Automatic circuit modeling method and system in lithium ion battery electrochemical impedance spectroscopy analysis

Abstract

The invention discloses an automatic circuit modeling method and system in electrochemical impedance spectroscopy analysis of a lithium ion battery. The method comprises the following steps: 1) acquiring electrochemical impedance spectroscopy data of the lithium ion battery to be analyzed; (2) deleting data with positive imaginary parts in the obtained electrochemical impedance spectroscopy Nyquist diagram of the lithium ion battery to be analyzed, and reserving other impedance spectroscopy data; and 3) calculating a fitting precision part in the fitness of the equivalent circuit model by using the retained impedance spectrum data, and obtaining a total fitness evaluation result of the single equivalent circuit model by combining the complexity of the equivalent circuit model. The system comprises a data acquisition module, a data processing module and a calculation module. According to the method, the circuit model can be quickly selected by using the genetic algorithm, and the circuit element parameter identification is performed by using the nonlinear least square method.

Description

Technical Field

[0001] The present invention belongs to the technical field of lithium-ion batteries, and in particular relates to an automated circuit modeling method and system in electrochemical impedance spectrum analysis of lithium-ion batteries. Background Art

[0002] Electrochemical impedance spectroscopy analysis can be described by (ordinary or partial or fractional) differential equations derived from physical and chemical models, but these equations are difficult to solve, and the physicochemical meaning of their components is still under construction, so they are rarely used. Equivalent circuit models have been widely used due to their simplicity and efficiency. However, it is still a challenge to select a suitable equivalent circuit model and determine its parameters. Traditional methods usually select equivalent circuit models based on experience. This method has great limitations. If the model is not selected appropriately, it will greatly increase the workload of the parameter identification process, and may even fail to obtain component parameters with good fitting effects. The existing method of automatically building models using genetic programming methods gives priority to ensuring the accuracy of the fitting model, but its model is too complex and needs to be simplified. However, the simplification process is very cumbersome and requires a lot of computing resources. Summary of the invention

[0003] The purpose of the present invention is to provide an automated circuit modeling method and system for the electrochemical impedance spectrum analysis of lithium-ion batteries, using a genetic algorithm to quickly select a circuit model, and using a nonlinear least squares method to identify circuit element parameters. The fitness function includes fitting accuracy and complexity, and no additional operation is required to simplify the circuit. It can automatically identify the best equivalent circuit model and corresponding circuit element parameters from the given lithium-ion battery electrochemical impedance spectrum data, and the algorithm is simple and runs fast. The automated modeling method does not need to rely on existing experience, and modeling can be achieved with only an electrochemical impedance spectrum.

[0004] In order to achieve the above object, the present invention adopts the following technical solution: Automated circuit modeling methods for electrochemical impedance spectroscopy analysis of lithium-ion batteries, including: 1) Obtain electrochemical impedance spectroscopy data of lithium-ion batteries to be analyzed; 2) Delete the data with positive imaginary part in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed, and keep the remaining impedance spectrum data; 3) The retained impedance spectrum data is used to calculate the fitting accuracy part of the fitness of the equivalent circuit model, and the overall fitness evaluation result of a single equivalent circuit model is obtained by combining the complexity of the equivalent circuit model.

[0005] A further improvement of the present invention is to delete the data with positive imaginary part in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed, and retain the remaining impedance spectrum data, including: Find the data points with positive imaginary parts in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed, delete them, and retain the remaining impedance spectrum data.

