Battery mechanism model parameter identification method and system based on differential evolution algorithm

Abstract

The present invention discloses a battery mechanism model parameter identification method and system based on a differential evolution algorithm, wherein the method includes: constructing a proxy model training data set; training a neural network proxy model to obtain a trained proxy model; screening individuals to obtain an initial population; using the trained proxy model as the objective function of the differential evolution algorithm; inputting the initial population into the differential evolution algorithm to obtain an optimal population; inputting each set of data in the optimal population into an experimental system and a simulation system for evaluation to obtain the true voltage error corresponding to each set of data in the optimal population; judging whether the true voltage error is less than a set threshold, and if so, directly outputting the parameter to be identified corresponding to the minimum value of the true voltage error; if not, setting a set proportion of the optimal population after evaluation as a dynamic data set, adding the dynamic data set to the proxy model training data set, and returning to the step of training the neural network proxy model.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a battery mechanism model parameter identification method and system based on a differential evolution algorithm. Background Art

[0002] In the field of battery system analysis and evaluation, battery system simulation is one of the important means to study battery performance and evaluate battery status. Therefore, studying the model mechanism of the battery system is crucial. In addition, studying the battery mechanism model is of great significance for researchers to gain a deep understanding of the chemical and physical processes of the battery to optimize battery performance and extend its service life. However, for complex battery mechanism models, many parameters need to be identified. Therefore, it is very important to develop accurate and efficient battery mechanism model identification methods.

[0003] At present, the common methods for identifying battery mechanism model parameters are mainly the following:

[0004] Heuristic optimization algorithms such as genetic algorithms and particle swarm optimization are used to identify the parameters of the mechanism model through experiments and model error minimization. This method is prone to falling into local optimality and has low convergence accuracy.

[0005] The least squares method is used to identify the parameters of the battery mechanism model by fitting the battery data and estimating the model parameters, thereby improving the prediction accuracy of the battery model. However, it is sensitive to noise and the data quality has a great impact on the results.

[0006] The Kalman filter method estimates the parameter values ​​at each moment using the state variables in the mathematical model and the known terminal voltage data, and then obtains the corresponding model parameter values. Its implementation is very complex.

[0007] Based on the defects of the above methods, there is an urgent need for a battery mechanism model parameter identification method with fast speed, low complexity and high convergence accuracy. Summary of the Invention

[0008] In order to solve the deficiencies of the prior art, the present invention provides a battery mechanism model parameter identification method and system based on differential evolution algorithm;

[0009] On the one hand, a battery mechanism model parameter identification method based on differential evolution algorithm is provided, including:

[0010] Based on each set of parameter sampling samples to be identified and the corresponding real errors between the experimental system and the simulation system, a surrogate model training data set is constructed;

[0011] Using the proxy model training data set to train the neural network proxy model, and obtaining a trained proxy model;

[0012] Based on the surrogate model training data set, individuals are screened to obtain the initial population; the trained surrogate model is used as the objective function of the differential evolution algorithm; the initial population is input into the differential evolution algorithm to obtain the optimal population;

[0013] Each set of data from the optimal population is input into the experimental system and the simulation system for evaluation, and the actual voltage error corresponding to each set of data in the optimal population is obtained;

[0014] Determine whether the true voltage error corresponding to each set of data in the optimal population is less than the set threshold. If so, directly output the parameter to be identified corresponding to the minimum value of the true voltage error; if not, set the set proportion of the optimal population after evaluation as the dynamic data set, add the dynamic data set to the proxy model training data set, and return to the training step of the neural network proxy model.

