Automated circuit modeling method and system for lithium-ion battery electrochemical impedance spectroscopy analysis

Abstract

The application discloses a method and system for automatically modeling a circuit in lithium ion battery electrochemical impedance spectrum analysis, and the method comprises the following steps: 1) obtaining electrochemical impedance spectrum data of a lithium ion battery to be analyzed; 2) deleting data with positive imaginary parts in a Nyquist diagram of the obtained electrochemical impedance spectrum data of the lithium ion battery to be analyzed, and retaining the rest of the impedance spectrum data; and 3) using the retained impedance spectrum data to calculate fitting precision in fitness of an equivalent circuit model, and obtaining a total fitness evaluation result of a single equivalent circuit model in combination with complexity of the equivalent circuit model. The system comprises a data acquisition module, a data processing module and a calculation module. The application can quickly select a circuit model by using a genetic algorithm, and can identify parameters of circuit elements by using a nonlinear least square method.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of lithium ion batteries, and particularly relates to an automatic circuit modeling method and system in lithium ion battery electrochemical impedance spectrum analysis. BACKGROUND

[0002] The electrochemical impedance spectrum analysis can be described by (ordinary or partial or fractional order) differential equations derived from physical and chemical models, but solving these equations is difficult, and the physical and chemical meanings of their elements are still under construction and rarely used. Equivalent circuit models have been widely used due to their simplicity and efficiency. However, selecting a suitable equivalent circuit model and determining its parameters is still a challenge. The traditional method usually selects the equivalent circuit model based on experience, which has great limitations. If the model is not selected properly, it will greatly increase the workload of the parameter identification process, and may even fail to obtain a good fitting effect of the element parameters. The existing method of automatically establishing a model using genetic programming prioritizes the accuracy of the fitting model, but the model is too complex and needs to be simplified. However, the simplification process is very tedious and requires a lot of computing resources. SUMMARY

[0003] The purpose of the present application is to provide an automatic circuit modeling method and system in lithium ion battery electrochemical impedance spectrum analysis, which uses genetic algorithm to quickly select the circuit model and uses nonlinear least squares method to identify the circuit element parameters. The fitness function includes the fitting accuracy and complexity, and there is no need for additional operation 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. The algorithm is simple and fast. The automatic modeling method does not need to rely on existing experience, and can realize modeling only with electrochemical impedance spectrum.

[0004] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0005] The automatic circuit modeling method in lithium ion battery electrochemical impedance spectrum analysis comprises:

[0006] 1) Obtain the electrochemical impedance spectrum data of the lithium ion battery to be analyzed;

[0007] 2) Delete the data with positive imaginary part in the obtained electrochemical impedance spectrum data of the lithium ion battery to be analyzed, and retain the remaining impedance spectrum data;

[0008] 3) Calculate the fitting accuracy part of the fitness of the equivalent circuit model using the retained impedance spectrum data, and obtain the total fitness evaluation result of a single equivalent circuit model in combination with the complexity of the equivalent circuit model.

[0009] The further improvement of the present application is that the positive imaginary part data of the obtained lithium ion battery electrochemical impedance spectrum to be analyzed is deleted, and the remaining impedance spectrum data is retained, including:

[0010] The positive imaginary part data of the obtained lithium ion battery electrochemical impedance spectrum to be analyzed is searched, deleted, and the remaining impedance spectrum data is retained.

[0011] The further improvement of the present application is that the fitting precision part of the equivalent circuit model fitness is calculated by using the retained impedance spectrum data, and the total fitness evaluation result of a single equivalent circuit model is obtained by combining the complexity of the equivalent circuit model, including:

[0012] 301) automatically generating an initial population, each population containing a preset number of individuals, each individual being a binary array, and 0 and 1 in the binary array representing the presence or absence of the corresponding circuit combination, so that an individual corresponds to a circuit model;

[0013] 302) fitness evaluation of each individual in the population; the evaluation is divided into two parts, the first part is precision evaluation, the residual error between the circuit model and the retained impedance spectrum data is calculated by fitting the retained impedance spectrum data through nonlinear least squares method, as precision evaluation; the second part is complexity evaluation, which is calculated according to the number of elements in the circuit model; then the population is selected, crossed, mutated and other operations to obtain a new generation of population;

[0014] 303) after fitness evaluation, the individual with the highest fitness is selected from the new population as the optimal individual of this generation, then the new population is taken as the input of the genetic algorithm to continue step 302), when the population generation is terminated, the optimal individuals of each generation are compared, and the individual with the highest fitness is selected as the final output of the algorithm.

