Lithium battery state-of-charge estimation method and system under multiple temperatures

Abstract

The invention provides a method and a system for estimating the state of charge (SOC) of a lithium battery under multiple temperatures, and the method combines a large time scale extended Kalman filter (EKF) algorithm and a small time scale fuzzy logic control adaptive extended Kalman filter (FAEKF), and is used for carrying out the online identification of battery model parameters and the estimation of the SOC respectively. The identification of model parameters is performed at a large time scale to accommodate long-term changes of the battery, and the estimation of SOC is performed at a small time scale to capture short-term dynamic changes of the battery. In this way, the calculation complexity is reduced, and the SOC estimation precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium ion battery state of charge estimation, and in particular to a method and system for estimating the state of charge of a lithium ion battery at multiple temperatures. Background Art

[0002] As the core energy storage device of electric vehicles, accurate estimation of the state of charge (SOC) of lithium-ion batteries is crucial for extending battery life, optimizing energy management, improving vehicle performance and driving safety. However, since SOC cannot be measured directly and is easily affected by factors such as self-discharge rate, aging degree, and temperature, it is difficult to estimate accurately. Existing SOC estimation methods include ampere-hour counting method, open circuit voltage method, data-driven estimation method, and filter-based and observer-based methods. However, these methods often ignore the time scale effect of model parameter changes in the actual working environment of the power battery, resulting in insufficient SOC estimation accuracy. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a method and system for estimating the state of charge of a lithium battery at multiple temperatures, which has high accuracy, low error and good adaptability, and is suitable for electric vehicles and other application scenarios requiring precise battery management.

[0004] To achieve the above object, the present invention adopts the following technical solution: a method for estimating the state of charge of a lithium battery at multiple temperatures, comprising the following steps:

[0005] Based on the lithium-ion battery model, the large-time scale extended Kalman filter algorithm and the small-time scale fuzzy logic controller adaptive extended Kalman filter algorithm are used to perform online estimation of battery model parameters and SOC.

[0006] On a large time scale, the extended Kalman filter algorithm is used to identify the main parameters in the battery model to cope with the long-term changes of the battery model;

[0007] At a small time scale, the battery SOC is estimated by an adaptive extended Kalman filter algorithm using a fuzzy logic controller to capture short-term dynamic changes.

[0008] In a preferred embodiment, the battery model is a second-order Thevenin equivalent circuit model, and the Kalman filter algorithm is used for battery model parameter identification and SOC estimation respectively; the second-order Thevenin equivalent circuit model includes the battery's ohmic internal resistance R0; electrochemical polarization internal resistance R1 and electrochemical concentration polarization internal resistance R2; electrochemical polarization capacitor C1 and concentration polarization capacitor C2;

[0009] According to Kirchhoff's voltage and current laws, the model equation is expressed as follows:

[0010]

[0011] Where U1 and U2 represent the voltage drops on the R1C1 and R2C2 circuits respectively; U oc Indicates the open circuit voltage.

[0012] In a preferred embodiment, the equivalent circuit model uses an extended Kalman filter algorithm and a fuzzy logic controller adaptive extended Kalman filter to perform parameter identification and SOC estimation on the circuit respectively. The definition of SOC is:

[0013]

[0014] in, represents the initial value of SOC, η represents the charge and discharge efficiency of the battery; C n The nominal capacity current i of the battery is positive in the discharge state and negative in the charge state;

[0015] Assume that the system state space equation is as follows:

[0016]

[0017] where x k =[U 1，k , U 2，k , SOC k ] T , and y k are the state vector and output vector of the system respectively; f(x k ,u k ) and h(x k ,u k ) are the state function and observation function of the system respectively; after first-order Taylor expansion, we get:

[0018]

[0019] make

[0020]

[0021] Right now

[0022]

[0023] Among them, x k+1 , x k Represent the state variables of the system at the next moment and the current moment respectively; y k represents the observed value of the system at time k; u k represents the system input variable at the current moment; A is the state transfer matrix; B is the state equation control matrix; C is the observation matrix; ω k and v krepresent the process noise and observation noise of the system respectively.

