Method, device and equipment for determining state of charge of lithium battery

Abstract

The invention discloses a method, a device and equipment for determining the state of charge of a lithium battery, and relates to the technical field of battery modeling. Based on a lithium battery relaxation time distribution theory, the method comprises the following steps: firstly, accurately obtaining a relaxation time distribution parameter and a distribution function of the lithium battery by using a genetic optimization algorithm; wherein the genetic algorithm takes the minimum error index and smooth index as an optimization target, considers optimization of regularization parameters and primary function width at the same time, and determines lithium battery relaxation time distribution based on the optimal regularization parameters and primary functions; two indexes are used to respectively avoid the problems of unsmooth false peaks caused by over-fitting and too large errors caused by under-fitting, so that the obtained relaxation time distribution result has characteristic peaks and does not have the false peaks, and then a characteristic peak parameter rapid extraction technology is provided. And the charge state of the lithium battery is measured by using the characteristic value of the target characteristic peak most related to the charge state of the lithium battery, so that a more accurate prediction result of the charge state of the lithium battery is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of battery modeling, and in particular to a method, device and equipment for determining the state of charge of a lithium battery. Background Art

[0002] At present, the state of charge (SOC) of batteries is difficult to estimate, and electrochemical impedance spectroscopy (EIS) is a non-invasive and non-destructive measurement technology that can obtain the AC impedance of the battery in an extremely wide frequency band to analyze the complex physical and chemical processes inside the battery. It is one of the important tools for studying the state of charge of the battery. The existing methods for estimating the battery SOC usually include the open circuit voltage method, the ampere-hour integration method, the data-driven method, and the model-based method. Among them, the accuracy of the open circuit voltage method is poor and the corresponding relationship between the battery voltage and SOC will change. The real-time performance of the ampere-hour integration method is poor. The interpretability of the data-driven method is poor and it cannot dynamically reflect the changes inside the battery. The model-based method is more robust but highly dependent on the accuracy of the model and cannot change the parameter value with the change of the battery impedance to obtain an accurate state of charge value.

[0003] The electrochemical impedance spectrum of the battery contains a large amount of internal information of the battery, which can be used to estimate the battery's state of charge, battery health status, battery internal temperature, etc. The Distribution of Relaxation Times (DRT) method can be used to analyze the impedance spectrum. It is a method of high fractional order model. The core idea of ​​the DRT method is to deconvolve the discrete frequency domain data into a continuous time domain DRT function. It not only directly obtains the distribution of the battery polarization internal resistance in the time domain, but also is more stable and effective in estimating the battery state of charge. Current research has shown that the battery relaxation time distribution has a strong correspondence with SOC and temperature. Compared with other methods, using DRT to estimate the battery state of charge can intuitively reflect the impedance change of the battery. At the same time, the accuracy of SOC estimation can be dynamically adjusted according to the change of polarization reaction impedance, avoiding the model instability caused by the change of internal resistance due to long battery use time. However, there are still many problems in the acquisition of DRT and the correlation analysis with SOC.

[0004] Existing methods for solving battery relaxation time distribution cannot avoid underfitting and overfitting during the relaxation time distribution function fitting process, and the obtained relaxation time distribution results cannot accurately distinguish the characteristic peaks of polarization resistance. In addition, there is currently no scientific way to estimate SOC using DRT. Traditional SOC estimation mostly relies on data-driven and battery model methods. Traditional methods have low applicability, poor dynamic response of the model, and cannot reflect the dynamic response process inside the battery. Using this battery model to estimate the SOC of aging or in use has low accuracy and robustness. The interpretability and estimation accuracy of the lithium battery state of charge estimation are not suitable for actual monitoring. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device and equipment for determining the state of charge of a lithium battery in response to the above technical problems.