[0015] The further improvement of the present application is that roulette is selected as the method of the selection part in step 302), specifically: the roulette method refers to normalizing the fitness values of the population of a preset number of individuals, and randomly selecting individuals from the population based on the normalized fitness values to perform the subsequent crossing and mutation in step 302).

[0016] The further improvement of the present application is that the crossing operation in step 302) uses single-point crossing, and the crossing point is randomly generated; in the equivalent circuit model, the circuit elements on the left represent the high-frequency part data in the electrochemical impedance spectrum; and the circuit elements on the right represent the low-frequency part data in the electrochemical impedance spectrum; the crossing point is not selected in the first two and the last four, and the crossing point is randomly selected in the remaining selected points for single-point crossing.

[0017] The further improvement of the present application is that the mutation operation in step 302) selects a method of reducing the mutation rate generation by generation; for individuals in the population, the mutation operation is to convert the numbers 0 and 1 into each other; performing mutation changes 1 to 0 and 0 to 1, or keeps the original number unchanged; for the initial population, an initial mutation rate is set, and the mutation rate is linearly reduced for each subsequent population.

[0018] The further improvement of the present application is that a total dictionary is established, and all the individuals that have appeared and their corresponding fitness values are placed in the total dictionary; when the fitness of an individual is evaluated, firstly, it is searched in the total dictionary whether the individual has already existed in the total dictionary; if yes, the stored fitness value is directly called, so that the fitness evaluation is not performed again; otherwise, the fitness of the individual is evaluated, and the individual is stored in the total dictionary.

[0019] The further improvement of the present application is that after the process of selection, crossover and mutation of each population is completed, random individuals in a proportion of the preset population number are randomly generated, and the individuals also ensure that they have not appeared in the total dictionary; the new individuals and the population after selection, crossover and mutation are used 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 random individuals are added is consistent with the preset population individual number.

[0020] An automatic circuit modeling system in lithium ion battery electrochemical impedance spectrum analysis comprises:

[0021] A data acquisition module is configured to acquire lithium ion battery electrochemical impedance spectrum data to be analyzed.

[0022] A data processing module is configured to delete data with positive imaginary parts in the acquired lithium ion battery electrochemical impedance spectrum to be analyzed and retain the rest of the impedance spectrum data.

[0023] A calculation module is configured to calculate the fitting precision part of the fitness of an equivalent circuit model by using the retained impedance spectrum data, and obtain the total fitness evaluation result of a single equivalent circuit model in combination with the complexity of the equivalent circuit model.

[0024] The further improvement of the present application is that the data processing module deletes the data with positive imaginary parts in the acquired lithium ion battery electrochemical impedance spectrum to be analyzed and retains the rest of the impedance spectrum data, which comprises:

[0025] The data points with positive imaginary parts in the acquired lithium ion battery electrochemical impedance spectrum to be analyzed are searched and deleted, and the rest of the impedance spectrum data is retained.

[0026] Compared with the prior art, the present application has at least the following beneficial technical effects:

[0027] 1. The circuit modeling method realizes automation. The traditional modeling method needs to select the circuit elements to be combined into an equivalent circuit model according to experience first, and then parameter identification is carried out. If the circuit model is not reasonable, the parameter identification may consume a lot of time, but still cannot get a good result. The method does not need to select the circuit model artificially, and the algorithm can automatically generate a large number of circuit models and carry out fitness evaluation to automatically select the most suitable equivalent circuit model from a large number of equivalent circuit models.

[0028] 2. The present application takes the fitting accuracy and the circuit complexity as the fitness evaluation standard, which can ensure the fitting accuracy and the circuit complexity. The process evaluates the two standards simultaneously during the algorithm running, and does not need to generate the equivalent circuit model first and then simplify the circuit model, thereby ensuring the simplicity of the algorithm and shortening the time required to obtain the optimal equivalent circuit model.