[0006] A further improvement of the present invention is that the retained impedance spectrum data is used to calculate the fitting accuracy part of the fitness of the equivalent circuit model, and the overall fitness evaluation result of a single equivalent circuit model is obtained in combination with the complexity of the equivalent circuit model, including: 301) Automatically generate an initial population, each population contains a preset number of individuals, each individual is a binary array, and the 0 and 1 in the binary array represent the presence or absence of the corresponding circuit combination, so one individual corresponds to one circuit model; 302) Perform fitness evaluation on each individual in the population; the evaluation is divided into two parts. The first part is the accuracy evaluation, which uses the nonlinear least squares method to fit the retained impedance spectrum data to calculate the residual between the circuit model and the retained impedance spectrum data as the accuracy evaluation; the second part is the complexity evaluation, which is calculated according to the number of components in the circuit model; then the population is subjected to selection, crossover, mutation and other operations to obtain a new generation of population; 303) After fitness evaluation, the individual with the highest fitness is selected from the new population as the best individual of this generation, and then the new population is used as the input of the genetic algorithm to continue to execute step 302). When the population generation is terminated, the best individuals of each generation are compared and the individual with the highest fitness is selected as the final output of the algorithm.

[0007] A further improvement of the present invention is that roulette is selected as the method for selecting the part in step 302), specifically: the roulette method refers to normalizing the fitness values ​​of a population of a preset number of individuals, and based on the normalized fitness values, randomly selecting individuals from the population to perform subsequent crossover and mutation in step 302).

[0008] A further improvement of the present invention is that the crossover operation in step 302) uses a single-point crossover, and the crossover point is randomly generated; in the equivalent circuit model, the circuit element on the left represents the high-frequency data of the electrochemical impedance spectrum; and the circuit element on the right represents the low-frequency data of the electrochemical impedance spectrum; the crossover point does not select the first two digits and the last four digits, and the crossover point is randomly selected at the remaining selected points to perform a single-point crossover.

[0009] A further improvement of the present invention is that, in the mutation operation in step 302), a method of decreasing the mutation rate from generation to generation is selected; for individuals in the population, the mutation operation is to convert the numbers 0 and 1 to each other; when performing mutation, 1 is replaced by 0, and 0 is replaced by 1, otherwise the original number remains unchanged; for the initial population, an initial mutation rate is set, and the mutation rate decreases linearly in each subsequent generation of the population.

[0010] A further improvement of the present invention is to establish a general dictionary, in which all individuals that have appeared and their corresponding fitness values ​​are placed; when evaluating the fitness of an individual, first search the general dictionary to see if the individual already exists; if so, directly call its stored fitness value, and no longer perform fitness evaluation; otherwise, perform fitness evaluation on the individual and store it in the general dictionary.

[0011] A further improvement of the present invention is that after each generation of population completes the process of selection, crossover and mutation, random individuals accounting for a set proportion of the preset population number are randomly generated, and these individuals are also guaranteed not to have appeared in the total dictionary; these newly added individuals are used together with the population that has completed selection, crossover and mutation to generate a new population, and then the individuals in the new population are randomly deleted to ensure that the number of individuals in the new population after the randomly generated individuals are added is consistent with the number of individuals in the preset population.

[0012] Automated circuit modeling system for electrochemical impedance spectroscopy analysis of lithium-ion batteries, including: A data acquisition module, used to acquire electrochemical impedance spectroscopy data of lithium-ion batteries to be analyzed; A data processing module, used for deleting the data with positive imaginary part in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed, and retaining the remaining impedance spectrum data; The calculation module is used to calculate the fitting accuracy part of the fitness of the equivalent circuit model using the retained impedance spectrum data, and obtain the overall fitness evaluation result of a single equivalent circuit model in combination with the complexity of the equivalent circuit model.

[0013] A further improvement of the present invention is that, in the data processing module, the data with positive imaginary part in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed is deleted, and the remaining impedance spectrum data is retained, including: Find the data points with positive imaginary parts in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed, delete them, and retain the remaining impedance spectrum data.

[0014] Compared with the prior art, the present invention has at least the following beneficial technical effects: 1. The circuit modeling method is automated. The traditional modeling method requires first selecting which circuit elements to combine into an equivalent circuit model based on experience, and then performing parameter identification. If the circuit model is not selected reasonably, parameter identification may take a lot of time, but still cannot get a good result. This method does not require manual selection of circuit models. The algorithm will automatically generate a large number of circuit models and perform fitness evaluation, and automatically select the most suitable equivalent circuit model from the massive equivalent circuit models.

