Parameter identification method, system, equipment and medium for secondary battery physical model

Abstract

The present application relates to a parameter identification method and system for a physical model of a secondary battery, wherein the parameter identification method includes: constructing a physical model of the secondary battery to be identified, and setting an optimization algorithm and its objective function used for the physical model of the secondary battery to be identified; collecting test data of non-destructive testing of the secondary battery to be identified; when all test data have not been collected, using the set optimization algorithm and its objective function, based on the parameters corresponding to the currently collected test data, to perform preliminary parameter identification on the physical model of the secondary battery to be identified; after all test data have been collected, using the set optimization algorithm and its objective function, based on the parameters corresponding to all the test data and the preliminary parameter identification results, to perform post-parameter identification on the physical model of the secondary battery to be identified, to obtain the final parameter identification result of the secondary battery. The present application can identify potential parameters in the early identification process and can be widely used in the field of parameter identification.

Description

Technical Field

[0001] The present application relates to the field of parameter identification, and in particular to a parameter identification method, system, device and medium for a secondary battery physical model. Background Art

[0002] Currently, secondary batteries, such as lithium-ion batteries, have become the mainstream choice for electric vehicles, grid energy storage, and consumer electronics. As electrochemical energy storage devices, their operating principles involve changes at multiple scales and in multiple physical fields. Accurate secondary battery models are fundamental to their intelligent R&D, design, and efficient and safe operation and management. Establishing these models requires accurate model parameters. However, these models at all scales and in all physical fields face the challenge of parameter identification.

[0003] Before parameter identification, the secondary battery needs to be subjected to destructive testing, that is, the battery cells and electrodes need to be disassembled, button batteries, symmetrical batteries, etc. need to be made and then tested before parameter identification can be performed. This has the following problems: 1) Long identification time and manual operation: In most identification processes, it is necessary to first obtain test data and then fit the test data. As the model shifts to a model that is more based on physical rules, the solution time of the relevant model increases and the identification efficiency gradually decreases. 2) Poor accuracy: In most models, the relationship between the relevant parameters and the target test data is non-convex, and the sensitivity between different parameters is inconsistent. 3) Model computability: The secondary battery model is sensitive to parameters. When there is an unreasonable combination of parameters during the identification process, the model solution will be wrong, which will cause the identification algorithm to be unable to continue working. Summary of the Invention

[0004] In response to the above problems, the purpose of this application is to provide a parameter identification method, system, device and medium for a secondary battery physical model, which can solve the problems of long identification time, low efficiency and poor accuracy in the secondary battery model parameter identification process.

[0005] To achieve the above objectives, the present application adopts the following technical solutions: In a first aspect, a method for parameter identification of a secondary battery physical model is provided, comprising:

[0006] Constructing a physical model of the secondary battery to be identified, and setting an optimization algorithm and its objective function used by the physical model of the secondary battery to be identified;

[0007] Collecting test data of non-destructive testing of secondary batteries to be identified;

[0008] When all test data have not been collected, the set optimization algorithm and its objective function are used to perform preliminary parameter identification on the physical model of the secondary battery to be identified based on the parameters corresponding to the currently collected test data to obtain preliminary parameter identification results;

[0009] After all test data are collected, the set optimization algorithm and its objective function are used to perform post-parameter identification on the physical model of the secondary battery to be identified based on the parameters corresponding to all test data and the pre-parameter identification results to obtain the final parameter identification results of the secondary battery.

[0010] Furthermore, the physical model of the secondary battery to be identified is constructed, and the optimization algorithm and its objective function used by the physical model of the secondary battery to be identified are set, including:

[0011] Constructing a physical model of the secondary battery to be identified, the model including several parameters to be identified;

[0012] Based on the historical data range and the accuracy requirements for model use, set the upper and lower limits, error calculation method and error threshold of the parameters to be identified. The error threshold includes the error threshold for complete data, the error threshold for partial data and the upper error limit.

[0013] Based on the computational complexity and number of parameters in the model solution, the optimization algorithm and its objective function are selected.