[0029] 3. The total dictionary stores all the individuals and the corresponding fitness in the algorithm loop, which improves the running speed of the algorithm. During the algorithm running process, due to the operations such as crossover and mutation, the individuals in each generation population may appear in the previous population, and the individuals that have appeared have been evaluated for fitness. If the same process of fitness evaluation is carried out again, only the same result will be obtained, which wastes time and slows down the running speed of the algorithm. Therefore, the total dictionary stores all the individuals and the fitness, and if the same individual appears in the subsequent running process, the fitness evaluation of the corresponding individual in the total dictionary is directly called, which saves the time of fitness evaluation.

[0030] 4. A certain number of random individuals that do not exist in the total dictionary are added in each generation, and the offspring individuals obtained by crossing and mutating the previous generation (parents) form the next generation population, which maintains the diversity of the population, expands the search range of each generation population, prevents the algorithm from converging to a local optimal solution too early, and gradually reduces the number of newly added individuals, which can also avoid slowing down the running speed of the algorithm. Because the population is close to the optimal solution at the later generation number, the search range does not need to be expanded. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0032] Figure 1 Flow chart of automatic circuit modeling method for lithium ion battery electrochemical impedance spectrum analysis.

[0033] Figure 2 Schematic diagram for different element combinations.

[0034] Figure 3 Structure block diagram of the automatic circuit modeling system in lithium ion battery electrochemical impedance spectrum analysis. DETAILED DESCRIPTION

[0035] In the following, only certain exemplary embodiments are simply described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the spirit or scope of the present application. Therefore, the drawings and the description are considered to be exemplary in nature rather than limiting.

[0036] It should be understood that the terms "comprises" and "comprising", when used in this specification and the following claims, indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0037] It should also be understood that the terms used in the present specification and the following claims are merely for the purpose of describing particular embodiments and do not intend to limit the present application. As used in the present specification and the following claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0038] It should be further understood that the term "and / or" used in the present specification and the following claims means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0039] Various structural schematic diagrams according to the disclosed embodiments of the present application are shown in the drawings. These diagrams are not drawn to scale, in which certain details are exaggerated for clarity of expression, and certain details can be omitted. The shapes of various regions, layers shown in the drawings and their relative sizes, positional relationships are only exemplary, and in actuality, there can be deviations due to manufacturing tolerances or technical limitations, and a person skilled in the art can additionally design regions / layers with different shapes, sizes, relative positions according to actual needs.

[0040] The embodiments of the present application are described in detail below in conjunction with the drawings.

[0041] Embodiment 1

[0042] As Figure 1 shown, the automatic circuit modeling method in lithium ion battery electrochemical impedance spectrum analysis provided by the present application comprises:

[0043] 1) Obtain the electrochemical impedance spectroscopy data of the lithium ion battery to be analyzed;

[0044] 2) Delete the data with positive imaginary part in the obtained electrochemical impedance spectroscopy data of the lithium ion battery to be analyzed, and retain the remaining impedance spectroscopy data;

[0045] 3) Calculate the fitting accuracy part of the fitness of the equivalent circuit model using the retained impedance spectroscopy data, and obtain the total fitness evaluation result of the single equivalent circuit model in combination with the complexity of the equivalent circuit model.

[0046] In this embodiment, the data with positive imaginary part in the obtained electrochemical impedance spectroscopy data of the lithium ion battery to be analyzed is deleted, and the remaining impedance spectroscopy data is retained, including:

[0047] Finding the data points with positive imaginary part in the obtained electrochemical impedance spectroscopy data of the lithium ion battery to be analyzed, deleting them, and retaining the remaining impedance spectroscopy data.

[0048] In this embodiment, the fitting accuracy part of the fitness of the equivalent circuit model is calculated using the retained impedance spectroscopy data, and the total fitness evaluation result of the single equivalent circuit model is obtained in combination with the complexity of the equivalent circuit model, including:

[0049] 301) Automatically generate an initial population, each population containing a predetermined number of individuals, each individual being a binary array, and 0 and 1 in the binary array representing the presence or absence of the corresponding circuit combination, so that one individual corresponds to one circuit model;

[0050] 302) Fitness evaluation is performed on each individual in the population; the evaluation is divided into two parts, the first part is accuracy evaluation, the retained impedance spectroscopy data is fitted by nonlinear least squares method to calculate the residual error between the circuit model and the retained impedance spectroscopy data as the accuracy evaluation; the second part is complexity evaluation, which is calculated according to the number of elements in the circuit model; then the population is selected, crossed, mutated, etc. to obtain a new generation of population;

[0051] 303) After fitness evaluation, the individual with the highest fitness is selected from the new population as the optimal individual of this generation, and then the new population is taken as the input of the genetic algorithm to continue step 302), when the population generation terminates, the optimal individuals of each generation are compared, and the individual with the highest fitness is selected as the final output of the algorithm.

