Battery parameter identification method, battery parameter identification device, medium and electronic equipment

Abstract

The present invention provides a battery parameter identification method, a battery parameter identification device, a medium, and an electronic device. The battery parameter identification method includes: receiving battery monitoring system data and extracting and processing the battery monitoring system data to obtain battery data under actual operating conditions; obtaining multiple parameter data of a battery model, wherein the multiple parameters of the battery model are all parameters to be identified; and obtaining a solution set of a multi-objective function in a multi-objective optimization algorithm based on the battery data and the multiple parameter data of the battery model, wherein the solution set includes parameter values ​​of multiple parameters to be identified, and the multi-objective function is a function of at least two parameters to be identified. The battery parameter identification method can improve the efficiency of battery parameter identification and has practical industrial application value.

Description

Technical Field

[0001] The present invention relates to the field of batteries, and in particular to a battery parameter identification method, a battery parameter identification device, a medium and an electronic device. Background Art

[0002] In recent years, lithium-ion batteries have become the mainstream battery technology for energy storage power stations in my country due to their unique advantages. To ensure the safe operation of lithium-ion batteries, internal battery parameters must be identified to prevent over-discharge, overcharging, overheating, and degradation. Current battery parameter identification methods generally employ single-target identification, which has relatively ideal operating conditions and is therefore difficult to apply to random and complex real-world operating conditions. Furthermore, the battery parameter identification accuracy of single-target identification methods is low. Summary of the Invention

[0003] In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a battery parameter identification method, a battery parameter identification device, a medium and an electronic device to solve the problem that the battery parameter identification method in the prior art is difficult to apply in actual working conditions and has low accuracy.

[0004] To achieve the above-mentioned purpose and other related purposes, the first aspect of the present invention provides a battery parameter identification method, which includes: receiving battery monitoring system data and extracting and processing the battery monitoring system data to obtain battery data under actual working conditions; obtaining multiple parameter data of the battery model, and the multiple parameters of the battery model are all parameters to be identified; based on the battery data and the multiple parameter data of the battery model, obtaining a solution set of a multi-objective function in a multi-objective optimization algorithm, the solution set includes parameter values ​​of multiple parameters to be identified, and the multi-objective function is a function of at least two of the parameters to be identified.

[0005] In one embodiment of the first aspect, the implementation method for obtaining the solution set of the multi-objective function in the multi-objective optimization algorithm includes: generating multiple initial populations based on multiple parameter data of the battery model, each of the initial populations including all of the parameters to be identified; processing each of the populations based on the battery data and the multi-objective function to obtain the solution set of the multi-objective function.

[0006] In an embodiment of the first aspect, an implementation method for receiving battery monitoring system data and extracting and processing the battery monitoring system data includes: receiving the battery monitoring system data; performing big data processing on the battery monitoring system data to obtain the battery data, wherein the battery data includes an actual voltage under actual operating conditions and an actual current under actual operating conditions.

[0007] In an embodiment of the first aspect, it also includes: obtaining a simulated voltage under the actual working conditions through the battery model on the actual current under the actual working conditions, and the multi-objective function includes a first objective function and a second objective function, the first objective function is a function of the simulated voltage and the actual voltage, and the second objective function is a function of at least two of the parameters to be identified.

[0008] In an embodiment of the first aspect, the parameters in the second objective function further include a battery stoichiometric number.

[0009] In an embodiment of the first aspect, the first objective function is expressed by the following formula:

[0010]

[0011] in, is the analog voltage, is the actual voltage, m is the number of the actual voltages, represents the i-th analog voltage, represents the i-th actual voltage;

[0012] The second objective function is expressed as follows:

[0013]

[0014] Among them, A + is the effective area of ​​the positive electrode plate, L + is the thickness of the positive electrode plate, is the maximum lithium concentration of the positive electrode, A - is the effective area of ​​the negative electrode plate, L - is the thickness of the negative electrode plate, is the maximum lithium concentration of the negative electrode, and the battery stoichiometric coefficients include and is the ratio of the initial positive electrode lithium concentration to the maximum positive electrode ion concentration, is the ratio of the final positive electrode lithium concentration to the maximum positive electrode ion concentration, is the ratio of the initial negative electrode lithium concentration to the negative electrode maximum ion concentration, It is the ratio of the final negative electrode lithium concentration to the negative electrode maximum ion concentration.