[0015] 2. The present invention uses fitting accuracy and circuit complexity as fitness evaluation criteria, which can not only ensure the accuracy of fitting, but also ensure that the circuit does not have too many complex components. This process evaluates the two criteria simultaneously during the operation of the algorithm, and does not require the generation of an equivalent circuit model first and then the simplification of the circuit model, thereby ensuring the simplicity of the algorithm and shortening the time required to obtain the optimal equivalent circuit model.

[0016] 3. The total dictionary stores all individuals and corresponding fitness that have appeared in the algorithm cycle, which improves the running speed of the algorithm. During the operation of the algorithm, due to operations such as crossover and mutation, individuals in each generation of the population may appear in the population before, and the fitness of the individuals that have appeared has been evaluated. If the fitness evaluation of the same process is performed again, only the same result will be obtained, which wastes time and slows down the operation speed of the algorithm. Therefore, the total dictionary stores all individuals and fitness that have appeared. If duplicate individuals appear in the subsequent operation, the fitness evaluation of the corresponding individuals in the total dictionary is directly called, saving the time of fitness evaluation.

[0017] 4. Each generation adds a certain number of random individuals that do not exist in the total dictionary, and together with the offspring individuals obtained by crossover mutation of the previous generation (parents), they form the next generation population, maintaining the diversity of the population, so that each generation of the population can expand the search range, preventing the algorithm from converging to the local optimal solution too early. At the same time, the number of newly added individuals gradually decreases, which can also avoid slowing down the algorithm's running speed. Because in the later generations, the population is close to the optimal solution, and there is no need to expand the search range. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 Flowchart of the automated circuit modeling method for electrochemical impedance spectroscopy analysis of lithium-ion batteries.

[0020] Figure 2 Schematic diagram of different component combinations.

[0021] Figure 3 The present invention is a structural block diagram of the automatic circuit modeling system in the electrochemical impedance spectroscopy analysis of lithium-ion batteries. DETAILED DESCRIPTION

[0022] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0023] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0024] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0025] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0026] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0027] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0028] Example 1 like Figure 1 As shown, the automated circuit modeling method for electrochemical impedance spectroscopy analysis of lithium-ion batteries provided by the present invention comprises: 1) Obtain electrochemical impedance spectroscopy data of lithium-ion batteries to be analyzed; 2) Delete the data with positive imaginary part in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed, and keep the remaining impedance spectrum data; 3) The retained impedance spectrum data is used to calculate the fitting accuracy part of the fitness of the equivalent circuit model, and the overall fitness evaluation result of a single equivalent circuit model is obtained by combining the complexity of the equivalent circuit model.

[0029] In this embodiment, the data with positive imaginary part in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed is deleted, and the remaining impedance spectrum data is retained, including: Find the data points with positive imaginary parts in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed, delete them, and retain the remaining impedance spectrum data.

[0030] In this embodiment, the retained impedance spectrum data is used to calculate the fitting accuracy part of the equivalent circuit model fitness, and the overall fitness evaluation result of a single equivalent circuit model is obtained in combination with the complexity of the equivalent circuit model, including: 301) Automatically generate an initial population, each population contains a preset number of individuals, each individual is a binary array, and the 0 and 1 in the binary array represent the presence or absence of the corresponding circuit combination, so one individual corresponds to one circuit model; 302) Perform fitness evaluation on each individual in the population; the evaluation is divided into two parts. The first part is the accuracy evaluation, which uses the nonlinear least squares method to fit the retained impedance spectrum data to calculate the residual between the circuit model and the retained impedance spectrum data as the accuracy evaluation; the second part is the complexity evaluation, which is calculated according to the number of components in the circuit model; then the population is subjected to selection, crossover, mutation and other operations to obtain a new generation of population; 303) After fitness evaluation, the individual with the highest fitness is selected from the new population as the best individual of this generation, and then the new population is used as the input of the genetic algorithm to continue to execute step 302). When the population generation is terminated, the best individuals of each generation are compared and the individual with the highest fitness is selected as the final output of the algorithm.