[0015] On the other hand, a battery mechanism model parameter identification system based on differential evolution algorithm is provided, including:

[0016] A data set construction module is configured to: construct a proxy model training data set based on each set of parameter sampling samples to be identified and the corresponding actual errors between the experimental system and the simulation system;

[0017] A training module is configured to: train the neural network proxy model using a proxy model training data set to obtain a trained proxy model;

[0018] The differential evolution module is configured to: screen individuals based on the surrogate model training data set to obtain an initial population; use the trained surrogate model as the objective function of the differential evolution algorithm; and input the initial population into the differential evolution algorithm to obtain the optimal population;

[0019] The evaluation module is configured to: input each set of data of the optimal population into the experimental system and the simulation system for evaluation, and obtain the actual voltage error corresponding to each set of data in the optimal population;

[0020] The judgment module is configured to: judge whether the true voltage error corresponding to each set of data in the optimal population is less than a set threshold; if so, directly output the parameter to be identified corresponding to the minimum value of the true voltage error; if not, set a set proportion of the optimal population after evaluation as a dynamic data set, add the dynamic data set to the proxy model training data set, and return to the training module.

[0021] In another aspect, an electronic device is provided, comprising:

[0022] a memory for non-transitory storage of computer-readable instructions; and

[0023] a processor for executing said computer-readable instructions,

[0024] When the computer-readable instructions are executed by the processor, the method described in the first aspect is executed.

[0025] On the other hand, a storage medium is provided, which non-temporarily stores computer-readable instructions, wherein when the non-temporary computer-readable instructions are executed by a computer, the method described in the first aspect is executed.

[0026] On the other hand, a computer program product is provided, comprising a computer program, wherein the computer program is configured to implement the method described in the first aspect when running on one or more processors.

[0027] The above technical solution has the following advantages or beneficial effects:

[0028] This method builds a neural network proxy model based on the correlation of identification parameters, effectively replacing the experimental system and numerical simulation model. Based on this, a differential evolution algorithm that selects the initial population is used to optimize the parameters to be identified. Each optimized parameter is re-evaluated using the experimental system and numerical simulation system and then added to the training dataset. The proxy model, initial population, and optimization results are continuously updated to achieve parameter identification.

[0029] The present invention combines the correlation analysis of the parameters to be identified and constructs a three-layer neural network proxy model combined with a basis function model, which reduces the neural network training hyperparameters and reduces the complexity of the proxy model.

[0030] The present invention improves the initial population selection rule of the differential evolution algorithm and selects better samples as the initial population in the data set for training the proxy model, which greatly improves the optimization starting point of the optimization algorithm.

[0031] The present invention implements a logical closed loop by re-experimenting and simulating the optimal population of the optimization algorithm and substituting it back into the training data set, continuously updating the proxy model, initial population, and optimal population until the parameters are identified, thereby improving the accuracy of the parameters to be identified. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0033] Figure 1 This is a flow chart of the proxy optimization method for battery mechanism model parameter identification according to the present invention;

[0034] Figure 2 A schematic diagram of a neural network model based on correlation analysis in the present invention;

[0035] Figure 3 A flow chart of the differential evolution algorithm for screening the initial population implemented in the present invention;

[0036] Figure 4 Flowchart of the cyclic update operation in the present invention. DETAILED DESCRIPTION

[0037] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0038] Example 1

[0039] like Figure 1 As shown, this embodiment provides a battery mechanism model parameter identification method based on differential evolution algorithm;

[0040] The battery mechanism model parameter identification method based on differential evolution algorithm includes:

[0041] S101: Construct a surrogate model training dataset based on each set of parameter sampling samples to be identified and the corresponding actual errors between the experimental system and the simulation system;

[0042] S102: Using the proxy model training data set, training the neural network proxy model to obtain a trained proxy model;

[0043] S103: Based on the surrogate model training data set, individuals are screened to obtain an initial population; the trained surrogate model is used as the objective function of the differential evolution algorithm; the initial population is input into the differential evolution algorithm to obtain the optimal population;

[0044] S104: Input each set of data of the optimal population into the experimental system and the simulation system for evaluation to obtain the actual voltage error corresponding to each set of data in the optimal population;

[0045] S105: Determine whether the true voltage error corresponding to each set of data in the optimal population is less than the set threshold. If so, directly output the parameter to be identified corresponding to the minimum value of the true voltage error; if not, set the set proportion of the optimal population after evaluation as the dynamic data set, add the dynamic data set to the proxy model training data set, and return to S102.

