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CN114035073B - A battery consistency evaluation method, device and equipment - Google Patents

A battery consistency evaluation method, device and equipment Download PDF

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Publication number
CN114035073B
CN114035073B CN202111357808.1A CN202111357808A CN114035073B CN 114035073 B CN114035073 B CN 114035073B CN 202111357808 A CN202111357808 A CN 202111357808A CN 114035073 B CN114035073 B CN 114035073B
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data
temperature difference
charging
characteristic
charging event
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CN114035073A (en
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王辰
王阳
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Mobai Beijing Information Technology Co Ltd
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Mobai Beijing Information Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Secondary Cells (AREA)

Abstract

The embodiment of the application provides a consistency evaluation method of a battery, which comprises the steps of obtaining a plurality of charging event data of the same battery in a preset time period, determining characteristic data of a charging event according to the charging event data, wherein the characteristic data of the charging event comprise a temperature difference characteristic value and a current characteristic value of the charging event, the temperature difference characteristic value of the charging event represents the temperature difference characteristic of a plurality of single battery cores in the battery in the charging event, and determining a consistency evaluation result of the battery according to the characteristic data of the charging event.

Description

Consistency assessment method, device and equipment for battery
Technical Field
The embodiment of the disclosure relates to the technical field of battery performance test, and more particularly relates to a method and equipment for evaluating consistency of batteries.
Background
The battery consistency means that after the single batteries with uniform specification and model form a battery pack, certain differences exist in parameters such as voltage, state of charge, maximum residual capacity, decay rate, internal resistance, change rate, service life, temperature influence, self-discharge rate and the like.
The consistency difference of the initial states of the battery cells in the battery pack caused by the production process and the use process caused by the structural design in the subsequent use can obviously influence the overall performance of the battery. The method is characterized by the damage of battery thermal runaway and the like caused by the local overheating of the battery pack, wherein the damage is caused by larger capacity attenuation and service life attenuation of the battery pack. The damage such as capacity loss, life loss, internal resistance increase, etc. is caused.
Therefore, it is necessary to evaluate the consistency of the battery and provide a reference for evaluating the performance and safety state of the battery.
Disclosure of Invention
An object of the present disclosure is to provide a new technical solution of a method, an apparatus and a device for evaluating consistency of a battery.
According to a first aspect of the present disclosure, there is provided an embodiment of a method of evaluating consistency of a battery, including:
acquiring a plurality of charging event data of the same battery in a preset time period;
Determining characteristic data of a charging event according to the charging event data, wherein the characteristic data of the charging event comprises a temperature difference characteristic value and a current characteristic value of the charging event, and the temperature difference characteristic value of the charging event represents the temperature difference characteristics of a plurality of single battery cores in the battery in the charging event;
and determining a consistency evaluation result of the battery according to the characteristic data of the plurality of charging events.
Optionally, the charging event data includes a plurality of pieces of charging data acquired from the charging event, the charging data including a sampling time and a temperature of each temperature sampling point corresponding to the sampling time;
the determining the characteristic data of the charging event according to the charging event data comprises the following steps:
Determining a maximum temperature difference between a plurality of temperature sampling points in the battery at the sampling time according to the charging data;
And determining the average value or the median of the maximum temperature difference corresponding to a plurality of sampling moments in the charging event as the temperature characteristic value of the charging event.
Optionally, the charging data includes a charging current corresponding to the sampling time;
The determining the characteristic data of the charging event according to the charging event data further comprises:
and determining the average value or the median of the charging current corresponding to a plurality of sampling moments in the charging event as the current characteristic value of the charging event.
Optionally, the determining the consistency evaluation result of the battery according to the feature data of the plurality of charging events includes:
Dividing a plurality of characteristic data into N classes through a maximum expected algorithm to obtain N characteristic data sets, wherein N is an integer and is more than or equal to 2;
determining whether the target feature data is the feature data with abnormal temperature difference according to the expected and covariance of the nth feature data set and the temperature difference feature value of the target feature data, wherein the nth feature data set is any one of the N feature data sets, and the target feature data is any one of the nth feature data sets;
And determining a consistency evaluation result of the battery according to the number of the characteristic data of the abnormal temperature difference of the battery in the preset time period.
Optionally, the N is 3, and before classifying the plurality of feature data into N classes by the maximum expectation algorithm, the method further comprises:
And estimating the expected initial value and the covariance initial value of the first characteristic data set, the expected initial value and the covariance initial value corresponding to the second characteristic data set and the expected initial value and the covariance initial value corresponding to the third characteristic data set according to the distribution condition of the charging data.
Optionally, the method further comprises:
And acquiring expected and covariance corresponding to each classification of the same battery in a plurality of preset time periods, and determining the consistency change trend of the same battery.
