CN111376793A

CN111376793A - Method, apparatus and computer readable medium for managing battery

Info

Publication number: CN111376793A
Application number: CN201811652876.9A
Authority: CN
Inventors: 张民; 杨重科; 秦兴权; 韩广璞
Original assignee: Qoros Automotive Co Ltd
Current assignee: Qoros Automotive Co Ltd
Priority date: 2018-12-29
Filing date: 2018-12-29
Publication date: 2020-07-07

Abstract

Embodiments of the present disclosure relate to methods, devices and computer program products for managing a battery. According to an exemplary implementation of the present disclosure, a battery model is obtained, the battery model characterizing an equivalent circuit of a battery; determining a first state of charge of the battery based on the battery model; and iteratively performing at least one of: determining a second state of charge of the battery based on the first state of charge; determining a first battery direct-current internal resistance of the battery based on the second state of charge; and updating the first state of charge based on the second state of charge.

Description

Method, apparatus and computer readable medium for managing battery

Technical Field

Implementations of the present disclosure relate to information processing, and more particularly, to a method, apparatus, and computer-readable medium for managing a battery.

Background

Currently, electric vehicles are increasingly popular because of their smaller environmental impact relative to conventional vehicles. The battery is one of the core components of the electric automobile. The electric automobile adopts the battery as the energy storage power source, provides the electric energy for the motor through the battery, and the drive motor moves to promote electric automobile to travel. Therefore, there is a need to accurately and real-time determine the State of the battery in the electric vehicle, such as the State of Charge (SOC) and State of Health (SOH) of the battery.

Disclosure of Invention

Embodiments of the present disclosure provide methods and apparatus for managing a battery.

In a first aspect of the disclosure, a method for managing a battery is provided. The method comprises the following steps: obtaining a battery model, wherein the battery model represents an equivalent circuit of a battery; determining a first state of charge of the battery based on the battery model; and iteratively performing at least one of: determining a second state of charge of the battery based on the first state of charge; determining a first battery direct-current internal resistance of the battery based on the second state of charge; and updating the first state of charge based on the second state of charge.

In a second aspect of the present disclosure, an apparatus for managing a battery is provided. The apparatus comprises at least one processing unit and at least one memory. At least one memory is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit. The instructions, when executed by the at least one processing unit, cause the apparatus to perform acts comprising: obtaining a battery model, wherein the battery model represents an equivalent circuit of a battery; determining a first state of charge of the battery based on the battery model; and iteratively performing at least one of: determining a second state of charge of the battery based on the first state of charge; determining a first battery direct-current internal resistance of the battery based on the second state of charge; and updating the first state of charge based on the second state of charge.

In a third aspect of the disclosure, a computer program product is provided. A computer program product is tangibly stored on a non-transitory computer-readable medium and includes machine executable instructions that, when executed, cause a machine to implement any of the steps of the method described in accordance with the first aspect of the disclosure.

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the disclosure, nor is it intended to be used to limit the scope of the disclosure.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be apparent from the following more particular descriptions of exemplary embodiments of the disclosure as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the disclosure.

FIG. 1 illustrates a schematic diagram of an example of a managed battery environment in which embodiments of the present disclosure may be implemented;

fig. 2 shows a flow diagram of one example of a method for managing a battery according to an embodiment of the present disclosure;

fig. 3 shows a flowchart of another example of a method for managing a battery according to an embodiment of the present disclosure;

FIG. 4 shows a schematic diagram of an example of a battery equivalent circuit model, in accordance with an embodiment of the present disclosure;

FIG. 5 shows a schematic diagram of an example of a mixed pulse power performance experimental voltage curve according to an embodiment of the present disclosure; and

FIG. 6 illustrates a schematic block diagram of an example device that can be used to implement embodiments of the present disclosure.

Like or corresponding reference characters designate like or corresponding parts throughout the several views.

Detailed Description

Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.