[0015] In an embodiment of the first aspect, the plurality of parameter data of the battery model are parameter value ranges of the plurality of parameters of the battery model.

[0016] The second aspect of the present invention provides a battery parameter identification device, including: a battery data acquisition module, used to receive battery monitoring system data and extract and process the battery monitoring system data to obtain battery data under multiple actual working conditions; a parameter data acquisition module, used to obtain multiple parameter data of a battery model, and the multiple parameters of the battery model are all parameters to be identified; a solution set acquisition module, used to obtain a solution set of a multi-objective function in a multi-objective optimization algorithm based on the battery data and the multiple parameter data of the battery model, the solution set contains parameter values ​​of multiple parameters to be identified, and the multi-objective function is a function of at least two parameters to be identified.

[0017] A third aspect of the present invention provides a computer-readable storage medium, which, when executed by a processor, implements the battery parameter identification method described in any one of the first aspects of the present invention.

[0018] The fourth aspect of the present invention provides an electronic device, comprising: a memory storing a computer program; a processor communicatively connected to the memory, for executing any one of the battery parameter identification methods described in the first aspect of the present invention when the computer program is called; and a display communicatively connected to the processor and the memory, for displaying a GUI interactive interface related to the battery parameter identification method.

[0019] As described above, the battery parameter identification method, battery parameter identification device, medium, and electronic device of the present invention have the following beneficial effects:

[0020] The battery parameter identification method includes receiving battery monitoring system data and extracting and processing the battery monitoring system data to obtain battery data under actual operating conditions; obtaining multiple parameter data of a battery model, wherein the multiple parameters of the battery model are all parameters to be identified; and based on the battery data and the multiple parameter data of the battery model, obtaining a solution set of a multi-objective function in a multi-objective optimization algorithm, wherein each solution set has the parameters to be identified, and the multi-objective function is an objective function with respect to at least two of the parameters to be identified. Solving the multi-objective identification model through the multi-objective optimization algorithm can improve the accuracy of parameter identification on the one hand, and improve the efficiency of battery parameter identification on the other hand.

[0021] In addition, by obtaining the battery data under actual working conditions and combining the battery data with a multi-objective battery parameter identification process, the battery parameter identification method can be made more suitable for actual industrial environments, thereby improving the practical application value of the battery parameter identification method. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Shown is a flow chart of a battery parameter identification method according to an embodiment of the present invention.

[0023] Figure 2 Shown is a flowchart of a method for obtaining a solution set of a multi-objective function in a multi-objective optimization algorithm in an embodiment of the present invention.

[0024] Figure 3 Shown is a flowchart of a method for receiving battery monitoring system data and extracting and processing the battery monitoring system data in an embodiment of the present invention.

[0025] Figure 4 Shown is a structural schematic diagram of the battery parameter identification device according to an embodiment of the present invention.

[0026] Figure 5 Shown is a schematic structural diagram of the electronic device according to an embodiment of the present invention.

[0027] Component number description

[0028] 400 Battery Parameter Identification Device

[0029] 410 Battery Data Acquisition Module

[0030] 420 parameter data acquisition module

[0031] 430 Solution Acquisition Module

[0032] 500 Electronic Equipment

[0033] 510 Memory

[0034] 520 processor

[0035] 530 Display

[0036] Steps S11-S13

[0037] Steps S21-S22

[0038] Steps S31-S32 DETAILED DESCRIPTION

[0039] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0040] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0041] Since current battery parameter identification methods generally adopt a single-target identification method, and the single-target identification method has relatively ideal working condition requirements, it is difficult to apply it in random and complex actual working conditions, and the battery parameters identified by the single-target identification method have low accuracy. At least in response to the above problems, the present invention provides a battery parameter identification method, the battery parameter identification method includes receiving battery monitoring system data and extracting and processing the battery monitoring system data to obtain battery data under actual working conditions; selecting a battery model, wherein multiple parameters of the battery model are all parameters to be identified; based on the battery data and the battery model, constructing a multi-objective function, and using a multi-objective optimization algorithm to obtain a Pareto optimal solution set, wherein each solution in the solution set contains parameter values ​​of all the parameters to be identified, and the multi-objective function is a function of at least two functions containing the parameters to be identified, and the union of the included identification parameters is all the parameters to be identified. By solving the multi-objective function through the multi-objective optimization algorithm, it is possible to obtain multiple parameters to be identified in one parameter identification process. Therefore, the battery parameter identification method can improve the identification efficiency of battery parameters. At the same time, compared with the single-objective identification algorithm, the synergy between each objective function can make the identification parameters more accurate.