[0006] The present invention adopts the following technical solutions:

[0007] The present invention provides a method for determining the state of charge of a lithium battery, comprising:

[0008] Obtaining electrochemical impedance spectroscopy data of a target lithium battery; determining an initial relaxation time distribution function of the target lithium battery using a ridge regression regularization method according to an initial regularization parameter of a preset ridge regression regularization method, an initial basis function half-maximum full width value, and the electrochemical impedance spectroscopy data;

[0009] Determine the fitted impedance spectrum data corresponding to the initial relaxation time distribution function, take the minimum deviation between the fitted impedance spectrum data and the electrochemical impedance spectrum data and the smoothness of the initial relaxation time distribution function as the optimization goal, perform parameter optimization from a preset regularization parameter search range and a preset basis function half-maximum full width value search range, and determine the preferred regularization parameter and the preferred basis function half-maximum full width value;

[0010] According to the preferred regularization parameter, the preferred basis function half-maximum full width value and electrochemical impedance spectroscopy data, a ridge regression regularization method is used to determine the relaxation time distribution function of the target lithium battery;

[0011] Determine the relaxation time distribution function of the same type of target lithium battery at different states of charge, and fit it by the least square method; determine the characteristic values ​​of each characteristic peak of the relaxation time distribution function of the same type of battery at different states of charge according to the fitting function, and determine the target characteristic peak that is most relevant to the change of the state of charge of the target lithium battery according to the Pearson correlation coefficient of the characteristic value of each characteristic peak and the corresponding state of charge;

[0012] Determine the characteristic value of the target characteristic peak in the relaxation time distribution function of the target lithium battery, and determine the state of charge of the target lithium battery based on the correlation between the characteristic value of the target characteristic peak in the relaxation time distribution function of the target lithium battery and the state of charge.

[0013] Optionally, the initial relaxation time distribution function of the target lithium battery is determined by a ridge regression regularization method using the following formula:

[0014]

[0015] Where N is the frequency of the impedance spectrum data, Z fit (f m ) is the frequency f m The fitted impedance under n is the vector coefficient of the relaxation time distribution function, φ n (τ) is the basis function, f m is the frequency corresponding to each data point of the electrochemical impedance spectroscopy data, τ is the time constant, i is the imaginary part, e z (f) is the fitting error, Z exp ' is the real part of the electrochemical impedance spectroscopy data, Z exp ” is the imaginary part of the electrochemical impedance spectroscopy data, A is the fitting coefficient matrix, λ is the regularization parameter, and L is the second-order derivative matrix.

[0016] Optionally, the optimization objective is to minimize the deviation between the fitted impedance spectrum data and the electrochemical impedance spectrum data and to smooth the initial relaxation time distribution function, perform parameter optimization from a preset regularization parameter search range and a preset basis function half-maximum full width value search range, and determine the preferred regularization parameter and the preferred basis function half-maximum full width value, specifically including:

[0017] Determining an error index of an initial relaxation time distribution function according to a deviation between the fitted impedance spectrum data and the electrochemical impedance spectrum data;

[0018] Determining a smoothing index of the initial relaxation time distribution function according to the number of peaks and the curvature variance of the initial relaxation time distribution function;

[0019] Taking the minimum error index and smoothness index of the relaxation time distribution function as the optimization goal, a multi-objective genetic optimization algorithm is used to optimize the parameters from the preset regularization parameter search range and the preset basis function half-maximum full width value search range to determine the non-dominated solution set of the regularization parameter and the half-maximum full width value of the basis function;

[0020] A plurality of preferred solutions are selected from the non-dominated solution set, and a mean of a regularization parameter is calculated according to each preferred solution as a preferred regularization parameter, and a mean of a basis function half-maximum full width value is calculated as a preferred basis function half-maximum full width value.

[0021] Optionally, the initial relaxation time distribution function of the target lithium battery is determined by a ridge regression regularization method using the following formula:

[0022]

[0023] Where N is the frequency of the impedance spectrum data, Z fit (f m ) is the frequency f m The fitted impedance under n is the vector coefficient of the relaxation time distribution function, φ n (τ) is the basis function, f m is the frequency corresponding to each data point of the electrochemical impedance spectroscopy data, τ is the time constant, i is the imaginary part, e z (f) is the fitting error, Z exp ' is the real part of the electrochemical impedance spectroscopy data, Z exp ” is the imaginary part of the electrochemical impedance spectroscopy data, A is the fitting coefficient matrix, λ is the regularization parameter, and L is the second-order derivative matrix.