As described above, there is a need for accurately and in real-time determining the state of a battery (such as the state of charge and state of health of the battery) within an electric vehicle. In conventional approaches, a combination of filtered or modified amp-time integral plus open circuit voltage is typically used for estimating the state of charge of the battery. However, this method is greatly affected by the measurement accuracy, and the initial value for correcting the state of charge using the open circuit voltage method must be under the system static condition to obtain an accurate value. Alternatively, for estimating the state of charge of the battery, an extended kalman filter method may also be generally used. However, the extended kalman filter method has a high requirement on the accuracy of model selection, and is poor in dealing with sudden changes after the system reaches a steady state.

For estimating the state of health, methods of calibrating parameters are generally performed using aging experiments. The method is performed offline in a laboratory environment, dynamic changes of the health state cannot be tracked in real time, and estimation accuracy is poor.

Furthermore, current joint estimation algorithms for battery state of charge and state of health typically use the double extended kalman filter method (DEKF). However, due to the limitation of the extended kalman filtering method, the tracking effect is poor when the state of charge encounters a current sudden change, especially when the current of a battery applied to an automobile frequently suddenly changes.

In addition, in the conventional scheme, the system model is not matched with the actual system after the system parameters are changed, so that the robustness of the conventional method is poor, and the accuracy of the state of charge estimation is influenced. To this end, solutions for managing batteries are proposed here.

In general, according to embodiments of the present disclosure, a battery model may be obtained, the battery model characterizing an equivalent circuit of a battery; determining a first state of charge of the battery based on the battery model; and iteratively performing at least one of: determining a second state of charge of the battery based on the first state of charge; determining a first battery direct-current internal resistance of the battery based on the second state of charge; and updating the first state of charge based on the second state of charge. In addition, the health state of the battery can be determined based on the direct current internal resistance of the first battery and the rated internal resistance of the battery.

Therefore, compared with an ampere-hour product method, the method can reduce the accumulated error and the relative error of the measurement precision, and improve the precision of the state of charge estimation; compared with the initial value of the state of charge when the system is started by an open-circuit voltage method, the initial value can be quickly iterated to find an accurate value; compared with the problems that the tracking performance is poor and the like under the condition that the extended Kalman algorithm is subjected to mutation during the estimation of the state of charge, the method has stronger tracking performance and further improves the estimation accuracy of the state of charge under multiple working conditions.

Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.

Fig. 1 illustrates a schematic diagram of an example environment 100 in which various embodiments of the present disclosure can be implemented. As shown, the example environment 100 includes a vehicle 110. It should be understood that vehicle 110 may be any entity capable of movement, such as a motor vehicle, a non-motor vehicle, or a wearable device.

Vehicle 110 includes a computing device 120 and a battery 130. Computing device 120 may be any suitable computing device, whether centralized or distributed, including but not limited to personal computers, servers, clients, hand-held or laptop devices, multiprocessors, microprocessors, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed clouds, combinations thereof, and the like. The computing device 120 may manage the battery 130. In certain embodiments, the computing device 120 may determine the state of charge and state of health of the battery 130.

Fig. 2 shows a flowchart of one example of a method 200 for managing a battery according to an embodiment of the present disclosure. Process 200 may be implemented by computing device 120. At 210, computing device 120 obtains a battery model that characterizes an equivalent circuit of battery 130. In some embodiments, the computing device 120 may determine a model internal dc resistance of the battery model, a rated capacity of the battery 130, and an open circuit voltage of the battery 130; determining the polarization impedance of the battery model based on the model direct current internal resistance; and determining a battery model based on the model direct current internal resistance, the polarization impedance, the rated capacity and the open circuit voltage.

At 220, computing device 120 determines a first state of charge of the battery based on the battery model. In some embodiments, the computing device 120 may initialize the first state of charge to a state of charge at a previous power down of the battery in response to a resting time after power up of the battery being below a predetermined threshold; and initializing the first state of charge to a state of charge corresponding to an open circuit voltage of the battery in response to a standing time after the battery is powered on exceeding a predetermined threshold.