According to a second aspect of the present disclosure, there is provided an embodiment of a uniformity evaluation apparatus for a battery, comprising:
The first acquisition module is used for acquiring a plurality of charging event data of the same battery in a preset time period;
The first determining module is used for determining characteristic data of a charging event according to the charging event data, wherein the characteristic data of the charging event comprises a temperature difference characteristic value and a current characteristic value of the charging event, and the temperature difference characteristic value of the charging event represents the temperature difference characteristics of a plurality of single electric cores in the battery in the charging event;
and the second determining module is used for determining the consistency evaluation result of the battery according to the characteristic data of the plurality of charging events.
Optionally, the charging event data includes a plurality of pieces of charging data acquired from the charging event, the charging data including a sampling time and a temperature of each temperature sampling point corresponding to the sampling time;
The first determining module comprises a temperature difference determining sub-module, and the temperature difference determining sub-module is specifically used for:
Determining a maximum temperature difference between a plurality of temperature sampling points in the battery at the sampling time according to the charging data;
And determining the average value or the median of the maximum temperature difference corresponding to a plurality of sampling moments in the charging event as the temperature characteristic value of the charging event.
Optionally, the charging event data includes a charging current corresponding to the sampling time;
The first determination module includes a current determination submodule to:
and determining the average value or the median of the charging current corresponding to a plurality of sampling moments in the charging event as the current characteristic value of the charging event.
According to a third aspect of the present disclosure, there is provided an embodiment of an electronic device, comprising a processor, a memory and a program or instructions stored on the memory and executable on the processor, which when executed by the processor, implement the steps of a method for evaluating the consistency of a battery as in any of the first aspects 1-6 of the present description.
The method and the device have the advantages that through the acquisition of the plurality of charging event data of the same battery in the preset time period, the temperature difference characteristic value and the current characteristic value of the charging event are determined according to the charging event data, and the battery consistency evaluation result is determined according to the characteristic data of the plurality of charging events. Thus, it can be found that the battery with poor consistency in the same time period has higher reliability compared with the median or average statistical result adopted by the embodiment when using the instantaneous data. Thus, a more accurate battery consistency assessment result can be provided.
Other features of the present specification and its advantages will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description, serve to explain the principles of the specification.
Fig. 1 is a functional block diagram of a hardware configuration of an exemplary communication system;
FIG. 2 is a flow diagram of a method of consistency assessment of a battery according to an embodiment of the present disclosure;
Fig. 3 is a schematic structural view of a uniformity evaluation apparatus of a battery according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of exemplary embodiments may have different values.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
< Hardware configuration >
As shown in fig. 1, the communication system 100 includes a server 1000, a terminal device 2000, and a vehicle 3000.
The server 1000 may be a unitary server or a distributed server across multiple computers or computer data centers. The server may be of various types such as, but not limited to, a web server, news server, mail server, message server, advertisement server, file server, application server, interaction server, database server, or proxy server. In some embodiments, each server may include hardware, software, or embedded logic components or a combination of two or more such components for performing the appropriate functions supported by or implemented by the server. For example, a server, such as a blade server, cloud server, etc., or may be a server group consisting of multiple servers, may include one or more of the types of servers described above, etc.
In one embodiment, the server 1000 may include a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, and an input device 1600, as shown in fig. 1.
In further embodiments, the server 1000 may also include speakers, microphones, etc., without limitation herein.
The processor 1100 is used for executing a computer program. The computer program may be written in an instruction set of an architecture such as x86, arm, RISC, MIPS, SSE, etc. The memory 1200 includes, for example, ROM (read only memory), RAM (random access memory), nonvolatile memory such as a hard disk, and the like. The interface device 1300 includes, for example, various bus interfaces such as a serial bus interface (including a USB interface), a parallel bus interface, and the like. The communication device 1400 can perform wired or wireless communication, for example. The display device 1500 is, for example, a liquid crystal display, an LED display, a touch display, or the like. The input device 1600 may include, for example, a touch screen, keyboard, etc.
Although a plurality of devices of the server 1000 are shown in fig. 1, the present disclosure may relate to only some of the devices thereof, for example, the server 1000 may relate to only the memory 1200 and the processor 1100.
The terminal device 2000 may be, for example, an electronic device, which may be, for example, a mobile phone, a tablet computer, a computer, or the like.
As shown in fig. 1, the terminal apparatus 2000 may include a processor 2100, a memory 2200, an interface device 2300, a communication device 2400, a display device 2500, an input device 2600, a speaker 2700, a microphone 2800, and the like.
The processor 2100 may be a mobile version of the processor. The memory 2200 includes, for example, ROM (read only memory), RAM (random access memory), nonvolatile memory such as a hard disk, and the like. The interface device 2300 includes, for example, a USB interface, a headphone interface, and the like. The communication device 2400 may be, for example, a wired or wireless communication device, and the communication device 2400 may include a short-range communication device, such as any device that performs short-range wireless communication based on a short-range wireless communication protocol such as Hilink protocol, wiFi (IEEE 802.11 protocol), mesh, bluetooth, zigBee, thread, Z-Wave, NFC, UWB, liFi, or the like, and the communication device 2400 may include a remote communication device, such as any device that performs WLAN, GPRS, 2G/3G/4G/5G remote communication. The display device 2500 is, for example, a liquid crystal display, a touch display, or the like. The input device 2600 may include, for example, a touch screen, a keyboard, and the like. The mobile terminal 2000 may output audio information through the speaker 2700 and may collect audio information through the microphone 2800.