[0042] In addition, by acquiring the battery data under actual working conditions and combining the battery data with the battery parameter identification process, the battery parameter identification method can be made more suitable for actual industrial environments, thereby improving the practical application value of the battery parameter identification method.

[0043] In one embodiment of the present invention, the battery parameter identification method includes:

[0044] S11, receiving battery monitoring system data and extracting and processing the battery monitoring system data to obtain battery data under actual working conditions. The battery monitoring system data may be battery status data stored by the battery management system during operation, such as battery voltage, battery pole temperature, battery loop current, battery pack terminal voltage, battery system insulation resistance, and other data stored by the BMS (Battery Management System). The battery data may include: a charge and discharge curve of the battery with a charge and discharge rate less than 0.5 under constant current, a charge and discharge curve of the battery with a charge and discharge rate substantially equal to 1 under constant current, a charge and discharge curve of the battery with a charge and discharge rate greater than 1.5 under constant current, and a mixed pulse curve and / or dynamic stress test curve of the battery under different working conditions. The charge and discharge rate being substantially 1 may be a charge and discharge rate approximately equal to 1. For example, a charge and discharge rate in the interval (0.9, 1.1) may be regarded as 1. The hybrid pulse curve may include a hybrid power pulse curve, which may be obtained by performing an HPPC (Hybrid Pulse Power Characterization) test on the battery, and the dynamic stress test curve may be obtained by performing a DST (Dynamic Stress Test) on the battery. The above-mentioned working conditions are all commonly used working conditions in the laboratory, but the above-mentioned working conditions do not exist in actual working conditions. Therefore, in actual working conditions, working conditions similar to the above-mentioned conditions or long-term working conditions (time>1h, soc variation range greater than 20%) are selected. Of course, in the long-term working conditions, some currents may be zero and the battery may be a lithium-ion battery.

[0045] S12, obtaining multiple parameter data of the battery model, wherein the multiple parameters of the battery model are all parameters to be identified.

[0046] Optionally, the multiple parameters of the battery model may include: positive electrode plate effective area, positive electrode plate thickness, positive electrode maximum lithium concentration, negative electrode plate effective area, negative electrode plate thickness, negative electrode maximum lithium concentration, initial electrolyte concentration, positive electrode maximum ion concentration, final electrolyte concentration, and negative electrode maximum ion concentration. The multiple parameter data of the battery model may be parameter value ranges of the multiple parameters of the battery model. For example, the parameter data of the positive electrode plate thickness may be 35-79 microns, and the parameter data of the positive electrode maximum lithium concentration may be 0.35-0.5.

[0047] S13: Initialize a population in a multi-objective optimization algorithm based on the battery data and multiple parameter data of the battery model and obtain a solution set of a multi-objective function, wherein the solution set includes parameter values ​​of multiple parameters to be identified, and the multi-objective function is a function of at least two parameters including the parameters to be identified. The multi-objective optimization algorithm may be an NSGA-II algorithm, and the solution set of the multi-objective function may be a Pareto non-dominated solution set of the multi-objective function.

[0048] Optionally, the battery parameter identification method further includes: processing the actual current under the actual working condition through the battery model to obtain the simulated voltage under the actual working condition, the multi-objective function includes a first objective function and a second objective function, the first objective function is a function of the simulated voltage and the actual voltage under the actual working condition, and the second objective function is a function of the battery capacity. The parameters in the second objective function include the battery stoichiometric number. The battery stoichiometric number may include and is the ratio of the initial positive electrode lithium concentration to the maximum positive electrode ion concentration, is the ratio of the final positive electrode lithium concentration to the maximum positive electrode ion concentration, is the ratio of the initial negative electrode lithium concentration to the negative electrode maximum ion concentration, The ratio of the final negative electrode lithium concentration to the maximum negative electrode ion concentration is obtained. By setting the stoichiometric coefficient in the parameters of the multi-objective function, the stoichiometric coefficient and multiple parameters to be identified can be obtained simultaneously in a single parameter identification process, avoiding the need to use a step-by-step identification method to sequentially obtain the stoichiometric coefficient and the parameters to be identified, thereby improving the efficiency of battery parameter identification.