[0052] In this embodiment, roulette is selected as the method of the selection part in step 302), specifically: the roulette method refers to normalizing the fitness values of the population of a predetermined number of individuals, and randomly selecting individuals from the population based on the normalized fitness values to perform the subsequent crossover and mutation in step 302).

[0053] In this embodiment, the crossover operation in step 302) uses single-point crossover, while the crossover point is randomly generated. In the equivalent circuit model, the circuit elements closer to the left represent the data of the high-frequency part of the electrochemical impedance spectrum, such as the combination of inductance and resistance; while the circuit elements closer to the right represent the data of the low-frequency part of the electrochemical impedance spectrum, such as the Warburg impedance and various combinations thereof. Therefore, the crossover point in this embodiment does not select the first two bits, i.e., the combination of resistance and inductance, nor does it select the last four bits, i.e., the combination of circuit elements with Warburg impedance. The crossover point is randomly selected from the remaining available bits.

[0054] 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; performing mutation changes 1 to 0 and 0 to 1, otherwise keeps the original number unchanged; for the initial population, set an initial mutation rate, and for each subsequent generation of population, the mutation rate decreases linearly. Since the ohmic resistance in the high-frequency part is always present in any equivalent circuit model, the mutation operation is not performed on the numbers representing the ohmic resistance.

[0055] In this embodiment, a total dictionary is established, in which all individuals that have appeared in the overall method and their corresponding fitness values are placed; when evaluating the fitness of an individual, first search the total dictionary to determine whether the individual already exists in the total dictionary; if it exists, directly call the stored fitness value, so that fitness evaluation is no longer performed; otherwise, evaluate the fitness of the individual and store it in the total dictionary.

[0056] In this embodiment, after the selection, crossover, and mutation processes of each generation of population are completed, randomly generate random individuals that account for a certain proportion of the preset population size, and these individuals also ensure that they have not 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 randomly delete individuals in the new population to ensure that the number of individuals in the new population after adding randomly generated individuals is consistent with the preset population size.

[0057] In this embodiment, the specific encoding method is as follows:

[0058] The first and second bits are encoded as follows: "00" and "01" select the ohmic resistance; "10" selects the inductance and ohmic resistance in series; and "11" selects the inductance and ohmic resistance in parallel, followed by an ohmic resistance in series.

[0059] The third bit is encoded as follows: "0" selects to skip this bit, and "1" selects the ohmic resistance and capacitor in parallel.

[0060] The fourth bit is encoded as follows: "0" selects to skip this bit, and "1" selects the ohmic resistance and capacitor in parallel.

[0061] Bit 5: "0" means to skip this bit, "1" means to select the element combination in row 5, column 2.

[0062] Bit 6: "0" means to skip this bit, "1" means to select the element combination in row 6, column 2.

[0063] Bit 7: "0" means to skip this bit, "1" means to select the element combination in row 7, column 2. Figure 1

[0064] Bit 8: "0" means to skip this bit, "1" means to select the element combination in row 8, column 3. Figure 1

[0065] Bit 9: "0" means to skip this bit, "1" means to select the element combination in row 9, column 3. Figure 1

[0066] Bits 10 and 11: These two bits determine the type of Weber impedance in the next two bits. "01" means to select the Weber impedance with finite diffusion length and with blocking condition; "10" means to select the Weber impedance with infinite diffusion length; "11" means to select the Weber impedance with finite diffusion length and without blocking condition.

