average differences and the long-term and short-term average differences; and The battery cell that satisfies the condition that the battery cell diagnosis deviation exceeds the diagnosis threshold is detected as a battery cell with abnormal voltage. 10. The battery cell diagnostic device according to claim 8, Wherein, the battery cell diagnostic device is configured to diagnose a plurality of battery cells, and Wherein, the first control unit is configured as: determining, for each battery cell of the plurality of battery cells, long-term and short-term average differences corresponding to a difference between the first average cell voltage and the second average cell voltage; determining an average of the long-term and short-term average differences for the plurality of battery cells; determining, for each battery cell of the plurality of battery cells, a cell diagnostic deviation corresponding to a deviation between an average of the long-term and short-term average differences and the long-term and short-term average differences; determining a statistical variable threshold value dependent on a standard deviation of the cell diagnostic deviations of the plurality of battery cells; filtering the time series data based on the statistical variable threshold to generate filtered time series data; and The voltage abnormality of the battery cell is detected based on the time or the number of data when the filtered time series data exceeds a diagnosis threshold. 11. The battery cell diagnostic device according to claim 8, Wherein, the battery cell diagnostic device is configured to diagnose a plurality of battery cells, and The first control unit is configured as: determining, for each battery cell of the plurality of battery cells, long-term and short-term average differences corresponding to a difference between the first average cell voltage and the second average cell voltage; determining a normalized value corresponding to an average of the long-term and short-term average differences for the plurality of battery cells; for each battery cell in the plurality of battery cells, normalizing the long-term and short-term average differences according to the normalized value; determining a statistical variable threshold value that is dependent on a standard deviation of normalized cell diagnostic deviations of the plurality of battery cells; for each battery cell in the plurality of battery cells, filtering the normalized long-term and short-term mean differences for each battery cell based on the statistical variable threshold to generate filtered time series data; and The voltage abnormality of the battery cell is detected based on the time or the number of data when the filtered time series data exceeds a diagnosis threshold. 12. The battery cell diagnostic device according to claim 1, Wherein, the first control unit is configured as: determining a plurality of sub-voltage curves by applying a moving window of a first time length to a time series of the cell voltages included in the first information; Determine a long-term average voltage value of each sub-voltage curve using a first average filter of the first time length; determining a short-term average voltage value of each sub-voltage curve using a second averaging filter of a second time length shorter than the first time length; determining a voltage deviation corresponding to a difference between the long-term average voltage value and the short-term average voltage value of each sub-voltage curve; and Each of the plurality of voltage deviations determined for the plurality of sub-voltage curves is compared with at least one of a first threshold deviation and a second threshold deviation to detect abnormal behavior of the battery cell. 13. The battery cell diagnostic device according to claim 12, The first control unit is configured to detect the behavioral anomaly corresponding to two voltage deviations among the plurality of voltage deviations that respectively meet the first condition, the second condition, and the third condition, Wherein, when a first voltage deviation of the two voltage deviations is equal to or greater than the first threshold deviation, the first condition is satisfied, Wherein, when the second voltage deviation of the two voltage deviations is equal to or less than the second threshold deviation, the second condition is satisfied, and When the time interval between the two voltage deviations is equal to or less than a threshold time, the third condition is satisfied. 14. The battery cell diagnostic device according to claim 1, The second information indicates whether the cumulative capacity difference change is greater than or equal to a threshold value. The cumulative capacity difference change is the sum of the capacity difference changes. Each of the capacity difference changes is the difference between the capacity difference of the kth charge-discharge cycle of the battery cell and the capacity difference of the k-1th charge-discharge cycle of the battery cell, The k is a natural number greater than or equal to 2, The capacity difference of each charge-discharge cycle corresponds to the difference between the charge capacity of the battery cell during the charge process of the charge-discharge cycle and the discharge capacity of the battery cell during the discharge process of the charge-discharge cycle, and Each of the charge capacity and the discharge capacity can be derived from data acquired from the current measurement unit and included in the first information. 15. The battery cell diagnostic device according to claim 1, The second information represents the capacity difference change between consecutive charge and discharge cycles of the battery cell, and The capacity difference of each charge and discharge cycle of the battery cell is the difference between (i) the charge capacity of the battery cell during the charge and discharge cycle of the battery cell and (ii) the discharge capacity of the battery cell during the discharge cycle of the battery cell. 16. The battery cell diagnostic device according to claim 1, The second information indicates whether the parallel connection of the plurality of unit cells included in the battery cell is abnormal based on the result of the external device monitoring the estimated capacity value over time. The estimated capacity value represents a full charge capacity of the battery cell based on charge and discharge data, and The charge and discharge data include a voltage time series indicating that the voltage of the battery cell changes with time and a current time series indicating that the charge and discharge current of the battery cell changes with time. 17. The battery cell diagnostic device according to claim 1, wherein the second information indicates whether the battery cell has an internal short circuit based on a first SOC change of the battery cell and a standard factor, determining the standard factor by applying a statistical algorithm to the first SOC variation of at least two battery cells of a plurality of battery cells, The first SOC change is the difference between a first SOC at a first charging time point and a second SOC at a second charging time point of each battery cell, estimating the first SOC by applying an SOC estimation algorithm to the state parameters of the battery cell at the first charging time point, estimating the second SOC by applying the SOC estimation algorithm to the state parameters of the battery cell at the second charging time point, and The state parameter is acquired based on the first information. 18. A battery cell diagnostic system, comprising an external device and the battery cell diagnostic apparatus according to claim 1, The external device is configured to derive the second information based on at least a portion of the first information. 19. A battery cell diagnosis method, the battery cell diagnosis method comprising the following steps: Acquiring, by means of a control unit, data including at least one of a charging current and a discharging current of a battery cell and a cell voltage of the battery cell; By means of the control unit, sending first information of the battery cell including the acquired data to an external device; receiving, by means of the control unit, second information from the external device, the second information including diagnostic information of the battery cell obtained based on the first information; and With the control unit, an abnormal state of the battery cell is diagnosed based on the first information and the second information. 20. The battery cell diagnosis method according to claim 19, further comprising the following steps: detecting, by means of the control unit, at least one of a voltage anomaly and a behavior anomaly of the battery cell based on the first information of the battery cell; and With the control unit, an abnormal state of the battery cell is diagnosed based on at least one of an abnormal voltage of the battery cell, an abnormal behavior of the battery cell, and the second information.