In the present embodiment, the memory 2200 of the terminal device 2000 is used to store instructions for controlling the processor 2100 to operate to realize communication of the terminal device 2000. The skilled artisan can design instructions in accordance with the disclosed aspects of the present disclosure. How the instructions control the processor to operate is well known in the art and will not be described in detail here.
Although a plurality of devices of the terminal apparatus 2000 are shown in fig. 1, the present disclosure may relate to only some of the devices, for example, the terminal apparatus 2000 may relate to only the memory 2200 and the processor 2100, the communication device 2400, and the display device 2500.
The vehicle 3000 may be a shared moped, an electric vehicle, or the like.
As shown in fig. 1, vehicle 3000 may include a processor 3100, a memory 3200, an interface device 3300, a communication device 3400, an output device 3500, an input device 3600, and so on. The processor 3100 may be a microprocessor MCU or the like. The memory 3200 includes, for example, ROM (read only memory), RAM (random access memory), nonvolatile memory such as a hard disk, and the like. The interface device 3300 includes, for example, a USB interface, a headphone interface, and the like. The communication device 3400 may include a short-range communication device, for example, any device that performs short-range wireless communication based on a Hilink protocol, wiFi (IEEE 802.11 protocol), mesh, bluetooth, zigBee, thread, Z-Wave, NFC, UWB, liFi, or the like, and the communication device 3400 may also include a remote communication device, for example, any device that performs WLAN, GPRS, 2G/3G/4G/5G remote communication. The output device 3500 may be, for example, a device that outputs a signal, and may be a display device such as a liquid crystal display, a touch display, or a speaker that outputs voice information. The input device 3600 may include, for example, a touch panel, a keyboard, or the like, and may input voice information by a microphone.
Although a plurality of devices of the vehicle 3000 are shown in fig. 1, the present disclosure may relate to only some of the devices, for example, the vehicle 3000 may relate to only the communication device 3400, the memory 3200, and the processor 3100.
The network 4000 may be a wireless communication network or a wired communication network, and may be a local area network or a wide area network. In the communication system 100 shown in fig. 1, the vehicle 3000 and the server 1000, and the terminal device 2000 and the server 1000 can communicate through the network 4000. The network 4000 on which the vehicle 3000 communicates with the server 1000 and the terminal device 2000 communicates with the server 1000 may be the same or different.
It should be understood that although fig. 1 shows only one server 1000, terminal device 2000, and vehicle 3000, it is not meant to limit the respective numbers, and that a plurality of servers 1000, a plurality of terminal devices 2000, and a plurality of vehicles 3000 may be included in the communication system 100.
< Method >
Fig. 2 is a flow chart of a method of evaluating consistency of a battery according to an embodiment of the present disclosure. The consistency evaluation method of the battery of the present embodiment may be performed by a consistency evaluation device of the battery, which may be provided in an electronic apparatus, for example.
As shown in fig. 2, the method for evaluating the consistency of the battery of the present embodiment may specifically include the following steps 2100 to 2300:
in step 2100, a plurality of charging event data for the same battery over a preset period of time is obtained.
In the present embodiment, the same type of battery may be any type of battery of the same model, and the present embodiment does not limit the type of battery.
In this embodiment, the preset time period may be one week, or one month. The preset time period may also be an nth week or an nth month after the battery starts to be used in the same type of battery. Or other predetermined time periods for acquiring a plurality of charging events.
The vehicle records vehicle operation data during operation, which may include a State Of Charge (SOC) Of the battery, a start time and an end time Of each charging event, a sampling time in each charging event, a charging current, a voltage, a temperature, and the like corresponding to the sampling time.
Acquiring a plurality of charging event data of the same battery within a preset time period includes acquiring data related to a charging event, that is, charging event data, from vehicle operation data. The charging event data may be obtained by sampling the battery management chip, and the sampling time interval may be flexibly set according to the actual situation, which is not limited herein. In one example, the charging event data includes a plurality of pieces of charging data acquired in the charging event, and the charging data includes a sampling time and a temperature of each temperature sampling point corresponding to the sampling time, and a charging current corresponding to the sampling time.
In practical situations, a person skilled in the art may set a temperature sampling point on each cell, or may set temperature sampling points in the cells at intervals. For example, for every few adjacent cells, a temperature sampling point is set at only one of them. It should be noted that, those skilled in the art can flexibly set this according to the actual situation.
The plurality of charge event data may include charge event data of all charge events of all electric vehicles using the same battery within a preset time period, and may also include charge event data of part of charge events of part of vehicles using the same battery within a preset time period. In practice, one skilled in the art can flexibly set a plurality of charging event data as needed.
Step 2200, determining characteristic data of a charging event according to the charging event data, wherein the characteristic data of the charging event comprises a temperature difference characteristic value and a current characteristic value of the charging event, and the temperature difference characteristic value of the charging event characterizes temperature difference characteristics of a plurality of single electric cores in the battery in the charging event.