[0046] It should be understood that the present invention continuously obtains the optimal population by cyclically updating the training data set, the proxy model and the initial population. When the true voltage error of the optimal individual in the optimal population meets the conditions, the optimization process is considered to be completed, and the optimal individual is the parameter to be identified.

[0047] Furthermore, the step S101: constructing a proxy model training data set based on each set of parameter sampling samples to be identified and the corresponding actual errors between the experimental system and the simulation system, includes:

[0048] (1-1): Construct a battery mechanism model of the target battery and obtain M parameters to be identified of the battery mechanism model;

[0049] (1-2): According to the Latin hypercube sampling rule, each parameter to be identified is divided into several intervals according to the parameter value range, and sampling is performed evenly in each interval to obtain several groups of parameter sampling samples to be identified, where each group of parameter sampling samples to be identified includes M types of parameters to be identified, and each type of parameter to be identified has a corresponding value;

[0050] (1-3): Under the set battery discharge conditions, a discharge experiment is performed on the target battery to obtain the reference voltage corresponding to each set of parameter sampling samples to be identified;

[0051] (1-4): Through numerical simulation, the numerical voltage corresponding to each set of parameter sampling samples to be identified is obtained;

[0052] (1-5): Calculate the difference between the reference voltage and the numerical voltage corresponding to each set of parameter sampling samples to be identified, and use the absolute difference as the error;

[0053] (1-6): Based on the sample samples of each set of parameters to be identified and the corresponding errors, a training dataset for the proxy model is constructed.

[0054] Furthermore, the (1-1) is to construct an electrochemical mechanism model of the target battery and obtain M parameters to be identified of the battery mechanism model, wherein the battery mechanism model to be identified is a classic P2D model, and the M parameters to be identified include physical parameters reflecting the battery itself, such as the radius of the electrode active particles, and parameters reflecting the electrochemical performance of the battery, such as the solid-phase electronic conductivity.

[0055] Furthermore, in (1-2): according to the Latin hypercube sampling rule, each parameter to be identified is divided into several intervals according to the value range of the parameter, and sampling is performed evenly in each interval to obtain several groups of sampled parameters to be identified, wherein each group of sampled parameters to be identified includes M types of parameters to be identified, and each type of parameter to be identified has a corresponding numerical value, including:

[0056] Assuming that the value range of the first parameter to be identified is 1 to 100, and it is divided into 5 intervals, the value ranges of the 5 intervals are 1 to 20, 21 to 40, 41 to 60, 61 to 80, and 81 to 100 respectively.

[0057] Among them, the value of each parameter to be identified is the M parameters to be identified of the battery mechanism model. Obtained by spatial Latin hypercube sampling.

[0058] Spatial Latin hypercube sampling refers to dividing the value range of each parameter to be identified into the same interval and performing uniform sampling in each interval to reduce the correlation between various parameters.

[0059] Furthermore, (1-3) is as follows: a discharge experiment is performed on the target battery under set battery discharge conditions to obtain a reference voltage corresponding to each group of parameter sampling samples to be identified; the discharge conditions include: constant current, discharge depth, discharge temperature and cut-off voltage.

[0060] Among them, the discharge experiment refers to connecting the battery to a load device, setting the battery discharge conditions to make it discharge, so as to test the battery's discharge performance and voltage changes during the discharge process.

[0061] The reference voltage u corresponding to each set of identification parameter sampling samples is obtained through battery discharge experiments to construct an accurate sample data set

[0062] Furthermore, (1-4) is as follows: numerical simulation is used to obtain the numerical voltage corresponding to each set of sampled parameters to be identified, wherein numerical simulation specifically refers to using a computer to simulate the battery discharge process based on the battery electrochemical model and according to the conditions of the discharge experiment to observe changes in the electrochemical performance of the battery.