[0014] Furthermore, when all test data have not been collected, the set optimization algorithm and its objective function are used to perform preliminary parameter identification on the physical model of the secondary battery to be identified based on the parameters corresponding to the currently collected test data, and obtain preliminary parameter identification results, including:

[0015] When all test data have not been collected, a set error calculation method is used to find the parameters closest to the currently collected test data in the historical identification records of secondary batteries of the same system type as the secondary battery to be identified;

[0016] By adopting the set optimization algorithm and its objective function and based on the determined parameters, preliminary parameter identification is performed on the physical model of the secondary battery to be identified, and preliminary parameter identification results are obtained.

[0017] Furthermore, the aforementioned use of the set optimization algorithm and its objective function, based on the determined parameters, performs preliminary parameter identification on the physical model of the secondary battery to be identified, and obtains preliminary parameter identification results, including:

[0018] Based on the determined parameters and the set error threshold of the partial data, corresponding initial candidate parameters are generated;

[0019] By adopting the set optimization algorithm and its objective function, and based on the generated initial candidate parameters, the physical model of the secondary battery to be identified is subjected to preliminary parameter identification to obtain preliminary parameter identification results.

[0020] Furthermore, after all test data are collected, a set optimization algorithm and its objective function are used to perform post-parameter identification on the physical model of the secondary battery to be identified based on the parameters corresponding to all test data and the pre-parameter identification results, to obtain the final parameter identification results of the secondary battery, including:

[0021] After all test data are collected, a set error calculation method is used to find the parameters closest to the current test data in the historical identification records of secondary batteries of the same system type as the secondary battery to be identified;

[0022] Determine whether the error corresponding to the parameter is greater than the set error threshold of the complete data. If the error corresponding to the parameter is not greater than the error threshold of the complete data, the parameter has met the accuracy requirement, the identification process is stopped, and the previous parameter identification result is output as the final parameter identification result;

[0023] If the error corresponding to the parameter is greater than the set error threshold of the complete data, the set optimization algorithm and its objective function are used to perform post-parameter identification on the physical model of the secondary battery to be identified based on the determined parameters to obtain the final parameter identification result of the secondary battery.

[0024] Furthermore, if the error corresponding to the parameter is greater than the set error threshold of the complete data, a set optimization algorithm and its objective function are used to perform post-parameter identification on the physical model of the secondary battery to be identified based on the determined parameters to obtain the final parameter identification result of the secondary battery, including:

[0025] Based on the determined parameters and the set error threshold of the partial data, the corresponding initial candidate parameters are generated, and a solution among the initial candidate parameters is randomly selected as the current optimal solution;

[0026] Using the set optimization algorithm and its objective function, based on the generated initial candidate parameters and their current optimal solutions, the physical model of the secondary battery to be identified is subjected to post-parameter identification to obtain the final parameter identification result of the secondary battery.

[0027] Furthermore, the initial candidate parameters are generated according to the following rules:

[0028] If the error of the determined parameter is less than or equal to the error threshold of the partial data, the initial candidate parameter X0 is generated according to the multidimensional normal distribution N(μ, ∑), where μ = θ p or θ f , θ p is the parameter closest to the current test data when all test data have not been collected. f The parameter that is closest to the current test data after all test data are collected is U=(u0, u1, u2, ..., ui ,…,u m-1 ) is the upper limit of the parameter to be identified, L=(l0, l1, l2,…, l i ,…,l m-1 ) is the lower limit of the parameter to be identified, m is the number of parameters to be identified, u i is the upper limit of the i-th parameter to be identified, l i is the lower limit of the i-th parameter to be identified;

[0029] If the error of the determined parameter is greater than the error threshold of the partial data, the initial candidate parameter X0 is generated within the upper limit U and lower limit L of the parameter according to a uniform distribution.

[0030] In a second aspect, a parameter identification system for a secondary battery physical model is provided, comprising:

[0031] A model building module, used to build a physical model of the secondary battery to be identified, and set the optimization algorithm and its objective function used by the physical model of the secondary battery to be identified;

[0032] A data acquisition module, used to collect test data of non-destructive testing of the secondary battery to be identified;

[0033] The early parameter identification module is used to use the set optimization algorithm and its objective function to perform early parameter identification on the physical model of the secondary battery to be identified based on the parameters corresponding to the currently collected test data when all test data have not been collected, and obtain early parameter identification results;

[0034] The post-parameter identification module is used to perform post-parameter identification on the physical model of the secondary battery to be identified after all test data are collected, using a set optimization algorithm and its objective function, based on the parameters corresponding to all test data and the pre-parameter identification results, to obtain the final parameter identification results of the secondary battery.