In this step, after the charging event data is acquired, the charging event data is analyzed to determine feature data of the charging event. Specifically, for any piece of charging data in the charging event, determining the sampling time corresponding to the charging data and the temperature of each temperature sampling point corresponding to the sampling time, and calculating the maximum temperature difference between a plurality of temperature sampling points corresponding to the sampling time. After obtaining the maximum temperature differences corresponding to the sampling moments in the charging event, determining the average value or the median of the maximum temperature differences corresponding to the sampling moments in the charging event as the temperature characteristic value of the charging event.
Specifically, for one of the sampling moments, the temperature of each temperature sampling point is read from the corresponding charging data, the highest temperature value T max and the lowest temperature value T min are determined, and the difference between the two values is calculated as the maximum temperature difference T between the plurality of temperature sampling points in the battery at the sampling moment, that is, t=t max-Tmin.
According to the maximum temperature difference of a plurality of sampling moments of the charging event, an average value T mean or a median T med of the maximum temperature difference is determined and is used as a temperature characteristic value of the charging event. In one example of the present embodiment, a total of 5 sampling times are recorded as T 1＝1℃,T2＝2℃,T3＝3℃,T4＝4℃,T5 =5 ℃ for a certain charging event, the maximum temperature differences corresponding to the 5 sampling times are arranged from small to large, and the median T med =3 ℃ of the maximum temperature differences is determined as the temperature characteristic value. In another example of this patent, a maximum temperature difference average T mean may be calculated as the temperature characteristic value from the maximum temperature difference corresponding to these 5 sampling instants.
In another example of the present embodiment, the charging current in the battery at the sampling time may be determined from the charging data. And determining the average value or the median of the charging current corresponding to a plurality of sampling moments in the charging event as the current characteristic value of the charging event.
According to the charging current at each sampling time, an average value I mean or a median I med of the charging current is determined as a current characteristic value of the charging event. In one example of the present embodiment, a total of 5 sampling times are counted as I 1＝1A,I＝2A,I3＝3A,I4＝4A,I5 =5a for a certain charging event, the charging current values corresponding to the 5 sampling times are arranged from small to large, and the median I med =3a of the charging current is determined as the charging current characteristic value.
In another example of the present patent, an average value I mean of the charging currents may be calculated as the charging current characteristic value from the charging currents corresponding to the 5 sampling times.
In this embodiment, the feature data may be a binary array, which may be represented as [ T med,Imed ] or [ T mean,Imean ], for example.
Step 2300, determining a consistency evaluation result of the battery according to the feature data of the plurality of charging events.
And determining the consistency evaluation result of the battery according to the characteristic data of the plurality of charging events comprises classifying the characteristic data through a maximum expected algorithm to obtain N characteristic data sets, wherein N is an integer and is more than or equal to 2. And determining the characteristic data of abnormal temperature difference in each characteristic data set according to the distribution condition of the characteristic data in the characteristic data set. Further description is provided below.
Generally, there are two charging modes for a charging event, namely a fast charging mode and a slow charging mode, the charging currents of which are different, in this example, the initial charging current for fast charging of the battery pack is 150A and the initial charging current for slow charging is 15A. Correspondingly, in one example, N is preset to 2, that is, the types of the preset feature data include 2 types in total. Then, the feature data is classified by a maximum expected algorithm, and based on the current characteristic and the temperature characteristic during the battery charging process, the first feature data set obtained by the final classification can be regarded as corresponding to a slow charging mode, and the second feature data set can be regarded as corresponding to a fast charging mode.
In practice, in the fast charge mode, if the SOC of the battery is low at the start of charging, the charging current will be generally larger, which is referred to as deep fast charge. If the SOC of the battery is high at the start of charging, the charging current will typically be smaller, which is referred to as shallow fast charging. In one example, N is preset to 3, that is, the types of preset feature data include 3 types in total. Then, the feature data is classified by a maximum expected algorithm, based on the current characteristic and the temperature characteristic in the battery charging process, the first feature data set obtained by the final classification can be regarded as corresponding to a slow charging mode, the second feature data set can be regarded as corresponding to a shallow charging mode, and the third feature data set can be regarded as corresponding to a deep charging mode.
Before the plurality of feature data are divided into N classes by the maximum expectation algorithm, the method of the embodiment further comprises estimating an expected initial value and a covariance initial value of the first feature data set, an expected initial value and a covariance initial value corresponding to the second feature data set, and an expected initial value and a covariance initial value corresponding to the third feature data set according to the distribution condition of the charging data.
In this example, taking N as 3 as an example, before classifying a plurality of the feature data into 3 classes by the maximum expectation algorithm, the initial parameter values of the feature data set may be set to:
θ0＝{μ1＝[1 0],u2＝[80 0],μ3=[100 0],ε=[0.3 0.4 0.3]}
Where mu 1 represents the desired initial value of the first feature dataset, mu 2 represents the desired initial value of the second feature dataset, and mu 3 represents the desired initial value of the third feature dataset. σ 1 represents the initial covariance value of the first feature dataset, σ 2 represents the initial covariance value of the second feature dataset, and σ 3 represents the initial covariance value of the third feature dataset. The numbers in epsilon represent the probability of the distribution of the three feature data sets, respectively.