[0031] In this embodiment, roulette is selected as the method for selecting part in step 302), specifically: the roulette method refers to normalizing the fitness values ​​of a population of a preset number of individuals, and based on the normalized fitness values, randomly selecting individuals from the population to perform subsequent crossover and mutation in step 302).

[0032] In this embodiment, the crossover operation in step 302) uses a single-point crossover, and the crossover point is randomly generated. In the equivalent circuit model, the circuit elements on the left represent the data of the high-frequency part of the electrochemical impedance spectrum, such as the combination of inductance and resistance; and the circuit elements on the right represent the data of the low-frequency part of the electrochemical impedance spectrum, such as Weber impedance and its various combinations. Therefore, the crossover point in this article does not select the first two digits, that is, the combination of resistance and inductance, nor the last four digits, that is, the combination of circuit elements with Weber impedance. The crossover point is randomly selected at the remaining selectable points for single-point crossover.

[0033] In this embodiment, the mutation operation in step 302) selects a method in which the mutation rate decreases from generation to generation; for individuals in the population, the mutation operation is to convert the numbers 0 and 1 to each other; when performing mutation, 1 is replaced by 0, and 0 is replaced by 1, otherwise the original number remains unchanged; for the initial population, an initial mutation rate is set, and the mutation rate decreases linearly in each subsequent generation of the population. Since the ohmic resistance of the high-frequency part must exist in any equivalent circuit model, the mutation operation is not performed on the number representing the ohmic resistance.

[0034] In this embodiment, a general dictionary is established, and all individuals that have appeared in the overall method and their corresponding fitness values ​​are placed in the general dictionary; when evaluating the fitness of an individual, the general dictionary is first searched to see whether the individual already exists in the general dictionary; if it exists, its stored fitness value is directly called, and no fitness evaluation is performed; otherwise, the fitness of the individual is evaluated and stored in the general dictionary.

[0035] In this embodiment, after each generation of population completes the selection, crossover, and mutation process, random individuals accounting for a set proportion of the preset population number are randomly generated, and these individuals are also guaranteed not to have appeared in the total dictionary. These newly added individuals will generate a new population together with the population that has completed selection, crossover, and mutation, and then the individuals in the new population will be randomly deleted to ensure that the number of individuals in the new population after adding the randomly generated individuals is consistent with the number of individuals in the preset population.

[0036] In this embodiment, the specific encoding method is: The first and second digit codes: "00" and "01" select an ohmic resistor; "10" selects an inductor and an ohmic resistor in series; "11" selects an inductor and an ohmic resistor in parallel, and then an ohmic resistor in series.

[0037] The third bit code: "0" selects to skip this bit, "1" selects to connect the ohmic resistor in parallel with the capacitor.

[0038] The 4th bit code: "0" selects to skip this bit, and "1" selects the ohmic resistor to be connected in parallel with the capacitor.

[0039] The fifth bit code: "0" selects to skip this bit, and "1" selects to connect the ohmic resistor in parallel with the constant phase element.

[0040] The sixth bit code: "0" selects to skip this bit, and "1" selects to connect an ohmic resistor in parallel with the constant phase element.

[0041] The 7th bit code: "0" selects to skip this bit, "1" selects Figure 1 The combination of components in the 3rd row and 2nd column.

[0042] The 8th bit code: "0" selects to skip this bit, "1" selects Figure 1 The combination of components in the 2nd row and 3rd column.

[0043] The 9th bit code: "0" selects to skip this bit, "1" selects Figure 1 The combination of components in the 3rd row and 3rd column.

[0044] The 10th and 11th digits determine the type of Weber impedance in the following 2 digits. "01" selects the Weber impedance with finite diffusion length and blocking conditions; "10" selects the Weber impedance with infinite diffusion length; "11" selects the Weber impedance with finite diffusion length and non-blocking conditions.