[0024] Optionally, the error index of the initial relaxation time distribution function is determined according to the fitted impedance spectrum data and the electrochemical impedance spectrum data by the following formula:

[0025]

[0026] in, is the error index of the relaxation time distribution function, Z' exp (f n ) is the real part of the electrochemical impedance spectroscopy data, Z” exp (f n ) is the imaginary part of the electrochemical impedance spectroscopy data, Z' fit (f n ) is the real part of the fitted impedance spectrum data, Z” fit (f n ) is the imaginary part of the fitted impedance spectrum data.

[0027] Optionally, the smoothing index of the initial relaxation time distribution function is determined by the following formula:

[0028] Idx smth =Sch*100*Var(K(τ)),

[0029]

[0030] Among them, K i is the curvature of the fitted data, S ch is the number of peaks in the relaxation time distribution, Var() is the variance, Idx smth is the smoothing index, τ is the time constant, and g(τ) is the relaxation time distribution function.

[0031] Optionally, the optimization objective is to minimize the error index and smoothness index of the relaxation time distribution function, and to use a multi-objective genetic optimization algorithm to perform parameter optimization from a preset regularization parameter search range and a preset basis function half-maximum full width value search range to determine a non-dominated solution set of the regularization parameter and the half-maximum full width value of the basis function, specifically including:

[0032] An initial population is created by randomly selecting parameters from a preset regularization parameter search range and a preset basis function half-maximum full width value search range, an error index and a smoothness index of the relaxation time distribution of each individual in the population are determined, and a progeny population is obtained through selection, crossover and mutation among individuals in the initial population;

[0033] The elite retention strategy is used to merge the initial population and the offspring population to obtain a merged population; the dominance relationship between individuals in the merged population is determined, and the non-dominated solutions and mutually non-dominated solutions are taken as the initial optimal solution set;

[0034] Perform fast non-dominated sorting on the initial optimal solution set and calculate the crowding distance of each individual. According to the crowding distance, select individuals through targeted optimization method to form the updated parent population.

[0035] An updated offspring population is obtained through selection, crossover and mutation among individuals in the updated parent population, and an error index and a smoothness index of the relaxation time distribution of each individual in the updated offspring population are determined;

[0036] Through multiple rounds of iterations until the updated optimal solution set obtained according to the updated parent population and the updated child population converges, the converged optimal solution set is used as the non-dominated solution set of the regularization parameter and the half-maximum full width of the basis function.

[0037] Optionally, determining the relaxation time distribution function of the target lithium battery under different states of charge of the same type of battery and fitting it by the least squares method specifically includes:

[0038] For each charge state preset for the same type of target lithium battery, determine the relaxation time distribution function under the charge state, and extract the characteristic peak height, center frequency and half-maximum full width in the relaxation time distribution function under the charge state;

[0039] According to the characteristic peak height, center frequency and half-maximum full width in the relaxation time distribution function under the charge state, a Gaussian function is used as the basis function to perform fitting by the least squares method according to the following formula to obtain multiple Gaussian functions corresponding to each characteristic peak:

[0040]

[0041] Among them, G m(x) is the Gaussian function corresponding to the mth characteristic peak, f m is the center frequency corresponding to the mth characteristic peak, A m is the peak height corresponding to the mth characteristic peak, Hf m is the full width at half maximum corresponding to the mth characteristic peak.

[0042] Optionally, the characteristic values ​​of each characteristic peak are the peak area and peak height of each characteristic peak;

[0043] The step of determining the state of charge of the target lithium battery according to the correlation between the characteristic value of the target characteristic peak in the relaxation time distribution function of the target lithium battery and the state of charge specifically includes:

[0044] The state of charge of the target lithium battery is determined by the following formula based on the correlation between the peak area and peak height of the target characteristic peak in the relaxation time distribution function of the target lithium battery and the state of charge:

[0045] SOC=k1S+k2A+k3Asoh;

[0046] Among them, SOC is the state of charge of the target lithium battery, S is the peak area of ​​the target characteristic peak, A is the peak height of the target characteristic peak, Asoh is the ratio of the first difference between the total impedance of the relaxation time distribution function of the target lithium battery and the corresponding target characteristic peak impedance to the second difference between the total impedance of the relaxation time distribution function of the same type of battery of the target lithium battery at the rated state of charge and the corresponding target characteristic peak impedance, k1, k2 and k3 are coefficients obtained by fitting the characteristic value parameters of the characteristic peak.