[0035] In a third aspect, a processing device is provided, comprising computer program instructions, wherein the computer program instructions, when executed by the processing device, are used to implement the steps corresponding to the above-mentioned parameter identification method of the secondary battery physical model.

[0036] In a fourth aspect, a computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions, when executed by a processor, are used to implement the steps corresponding to the parameter identification method of the secondary battery physical model.

[0037] Due to the adoption of the above technical solution, this application has the following advantages:

[0038] 1. This application starts parameter identification during the test data acquisition process, which can identify potential parameters in the early identification process and improve the search process in the parameter space during the identification process.

[0039] 2. This application can improve the reinforcement process of the identification process by using historical identification records in the later parameter identification and generating an initial candidate parameter group from potential parameters, which can significantly improve the parameter identification efficiency and parameter identification accuracy, especially in solving complex physical models of secondary batteries, and shorten the identification time.

[0040] In summary, the present application can be widely used in the field of parameter identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. Throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0042] Figure 1 This is a flow chart of a method provided by an embodiment of the present application;

[0043] Figure 2 This is a schematic diagram comparing the model calculation results and target data at a 45°C magnification of 1C provided in one embodiment of the present application;

[0044] Figure 3 is a schematic diagram of a temporary candidate parameter group generated in the later parameter identification stage according to an embodiment of the present application, wherein: Figure 3 (a) is a schematic diagram of the generated temporary candidate parameter group. Figure 3 (b) is a schematic diagram after adding the temporary candidate parameter group to the boundary restriction;

[0045] Figure 4 : is a schematic diagram comparing the model calculation results and target data of the post-parameter identification provided by an embodiment of the present application, wherein: Figure 4 (a) is a schematic diagram showing the comparison between the diaphragm pore A and the target data after the later parameter identification. Figure 4 (b) is a schematic diagram showing the comparison between the negative electrode diffusion activation energy B and the target data in the later parameter identification. Figure 4 (c) Schematic diagram of the comparison between the positive electrode Bruggeman coefficient C and the target data in the later parameter identification. DETAILED DESCRIPTION

[0046] The following will describe exemplary embodiments of the present application in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. Instead, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0047] It should be understood that the terms used herein are for the purpose of describing specific example embodiments only and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "comprise", "include", "contain" and "have" are inclusive and therefore specify the presence of stated features, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring them to be performed in the specific order described or illustrated, unless the order of execution is clearly indicated. It should also be understood that additional or alternative steps may be used.

[0048] Although the terms first, second, third, etc. can be used in the text to describe multiple elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms can only be used to distinguish an element, component, region, layer or section from another region, layer or section. Unless the context clearly indicates otherwise, terms such as "first", "second" and other numerical terms do not imply order or sequence when used in the text. Therefore, the first element, component, region, layer or section discussed below can be referred to as the second element, component, region, layer or section without departing from the teaching of the example embodiments.

[0049] To address the issues of long identification time, low efficiency, and poor accuracy during secondary battery model parameter identification, the present invention provides a method, system, device, and medium for parameter identification of a secondary battery physical model. During the test data acquisition process, partial parameters are used for preliminary parameter identification, and then subsequent parameter identification is performed based on the preliminary parameter identification results and the complete test data. This improves the efficiency and accuracy of parameter identification of the secondary battery physical model. In this application, secondary batteries include lithium-ion secondary batteries and sodium-ion secondary batteries.

[0050] Example 1

[0051] like Figure 1 As shown, this embodiment provides a parameter identification method for a secondary battery physical model, comprising the following steps:

[0052] 1) Identification initialization: constructing a physical model of the secondary battery to be identified, and setting the optimization algorithm and its objective function used by the physical model of the secondary battery to be identified.

[0053] 2) Data collection: Collecting test data of the secondary battery to be identified through non-destructive testing.

[0054] 3) Preliminary parameter identification: When all test data have not been collected, the set optimization algorithm and its objective function are used to perform preliminary parameter identification on the physical model of the secondary battery to be identified based on the parameters corresponding to the currently collected test data to obtain preliminary parameter identification results.

[0055] 4) Post-parameter identification: After all test data are collected, the set optimization algorithm and its objective function are used to perform post-parameter identification on the physical model of the secondary battery to be identified based on the parameters corresponding to all test data and the early parameter identification results to obtain the final parameter identification results of the secondary battery.