[0045] The 12th and 13th digits are coded as follows: "00" Figure 1 The combination of components in the first row and the fourth column; "01" selects Figure 1 The combination of components in the 3rd row and 4th column; "10" selects Figure 1 The combination of components in the first row and third column; "11" selects Figure 1 The combination of components in the 2nd row and 4th column.

[0046] Example 2 1. Use a binary array as an individual in the genetic algorithm, and each digit corresponds to Figure 2 There are 12 combinations of circuits in total. If this digit is 0, it means that the corresponding circuit combination is not selected to form the total circuit, and if the digit is 1, it means that the circuit combination corresponding to this digit is selected to form the total circuit. Since there are three types of Weber impedance, two additional digits are selected to determine which Weber impedance is selected. The three types of Weber impedance are as follows: impedance under infinite diffusion length, impedance under finite diffusion length and non-blocking conditions, and impedance under finite diffusion length and blocking conditions. In this way, each array, that is, a string of 01, will represent a circuit structure. Each digit in the individual is randomly generated as 0 or 1.

[0047] 2. After randomly generating each individual, a population is formed. Nonlinear least squares fitting is performed on each individual in the population to identify the circuit component parameters. The objective function to be minimized is As shown in formula (1): (1) (1) In the formula, N Represents the number of frequency points, represents the real part of the original impedance data, represents the real part of the impedance data of the circuit model obtained by fitting, represents the imaginary part of the original impedance data, Represents the imaginary part of the circuit model impedance data obtained by fitting.

[0048] Subsequently, the fitness of each individual is evaluated. The fitness function consists of two parts: precision fitness and complexity fitness. Precision fitness ensures the fitting effect of the original data, so that the ECM can be considered to successfully represent the original EIS data within a small error range; complexity fitness ensures that the obtained ECM structure is relatively simple and reasonable, without redundant circuit elements. The total fitness function in the algorithm , obtained from formula (2): (2) In the formula, A is a fixed constant, k 1 and k 2 is a fixed positive coefficient, Res is the initial accuracy fitness, Cpl is the initial complexity. Initial accuracy fitness Res From formula (3), we get: (3) Initial complexity fitness Cpl Defined as the number of components in a circuit combination. Cpl =1, RC circuit with resistor and capacitor in parallel Cpl is 2, and so on. Among all circuit combinations, the largest Cpl It is 4, that is, it is composed of 4 circuit elements.

[0049] k 1 and k 2 Make the initial accuracy fit Res Fitness with initial complexity Cpl To the same order of magnitude, k 1 and k The size of 2 determines the proportion of accuracy and complexity in the total fitness function. k If the value of 1 is increased, the proportion of precision fitness is increased, that is, we hope to obtain an ECM with higher fitting accuracy without too high requirements on complexity; on the contrary, if we increase k If the value is 2, it means that when the fitting accuracy meets certain requirements, we hope to obtain an ECM with a simpler circuit structure. The relationship between the fitness function and the individual's excellence is positively correlated. The larger the fitness function value, the better the individual is and the closer it is to the original EIS data.

[0050] 3. After fitness evaluation, the population is used as the input of the genetic algorithm to run the genetic algorithm. A new offspring population is generated through selection, crossover, and mutation. The selection operation is to select individuals from the population according to specific rules as the parents of the crossover operation. This paper selects the roulette method as the selection operation rule in the identification algorithm. Select the single-point crossover method and randomly select one of the selectable points as the crossover point. The crossover point does not select the first and last digits. In the mutation operation, the conversion between the numbers 0 and 1 corresponds to the conversion of a circuit combination from nothing to something or from something to nothing in the actual circuit. According to experience, the ohmic resistor connected in series in the circuit is fixed, so the number representing the position of the individual ohmic resistor in each individual does not participate in the mutation and always remains 1. Select a mutation algorithm with a decreasing mutation rate from generation to generation, and the mutation rate of each generation of the population decreases with the increase of the number of generations.