[0063] The numerical voltage corresponding to each set of identification parameter sampling samples is obtained through parallel numerical simulation Constructing a simulation sample dataset

[0064] Battery discharge conditions include constant current I(t), discharge depth, temperature and cut-off voltage. At the same time, the numerical simulation is consistent with the discharge experiment conditions. The numerical simulation solution methods include but are not limited to finite element and finite difference. The open circuit voltage at time T is recorded as the reference voltage u and the numerical voltage

[0065] Furthermore, the step (1-5): calculating the difference between the reference voltage and the numerical voltage corresponding to each set of parameter sampling samples to be identified, and taking the difference as the error e, includes:

[0066]

[0067] Where u represents the reference voltage corresponding to each set of parameter sampling samples to be identified obtained by the experimental system; Indicates the numerical simulation voltage corresponding to each set of parameter sampling samples to be identified.

[0068] Furthermore, (1-6): based on each set of parameter sampling samples to be identified and the corresponding errors, a proxy model training data set is constructed. It includes: a number of samples, each sample includes: a group of parameters to be identified and corresponding errors, each group of parameters to be identified includes: M parameters to be identified, and each parameter to be identified has a corresponding value.

[0069] Furthermore, the step S102 of training the proxy model using the proxy model training data set to obtain a trained proxy model includes:

[0070] (2-1): Calculate the correlation coefficient between each parameter to be identified and the error in each set of parameter sampling samples, and determine whether the absolute value of the correlation coefficient is greater than the set threshold. If so, it means that the current parameter to be identified is a high-correlation parameter; otherwise, it means that the current parameter to be identified is a low-correlation parameter.

[0071] (2-2): If it is a highly correlated parameter, the current parameter to be identified is used as the input value of the linear basis function model, and the corresponding error is used as the output value of the linear basis function model to obtain the trained linear basis function model;

[0072] (2-3): If it is a low correlation parameter, the current parameter to be identified is used as the input value of the fully connected neural network, and the corresponding error is used as the output value of the fully connected neural network to obtain the trained fully connected neural network;

[0073] (2-4): Based on the trained linear basis function model and the trained fully connected neural network, a trained proxy model is obtained.

[0074] Furthermore, the (2-1) is to calculate the correlation coefficient between each parameter to be identified and the error in each group of parameter sampling samples to be identified, wherein the correlation coefficient is obtained by a Spearman correlation analysis algorithm.

[0075] The calculation formula of the Spearman correlation analysis algorithm is as follows:

[0076]

[0077] Among them, d i It represents the difference in the rank value of the i-th data pair, and n represents the number of data points.

[0078] According to the parameters to be identified Correlation analysis between the error e and the parameters is divided into high correlation parameters With low correlation parameters And get the corresponding data set and The neural network proxy model is trained using two datasets. It is generally considered that the absolute value of the correlation coefficient |ρ| ≥ 0.5 is highly correlated, otherwise it is considered low correlation.

[0079] Furthermore, (2-2): If it is a highly correlated parameter, the current parameter to be identified is used as the input value of the linear basis function model, and the corresponding error is used as the output value of the linear basis function model to obtain the trained linear basis function model. The linear basis function model includes: a first input layer, a first hidden layer and a first output layer connected in sequence.

[0080] In order to take advantage of the strong overall correlation between high-correlation parameters and outputs, a linear basis function model is used to characterize the mapping relationship between the two. The formula is as follows:

[0081]

[0082] Among them, the basis function θ(x i )You can choose Gaussian basis function, etc.

[0083] Based on the dataset Using genetic algorithm to identify the basic model parameters w i , where the loss function Loss in the identification process is defined as the output of the base model f(x) The root mean square error with e in the training data set is calculated as follows:

[0084]

[0085] Where n is the number of data points.