[0056] In the above step 1), a physical model of the secondary battery to be identified is constructed, and an optimization algorithm and its objective function used by the physical model of the secondary battery to be identified are set, including:

[0057] 1.1) Construct a physical model of the secondary battery to be identified, wherein the physical model of the secondary battery includes m parameters to be identified θ=(θ0,θ1,θ2,…θ m-1 ).

[0058] Specifically, the physical models of secondary batteries include but are not limited to particle discrete element models and electrochemical models (such as P2D mechanism models, single particle models, equivalent circuit RC models) as well as finite element mechanics, thermal models and thermo-electrical coupling models at the pole piece, cell and module scales.

[0059] 1.2) Based on the historical data range, set the upper limit U and lower limit L of the parameter to be identified θ. The upper limit and lower limit are the range of parameter identification.

[0060] Specifically, the upper limit of the parameter θ to be identified is U=(u0, u1, u2, ..., u i ,…,u m-1 ), the lower limit of the parameter θ to be identified is L=(l0,l1,l2,…,l i ,…,l m-1 ), where u i is the upper limit of the i-th parameter to be identified, l i is the lower limit of the i-th parameter to be identified, and l i <u i .

[0061] 1.3) Based on the accuracy requirements of the model, set the error calculation method used to guide the parameter identification direction and the error threshold used to evaluate whether the parameter identification results meet the requirements.

[0062] Specifically, the error calculation method F includes commonly used error indicators, such as mean square error, root mean square error, and maximum error.

[0063] Specifically, the error thresholds include the error threshold ef for complete data, the error threshold ep for partial data, and the upper error limit emax. Where 0≤ef<ep<emax, ef is used for error evaluation of complete test data, and ep is used for error evaluation of partial test data. Due to the correlation between different parameters in the secondary battery physical model, unreasonable parameter combinations can sometimes cause the secondary battery physical model to fail to solve. In this case, the error calculation returns the upper error limit emax.

[0064] 1.4) Based on the computational effort and number of parameters required to solve the model, select an optimization algorithm and its objective function obj(θ).

[0065] Specifically, optimization algorithms include gradient-free optimization algorithms such as evolutionary algorithms (genetic algorithms, differential evolution algorithms), population intelligence algorithms (particle swarm algorithms, ant colony algorithms) and agent optimization algorithms (Bayesian optimization algorithms). The optimization object of the optimization algorithm is the parameter, and the objective function of the optimization algorithm is to minimize the error function.

[0066] Specifically, the parameter identification process is the process of finding the minimum value of the objective function using an optimization algorithm, including:

[0067] If the parameter θ is used and an error occurs in solving the physical model of the secondary battery to be identified, the upper limit of the error emax is directly returned;

[0068] If the parameter θ is used, the physical model of the secondary battery to be identified is calculated to obtain the parameter identification result Y θ When Among them, n f is the data length of the complete test data, n p is the length of the test data used in the current calculation, Y target is the test data, F is the objective function, that is, the error calculation method in step 1.3).

[0069] In the above step 3), when all test data have not been collected, the set optimization algorithm and its objective function are used to perform preliminary parameter identification of the physical model of the secondary battery to be identified based on the parameters corresponding to the currently collected test data, including:

[0070] 3.1) When all test data have not been collected, the error calculation method is used to find the parameter θ that is closest to the current test data (i.e., the part of the test data currently collected) in the historical identification records of secondary batteries of the same system type as the secondary battery to be identified. p , where the historical identification record is a data table including parameters, result data and metadata. The result data is the model parameters and the calculation results of the model using the parameters, and the metadata is other data except the calculation results.

[0071] Specifically, each pair of parameter and result data in the historical identification records of secondary batteries of the same system type as the secondary battery to be identified is traversed, and the error calculation method set in step 1.3) is used to calculate the error between each result data and the currently collected partial test data, i.e., the target data. The parameter corresponding to the result data with the lowest error is the parameter θ that is closest to the current test data. p .

[0072] 3.2) Using the set optimization algorithm and its objective function, based on the determined parameters θ p , perform preliminary parameter identification on the physical model of the secondary battery to be identified, and obtain the preliminary parameter identification results:

[0073] 3.2.1) Based on the determined parameter θ p And the error threshold ep of the set partial data is used to generate the corresponding initial candidate parameter X0.