[0049] In addition, by establishing the multi-objective function based on parameters with electrochemical properties such as the initial electrolyte concentration and the maximum ion concentration of the positive electrode, the solution set of the multi-objective function can be made more consistent with the actual physical operating state of the battery, and all the parameters to be identified are obtained through a single parameter identification process, which can make the identification results more accurate.

[0050] The first objective function is expressed as follows:

[0051]

[0052] in, is the analog voltage, is the actual voltage, m is the number of the actual voltages, represents the i-th analog voltage, represents the i-th actual voltage;

[0053] The second objective function is expressed as follows:

[0054]

[0055] Among them, A + is the effective area of ​​the positive electrode plate, L + is the thickness of the positive electrode plate, is the maximum lithium concentration of the positive electrode, A - is the effective area of ​​the negative electrode plate, L - is the thickness of the negative electrode plate, is the maximum lithium concentration of the negative electrode, and the battery stoichiometric coefficients include and is the ratio of the initial positive electrode lithium concentration to the maximum positive electrode ion concentration, is the ratio of the final positive electrode lithium concentration to the maximum positive electrode ion concentration, is the ratio of the initial negative electrode lithium concentration to the negative electrode maximum ion concentration, It is the ratio of the final negative electrode lithium concentration to the maximum negative electrode ion concentration, wherein the initial electrode lithium concentration may be the electrode lithium concentration at the beginning of battery discharge, and the final electrode lithium concentration may be the electrode lithium concentration at the end of battery discharge.

[0056] According to the above description, the battery parameter identification method of this embodiment includes receiving battery monitoring system data and extracting and processing the battery monitoring system data to obtain battery data under actual working conditions; obtaining multiple parameter data of the battery model, wherein the multiple parameters of the battery model are all parameters to be identified; based on the battery data and the multiple parameter data of the battery model, obtaining a solution set of a multi-objective function in a multi-objective optimization algorithm, wherein the solution set includes parameter values ​​of multiple parameters to be identified, and the multi-objective function is a function of at least two parameters to be identified. By setting the multi-objective function about multiple parameters to be identified and solving the multi-objective function through the multi-objective optimization algorithm, it is possible to obtain multiple parameters to be identified in one parameter identification process, so the battery parameter identification method can improve the identification efficiency of battery parameters.

[0057] In addition, by acquiring the battery data under actual working conditions and combining the battery data with the battery parameter identification process, the battery parameter identification method can be made more suitable for actual industrial environments, thereby improving the practical application value of the battery parameter identification method.

[0058] See also Figure 2 In one embodiment of the present invention, a method for obtaining a solution set of a multi-objective function in a multi-objective optimization algorithm includes:

[0059] S21: Generate multiple initial populations based on multiple parameter data of the battery model, each of the initial populations including all of the parameters to be identified. The multiple parameter data of the battery model may be parameter value ranges of the multiple parameters of the battery model.

[0060] Optionally, the method for generating multiple initial populations based on multiple parameter data of the battery model may include: obtaining multiple random parameters within each parameter value range based on the parameter value range; and generating the multiple initial populations based on the random parameters.

[0061] S22: Process the multiple initial populations based on the battery data and the multi-objective function to obtain a solution set of the multi-objective function.

[0062] Optionally, the method for processing the multiple initial populations based on the battery data and the multi-objective function includes:

[0063] Sorting step: Based on the multi-objective function, the multiple initial populations are fast non-dominated sorted to obtain a graded population, where the graded population has grade information, for example, the grade of population A is 2, and the grade of population B is 1.

[0064] Congestion degree calculation step: based on the objective function and the hierarchical population, obtaining the congestion degree of the hierarchical population.

[0065] Selection step: obtaining a selected population based on the crowding degree.

[0066] Crossover and mutation step: Based on the selected population, the parent and child populations generated in the crossover process and the mutation process are merged to obtain a pending population.

[0067] A judgment step is performed, based on the pending population, to determine whether a threshold is met or whether a maximum number of iterations is reached. If so, the pending population is output as a Pareto non-dominated solution set of the objective function. If not, the process proceeds to the sorting step. When entering the sorting step, the pending population can be regarded as the initial population.

[0068] According to the above description, the method for obtaining the solution set of the multi-objective function in the multi-objective optimization algorithm in this embodiment includes: generating multiple initial populations based on multiple parameter data of the battery model, each of the initial populations including all of the parameters to be identified; and processing the multiple initial populations based on the battery data and the multi-objective function to obtain the solution set of the multi-objective function. By generating multiple initial populations based on multiple parameter data of the battery model and processing the multiple initial populations based on the battery data and the multi-objective function, the parameters to be identified can be quickly identified, thereby improving identification efficiency.