At 230, computing device 120 iteratively performs the following actions at least once. Specifically, at 232, the computing device 120 determines a second state of charge of the battery based on the first state of charge. In some embodiments, to determine the second state of charge, the computing device 120 may obtain the second internal dc battery resistance determined in the previous iteration; determining a state of charge of the intermediate battery and a residual error associated with a terminal voltage output value of the battery based on the first state of charge and the direct current internal resistance of the second battery; determining a time-varying fading factor associated with the intermediate battery state of charge and the second battery dc internal resistance based on the residual error; determining a Kalman gain associated with the intermediate battery state of charge and the second battery DC internal resistance based on a time-varying fading factor; and determining a second state of charge based on the kalman gain.

At 234, the computing device 120 determines a first internal battery dc resistance of the battery based on the second state of charge. In some embodiments, to determine the first internal dc battery resistance, the computing device 120 may obtain a second internal dc battery resistance determined in a previous iteration; determining the intermediate battery direct current internal resistance and a residual error associated with the terminal voltage output value of the battery based on the second state of charge and the second battery direct current internal resistance; determining a time-varying fading factor associated with the second state of charge and the intermediate battery DC internal resistance based on the residual error; determining a Kalman gain associated with the second state of charge and the intermediate battery DC internal resistance based on a time-varying fading factor; and determining the direct current internal resistance of the first battery based on the Kalman gain.

At 236, the computing device 120 updates the first state of charge based on the second state of charge. In certain embodiments, the computing device 120 may determine the second state of charge as the first state of charge.

Further, the computing device 120 may also determine the state of health of the battery based on the first battery internal dc resistance and the rated internal resistance of the battery. Thus, the computing device 120 may determine the state of charge and state of health of a battery within an electric vehicle accurately and in real-time.

Fig. 3 shows a flowchart of another example of a method 300 for managing a battery according to an embodiment of the present disclosure. Process 300 may be implemented by computing device 120.

Generally, the invention solves the problem that the SOC value of the battery can be accurately estimated under multiple working conditions by establishing an equivalent circuit model of the first-order RC of the battery and adding a strong tracking algorithm on the basis of an extended Kalman filtering algorithm, thereby ensuring the estimation accuracy of the SOC even under suburban working conditions and when the discharge fluctuation of a battery system is large. According to the principle of the strong tracking extended Kalman algorithm, a dual-strong tracking extended Kalman filtering algorithm is adopted to estimate the SOC and the system parameter internal resistance at the same time, and the internal resistance is used for representing the SOH.

Process 300 begins at 310. At 310, computing device 120 may control the voltage and current provided to battery 130. In addition, the ambient temperature will also affect the characteristics of the battery 130.

At 315, computing device 120 may build a battery model. Specifically, the computing device 120 may select the battery equivalent circuit model 400 of the first-order RC as shown in fig. 4, collect experimental data according to the charge and discharge experimental standard to identify the battery model parameters and verify the accuracy of the model. In some embodiments, a selected battery is used for conducting a specified charge-discharge experiment to obtain voltage and current, an OCV-SOC curve of the battery is obtained, then direct-current internal resistance and polarization impedance of the battery are obtained according to HPPC (Power Perkin Elder cell) experiment by using battery characteristics, parameters of a battery model are obtained, and model accuracy is verified according to actual output voltage of the battery and data voltage of the battery model under the condition of the same current.

The battery equivalent circuit model 400 adopts a first-order RC Thevenin equivalent circuit model in which direct-current internal resistance R₀The abrupt change state of the battery is reflected, the RC loop is used for representing the polarization effect of the battery, the model is favorable for engineering realization, and compared with other models, the accuracy is higher at present, and the estimation requirement of the state of charge is met.

In certain embodiments, the voltage current curves for charging and discharging over multiple cycles are obtained according to a mixed pulse power performance test, as shown in fig. 5. Wherein the charge-discharge multiplying power is 1C. Based on the instantaneous fluctuation of the voltage (line segment indicated by points 520 to 530), R is used₀＝(U₄-U₂) And I, calculating the direct current internal resistance of the model.

Furthermore, the line segment as indicated by points 530 through 540 may better represent the progressiveness of the battery 130. Fitting is then performed (using, for example, cftool kit) according to the terminal voltage formula U₀＝U_oc-U₁-I×R₀Fitting into a cftool box to obtain the polarization impedance R of the battery model₁、C₁。

In addition, under experimental conditions, after the battery 130 is charged at a constant current and a constant voltage to ensure that the battery is fully charged, the battery 130 is discharged until the maximum cut-off voltage of battery discharge, and the capacity of the battery is calculated by ampere-hour integration according to the discharge current.