[0074] Specifically, the optimization algorithm needs to calculate one or more sets of parameters in each iteration. The optimization algorithm generates the initial candidate parameters X0 according to the following rules:

[0075] ① If the parameter θ is determined p If the error is less than or equal to the error threshold ep of some data, the initial candidate parameter X0 is generated according to the multidimensional normal distribution N(μ, ∑), where μ = θ p , diag represents a diagonal matrix. If any of the initial candidate parameters X0 exceeds the upper limit U and lower limit L, the corresponding position will be replaced by the upper limit U and lower limit L.

[0076] ② If there is no historical identification record or confirmed parameter θ p If the error is greater than the error threshold ep of some data, the initial candidate parameter X0 is generated within the upper limit U and lower limit L of the parameter according to the uniform distribution.

[0077] 3.2.2) Using the specified optimization algorithm and its objective function, and based on the generated initial candidate parameters X0, perform preliminary parameter identification on the physical model of the secondary battery to be identified, and obtain preliminary parameter identification results, wherein the stopping condition is that the number of iterations exceeds the maximum number of iterations of the optimization algorithm or the test data has been collected.

[0078] 3.3) If the test data has been collected, the current identification and model calculation tasks are stopped, and the parameters and corresponding result data during the identification process are saved in the historical identification record.

[0079] In the above step 4), after the test data collection is completed, the set optimization algorithm and its objective function are used to perform post-parameter identification on the physical model of the secondary battery to be identified based on the parameters corresponding to all the test data and the previous parameter identification results, and the final parameter identification results of the secondary battery are obtained, including:

[0080] 4.1) After all test data are collected, use the set error calculation method to find the parameter θ that is closest to the current test data in the historical identification records of batteries of the same system type as the secondary battery to be identified. f .

[0081] 4.2) Determine parameter θ f Is the corresponding error greater than the error threshold ef of the set complete data? If so, go to step 4.3); if not, then the parameter θ f If the accuracy requirement is met, the identification process is stopped and the previous parameter identification results are output as the final parameter identification results.

[0082] 4.3) Using the set optimization algorithm and its objective function, based on the determined parameters θ f , perform subsequent parameter identification on the physical model of the secondary battery to be identified, and obtain the final parameter identification result of the secondary battery:

[0083] 4.3.1) Based on the determined parameter θ f And the error threshold ep of the set partial data is used to generate the corresponding initial candidate parameters X0, and a solution in the initial candidate parameters X0 is randomly selected as the current optimal solution for parameter identification.

[0084] Specifically, the optimization algorithm needs to calculate one or more sets of parameters in each iteration. The optimization algorithm generates the initial candidate parameters X0 according to the following rules:

[0085] ① If the parameter θ is determined f If the error is less than or equal to the error threshold ep of some data, the initial candidate parameter X0 is generated according to the multidimensional normal distribution N(μ, ∑), where μ = θ f , If any parameter in the initial candidate parameter X0 exceeds the set upper limit U and lower limit L, the corresponding position will be replaced by the upper limit U and lower limit L.

[0086] ② If the parameter θ is determined f If the error is greater than the error threshold ep of some data, the initial candidate parameters X0 are generated within the upper limit U and lower limit L of the parameters according to the uniform distribution, and a solution in the initial candidate parameters X0 is randomly selected as the current optimal solution for parameter identification.

[0087] 4.3.2) Using the set optimization algorithm and its objective function, based on the generated initial candidate parameters X0 and its current optimal solution, perform post-parameter identification on the physical model of the secondary battery to be identified. The optimal parameters obtained are the final parameter identification results of the secondary battery, where the stopping condition is when the number of iterations exceeds the maximum number of iterations of the optimization algorithm or when the accuracy of the objective function F is less than the set error threshold ef of the complete data.

[0088] The parameter identification method of the secondary battery physical model of the present application is described in detail below through specific examples:

[0089] 1) Construct a physical model of the secondary battery to be identified, and set the optimization algorithm and its objective function used by the physical model of the secondary battery to be identified:

[0090] This embodiment adopts the DFN electrochemical model as the physical model of the secondary battery to be identified. The parameters to be identified of the model include the diaphragm pore A, the negative electrode diffusion activation energy B, and the positive electrode Bruggeman coefficient C.