[0024] The present invention also provides a multi-temperature lithium battery state of charge estimation system, which adopts the multi-temperature lithium battery state of charge estimation method described above, and the system includes:

[0025] Sensor module, used to collect the current, voltage and temperature operating parameters of lithium-ion batteries;

[0026] The processor module is used to execute the multi-time scale EKF-FAEKF algorithm to perform online estimation of battery model parameters and SOC: the large time scale filter unit is used to implement the extended Kalman filter algorithm under large time scale to identify the battery model parameters; the small time scale filter unit is used to implement the fuzzy logic controller adaptive extended Kalman filter algorithm under small time scale to estimate the battery SOC.

[0027] The present invention also provides a computer program product, and when the processor executes the computer program, the processor implements the method for estimating the state of charge of a lithium battery at multiple temperatures as described above.

[0028] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for estimating the state of charge of a lithium battery under multiple temperatures is implemented.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The present invention introduces a multi-time scale parameter identification method to perform parameter identification on different RC links in the battery model, so as to more accurately describe the internal reaction of the battery.

[0031] Compared with the conventional SOC estimation model, the model of the present invention has better accuracy and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A schematic diagram of a method according to a preferred embodiment of the present invention;

[0033] Figure 2 Schematic diagram of a second-order Thevenin equivalent circuit model of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0035] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0036] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application; as used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations.

[0037] like Figure 1-2 As shown, this embodiment provides a method for estimating the state of charge of a lithium battery, which specifically includes the following steps:

[0038] Based on the lithium-ion battery model, the large-time scale extended Kalman filter algorithm and the small-time scale fuzzy logic controller adaptive extended Kalman filter algorithm are used to perform online estimation of battery model parameters and SOC.

[0039] On a large time scale, the extended Kalman filter algorithm is used to identify the main parameters in the battery model to cope with the long-term changes of the battery model;

[0040] At a small time scale, the battery SOC is estimated by an adaptive extended Kalman filter algorithm using a fuzzy logic controller to capture short-term dynamic changes;

[0041] The battery model is a second-order Thevenin equivalent circuit model, and the two Kalman filter algorithms are used for battery model parameter identification and SOC estimation, respectively.

[0042] Figure 2 In the figure, R0 represents the ohmic internal resistance of the battery; R1 and R2 represent the electrochemical polarization internal resistance and the concentration polarization internal resistance respectively; C1 and C2 represent the electrochemical polarization capacitance and the concentration polarization capacitance respectively; U m and i represent the terminal voltage and current of the battery respectively.

[0043] According to Kirchhoff's voltage and current laws, the model equation is expressed as follows:

[0044]

[0045] Where U1 and U2 represent the voltage drops on the R1C1 and R2C2 circuits respectively. oc Indicates the open circuit voltage.

[0046] The extended Kalman filter algorithm and the fuzzy logic controller adaptive extended Kalman filter are used to perform circuit parameter identification and SOC estimation respectively. The definition of SOC is:

[0047]

[0048] in, represents the initial value of SOC, and η represents the charge and discharge efficiency of the battery. n The nominal capacity current i representing the battery is positive in the discharge state and negative in the charge state.

[0049] Assume that the system state space equation is as follows:

[0050]

[0051] where x k =[U 1，k , U 2，k , SOC k ] T , and y k are the state vector and output vector of the system respectively; f(x k ,u k ) and h(x k ,u k ) are the state function and observation function of the system respectively. Performing a first-order Taylor expansion, we can get:

[0052]

[0053] make

[0054]

[0055] Right now

[0056]

[0057] Among them, x k+1 , x k Represent the state variables of the system at the next moment and the current moment respectively; y k represents the observed value of the system at time k; u k represents the system input variable at the current moment; A is the state transfer matrix; B is the state equation control matrix; C is the observation matrix; ω k and v k represent the process noise and observation noise of the system respectively.

[0058] Simulation and verification: Through actual charging and discharging experimental platforms, such as DST (dynamic stress test) and UDDS (urban driving conditions), the SOC estimation accuracy of the established model and algorithm under different working conditions was verified.