Further, the charging and discharging cabinet is utilized to collect the voltage U and the current I of the selected battery cell according to the experimental conditions, and the corresponding open-circuit voltage U when the SOC is respectively 0.05, 0.1, 0.15, … and 1 is calculated by an ampere-hour integration method_ocvObtaining the relation between the open-circuit voltage and the state of charge (SOC);

therefore, by finding the model parameter R₀、R₁、C₁And open circuit voltage U_ocAnd finally obtaining a state space equation of the battery model. The battery model was established as follows:

the state equation for state of charge, SOC, estimation is:

wherein SOC represents an estimated target value of state of charge, U₁Representing the RC loop voltage, R₁Representing the resistance of the RC circuit, C₁Representing RC loop capacitance, SOC₀Initial value representing state of charge, T represents sampling time, Q_ratIndicating the rated capacity of the battery and I the sampled current.

Internal resistance of DC R₀The equation of state of (a) is:

R₀(k)＝R₀(k-1)+Q(k-1) (2)，

wherein R is₀(k) Direct current internal resistance R representing current time₀Is estimated by R₀(k-1) is the direct current internal resistance R at the previous moment₀Q (k-1) represents the state noise at the previous time.

The measurement equation is:

U₀＝U_oc-U₁-I×R₀(3)，

wherein U is₀Indicating the terminal voltage output value, U_ocRepresents an open circuit voltage, which is approximated by a cell balance electromotive force E₀，U₁Representing the RC loop voltage, I representing the sample current, R₀Indicating the dc internal resistance.

In the following description, for the sake of clarity, the above equations (1) and (2) may be expressed as follows:

wherein

A state variable representing the predicted time k + 1; a (k) represents that the system state variable x is a used transfer matrix from the time k to the time k +1, and is used for reflecting the mapping of the previous time to the current time; x (k) represents a state variable at time k; b (k) represents a system input matrix; u (k) represents the excitation used by the system. Note that, although equations (1) and (2) are expressed in a unified form, equation (4) should be expressed as equation (1) when predicting the state of charge SOC, and equation (1) when predicting the direct-current internal resistance R₀Equation (4) should be expressed as equation (2).

Further, the computing device 120 may obtain a state initial value of the state of charge, SOC. At 520, computing device 120 may compare the resting time of the first power-up of the battery to a predetermined threshold (e.g., 1 hour). If the rest time is below a predetermined threshold, then at 325, the SOC value stored by the last power down EEPROM is used. If the time to rest is above a predetermined threshold, then at 330, the initial value is looked up by looking up a table.

Then, at 335, the computing device 120 may initialize the state of charge SOC. Further, at 360, the computing device 120 may initialize the direct current internal resistance R₀. In addition, the computing device 120 may also initialize the error covariance P. Initialization may give these variables arbitrary values. In some embodiments, SOC and R₀Is typically assigned the actual value of the current battery and P is typically assigned a smaller arbitrary value.

At 340, the computing device 120 may predict the state of charge, SOC, by equation (4) above, and calculate the observed output by the following equation:

wherein

Representing the output voltage U₀The estimated value at the time k + 1; c (k) represents a system measurement matrix; d (k) represents a system feedforward matrix.

At 345, computing device 120 may calculate the residual error by the following equation:

where Δ y (k +1) represents the residual error and y (k +1) represents the acquisition voltage.

Further, the computing device 120 may calculate a time-varying fading factor. The strong tracking algorithm is to introduce a time-varying fading factor lambda when the Kalman filtering algorithm predicts the covariance matrix P, and to make the residual error sequences mutually orthogonal at each step by adjusting a proper Kalman gain matrix K. Namely:

E[△y(k+1+j)△y^T(k+1)]＝0,k＝0,1,...j＝1,2,... (7)，

where E represents averaging.