[0091] The upper and lower limits of the parameters to be identified are set: the membrane pore size A is 0.2-0.6, the negative electrode diffusion activation energy B is 10-100 kJ / mol, and the positive electrode Bruggeman coefficient C is 1.2-1.8, i.e., the lower limit L = (0.2, 10, 1.2) and the upper limit U = (0.6, 100, 1.8). In this example, the model parameters are identified using voltage data at -10°C with a magnification of 0.2°C, 25°C with a magnification of 1°C, and 45°C with a magnification of 1°C.

[0092] The error calculation method uses the root mean square error. The error threshold ef of the complete data is 10mV, the error threshold ep of the partial data is 100mV, and the error upper limit emax is 1000mV.

[0093] The optimization algorithm selected is the particle swarm optimization algorithm.

[0094] 2) Collecting test data of non-destructive testing of secondary batteries to be identified:

[0095] The test data were acquired in the order of 45°C magnification 1C, 25°C magnification 1C and -10°C magnification 0.2C.

[0096] 3) When all test data have not been collected, the set optimization algorithm is used to perform preliminary parameter identification of the physical model of the secondary battery to be identified based on the parameters corresponding to the currently collected test data:

[0097] After completing the 45°C rate and 1C rate test, the preliminary parameter identification was started. The population size of the particle swarm algorithm was 80, the hyperparameters w, c1, and c2 were 0.79, 1.42, and 1.42, respectively, and the maximum number of optimization iterations was 100.

[0098] The results of the preliminary parameter identification include: the diaphragm pore A is 0.4, the negative electrode diffusion activation energy B is 32.7kJ / mol and the positive electrode Bruggeman coefficient C is 1.36. The model calculation results of the 45℃ magnification 1C are consistent with the target data as shown in the figure. Figure 2 shown.

[0099] 4) After the test data collection is completed, the set optimization algorithm is used to perform post-parameter identification on the physical model of the secondary battery to be identified based on the parameters corresponding to all the test data and the previous parameter identification results, and the final parameter identification results of the secondary battery are obtained:

[0100] The error e corresponding to the early parameter identification result in step 3) is between the error threshold ef of the complete data and the error threshold ep of the partial data, that is, ef<e<ep. Therefore, by using the parameter θ p Generate the initial candidate parameter X0 according to μ = (0.4, 32.7, 1.36), Generate a temporary candidate parameter group such as Figure 3 As shown in (a), after adding the boundary restriction, Figure 3 (b) shown.

[0101] The complete test data of 45℃ magnification 1C, 25℃ magnification 1C and -10℃ magnification 0.2C were used for post-parameter identification. The maximum number of optimization iterations was 200. The final parameter identification results were: the diaphragm pore A was 0.4283, the negative electrode diffusion activation energy B was 35.6444 kJ / mol, the positive electrode Bruggeman coefficient C was 1.3532, and the error threshold ep of some data was 5 mV. The model results were consistent with the test data as shown in the figure. Figure 4 shown.

[0102] It can be seen that by adopting the method of the present application, parameter identification can be started during the test data acquisition process, potential parameters can be identified in the early identification process, and the efficiency and accuracy of the parameter identification results are very high.

[0103] Example 2

[0104] This embodiment provides a parameter identification system for a secondary battery physical model, including:

[0105] The model building module is used to build a physical model of the secondary battery to be identified and set the optimization algorithm and its objective function used by the physical model of the secondary battery to be identified.

[0106] The data acquisition module is used to collect test data of the non-destructive test of the secondary battery to be identified.

[0107] The early parameter identification module is used to perform early parameter identification on the physical model of the secondary battery to be identified based on the parameters corresponding to the currently collected test data, using the set optimization algorithm and its objective function when all test data have not been collected.

[0108] The post-parameter identification module is used to perform post-parameter identification on the physical model of the secondary battery to be identified after all test data are collected, using the set optimization algorithm and its objective function, based on the parameters corresponding to all test data and the early parameter identification results, to obtain the final parameter identification results of the secondary battery.

[0109] The system provided in this embodiment is used to execute the above-mentioned method embodiments. Please refer to the above-mentioned embodiments for specific processes and detailed contents, which will not be repeated here.