And (3) carrying out m iterations through a maximum expected algorithm until parameters are converged, so as to obtain:
Wherein the method comprises the steps of Representing the expectation of the first feature data set after the end of the maximum expectation algorithm iteration,Representing the expectation of the characteristic value of the first characteristic data collection flow after the iteration of the maximum expectation algorithm is finished,Representing the expectation of the temperature difference characteristic value of the first characteristic data set after the iteration of the maximum expectation algorithm is finished.Representing the covariance of the first feature data set after the end of the maximum expected algorithm iteration,Represents the upper bound of the covariance of the eigenvalues of the first eigenvalue set after the end of the maximum expected algorithm iteration,Represents the lower bound of the covariance of the eigenvalues of the first eigenvalue set after the end of the maximum expected algorithm iteration,Represents the upper bound of the covariance of the temperature difference characteristic values of the first characteristic data set after the iteration of the maximum expected algorithm is finished,Represents the lower bound of the covariance of the temperature difference characteristic values of the first characteristic data set after the iteration of the maximum expected algorithm is finished,Representing the probability of the first feature data set distribution after the end of the maximum expected algorithm iteration. Similarly, after the iteration of the maximum expected algorithm is finished, various values of the second feature data set and the third feature data set are not repeated.
And determining whether the target feature data is the feature data with abnormal temperature difference according to the expected and covariance of the nth feature data set and the temperature difference feature value of the target feature data, wherein the nth feature data set is any one of the N feature data sets, and the target feature data is any one of the nth feature data sets.
Specifically, whether the temperature difference characteristic value of the target characteristic data in the nth characteristic data set is the characteristic data of abnormal temperature difference may be determined according to the expectation of the temperature difference characteristic value of the nth characteristic data set and the upper limit of the covariance of the temperature difference characteristic value.
In one example of the present embodiment, the target feature data is data in the first feature data set, and the target feature data may be a target feature data set according to the temperature difference feature value T med, and the first feature data set is expected to be a temperature difference feature valueAnd an upper limit of covariance of the temperature difference eigenvalues of the first eigenvalue setAnd determining whether the characteristic data is abnormal temperature difference. Temperature difference characteristic value of target characteristic dataAnd determining the target characteristic data as characteristic data with abnormal temperature difference. In this example, q can be set to 3, where And determining the target characteristic data as characteristic data with abnormal temperature difference. It should be noted that, a person skilled in the art can flexibly set the judging range of the temperature difference characteristic value of the target characteristic data according to the actual situation
And determining a consistency evaluation result of the battery according to the number of the characteristic data of the abnormal temperature difference of the battery in the preset time period. The consistency evaluation result of the battery may be determined by the number of the abnormal temperature difference characteristic data of the battery in the preset time period, or may be determined by the ratio of the number of the abnormal temperature difference characteristic data of the battery to the total number of all the characteristic data of the battery in the preset time period, which is not limited herein.
In another example of this embodiment, the method further includes obtaining expected and covariance corresponding to each class of the same battery over a plurality of the preset time periods, and determining a uniformity variation trend of the same battery.
Specifically, the preset time period is 1 month, the multiple preset time periods can be 6 continuous months, and the expected temperature difference characteristic value of the first characteristic data set in each month is obtained through iterative calculationAnd an upper limit of covariance of the temperature difference eigenvalues of the first eigenvalue setExpectations of temperature difference characteristic values of the second characteristic data setAnd an upper limit of covariance of the temperature difference characteristic values of the second characteristic data setExpectations of temperature difference characteristic values of third characteristic data setAnd an upper limit of covariance of the temperature difference characteristic values of the third characteristic data setThe trend of the change of the characteristic value of the temperature difference of the battery with time can be determined.
For example, for a certain battery, the expected temperature difference characteristic value of the 6 th month is calculated and compared with the expected temperature difference characteristic value of the first 5 months, and the expected temperature difference characteristic value of the 6 th month is foundAndA dramatic increase may indicate that the uniformity trend for such batteries includes a dramatic decrease in the uniformity of the battery at month 6.
The method for evaluating the consistency of the battery according to the present embodiment has been described above with reference to the accompanying drawings, in which a plurality of charging event data of the same battery in a preset period of time are obtained, a temperature difference characteristic value and a current characteristic value of a charging event are determined according to the charging event data, and a battery consistency evaluation result is determined according to the characteristic data of the plurality of charging events. Thus, it can be found that the battery with poor consistency in the same time period has higher reliability than the median or average statistical result adopted in the embodiment by using the instantaneous data. Thus, a more accurate battery consistency assessment result can be provided.