[0086] Furthermore, (2-3): if it is a low-correlation parameter, the current parameter to be identified is used as the input value of the fully connected neural network, and the corresponding error is used as the output value of the fully connected neural network to obtain the trained fully connected neural network; wherein, the fully connected neural network includes: a second input layer, a second hidden layer and a second output layer connected in sequence.

[0087] The mapping relationship between low-correlation parameters and output is represented by a fully connected neural network, and the nonlinear function in its hidden layer is set to the rectified linear unit ReLU, and the calculation formula is:

[0088] ReLU(x)=max{0,x}+min{0,x}*λ

[0089] Where x is the input value of the rectified linear unit, λ is the preset weight parameter, max{0,x} and min{0,x} respectively represent the maximum and minimum values ​​in {0,x}.

[0090] In the case that the basis function parameters remain unchanged, through the training data set and The parameters of the fully connected neural network are obtained, and the training method of the fully connected neural network adopts the gradient descent method.

[0091] Furthermore, the (2-4): based on the trained linear basis function model and the trained fully connected neural network, a trained proxy model is obtained, including:

[0092] The first output layer and the second output layer are merged into a total output layer to obtain the trained proxy model.

[0093] It should be understood that Figure 2 As shown, the first input layer and the second input layer have a total of M nodes, and the first hidden layer has There is only one related node in the second hidden layer. There are N related nodes, each of the second hidden nodes is added with a bias Bias1, and the total output layer has one node; the input of the trained proxy model is the parameter to be identified The output is the voltage error e, and the first hidden layer is The nodes related to the second hidden layer are basis functions. The relevant nodes are activation functions, the complexity of the proxy model is reduced, the training speed is increased, and it is helpful to avoid overfitting.

[0094] Furthermore, the step S103 of screening individuals to obtain an initial population based on the surrogate model training data set includes:

[0095] The data in the surrogate model training dataset is sorted in ascending order of voltage error, and the top-ranked data sets are selected as the initial population. Each data set includes M parameters to be identified and their corresponding errors. The differential evolution algorithm aims to minimize the error e.

[0096] Furthermore, the said S103: the said optimal population includes: a plurality of groups of data, each group of data includes: M types of optimized parameters to be identified.

[0097] The objective function of the trained proxy model is implemented using the trained proxy model, with the parameters to be identified as optimization parameters and the minimization of the error e as the goal.

[0098] Arrange the data set of the training agent model according to the error from small to large, and select the better data as the initial population to improve the starting point of the evolutionary algorithm and increase the optimization speed, such as Figure 3 shown.

[0099] like Figure 3 As shown, the steps of the differential evolution algorithm include:

[0100] (1): Screening the initial population;

[0101] (2): Determine whether the termination criteria are met. If so, output the optimal population and the optimal individual, and end;

[0102] (3): If not, perform mutation and crossover operations, process the boundary conditions, calculate the objective function, implement the objective function using the trained proxy model, perform selection operations, and return to (2).

[0103] Further, S105: if not, setting a set proportion of the optimal population after evaluation as a dynamic data set, and adding the dynamic data set to the agent model training data set, including:

[0104] Output the optimal population from the differential evolution algorithm Conduct experimental system evaluation best and numerical simulation evaluation In order to improve the optimization speed, the numerical simulation process adopts parallel computing or GPU heterogeneous computing;

[0105] Calculate the true voltage error of the output optimal population The optimal population individuals are arranged from small to large according to the true voltage error, and the individuals with a set proportion of the top rankings are used as a dynamic data set, which is added to the training data set.

[0106] For the supplemented training data set, the basis function model and the neural network are retrained to obtain a proxy model with higher accuracy, and the data is reselected as the initial population of the differential evolution algorithm.