[0110] Example 3

[0111] This embodiment provides a processing device corresponding to the parameter identification method of the secondary battery physical model provided in this embodiment 1. The processing device can be applicable to a client processing device, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the parameter identification method of the secondary battery physical model of embodiment 1.

[0112] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to facilitate communication between them. The memory stores a computer program executable on the processing device. When the processing device executes the computer program, it executes the parameter identification method for the secondary battery physical model provided in Example 1.

[0113] In some implementations, the memory may be a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage.

[0114] In other implementations, the processor may be a central processing unit (CPU), a digital signal processor (DSP), or other general-purpose processors, which are not limited herein.

[0115] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0116] Those skilled in the art will understand that the structure of the above-mentioned computing device is only a partial structure related to the solution of the present application, and does not constitute a limitation on the computing device to which the solution of the present application is applied. The specific computing device may include more or fewer components, or combine certain components, or have a different component arrangement.

[0117] Example 4

[0118] This embodiment provides a computer program product corresponding to the parameter identification method of the secondary battery physical model provided in this embodiment 1. The computer program product may include a computer-readable storage medium on which computer-readable program instructions for executing the parameter identification method of the secondary battery physical model described in this embodiment 1 are loaded.

[0119] Computer readable storage media can be tangible devices that hold and store instructions used by instruction execution devices. Computer readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any combination thereof.

[0120] The above embodiment provides a computer-readable storage medium, whose implementation principle and technical effects are similar to those of the above method embodiment, and will not be repeated here.

[0121] 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 produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. 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.

[0122] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0124] The above embodiments are only used to illustrate the present application, wherein the structure, connection method and manufacturing process of each component can be changed. Any equivalent transformations and improvements based on the technical solution of the present application should not be excluded from the scope of protection of the present application.

Claims

1. A parameter identification method for a secondary battery physical model, characterized in that: include: Constructing a physical model of the secondary battery to be identified, and setting an optimization algorithm and its objective function used by the physical model of the secondary battery to be identified; Collecting test data of non-destructive testing of secondary batteries to be identified; When all test data have not been collected, the set optimization algorithm and its objective function are used to perform preliminary parameter identification on the physical model of the secondary battery to be identified based on the parameters corresponding to the currently collected test data to obtain preliminary parameter identification results; After all test data are collected, a set optimization algorithm and its objective function are used to perform post-parameter identification on the physical model of the secondary battery to be identified based on the parameters corresponding to all test data and the pre-parameter identification results, thereby obtaining a final parameter identification result of the secondary battery; The step of constructing a physical model of the secondary battery to be identified and setting an optimization algorithm and its objective function used by the physical model of the secondary battery to be identified includes: Constructing a physical model of the secondary battery to be identified, the physical model including a number of parameters to be identified; Based on the historical data range and the accuracy requirements for model use, set the upper and lower limits, error calculation method and error threshold of the parameters to be identified. The error threshold includes the error threshold for complete data, the error threshold for partial data and the upper error limit. Select the optimization algorithm and its objective function based on the computational effort and number of parameters required to solve the model; When all test data have not been collected, the set optimization algorithm and its objective function are used to perform preliminary parameter identification on the physical model of the secondary battery to be identified based on the parameters corresponding to the currently collected test data, and obtain preliminary parameter identification results, including: When all test data have not been collected, a set error calculation method is used to find the parameters closest to the currently collected test data in the historical identification records of secondary batteries of the same system type as the secondary battery to be identified; By adopting the set optimization algorithm and its objective function and based on the determined parameters, preliminary parameter identification is performed on the physical model of the secondary battery to be identified, and preliminary parameter identification results are obtained.

2. The parameter identification method of a secondary battery physical model according to claim 1, characterized in that: The preset optimization algorithm and its objective function are used to perform preliminary parameter identification on the physical model of the secondary battery to be identified based on the determined parameters, and obtain preliminary parameter identification results, including: Based on the determined parameters and the set error threshold of the partial data, corresponding initial candidate parameters are generated; Using the set optimization algorithm and its objective function, based on the generated initial candidate parameters, the physical model of the secondary battery to be identified is subjected to preliminary parameter identification to obtain preliminary parameter identification results; If the test data has been collected, the current identification and model calculation tasks are stopped, and the parameters and corresponding result data in the identification process are saved to the historical identification record.