The computing device 120 may calculate the time-varying fading factor by the following equation:

N(k+1)＝S₀(k+1)-C(k)Q(k)C^T(k)-βR(k+1)

M(k+1)＝C(k)A(k)P(k)A^T(k)C^T(k) (10)，

wherein rho is a forgetting factor, a value between 0.9 and 1 is generally taken, β is more than or equal to 1 is a weakening factor, the introduction of the weakening factor can make a state estimation value smoother, A (k) is a transfer matrix used by a system state variable x from the current k moment to the k +1 moment and is used for reflecting the mapping of the previous moment to the current moment, C (k) is a system measurement matrix, D (k) is a system feedforward matrix, Q (k) represents the state noise of the system, R (k) represents the measurement noise of the system, and S (k) represents the measurement noise of the system₀Indicating a disabilityThe difference covariance.

At 350, the computing device 120 calculates a kalman gain. To calculate the kalman gain, the computing device 120 first calculates and updates the state covariance matrix P by the following equation:

P(k+1)＝λ(k+1)A(k)P(k)A^T(k)+Q(k) (11)

the computing device 120 may then calculate the kalman gain matrix K by the following equation:

K(k+1)＝P(k+1)K^T(k)[K(k)P(k+1)K^T(k)+R(k+1)]^-1(12)

at 355, the computing device 120 may update the state of charge SOC:

the SOC and the SOH of the battery are estimated by combining the suboptimal fading factor obtained by a strong tracking theory with an extended Kalman filtering algorithm, the SOC and the internal resistance are used as two state variables, the state of the SOH of the battery is reflected by using the change of the internal resistance, and meanwhile, the parameters of the battery are updated in real time, so that the service efficiency of the battery is improved. Firstly, the SOC updated for the first time is used as the state quantity of the internal resistance predicted for the next time, the internal resistance optimal value at the current moment is estimated and used for the SOC system parameters at the next time, and the SOC and the internal resistance optimal values are obtained through alternate circulation in this way.

Based on the principle, firstly, the SOC is estimated by using a strong tracking algorithm, the SOC value of which the current time k is 2n +1 is obtained and is substituted into the estimated direct current internal resistance R₀Obtaining the optimal estimated value R of the direct current internal resistance at the moment when k is 2n +1₀. Calculating the optimal estimated value R of the direct current internal resistance at the moment when k is 2n +1₀The method of (1) is similar to the algorithm for calculating the state of charge (SOC), except that the DC internal resistance R is calculated₀The adopted state space equation is direct current internal resistance R₀Out of the state space equation of (a). Therefore, the description of steps 365-380 is omitted herein.

Then, the obtained optimal estimated value R of the dc internal resistance at the time when k is 2n +1 may be obtained₀The parameter value is substituted into the SOC estimation at the time k 2n +2, and the time k 2n +2 is obtainedIs estimated. And updating alternately in such a way to obtain the optimal estimated values of the SOC and the direct current internal resistance. And finally, converting the obtained relation between the direct current internal resistance value and the SOH of the health state into the SOH, and finally obtaining the optimal estimated values of the SOC and the SOH.

Specifically, the internal resistance can be used as an indicative quantity of the SOH of the battery:

internal resistance loss of battery in actual operation (actual internal resistance R of battery in use)_reaMinus the rated internal resistance R of the battery_rat) Rated internal resistance R of battery_ratThe ratio of (a) to (b).

Thus, the computing device 120 may determine the state of charge and state of health of a battery within an electric vehicle accurately and in real-time.

Fig. 6 illustrates a schematic block diagram of an example device 600 that can be used to implement embodiments of the present disclosure. For example, computing device 120 as shown in FIG. 1 may be implemented by device 600. As shown, device 600 includes a Central Processing Unit (CPU)610 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM)620 or loaded from a storage unit 680 into a Random Access Memory (RAM) 630. In the RAM630, various programs and data required for the operation of the device 600 can also be stored. The CPU610, ROM620, and RAM630 are connected to each other via a bus 640. An input/output (I/O) interface 650 is also connected to bus 640.