[0107] By cyclically updating the training data set, the agent model, and the initial population, the optimal population is continuously obtained. When the voltage error of the optimal individual in the optimal population meets the conditions, the optimization process is considered to be completed, and the parameters of this individual are identified, such as Figure 4 shown.

[0108] Set the end standard of the entire loop optimization, the calculation formula is:

[0109]

[0110] in, For parameter identification, J is the preset minimum voltage error.

[0111] Example 2

[0112] This embodiment provides a battery mechanism model parameter identification system based on a differential evolution algorithm, including:

[0113] A data set construction module is configured to: construct a proxy model training data set based on each set of parameter sampling samples to be identified and the corresponding actual errors between the experimental system and the simulation system;

[0114] A training module is configured to: train the neural network proxy model using a proxy model training data set to obtain a trained proxy model;

[0115] The differential evolution module is configured to: screen individuals based on the surrogate model training data set to obtain an initial population; use the trained surrogate model as the objective function of the differential evolution algorithm; and input the initial population into the differential evolution algorithm to obtain the optimal population;

[0116] The evaluation module is configured to: input each set of data of the optimal population into the experimental system and the simulation system for evaluation, and obtain the actual voltage error corresponding to each set of data in the optimal population;

[0117] The judgment module is configured to: judge whether the true voltage error corresponding to each set of data in the optimal population is less than a set threshold; if so, directly output the parameter to be identified corresponding to the minimum value of the true voltage error; if not, set a set proportion of the optimal population after evaluation as a dynamic data set, add the dynamic data set to the proxy model training data set, and return to the training module.

[0118] It should be noted that the dataset construction module, training module, differential evolution module, evaluation module, and judgment module described above correspond to steps S101 to S105 in Example 1. The examples and application scenarios implemented by these modules and the corresponding steps are the same, but are not limited to the contents disclosed in Example 1. It should be noted that the above modules, as part of a system, can be executed in a computer system, such as a set of computer-executable instructions.

[0119] The descriptions of the various embodiments in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0120] The proposed system can be implemented in other ways. For example, the system embodiment described above is merely illustrative. For example, the above module division is only a logical function division. In actual implementation, other division methods may be used. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not implemented.

[0121] Example 3

[0122] This embodiment also provides an electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory. When the electronic device is running, the processor executes the one or more computer programs stored in the memory, so that the electronic device executes the method described in the above embodiment one.

[0123] It should be understood that in this embodiment, the processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), off-the-shelf field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0124] The memory may include a read-only memory and a random access memory, and provides instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0125] During implementation, each step of the above method may be completed by an integrated logic circuit of hardware in a processor or by instructions in the form of software.

[0126] The method in Example 1 can be directly implemented as being executed by a hardware processor, or by a combination of hardware and software modules within the processor. The software module can be located in a storage medium well-established in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not given here.

[0127] Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with this embodiment can be implemented using electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0128] Example 4

[0129] This embodiment further provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the method described in the first embodiment is performed.