[0067] Bits 12 and 13: "00" means to select the element combination in row 1, column 4; "01" means to select the element combination in row 3, column 4; "10" means to select the element combination in row 1, column 3; "11" means to select the element combination in row 2, column 4. Figure 1 Figure 1 Figure 1 Figure 1

[0068] Example 2

[0069] 1. Use a binary array as an individual in the genetic algorithm, each bit number corresponds to Figure 2 one circuit combination in the table, and there are 12 combinations. If the bit number is 0, it means not to select the corresponding circuit combination to form the total circuit, and if the number is 1, it means to select the circuit combination corresponding to the bit number to form the total circuit. Since there are three types of Weber impedance, two additional bit numbers are selected to determine which type of Weber impedance is selected. The three types of Weber impedance are: impedance with infinite diffusion length, impedance with finite diffusion length and without blocking condition, and impedance with finite diffusion length and with blocking condition. In this way, each array, that is, a string of 01, will represent a circuit structure. Each bit number in the individual is randomly generated as 0 or 1.

[0070] ​​​​​​​2. After each individual is randomly generated, a population is formed. Nonlinear least square fitting is performed on each individual in the population, so that the circuit element parameters are identified. The objective function to be minimized As shown in equation (1):

[0071] (1) (1)

[0072] In the equation, 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 fitted circuit model, represents the imaginary part of the original impedance data, represents the imaginary part of the impedance data of the fitted circuit model.

[0073] Subsequently, fitness evaluation is performed on each individual. The fitness function is composed of two parts: accuracy fitness and complexity fitness. The accuracy fitness ensures the fitting effect on the original data, so that the ECM can be considered to successfully represent the original EIS data within a small error range; the complexity fitness ensures that the obtained ECM structure is relatively simple and reasonable, without redundant circuit elements. The total fitness function in the algorithm is obtained from equation (2):

[0074] (2)

[0075] In the equation, A is a fixed constant, k 1 and k 2 are fixed positive coefficients, Res is the initial accuracy fitness, Cpl is the initial complexity. The initial accuracy fitness Res is obtained from equation (3):

[0076] (3)

[0077] The initial complexity fitness Cpl is defined as the number of elements in the circuit combination. The Cpl of a single ohmic resistor is 1, the RC circuit with a resistor and a capacitor in parallel Cpl is 2, and so on. The maximum Cpl in all circuit combinations is 4, that is, composed of 4 circuit elements.

[0078] k 1 and k 2 make the initial accuracy fitness Res and the initial complexity fitness Cpl reach the same order of magnitude, k 1 and kThe value of 2 determines the proportion of precision and complexity in the overall fitness function. If we increase... k A value of 1 increases the proportion of accuracy fitness, meaning the goal is to obtain an ECM with higher fitting accuracy, without requiring high complexity; conversely, increasing... k A value of 2 indicates that, while meeting certain requirements for fitting accuracy, a simpler ECM circuit structure is desired. The fitness function in this paper... The relationship between fitness function value and individual excellence is positive; the larger the fitness function value, the better the individual is and the closer it is to the original EIS data.

[0079] 3. After fitness evaluation, the population is used as input for the genetic algorithm, which is then run. A new offspring population is generated through selection, crossover, and mutation. The selection operation involves choosing individuals from the population according to specific rules to serve as parents for the crossover operation. This paper chooses the roulette wheel method as the selection rule in the identification algorithm. A single-point crossover method is chosen, randomly selecting a crossover point from the available options. The first and last digits are not selected for the crossover. In the mutation operation, the transition between the numbers 0 and 1 corresponds to the transition of a circuit combination in a real circuit from non-existent to present or from present to non-existent. Empirically, the ohmic resistor connected in series in the circuit is fixed; therefore, the number representing the individual ohmic resistor position in each individual does not participate in mutation and remains 1. A mutation algorithm with a progressively decreasing mutation rate is chosen, where the mutation rate of each generation decreases as the number of generations increases.

[0080] 4. After generating a new generation of population, the fitness of all individuals in the population is evaluated using a fitness function, and the individual with the highest fitness is selected and retained in the optimal fitness list. When the population reaches its maximum generation count, the algorithm terminates. At this point, the individual with the highest fitness in the optimal fitness list is output, indicating that the equivalent circuit model and circuit element parameters corresponding to this individual can fit the input electrochemical impedance spectroscopy data well.

[0081] A general dictionary is established to store all individuals that have appeared in the algorithm loop and their corresponding fitness. When evaluating the fitness of an individual in the new generation of the population, the individual is searched in the general dictionary. If it has appeared before, the corresponding fitness evaluation in the general dictionary is directly called without fitting. Otherwise, fitting is performed to obtain the fitness evaluation, and the individual and its corresponding fitness are added to the general dictionary.