[0047] The present invention provides a device for determining the state of charge of a lithium battery, comprising:

[0048] An acquisition module is used to acquire electrochemical impedance spectroscopy data of a target lithium battery; according to the initial regularization parameter of a preset ridge regression regularization method, the full width at half maximum of an initial basis function and the electrochemical impedance spectroscopy data, an initial relaxation time distribution function of the target lithium battery is determined by using a ridge regression regularization method;

[0049] An optimization module is used to determine the fitting impedance spectrum data corresponding to the initial relaxation time distribution function, with the minimum deviation between the fitting impedance spectrum data and the electrochemical impedance spectrum data and the smoothness of the initial relaxation time distribution function as the optimization goal, and to perform parameter optimization from a preset regularization parameter search range and a preset basis function half-maximum full width value search range to determine the preferred regularization parameter and the preferred basis function half-maximum full width value;

[0050] A fitting module, for determining a relaxation time distribution function of a target lithium battery by using a ridge regression regularization method according to a preferred regularization parameter, a preferred basis function half-maximum full width value and electrochemical impedance spectroscopy data;

[0051] A selection module is used to determine the relaxation time distribution function of the same type of target lithium battery at different charge states, and fit it by the least square method; according to the fitting function, the characteristic value of each characteristic peak of the relaxation time distribution function of the same type of battery at different charge states is determined, and according to the characteristic value of each characteristic peak and the Pearson correlation coefficient of the corresponding charge state, the target characteristic peak most correlated with the charge state change of the target lithium battery is determined;

[0052] The determination module is used to determine the characteristic value of the target characteristic peak in the relaxation time distribution function of the target lithium battery, and determine the charge state of the target lithium battery according to the correlation between the characteristic value of the target characteristic peak in the relaxation time distribution function of the target lithium battery and the charge state.

[0053] The present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for determining the state of charge of a lithium battery when executing the program.

[0054] At least one of the above technical solutions adopted by the present invention can achieve the following beneficial effects:

[0055] The present invention first takes the minimum error index and smoothness index of the relaxation time distribution function as the optimization target, and considers the optimization of the regularization parameter and the basis function width at the same time, and then determines the relaxation time distribution function of the target lithium battery, avoids the problem of the pseudo peak not being smooth due to overfitting and the error being too large due to underfitting, so that the obtained relaxation time distribution result has characteristic peaks while no pseudo peaks appear, improves the recognition of the analytical result of the relaxation time distribution of the lithium battery, and better reflects the response of the internal polarization resistance of the battery. And further, according to the correlation between the characteristic values ​​of each characteristic peak of the relaxation time distribution function of the same type of battery of the target lithium battery under different charge states and the corresponding charge state, the characteristic peak in the relaxation time distribution function of the target lithium battery is optimized, so as to determine the charge state of the target lithium battery according to the correlation between the characteristic value of the target characteristic peak obtained by optimization and the charge state, and improves the interpretability, estimation accuracy and application scope of the lithium battery charge state estimation of the lithium battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The drawings described herein are used to provide a further understanding of the present invention and constitute a part 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 an improper limitation of the present invention. In the drawings:

[0057] Figure 1 A schematic flow chart of a method for determining a state of charge of a lithium battery provided by the present invention;

[0058] Figure 2 A relaxation process distribution optimizer flow chart provided by the present invention;

[0059] Figure 3 A schematic diagram of the final DRT result obtained under the optimal value of the impedance spectrum in the range of 0.1Hz to 3000Hz under different SOCs provided by the present invention;

[0060] Figure 4 A schematic diagram of fitting a DRT extraction parameter using a Gaussian function provided by the present invention;

[0061] Figure 5 A schematic diagram of a device for determining the state of charge of a lithium battery provided by the present invention;

[0062] Figure 6 A schematic diagram of a computer device for implementing a method for determining a state of charge of a lithium battery provided by the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0064] Chinese invention patent CN104914312A discloses a method for calculating the relaxation time distribution of AC impedance spectrum. When applying the Tikhonov regularization method, the method directly specifies the regularization coefficient manually, and does not clearly propose the selection method of the regularization coefficient. The reasonable selection of the regularization parameter is the core problem of the regularization method. Too large regularization parameters will cause the characteristic peaks to couple together and be difficult to separate. Too small regularization parameters will cause unnecessary pseudo peaks and burrs. At the same time, this method does not take into account the pure capacitive behavior of battery EIS at low frequencies, and the Tikhonov regularization method may show non-convergence.