[0130] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A battery mechanism model parameter identification method based on differential evolution algorithm, characterized by: include: Based on each set of parameter sampling samples to be identified and the corresponding real errors between the experimental system and the simulation system, a surrogate model training data set is constructed; The neural network proxy model is trained using the proxy model training dataset to obtain the trained proxy model, including: Calculate the correlation coefficient between each parameter to be identified and the error in each set of parameter sampling samples to be identified, and determine whether the absolute value of the correlation coefficient is greater than the set threshold. If so, it means that the current parameter to be identified is a high correlation parameter; otherwise, it means that the current parameter to be identified is a low correlation parameter; If it is a highly correlated parameter, the current parameter to be identified is used as the input value of the linear basis function model, and the corresponding error is used as the output value of the linear basis function model to obtain the trained linear basis function model; If it is a low-correlation parameter, the current parameter to be identified is used as the input value of the fully connected neural network, and the corresponding error is used as the output value of the fully connected neural network to obtain the trained fully connected neural network; Based on the trained linear basis function model and the trained fully connected neural network, a trained proxy model is obtained; Based on the surrogate model training data set, individuals are screened to obtain an initial population. The trained surrogate model is used as the objective function of the differential evolution algorithm. The initial population is input into the differential evolution algorithm to obtain the optimal population. Each set of data from the optimal population is input into the experimental system and simulation system for evaluation to obtain the actual voltage error corresponding to each set of data in the optimal population. Determine whether the true voltage error corresponding to each set of data in the optimal population is less than the set threshold. If so, directly output the parameter to be identified corresponding to the minimum value of the true voltage error; if not, set a set proportion of the optimal population after evaluation as the dynamic data set, add the dynamic data set to the proxy model training data set, and return to the training step of the neural network proxy model; The optimal population includes: a plurality of data sets, each of which includes: M optimized parameters to be identified; The objective function of the trained surrogate model is implemented using the trained surrogate model, with the parameters to be identified as optimization parameters and the minimization of the error e as the goal; If not, a set proportion of the optimal population after evaluation is set as a dynamic dataset, and the dynamic dataset is added to the agent model training dataset; Output the optimal population from the differential evolution algorithm Conduct experimental system evaluation and numerical simulation evaluation ,In order to improve the optimization speed, the numerical simulation process adopts ,parallel computing or GPU heterogeneous computing; Calculate the true voltage error of the output optimal population , the optimal population individuals are arranged from small to large according to the true voltage error, and the individuals with a set proportion of the top rankings are used as the dynamic data set, and the dynamic data set is added to the training data set.

2. The battery mechanism model parameter identification method based on the differential evolution algorithm according to claim 1, characterized in that: Based on each set of parameter sampling samples to be identified and the corresponding actual errors between the experimental system and the simulation system, a surrogate model training dataset is constructed, including: Constructing a battery mechanism model of the target battery and obtaining M parameters to be identified of the battery mechanism model; According to the Latin hypercube sampling rule, each parameter to be identified is divided into several intervals according to the parameter value range, and sampling is performed evenly in each interval to obtain several groups of parameter sampling samples to be identified, where each group of parameter sampling samples to be identified includes M types of parameters to be identified, and each type of parameter to be identified has a corresponding value; Under the set battery discharge conditions, a discharge experiment is performed on the target battery to obtain the reference voltage corresponding to each group of parameter sampling samples to be identified; Through numerical simulation, the numerical voltage corresponding to each set of parameter sampling samples to be identified is obtained; Calculate the difference between the reference voltage and the numerical voltage corresponding to each set of parameter sampling samples to be identified, and use the absolute difference as the error; Based on the sampling samples of each group of parameters to be identified and the corresponding errors, a training data set of the proxy model is constructed.

3. The battery mechanism model parameter identification method based on the differential evolution algorithm according to claim 1, characterized in that: If it is a highly correlated parameter, the current parameter to be identified is used as an input value of the linear basis function model, and the corresponding error is used as an output value of the linear basis function model to obtain a trained linear basis function model, wherein the linear basis function model includes: a first input layer, a first hidden layer, and a first output layer connected in sequence; A linear basis function model is used to characterize the mapping relationship between high correlation parameters and outputs. The formula is as follows: ； Among them, the basis function Select the Gaussian basis function; Based on the dataset , using genetic algorithm to identify the base model parameters , where the loss function Loss in the identification process is defined as the output of the base model f(x) With the training data set The root mean square error is calculated as follows: Where n is the number of data points.