[0082] Each generation adds a certain number of random individuals not found in the total dictionary, which, together with the offspring obtained through crossover mutation of the previous generation (parents), form the next generation population. The first generation selects individuals that increase the population size by 10%, and the number added in each subsequent generation decreases linearly with the number of generations.

[0083] Example 3

[0084] As Figure 2 shown, the application provides an automatic circuit modeling system in lithium ion battery electrochemical impedance spectrum analysis, comprising:

[0085] A data acquisition module is configured to acquire lithium ion battery electrochemical impedance spectrum data to be analyzed.

[0086] A data processing module is configured to delete data with positive imaginary parts in the acquired lithium ion battery electrochemical impedance spectrum to be analyzed, and retain the rest of the impedance spectrum data.

[0087] A calculation module is configured to calculate the fitting accuracy part of the equivalent circuit model fitness by using the retained impedance spectrum data, and obtain the total fitness evaluation result of a single equivalent circuit model in combination with the complexity of the equivalent circuit model.

[0088] In the embodiment, the data processing module deletes data with positive imaginary parts in the acquired lithium ion battery electrochemical impedance spectrum to be analyzed, and retains the rest of the impedance spectrum data, including:

[0089] Finding data points with positive imaginary parts in the acquired lithium ion battery electrochemical impedance spectrum to be analyzed, deleting the data points, and retaining the rest of the impedance spectrum data.

[0090] The above shows and describes the basic principles and main features of the application and the advantages of the application. It is obvious for those skilled in the art that the application is not limited to the details of the above exemplary embodiments, and the application can be implemented in other specific forms without departing from the spirit or essential characteristics of the application. Therefore, the embodiments should be regarded as exemplary and non-limiting in any respect, and the scope of the application is defined by the appended claims rather than the above description, and it is intended to include all changes falling within the meaning and scope of the equivalent elements of the claims. Any reference signs in the claims should not be regarded as limiting the claims involved.

[0091] In addition, it should be understood that although the present specification is described in terms of embodiments, each embodiment does not contain only one independent technical solution, and the description manner of the specification is only for clarity, and those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be properly combined to form other embodiments that can be understood by those skilled in the art. The above is only to illustrate the technical idea of the application, and cannot limit the protection scope of the application, and any modification made according to the technical idea of the application on the basis of the technical solution falls within the protection scope of the claims of the application.

Claims

1. A method for automated circuit modeling in lithium-ion battery electrochemical impedance spectroscopy analysis, characterized in that, Comprise: 1) Obtain the lithium ion battery electrochemical impedance spectrum data to be analyzed; 2) Delete the positive imaginary part data of the obtained lithium ion battery electrochemical impedance spectrum to be analyzed, and retain the remaining impedance spectrum data; 3) Calculate the fitting accuracy part of the equivalent circuit model fitness by using the retained impedance spectrum data, and obtain the total fitness evaluation result of a single equivalent circuit model combined 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 0 and 1 in the binary array represent the presence or absence of the corresponding circuit combination, so an individual corresponds to a circuit model; 302) Evaluate the fitness of each individual in the population; The evaluation is divided into two parts, the first part is the accuracy evaluation, the residual error between the circuit model and the retained impedance spectrum data is calculated by fitting the retained impedance spectrum data through the nonlinear least squares method, which is used as the accuracy evaluation; The second part is the complexity evaluation, which is calculated according to the number of elements in the circuit model; Then select, cross and mutate the population to get a new generation of population; 303) After fitness evaluation, select the individual with the highest fitness from the new population as the optimal individual of this generation, then continue to execute step 302) with the new population as the input of the genetic algorithm, when the population generation terminates, compare the optimal individuals of each generation and select the individual with the highest fitness as the final output of the algorithm.

2. The method of claim 1, wherein the method is used for automatic circuit modeling in lithium-ion battery electrochemical impedance spectroscopy analysis. Delete the positive imaginary part data of the obtained lithium ion battery electrochemical impedance spectrum to be analyzed, and retain the remaining impedance spectrum data, including: Find the data points with positive imaginary part in the obtained lithium ion battery electrochemical impedance spectrum to be analyzed, delete them, and retain the remaining impedance spectrum data.