< Device >
Fig. 3 is a schematic structural view of a uniformity evaluation apparatus of a battery according to an embodiment of the present disclosure. As shown in fig. 3, the consistency evaluating apparatus 300 of the battery of the present embodiment may include:
The first obtaining module 310 is configured to obtain a plurality of charging event data of the same battery in a preset period of time. The first determining module 320 is configured to determine, according to the charging event data, characteristic data of a charging event, where the characteristic data of the charging event includes a temperature difference characteristic value and a current characteristic value of the charging event, and the temperature difference characteristic value of the charging event characterizes temperature difference characteristics of a plurality of unit cells in the battery in the charging event. And a second determining module 330, configured to determine a consistency evaluation result of the battery according to the feature data of the plurality of charging events.
In one embodiment, the charging event data includes a plurality of pieces of charging data acquired from the charging event, the charging data including a sampling time and a temperature of each temperature sampling point corresponding to the sampling time. The first determining module 310 includes a temperature difference determining module, specifically configured to determine a maximum temperature difference between a plurality of temperature sampling points in the battery at the sampling time according to the charging data. And determining the average value or the median of the maximum temperature difference corresponding to a plurality of sampling moments in the charging event as the temperature characteristic value of the charging event.
In one embodiment, the charging event data includes a plurality of pieces of charging data acquired from the charging event, the charging data including sampling time instants and charging currents corresponding to the sampling time instants. The first determining module 310 includes a current determining module configured to determine an average value or a median of charging currents corresponding to a plurality of sampling moments in a charging event as a current characteristic value of the charging event.
In one embodiment, the second determining module 330 includes:
and the maximum expectation module is used for dividing the plurality of characteristic data into N classes through a maximum expectation algorithm to obtain N characteristic data sets, wherein N is an integer and is more than or equal to 2.
The system comprises an anomaly determination module, a target feature data processing module and a temperature difference determination module, wherein the anomaly determination module is used for determining whether the target feature data is the feature data with abnormal temperature difference according to the expected and covariance of an nth feature data set and the temperature difference feature value of the target feature data;
And the consistency determination module is used for determining a consistency evaluation result of the battery according to the number of the characteristic data of the abnormal temperature difference of the battery in the preset time period.
In one embodiment, N is 3, and the maximum expected module includes an initial setting sub-module, where the initial setting sub-module is configured to estimate, according to a distribution of charging data, an expected initial value and a covariance initial value of the first feature data set, an expected initial value and a covariance initial value corresponding to the second feature data set, and an expected initial value and a covariance initial value corresponding to the third feature data set.
In one embodiment, the device 300 for evaluating the consistency of batteries further includes a third determining module, configured to obtain the expected and covariance corresponding to each category of the same battery in a plurality of preset time periods, and determine the consistency variation trend of the same battery.
The battery consistency assessment device of the present embodiment may be used to implement the technical solution of the foregoing method embodiment, and its implementation principle and technical effects are similar and will not be described herein.
< Electronic device >
Fig. 4 is a schematic diagram of a hardware structure of an electronic device according to another embodiment.
As shown in fig. 4, the embodiment of the present application further provides an electronic device 400, which includes a processor 410, a memory 420, and a program or an instruction stored in the memory 420 and capable of running on the processor 410, where the program or the instruction implements each process of the above embodiment of the method for evaluating the consistency of the battery when executed by the processor 410, and the process can achieve the same technical effect, and for avoiding repetition, a detailed description is omitted herein.
< Computer-readable storage Medium >
The present embodiment provides a computer-readable storage medium having stored therein executable instructions that, when executed by a processor, perform the method described in any of the method embodiments of the present specification.
One or more embodiments of the present description may be a system, method, and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement aspects of the present description.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical encoding device, punch cards or intra-groove protrusion structures such as those having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of embodiments of the present description may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as SMALLTALK, C ++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present description are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information for computer-readable program instructions, which may execute the computer-readable program instructions.
Various aspects of the present description are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present description. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.
The embodiments of the present specification have been described above, and the above description is illustrative, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvement in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the application is defined by the appended claims.