3. The parameter identification method of a secondary battery physical model according to claim 1, characterized in that: After all test data are collected, the set optimization algorithm and its objective function are used to perform post-parameter identification on the physical model of the secondary battery to be identified based on the parameters corresponding to all test data and the pre-parameter identification results, and obtain the final parameter identification results of the secondary battery, including: After all test data are collected, a set error calculation method is used to find the parameters closest to the current test data in the historical identification records of secondary batteries of the same system type as the secondary battery to be identified; Determine whether the error corresponding to the parameter is greater than the set error threshold of the complete data. If the error corresponding to the parameter is not greater than the error threshold of the complete data, stop the identification process and output the previous parameter identification result as the final parameter identification result; If the error corresponding to the parameter is greater than the set error threshold of the complete data, the set optimization algorithm and its objective function are used to perform post-parameter identification on the physical model of the secondary battery to be identified based on the determined parameters to obtain the final parameter identification result of the secondary battery.

4. The parameter identification method of a secondary battery physical model according to claim 3, characterized in that: If the error corresponding to the parameter is greater than the set error threshold of the complete data, the set optimization algorithm and its objective function are used to perform post-parameter identification on the physical model of the secondary battery to be identified based on the determined parameters to obtain the final parameter identification result of the secondary battery, including: Based on the determined parameters and the set error threshold of the partial data, the corresponding initial candidate parameters are generated, and a solution among the initial candidate parameters is randomly selected as the current optimal solution; Using the set optimization algorithm and its objective function, based on the generated initial candidate parameters and their current optimal solutions, the physical model of the secondary battery to be identified is subjected to post-parameter identification to obtain the final parameter identification result of the secondary battery.

5. The parameter identification method of a secondary battery physical model according to claim 4, characterized in that: The initial candidate parameters are generated according to the following rules: If the error of the determined parameter is less than or equal to the error threshold of the partial data, the initial candidate parameters are generated according to the multidimensional normal distribution; If the error of the determined parameter is greater than the error threshold of some data, initial candidate parameters are generated within the upper and lower limits of the parameter according to a uniform distribution.

6. A parameter identification system for a secondary battery physical model, characterized in that: include: A model building module, used to build a physical model of the secondary battery to be identified, and set the optimization algorithm and its objective function used by the physical model of the secondary battery to be identified; A data acquisition module, used to collect test data of non-destructive testing of the secondary battery to be identified; The early parameter identification module is used to use the set optimization algorithm and its objective function to perform early parameter identification on the physical model of the secondary battery to be identified based on the parameters corresponding to the currently collected test data when all test data have not been collected, and obtain early parameter identification results; a post-parameter identification module, configured to, after all test data are collected, perform post-parameter identification on the physical model of the secondary battery to be identified using a set optimization algorithm and its objective function based on the parameters corresponding to all test data and the pre-parameter identification results, to obtain a final parameter identification result of the secondary battery; The step of constructing a physical model of the secondary battery to be identified and setting an optimization algorithm and its objective function used by the physical model of the secondary battery to be identified includes: Constructing a physical model of the secondary battery to be identified, the physical model including a number of parameters to be identified; Based on the historical data range and the accuracy requirements for model use, set the upper and lower limits, error calculation method and error threshold of the parameters to be identified. The error threshold includes the error threshold for complete data, the error threshold for partial data and the upper error limit. Select the optimization algorithm and its objective function based on the computational effort and number of parameters required to solve the model; When all test data have not been collected, the set optimization algorithm and its objective function are used to perform preliminary parameter identification on the physical model of the secondary battery to be identified based on the parameters corresponding to the currently collected test data, and obtain preliminary parameter identification results, including: When all test data have not been collected, a set error calculation method is used to find the parameters closest to the currently collected test data in the historical identification records of secondary batteries of the same system type as the secondary battery to be identified; By adopting the set optimization algorithm and its objective function and based on the determined parameters, preliminary parameter identification is performed on the physical model of the secondary battery to be identified, and preliminary parameter identification results are obtained.

7. A processing device, characterized in that: The method comprises computer program instructions, wherein when the computer program instructions are executed by a processing device, they are used to implement the steps corresponding to the parameter identification method of the secondary battery physical model according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, wherein the computer program instructions, when executed by a processor, are used to implement the steps corresponding to the parameter identification method for a secondary battery physical model according to any one of claims 1 to 5.

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