3. The method of claim 1, wherein the method is used for automatic circuit modeling in lithium-ion battery electrochemical impedance spectroscopy analysis. Select roulette as the method of step 302) selection, specifically: the roulette method refers to normalizing the fitness values of the population of a preset number of individuals, and randomly selecting individuals from the population based on the normalized fitness values to perform the subsequent crossover and mutation in step 302).

4. The method of claim 1, wherein the method is used for automatic circuit modeling in lithium-ion battery electrochemical impedance spectroscopy analysis. The crossover operation in step 302) uses 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; And the circuit elements on the right represent the data of the low frequency part of the electrochemical impedance spectrum; The first two and the last four are not selected as the crossover point, and the remaining selected points are randomly selected as the crossover point for single-point crossover.

5. The method of claim 1, wherein the method is used for automatic circuit modeling in lithium-ion battery electrochemical impedance spectroscopy analysis. The mutation operation in step 302) selects the method of decreasing the mutation rate generation by generation; For individuals in the population, the mutation operation is to convert the numbers 0 and 1 to each other; Perform mutation to change 1 to 0 and 0 to 1, otherwise keep the original number unchanged; For the initial population, set an initial mutation rate, and the mutation rate decreases linearly for each subsequent generation of population.

6. The method of claim 1, wherein the method is used for automatic circuit modeling in lithium-ion battery electrochemical impedance spectroscopy analysis. A total dictionary is established, and all the individuals and their corresponding fitness values are placed in the total dictionary; when the fitness of an individual is evaluated, firstly, it is checked whether the individual exists in the total dictionary; if it exists, the stored fitness value is directly called, so that the fitness evaluation is not performed again; otherwise, the individual is evaluated, and is stored in the total dictionary.

7. The method of claim 1, wherein the method is used for automatic circuit modeling in lithium-ion battery electrochemical impedance spectroscopy analysis. After the selection, crossover and mutation of each population are completed, random individuals are generated, which account for a preset proportion of the population number, and the individuals are also ensured to not exist in the total dictionary; the new individuals and the population after the selection, crossover and mutation are combined 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 addition of the randomly generated individuals is consistent with the preset population number.

8. An automated circuit modeling system in lithium-ion battery electrochemical impedance spectroscopy analysis, characterized in that, Comprise: The data acquisition module is used for acquiring the lithium ion battery electrochemical impedance spectrum data to be analyzed; The data processing module is used for deleting the data with positive imaginary part in the acquired lithium ion battery electrochemical impedance spectrum to be analyzed, and retaining the remaining impedance spectrum data; The calculation module is used for calculating the fitting precision part of the fitness of the equivalent circuit model by using the retained impedance spectrum data, and obtaining the total fitness evaluation result of a single equivalent circuit model in combination with the complexity of the equivalent circuit model, comprising: 301) automatically generating an initial population, each population containing a preset number of individuals, each individual being a binary array, and 0 and 1 in the binary array representing the presence or absence of the corresponding circuit combination, so that one individual corresponds to one circuit model; 302) evaluating the fitness of each individual in the population; the evaluation is divided into two parts, the first part is precision evaluation, the residual error between the circuit model and the retained impedance spectrum data is calculated by fitting the retained impedance spectrum data through the nonlinear least square method, and is taken as the precision evaluation; the second part is complexity evaluation, which is calculated according to the number of elements in the circuit model; then the population is selected, crossed and mutated to obtain a new generation of population; 303) after the fitness evaluation, the individual with the highest fitness is selected from the new population as the optimal individual of this generation, and then the new population is taken as the input of the genetic algorithm to continue step 302); when the population generation is terminated, the optimal individuals of each generation are compared, and the individual with the highest fitness is selected as the final output of the algorithm.

9. The system for automated circuit modeling in lithium-ion battery electrochemical impedance spectroscopy analysis of claim 8, wherein, In the data processing module, the data with positive imaginary part in the acquired lithium ion battery electrochemical impedance spectrum to be analyzed is deleted, and the remaining impedance spectrum data is retained, comprising: Finding the data points with positive imaginary part in the acquired lithium ion battery electrochemical impedance spectrum to be analyzed, deleting the data points, and retaining the remaining impedance spectrum data.

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