Claims (9)

1.一种电池的一致性评估方法，其特征在于，包括：1. A battery consistency assessment method, comprising: 获取同一种电池在预设时间段内的多个充电事件数据；Acquire multiple charging event data of the same battery within a preset time period; 根据所述充电事件数据确定充电事件的特征数据，所述充电事件的特征数据包含所述充电事件的温差特征值和电流特征值，所述充电事件的温差特征值表征所述电池中的多个单体电芯在所述充电事件中的温差特性；Determining characteristic data of a charging event according to the charging event data, wherein the characteristic data of the charging event includes a temperature difference characteristic value and a current characteristic value of the charging event, and the temperature difference characteristic value of the charging event represents a temperature difference characteristic of a plurality of single cells in the battery during the charging event; 根据多个所述充电事件的特征数据，确定所述电池的一致性评估结果；Determining a consistency evaluation result of the battery according to the characteristic data of the plurality of charging events; 其中，所述根据多个所述充电事件的特征数据，确定所述电池的一致性评估结果，包括：Wherein, determining the consistency evaluation result of the battery according to the characteristic data of the plurality of charging events includes: 通过最大期望算法将多个所述特征数据分为N类，以得到N个特征数据集，所述N为整数并且N≥2；Classifying the plurality of feature data into N categories by using a maximum expectation algorithm to obtain N feature data sets, where N is an integer and N≥2; 根据第n个特征数据集的期望和协方差以及目标特征数据的温差特征值，确定所述目标特征数据是否为温差异常的特征数据；所述第n个特征数据集为N个特征数据集中的任一个特征数据集，所述目标特征数据为所述第n个特征数据集中的任一个特征数据；Determine whether the target feature data is feature data of temperature difference abnormality according to the expectation and covariance of the nth feature data set and the temperature difference feature value of the target feature data; the nth feature data set is any feature data set among the N feature data sets, and the target feature data is any feature data among the nth feature data set; 根据所述电池在所述预设时间段内出现的所述温差异常的特征数据的个数，确定所述电池的一致性评估结果；Determining a consistency evaluation result of the battery according to the number of characteristic data of the temperature difference abnormality occurring in the battery within the preset time period; 其中，根据第n个特征数据集的温差特征值的期望和温差特征值的协方差的上限，确定第n个特征数据集中的目标特征数据的温差特征值是否为温差异常的特征数据；Wherein, according to the expectation of the temperature difference characteristic value of the nth characteristic data set and the upper limit of the covariance of the temperature difference characteristic value, it is determined whether the temperature difference characteristic value of the target characteristic data in the nth characteristic data set is characteristic data of temperature difference abnormality; 当所述目标特征数据的温差特征值时，将所述目标特征数据确定为温差异常的特征数据，其中，温差特征值的期望为温差特征值的协方差的上限为q为系数。When the temperature difference characteristic value of the target characteristic data When , the target characteristic data is determined as the characteristic data of temperature difference abnormality, wherein the expectation of the temperature difference characteristic value is The upper limit of the covariance of the temperature difference eigenvalue is q is the coefficient. 2.根据权利要求1所述的方法，其特征在于，所述充电事件数据包括从所述充电事件中获取的多条充电数据，所述充电数据包括采样时刻和与所述采样时刻对应的每个温度采样点的温度；2. The method according to claim 1, characterized in that the charging event data comprises a plurality of charging data obtained from the charging event, and the charging data comprises a sampling time and a temperature of each temperature sampling point corresponding to the sampling time; 所述根据所述充电事件数据确定充电事件的特征数据，包括：The determining characteristic data of the charging event according to the charging event data includes: 根据所述充电数据，确定在所述采样时刻所述电池中的多个温度采样点之间的最大温差；determining, based on the charging data, a maximum temperature difference between a plurality of temperature sampling points in the battery at the sampling time; 确定所述充电事件中的多个采样时刻对应的所述最大温差的平均值或者中位数，作为所述充电事件的温度特征值。An average value or a median value of the maximum temperature difference corresponding to a plurality of sampling moments in the charging event is determined as a temperature characteristic value of the charging event. 3.根据权利要求2所述的方法，其特征在于，所述充电数据包括与所述采样时刻对应的充电电流；3. The method according to claim 2, characterized in that the charging data comprises a charging current corresponding to the sampling time; 所述根据所述充电事件数据确定充电事件的特征数据，还包括：The determining characteristic data of the charging event according to the charging event data further includes: 确定所述充电事件中的多个采样时刻对应的所述充电电流的平均值或者中位数，作为所述充电事件的电流特征值。An average value or a median value of the charging current corresponding to a plurality of sampling moments in the charging event is determined as a current characteristic value of the charging event. 4.根据权利要求2所述的方法，其特征在于，所述N为3；在通过最大期望算法将多个所述特征数据分为N类之前，所述方法还包括：4. The method according to claim 2, characterized in that N is 3; before the plurality of feature data are classified into N categories by the maximum expectation algorithm, the method further comprises: 根据所述充电数据的分布情况，预估第一个特征数据集的期望初始值和协方差初始值、对应于第二个特征数据集的期望初始值和协方差初始值、对应于第三个特征数据集的期望初始值和协方差初始值。According to the distribution of the charging data, the expected initial value and the initial covariance value of the first characteristic data set, the expected initial value and the initial covariance value corresponding to the second characteristic data set, and the expected initial value and the initial covariance value corresponding to the third characteristic data set are estimated. 5.根据权利要求1所述的方法，其特征在于，所述方法还包括：5. The method according to claim 1, characterized in that the method further comprises: 获取所述同一种电池在多个所述预设时间段内的每一个分类对应的期望和协方差，确定所述同一种电池的一致性变化趋势。