[0069] See also Figure 3 In one embodiment of the present invention, a method for receiving battery monitoring system data and extracting and processing the battery monitoring system data includes:

[0070] S31, receiving the battery monitoring system data.

[0071] S32: Perform big data processing on the battery monitoring system data to obtain the battery data, where the battery data includes an actual voltage and an actual current under actual working conditions.

[0072] Optionally, the method for implementing big data processing on the battery monitoring system data may be to process the battery monitoring system data using a big data tool to obtain the battery data. The big data tool may include a Hadoop framework and a Spark engine.

[0073] As can be seen from the above description, the method for receiving and extracting battery monitoring system data described in this embodiment includes: receiving the battery monitoring system data; performing big data processing on the battery monitoring system data to obtain battery data, wherein the battery data includes actual voltage and actual current under actual operating conditions. By combining big data processing with battery parameter identification, battery data that is more consistent with actual industrial environments can be obtained, thereby making the battery parameter identification method more practical in industrial application and economic value.

[0074] In one embodiment of the present invention, a battery parameter identification device 400 is provided. Specifically, please refer to Figure 4 , the battery parameter identification device 400 includes:

[0075] A battery data acquisition module 410 is configured to receive battery monitoring system data and extract and process the battery monitoring system data to obtain battery data under actual operating conditions;

[0076] The parameter data acquisition module 420 is used to acquire multiple parameter data of the battery model, wherein the multiple parameters of the battery model are all parameters to be identified;

[0077] The solution set acquisition module 430 is used to obtain a solution set of a multi-objective function in a multi-objective optimization algorithm based on the battery data and multiple parameter data of the battery model, wherein the solution set includes parameter values ​​of multiple parameters to be identified, and the multi-objective function is a function of at least two parameters to be identified.

[0078] According to the above description, the battery parameter identification device can obtain multiple parameters to be identified in one parameter identification process by setting the multi-objective function about the multiple parameters to be identified and solving the multi-objective function through the multi-objective optimization algorithm. Therefore, the battery parameter identification method can improve the identification efficiency of battery parameters. At the same time, compared with the single-objective identification algorithm, the synergistic effect of each objective function can make the identification parameters more accurate.

[0079] In addition, by acquiring the battery data under actual working conditions and combining the battery data with the battery parameter identification process, the battery parameter identification method can be made more suitable for actual industrial environments, thereby improving the practical application value of the battery parameter identification method.

[0080] Based on the above description of the battery parameter identification method, the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, Figure 1 The battery parameter identification method shown in FIG.

[0081] Based on the above description of the battery parameter identification method, the present invention also provides an electronic device. Figure 5 In one embodiment of the present invention, the electronic device 500 includes a memory 510 storing a computer program; a processor 520 communicating with the memory 510 and executing the computer program when calling the computer program. Figure 1 The battery parameter identification method shown; the display 530 is communicatively connected to the processor 520 and the memory 510, and is used to display the relevant GUI interactive interface of the battery parameter identification method.

[0082] The protection scope of the battery parameter identification method of the present invention is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, subtracting, or replacing steps in the prior art based on the principles of the present invention are included in the protection scope of the present invention.