[0051] 4. After a new generation of population is generated, the fitness of all individuals in the population is evaluated through the fitness function, and the individuals with the highest fitness are selected and retained in the best fitness list. When the population generation number reaches the maximum value, the algorithm terminates. At this time, the individual with the highest fitness in the best fitness list is output, and it is believed that the equivalent circuit model and circuit element parameters corresponding to this individual can better fit the input electrochemical impedance spectroscopy data.

[0052] Establish a general dictionary to store all individuals and their corresponding fitness that have appeared in the algorithm cycle. When evaluating the fitness of individuals in the new generation population, look for this individual in the general dictionary. If it has appeared before, directly call the corresponding fitness evaluation in the general dictionary without fitting. Otherwise, fit to get the fitness evaluation, and add the individual and its corresponding fitness to the general dictionary.

[0053] Each generation adds a certain number of random individuals that do not exist in the total dictionary, and together with the offspring individuals obtained by crossover mutation in the previous generation (parents), they form the next generation population. In the first generation, individuals that increase the population by 10% are selected, and the number of individuals added in each generation decreases linearly with the number of generations.

[0054] Example 3 like Figure 2 As shown, the automatic circuit modeling system for electrochemical impedance spectroscopy analysis of lithium-ion batteries provided by the present invention comprises: A data acquisition module, used to acquire electrochemical impedance spectroscopy data of lithium-ion batteries to be analyzed; A data processing module, used for deleting the data with positive imaginary part in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed, and retaining the remaining impedance spectrum data; The calculation module is used to calculate the fitting accuracy part of the fitness of the equivalent circuit model using the retained impedance spectrum data, and obtain the overall fitness evaluation result of a single equivalent circuit model in combination with the complexity of the equivalent circuit model.

[0055] In this embodiment, in the data processing module, the data with positive imaginary part in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed is deleted, and the remaining impedance spectrum data is retained, including: Find the data points with positive imaginary parts in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed, delete them, and retain the remaining impedance spectrum data.

[0056] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0057] In addition, it should be understood that although this specification is described in accordance with the implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art. The above content is only to illustrate the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. An automated circuit modeling method for electrochemical impedance spectroscopy analysis of lithium-ion batteries, characterized in that: include: 1) Obtain electrochemical impedance spectroscopy data of lithium-ion batteries to be analyzed; 2) Delete the data with positive imaginary part in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed, and keep the remaining impedance spectrum data; 3) The retained impedance spectrum data is used to calculate the fitting accuracy part of the fitness of the equivalent circuit model, and the overall fitness evaluation result of a single equivalent circuit model is obtained by combining the complexity of the equivalent circuit model.

2. The method for automated circuit modeling in electrochemical impedance spectroscopy analysis of lithium-ion batteries according to claim 1, characterized in that: Delete the data with positive imaginary part in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed, and keep the remaining impedance spectrum data, including: Find the data points with positive imaginary parts in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed, delete them, and retain the remaining impedance spectrum data.

3. The automated circuit modeling method in electrochemical impedance spectroscopy analysis of a lithium-ion battery according to claim 1, characterized in that: The retained impedance spectrum data is used to calculate the fitting accuracy part of the equivalent circuit model fitness. Combined with the complexity of the equivalent circuit model, the overall fitness evaluation result of a single equivalent circuit model is obtained, including: 301) Automatically generate an initial population, each population contains a preset number of individuals, each individual is a binary array, and the 0 and 1 in the binary array represent the presence or absence of the corresponding circuit combination, so one individual corresponds to one circuit model; 302) Perform fitness evaluation on each individual in the population; the evaluation is divided into two parts. The first part is the accuracy evaluation, which uses the nonlinear least squares method to fit the retained impedance spectrum data to calculate the residual between the circuit model and the retained impedance spectrum data as the accuracy evaluation; the second part is the complexity evaluation, which is calculated according to the number of components in the circuit model; then the population is subjected to selection, crossover, mutation and other operations to obtain a new generation of population; 303) After fitness evaluation, the individual with the highest fitness is selected from the new population as the best individual of this generation, and then the new population is used as the input of the genetic algorithm to continue to execute step 302). When the population generation is terminated, the best individuals of each generation are compared and the individual with the highest fitness is selected as the final output of the algorithm.