The expectation and covariance corresponding to each classification of the same type of battery in the plurality of preset time periods are obtained to determine the consistency change trend of the same type of battery. 6.一种电池的一致性评估装置，其特征在于，包括：6. A battery consistency assessment device, comprising: 第一获取模块，用于获取同一种电池在预设时间段内的多个充电事件数据；A first acquisition module is used to acquire multiple charging event data of the same type of battery within a preset time period; 第一确定模块，用于根据所述充电事件数据确定充电事件的特征数据，所述充电事件的特征数据包含所述充电事件的温差特征值和电流特征值，所述充电事件的温差特征值表征所述电池中的多个单体电芯在所述充电事件中的温差特性；A first determination module is used to determine characteristic data of a charging event according to the charging event data, wherein the characteristic data of the charging event includes a temperature difference characteristic value and a current characteristic value of the charging event, and the temperature difference characteristic value of the charging event represents a temperature difference characteristic of a plurality of single cells in the battery during the charging event; 第二确定模块，用于根据多个所述充电事件的特征数据，确定所述电池的一致性评估结果；A second determination module, configured to determine a consistency evaluation result of the battery according to the characteristic data of the plurality of charging events; 其中，所述根据多个所述充电事件的特征数据，确定所述电池的一致性评估结果，包括：Wherein, determining the consistency evaluation result of the battery according to the characteristic data of the plurality of charging events includes: 通过最大期望算法将多个所述特征数据分为N类，以得到N个特征数据集，所述N为整数并且N≥2；Classifying the plurality of feature data into N categories by using a maximum expectation algorithm to obtain N feature data sets, where N is an integer and N≥2; 根据第n个特征数据集的期望和协方差以及目标特征数据的温差特征值，确定所述目标特征数据是否为温差异常的特征数据；所述第n个特征数据集为N个特征数据集中的任一个特征数据集，所述目标特征数据为所述第n个特征数据集中的任一个特征数据；Determine whether the target feature data is feature data of temperature difference abnormality according to the expectation and covariance of the nth feature data set and the temperature difference feature value of the target feature data; the nth feature data set is any feature data set among the N feature data sets, and the target feature data is any feature data among the nth feature data set; 根据所述电池在所述预设时间段内出现的所述温差异常的特征数据的个数，确定所述电池的一致性评估结果；Determining a consistency evaluation result of the battery according to the number of characteristic data of the temperature difference abnormality occurring in the battery within the preset time period; 其中，根据第n个特征数据集的温差特征值的期望和温差特征值的协方差的上限，确定第n个特征数据集中的目标特征数据的温差特征值是否为温差异常的特征数据；Wherein, according to the expectation of the temperature difference characteristic value of the nth characteristic data set and the upper limit of the covariance of the temperature difference characteristic value, it is determined whether the temperature difference characteristic value of the target characteristic data in the nth characteristic data set is characteristic data of temperature difference abnormality; 当所述目标特征数据的温差特征值时，将所述目标特征数据确定为温差异常的特征数据，其中，温差特征值的期望为温差特征值的协方差的上限为q为系数。When the temperature difference characteristic value of the target characteristic data When , the target characteristic data is determined as the characteristic data of temperature difference abnormality, wherein the expectation of the temperature difference characteristic value is The upper limit of the covariance of the temperature difference eigenvalue is q is the coefficient. 7.根据权利要求6所述的装置，其特征在于，所述充电事件数据包括从所述充电事件中获取的多条充电数据，所述充电数据包括采样时刻和与所述采样时刻对应的每个温度采样点的温度；7. The device according to claim 6, characterized in that the charging event data comprises a plurality of charging data obtained from the charging event, and the charging data comprises a sampling time and a temperature of each temperature sampling point corresponding to the sampling time; 所述第一确定模块包括温差确定子模块，所述温差确定子模块具体用于：The first determination module includes a temperature difference determination submodule, and the temperature difference determination submodule is specifically used to: 根据所述充电数据，确定在所述采样时刻所述电池中的多个温度采样点之间的最大温差；determining, based on the charging data, a maximum temperature difference between a plurality of temperature sampling points in the battery at the sampling time; 确定所述充电事件中的多个采样时刻对应的所述最大温差的平均值或者中位数，作为所述充电事件的温度特征值。An average value or a median value of the maximum temperature difference corresponding to a plurality of sampling moments in the charging event is determined as a temperature characteristic value of the charging event. 8.根据权利要求7所述的装置，其特征在于，所述充电事件数据包括与所述采样时刻对应的充电电流；8. The device according to claim 7, characterized in that the charging event data comprises a charging current corresponding to the sampling time; 所述第一确定模块包括电流确定子模块，所述电流确定子模块用于：The first determination module includes a current determination submodule, and the current determination submodule is used to: 确定所述充电事件中的多个采样时刻对应的所述充电电流的平均值或者中位数，作为所述充电事件的电流特征值。An average value or a median value of the charging current corresponding to a plurality of sampling moments in the charging event is determined as a current characteristic value of the charging event. 9.一种电子设备，其特征在于，包括处理器，存储器及存储在所述存储器上并可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如权利要求1～5中任一项所述的电池的一致性评估方法的步骤。9. An electronic device, characterized in that it comprises a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein when the program or instruction is executed by the processor, the steps of the battery consistency evaluation method as described in any one of claims 1 to 5 are implemented.
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