4. The battery mechanism model parameter identification method based on the differential evolution algorithm according to claim 1, characterized in that: If it is a low-correlation parameter, the current parameter to be identified is used as the input value of the fully connected neural network, and the corresponding error is used as the output value of the fully connected neural network to obtain the trained fully connected neural network; wherein, the fully connected neural network includes: a second input layer, a second hidden layer, and a second output layer connected in sequence; the mapping relationship between the low-correlation parameter and the output is represented by a fully connected neural network, and the nonlinear function in the hidden layer is set to the rectified linear unit ReLU, and the calculation formula is: in, is the input value of the rectified linear unit, is the preset weight parameter, and Respectively represent The maximum and minimum values ​​in ; In the case that the basis function parameters remain unchanged, through the training data set and , obtain the parameters of the fully connected neural network, and the training method of the fully connected neural network adopts the gradient descent method.

5. The battery mechanism model parameter identification method based on the differential evolution algorithm according to claim 1, characterized in that: Based on the surrogate model training dataset, individuals are screened to obtain the initial population, including: The data in the surrogate model training data set are sorted in ascending order of voltage error, and several groups of data with the highest sorting order are selected as the initial population; each group of data includes: M parameters to be identified and corresponding errors.

6. The battery mechanism model parameter identification system based on differential evolution algorithm is characterized by: include: A data set construction module is configured to: construct a proxy model training data set based on each set of parameter sampling samples to be identified and the corresponding actual errors between the experimental system and the simulation system; The training module is configured to train the neural network proxy model using the proxy model training dataset to obtain a trained proxy model, including: Calculate the correlation coefficient between each parameter to be identified and the error in each set of parameter sampling samples to be identified, and determine whether the absolute value of the correlation coefficient is greater than the set threshold. If so, it means that the current parameter to be identified is a high correlation parameter; otherwise, it means that the current parameter to be identified is a low correlation parameter; If it is a highly correlated parameter, the current parameter to be identified is used as the input value of the linear basis function model, and the corresponding error is used as the output value of the linear basis function model to obtain the trained linear basis function model; If it is a low-correlation parameter, the current parameter to be identified is used as the input value of the fully connected neural network, and the corresponding error is used as the output value of the fully connected neural network to obtain the trained fully connected neural network; Based on the trained linear basis function model and the trained fully connected neural network, a trained proxy model is obtained; The differential evolution module is configured to: screen individuals based on the surrogate model training data set to obtain an initial population; use the trained surrogate model as the objective function of the differential evolution algorithm; and input the initial population into the differential evolution algorithm to obtain the optimal population; The evaluation module is configured to: input each set of data of the optimal population into the experimental system and the simulation system for evaluation, and obtain the actual voltage error corresponding to each set of data in the optimal population; A judgment module is configured to: determine whether the true voltage error corresponding to each set of data in the optimal population is less than a set threshold; if so, directly output the to-be-identified parameter corresponding to the minimum value of the true voltage error; if not, set a set proportion of the optimal population after evaluation as a dynamic data set, add the dynamic data set to the proxy model training data set, and return to the training module; The optimal population includes: a plurality of data sets, each of which includes: M optimized parameters to be identified; The objective function of the trained surrogate model is implemented using the trained surrogate model, with the parameters to be identified as optimization parameters and the minimization of the error e as the goal; If not, a set proportion of the optimal population after evaluation is set as a dynamic dataset, and the dynamic dataset is added to the agent model training dataset; Output the optimal population from the differential evolution algorithm Conduct experimental system evaluation and numerical simulation evaluation ,In order to improve the optimization speed, the numerical simulation process adopts ,parallel computing or GPU heterogeneous computing; Calculate the true voltage error of the output optimal population , the optimal population individuals are arranged from small to large according to the true voltage error, and the individuals with a set proportion of the top rankings are used as the dynamic data set, and the dynamic data set is added to the training data set.

7. An electronic device, comprising: a memory for non-transitory storage of computer-readable instructions; as well as a processor for executing said computer-readable instructions, When the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 5 is executed.

8. A storage medium, characterized in that: Non-transitory storage of computer-readable instructions, wherein when the non-transitory computer-readable instructions are executed by a computer, the method according to any one of claims 1 to 5 is performed.

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