4. The method for automated circuit modeling in electrochemical impedance spectroscopy analysis of lithium-ion batteries according to claim 3, characterized in that: Roulette is selected as the method for selecting the part in step 302), specifically: the roulette method refers to normalizing the fitness values ​​of a population of a preset number of individuals, and based on the normalized fitness values, randomly selecting individuals from the population to perform subsequent crossover and mutation in step 302).

5. The method for automated circuit modeling in electrochemical impedance spectroscopy analysis of lithium-ion batteries according to claim 3, characterized in that: The crossover operation in step 302) uses a single-point crossover, and the crossover point is randomly generated; in the equivalent circuit model, the circuit elements on the left usually represent the data of the high-frequency part of the electrochemical impedance spectrum; and the circuit elements on the right usually represent the data of the low-frequency part of the electrochemical impedance spectrum; the crossover point does not select the first two digits and the last four digits, and the crossover point is randomly selected at the remaining selected points to perform a single-point crossover.

6. The method for automated circuit modeling in electrochemical impedance spectroscopy analysis of lithium-ion batteries according to claim 3, characterized in that: The mutation operation in step 302) selects a method in which the mutation rate decreases from generation to generation; for individuals in the population, the mutation operation is to convert the numbers 0 and 1 to each other; when performing mutation, 1 is replaced by 0, and 0 is replaced by 1, otherwise the original number remains unchanged; for the initial population, an initial mutation rate is set, and the mutation rate decreases linearly in each subsequent generation of the population.

7. The method for automated circuit modeling in electrochemical impedance spectroscopy analysis of lithium-ion batteries according to claim 3, characterized in that: Establish a general dictionary and put all the individuals that have appeared and their corresponding fitness values ​​in the general dictionary; when evaluating the fitness of an individual, first search the general dictionary to see if the individual already exists in the general dictionary; if it does, directly call its stored fitness value, and no longer perform fitness evaluation; otherwise, evaluate the fitness of the individual and store it in the general dictionary.

8. The method for automated circuit modeling in electrochemical impedance spectroscopy analysis of lithium-ion batteries according to claim 3, characterized in that: After each generation of population completes the process of selection, crossover, and mutation, random individuals accounting for a set proportion of the preset population size are randomly generated. These individuals are also guaranteed to have never appeared in the total dictionary; these newly added individuals are combined with the population that has completed selection, crossover, and mutation to generate a new population, and then individuals in the new population are randomly deleted to ensure that the number of individuals in the new population after the randomly generated individuals are added is consistent with the number of individuals in the preset population.

9. An automated circuit modeling system for electrochemical impedance spectroscopy analysis of lithium-ion batteries, characterized in that: include: A data acquisition module, used to acquire electrochemical impedance spectroscopy data of the lithium-ion battery to be analyzed; A data processing module, used for deleting the data with positive imaginary part in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed, and retaining the remaining impedance spectrum data; The calculation module is used to calculate the fitting accuracy part of the fitness of the equivalent circuit model using the retained impedance spectrum data, and obtain the overall fitness evaluation result of a single equivalent circuit model in combination with the complexity of the equivalent circuit model.

10. The automated circuit modeling system for electrochemical impedance spectroscopy analysis of lithium-ion batteries according to claim 9, characterized in that: In the data processing module, the data with positive imaginary part in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed is deleted, and the remaining impedance spectrum data is retained, including: Find the data points with positive imaginary parts in the electrochemical impedance spectrum of the lithium-ion battery to be analyzed, delete them, and retain the remaining impedance spectrum data.

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