[0083] In summary, the battery parameter identification method, battery parameter identification device, medium, and electronic device of the present invention are used to improve the efficiency of battery parameter identification. Therefore, the present invention effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0084] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. A battery parameter identification method, characterized in that: The battery parameter identification method includes: Receiving battery monitoring system data and extracting and processing the battery monitoring system data to obtain battery data under actual working conditions, the battery data including actual voltage and actual current under actual working conditions; Acquiring multiple parameter data of a battery model, wherein the multiple parameters of the battery model are all parameters to be identified; Obtaining a solution set of a multi-objective function in a multi-objective optimization algorithm based on the battery data and multiple parameter data of the battery model, wherein the solution set includes parameter values ​​of multiple parameters to be identified, and the multi-objective function is a function of at least two of the parameters to be identified; The method for obtaining a solution set of a multi-objective function in a multi-objective optimization algorithm includes: generating multiple initial populations based on multiple parameter data of the battery model, each of the initial populations including all of the parameters to be identified; processing the multiple initial populations based on the battery data and the multi-objective function to obtain a solution set of the multi-objective function; The battery parameter identification method further includes: processing the actual current under the actual operating condition using the battery model to obtain a simulated voltage under the actual operating condition, the multi-objective function including a first objective function and a second objective function, the first objective function being a function of the simulated voltage and the actual voltage under the actual operating condition, and the second objective function being a function of at least two of the parameters to be identified; The first objective function is expressed as follows: in, is the analog voltage, is the actual voltage, is the actual voltage quantity, Indicates the An analog voltage, Indicates the The actual voltage; The second objective function is expressed as follows: in, is the effective area of ​​the positive electrode plate, is the thickness of the positive electrode plate, is the maximum lithium concentration of the positive electrode, is the effective area of ​​the negative electrode plate, is the thickness of the negative electrode plate, is the maximum lithium concentration of the negative electrode, and the battery stoichiometric factors include 、 、 ,and , is the ratio of the initial positive electrode lithium concentration to the maximum positive electrode ion concentration, is the ratio of the final positive electrode lithium concentration to the maximum positive electrode ion concentration, is the ratio of the initial negative electrode lithium concentration to the maximum negative electrode ion concentration, It is the ratio of the final negative electrode lithium concentration to the maximum ion concentration of the negative electrode.

2. The battery parameter identification method according to claim 1, characterized in that: The method for receiving battery monitoring system data and extracting and processing the battery monitoring system data includes: receiving the battery monitoring system data; Big data processing is performed on the battery monitoring system data to obtain the battery data.

3. The battery parameter identification method according to claim 2, characterized in that: The parameters in the second objective function also include the battery stoichiometry.

4. The battery parameter identification method according to claim 1, characterized in that: The plurality of parameter data of the battery model are parameter value ranges of the plurality of parameters of the battery model.

5. A battery parameter identification device, characterized in that: include: a battery data acquisition module, configured to receive and extract battery monitoring system data to obtain battery data under actual operating conditions, wherein the battery data includes actual voltage and actual current under actual operating conditions; A parameter data acquisition module is used to acquire multiple parameter data of the battery model, wherein the multiple parameters of the battery model are all parameters to be identified; a solution set acquisition module, configured to acquire a solution set of a multi-objective function in a multi-objective optimization algorithm based on the battery data and multiple parameter data of the battery model, wherein the solution set includes parameter values ​​of multiple parameters to be identified, and the multi-objective function is a function of at least two parameters to be identified; The method for obtaining a solution set of a multi-objective function in a multi-objective optimization algorithm includes: generating multiple initial populations based on multiple parameter data of the battery model, each of the initial populations including all of the parameters to be identified; processing the multiple initial populations based on the battery data and the multi-objective function to obtain a solution set of the multi-objective function; The battery parameter identification device further includes: processing the actual current under the actual operating condition using the battery model to obtain a simulated voltage under the actual operating condition, the multi-objective function including a first objective function and a second objective function, the first objective function being a function of the simulated voltage and the actual voltage under the actual operating condition, and the second objective function being a function of at least two of the parameters to be identified; The first objective function is expressed as follows: in, is the analog voltage, is the actual voltage, is the actual voltage quantity, Indicates the An analog voltage, Indicates the The actual voltage; The second objective function is expressed as follows: in, is the effective area of ​​the positive electrode plate, is the thickness of the positive electrode plate, is the maximum lithium concentration of the positive electrode, is the effective area of ​​the negative electrode plate, is the thickness of the negative electrode plate, is the maximum lithium concentration of the negative electrode, and the battery stoichiometric factors include 、 、 ,and , is the ratio of the initial positive electrode lithium concentration to the maximum positive electrode ion concentration, is the ratio of the final positive electrode lithium concentration to the maximum positive electrode ion concentration, is the ratio of the initial negative electrode lithium concentration to the maximum negative electrode ion concentration, It is the ratio of the final negative electrode lithium concentration to the maximum ion concentration of the negative electrode.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the battery parameter identification method according to any one of claims 1 to 4 is implemented.

7. An electronic device, characterized in that: The electronic device comprises: a memory storing a computer program; a processor, communicatively connected to the memory, and configured to execute the battery parameter identification method according to any one of claims 1 to 4 when calling the computer program; A display is communicatively connected to the processor and the memory, and is used to display a GUI interactive interface related to the battery parameter identification method.

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