[0059] This embodiment also provides a lithium battery state of charge estimation system based on multiple temperatures, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described above when executing the computer program. The system includes:

[0060] a. Sensor module, used to collect operating parameters of lithium-ion batteries such as current, voltage, and temperature.

[0061] b. Processor module, used to execute the multi-time scale EKF-FAEKF algorithm to estimate the battery model parameters and SOC online: ① Large time scale filter unit, used to implement the extended Kalman filter algorithm under large time scale to identify the battery model parameters. ② Small time scale filter unit, used to implement the fuzzy logic controller adaptive extended Kalman filter algorithm under small time scale to estimate the battery SOC.

[0062] This embodiment further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0063] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0064] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0067] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A method for estimating the state of charge of a lithium battery at multiple temperatures, characterized in that: The following steps are involved: Based on the lithium-ion battery model, the large-time scale extended Kalman filter algorithm and the small-time scale fuzzy logic controller adaptive extended Kalman filter algorithm are used to perform online estimation of battery model parameters and SOC. On a large time scale, the extended Kalman filter algorithm is used to identify the main parameters in the battery model to cope with the long-term changes of the battery model; At a small time scale, the battery SOC is estimated by an adaptive extended Kalman filter algorithm using a fuzzy logic controller to capture short-term dynamic changes.

2. The method for estimating the state of charge of a lithium battery at multiple temperatures according to claim 1, characterized in that: The battery model is a second-order Thevenin equivalent circuit model, and the Kalman filter algorithm is used for battery model parameter identification and SOC estimation respectively; the second-order Thevenin equivalent circuit model includes the battery's ohmic internal resistance R0; electrochemical polarization internal resistance R1 and electrochemical concentration polarization internal resistance R2; electrochemical polarization capacitor C1 and concentration polarization capacitor C2; According to Kirchhoff's voltage and current laws, the model equation is expressed as follows: Where U1 and U2 represent the voltage drops on the R1C1 and R2C2 circuits respectively; U oc Indicates the open circuit voltage.

3. The method for estimating the state of charge of a lithium battery at multiple temperatures according to claim 2, characterized in that: The equivalent circuit model uses the extended Kalman filter algorithm and the fuzzy logic controller adaptive extended Kalman filter to perform circuit parameter identification and SOC estimation respectively. The definition of SOC is: in, represents the initial value of SOC, η represents the charge and discharge efficiency of the battery; C n The nominal capacity current i of the battery is positive in the discharge state and negative in the charge state; Assume that the system state space equation is as follows: where x k =[U 1，k , U 2，k , SOC k ] T , and y k are the state vector and output vector of the system respectively; f(x k ,u k ) and h(x k ,u k ) are the state function and observation function of the system respectively; after first-order Taylor expansion, we get: make Right now Among them, x k+1 , x k Represent the state variables of the system at the next moment and the current moment respectively; y k represents the observed value of the system at time k; u k represents the system input variable at the current moment; A is the state transfer matrix; B is the state equation control matrix; C is the observation matrix; ω k and v k represent the process noise and observation noise of the system respectively.

4. A multi-temperature lithium battery charge state estimation system, characterized in that The method for estimating the state of charge of a lithium battery at multiple temperatures as described in any one of claims 1 to 3 above is adopted, and the system comprises: Sensor module, used to collect the current, voltage and temperature operating parameters of lithium-ion batteries; The processor module is used to execute the multi-time scale EKF-FAEKF algorithm to perform online estimation of battery model parameters and SOC: the large time scale filtering unit is used to implement the extended Kalman filtering algorithm under large time scale to identify the battery model parameters; the small time scale filtering unit is used to implement the fuzzy logic controller adaptive extended Kalman filtering algorithm under small time scale to estimate the battery SOC.

5. A computer program product, characterized in that When executing the computer program, the processor implements a method for estimating the state of charge of a lithium battery at multiple temperatures as described in any one of claims 1 to 3.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a method for estimating the state of charge of a lithium battery at multiple temperatures as described in any one of claims 1 to 3 is implemented.

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