Various components in device 600 are connected to I/O interface 650, including: an input unit 660 such as a keyboard, a mouse, etc.; an output unit 670 such as various types of displays, speakers, and the like; a storage unit 680, such as a magnetic disk, optical disk, or the like; and a communication unit 690 such as a network card, modem, wireless communication transceiver, etc. The communication unit 690 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The various processes and processes described above, such as

methods

200 and 300, may be performed by processing unit 610. For example, in some embodiments,

methods

200 and 300 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 680. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM620 and/or the communication unit 690. When the computer program is loaded into RAM630 and executed by CPU610, one or more acts of

methods

200 and 300 described above may be performed.

The present disclosure may be methods, apparatus, systems, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for carrying out various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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 coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical 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 via 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 transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter 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 the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Java, 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 execute 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 type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure 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 disclosure. 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 processing unit 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 processing unit 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 storing the instructions comprises 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 flowchart 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 disclosure. 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.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for managing a battery, comprising:

obtaining a battery model, wherein the battery model represents an equivalent circuit of the battery;

determining a first state of charge of the battery based on the battery model; and

iteratively performing at least one of:

determining a second state of charge of the battery based on the first state of charge;

determining a first battery direct current internal resistance of the battery based on the second state of charge; and

updating the first state of charge based on the second state of charge.

2. The method of claim 1, wherein obtaining the battery model comprises:

determining a model direct current internal resistance of the battery model, a rated capacity of the battery and an open-circuit voltage of the battery;

determining the polarization impedance of the battery model based on the model direct current internal resistance; and

and determining the battery model based on the model direct current internal resistance, the polarization impedance, the rated capacity and the open-circuit voltage.

3. The method of claim 1, wherein determining the first state of charge comprises:

initializing the first state of charge to a state of charge at a previous power down of the battery in response to a standing time after power up of the battery being below a predetermined threshold; and

initializing the first state of charge to a state of charge corresponding to an open circuit voltage of the battery in response to a standing time after the battery is powered up exceeding a predetermined threshold.

4. The method of claim 1, wherein determining the second state of charge comprises:

acquiring the direct current internal resistance of the second battery determined in the previous iteration;

determining an intermediate battery state of charge and a residual associated with a terminal voltage output of the battery based on the first state of charge and the second battery dc internal resistance;

determining a time-varying fade-out factor associated with the intermediate battery state of charge and the second battery DC internal resistance based on the residual error;

determining a Kalman gain associated with the intermediate battery state of charge and the second battery DC internal resistance based on the time-varying fading factor; and

determining the second state of charge based on the Kalman gain.

5. The method of claim 1, wherein determining the first battery internal dc resistance comprises:

determining an intermediate battery DC internal resistance and a residual associated with a terminal voltage output value of the battery based on the second state of charge and the second battery DC internal resistance;

determining a time-varying fading factor associated with the second state of charge and the intermediate battery DC internal resistance based on the residual error;

determining a Kalman gain associated with the second state of charge and the intermediate cell DC internal resistance based on the time-varying fading factor; and

and determining the direct current internal resistance of the first battery based on the Kalman gain.

6. The method of claim 1, wherein updating the first state of charge comprises:

determining the second state of charge as the first state of charge.

7. The method of claim 1, further comprising:

and determining the health state of the battery based on the first battery direct current internal resistance and the rated internal resistance of the battery.

8. An apparatus for managing a battery, comprising:

at least one processing unit;

at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, which when executed by the at least one processing unit, cause the apparatus to perform acts comprising:

iteratively performing at least one of:

updating the first state of charge based on the second state of charge.

9. The apparatus of claim 8, wherein obtaining the battery model comprises:

10. The apparatus of claim 8, wherein determining the first state of charge comprises:

11. The apparatus of claim 8, wherein determining the second state of charge comprises:

determining the second state of charge based on the Kalman gain.

12. The apparatus of claim 8, wherein determining the first battery dc internal resistance comprises:

13. The apparatus of claim 8, wherein updating the first state of charge comprises:

determining the second state of charge as the first state of charge.

14. The apparatus of claim 8, the acts further comprising:

15. A computer program product tangibly stored on a non-transitory computer readable medium and comprising machine executable instructions that, when executed, cause a machine to perform the steps of the method of any of claims 1 to 7.