[0065] Chinese invention patent CN112540316A discloses a complex battery impedance spectrum analysis method, which does not make a hypothetical extension in the low-frequency region, resulting in insufficient integrity of the DRT function. The selection of basis function width is not considered, which will affect the results. A larger basis function width will cause excessive interference from multiple functions at a certain point, resulting in a coupling peak without characteristics. A smaller basis function width will cause each peak to only affect a shorter period, ultimately causing the result to oscillate.

[0066] There are currently two main methods for selecting regularization parameters: the first is the cross-check method, which calculates the regularization result errors of the real and imaginary parts of the electrochemical impedance spectroscopy data respectively. The parameter with the smallest error difference between the two is the best regularization parameter. However, this method requires continuous verification of the difference, which is cumbersome to calculate, and cannot guarantee that the result will not have pseudo peaks or overfitting when the difference is minimized. The second is the L curve method, which calculates all the DRT result errors corresponding to different regularization parameters and selects the regularization parameter with the smallest error as the best parameter. However, the second method is very computationally intensive and time-consuming, and cannot guarantee smooth results or overfitting.

[0067] At present, there is no clear selection method for the basis function width. It is usually selected based on experience that 2Δlnτ is used as the basis function width of Tikhonov regularization. However, the impedance spectra of different frequency points and frequency bands cannot be selected based on experience. The coupling degree of polarization resistance in different frequency bands is different, and the requirements for basis function width at different frequency points are also different. When the number of frequency points is large, the basis function width needs to be reduced. When the number of frequency points is large, the coupling relationship is not obvious and pseudo peaks are likely to appear. In this case, the basis function width needs to be increased. Therefore, a clear selection method is needed to select the corresponding basis function width for different impedance spectrum data.

[0068] There is still no method for simultaneously optimizing the regularization parameter and the basis function width. The present invention provides a fast optimization method based on a multi-objective genetic optimization algorithm to select the optimal regularization parameter and basis function width to avoid overfitting and underfitting, and finally obtain a characteristic DRT result.

[0069] The SOC estimation based on DRT is well applicable to most lithium-ion batteries. It only needs to analyze and calculate the relaxation time distribution function to get a relatively accurate SOC value, and can dynamically determine the change in the contribution of polarization impedance during the aging process. For a specific model of battery, each DRT peak value obtained each time can be used to analyze the change in the degree of aging. In this way, the changing relationship between the SOC of the lithium battery and the DRT characteristic peak throughout the life cycle can be obtained, and a large amount of preliminary data research can be avoided.

[0070] The technical solutions provided by various embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0071] Figure 1 The present invention is a schematic flow chart of a method for determining the state of charge of a lithium battery, which specifically includes the following steps:

[0072] S101: Obtain electrochemical impedance spectroscopy data of a target lithium battery; determine an initial relaxation time distribution function of the target lithium battery using a ridge regression regularization method according to an initial regularization parameter of a preset ridge regression regularization method, an initial basis function half-maximum full width value, and the electrochemical impedance spectroscopy data.

[0073] Generally, before determining the relaxation time distribution of a lithium battery, the electrochemical impedance spectroscopy data of the target lithium battery can be measured by a measuring instrument such as an electrochemical workstation. The electrochemical impedance spectroscopy data here can be full-band electrochemical impedance spectroscopy data.

[0074] Based on this, in one or more embodiments of the present invention, the server of the business platform may first perform a KK check on the acquired electrochemical impedance spectrum data, and first screen out unqualified impedance spectrum data through the KK check.

[0075] Further, in one or more embodiments of the present invention, after completing the KK check, the server may fit the high-frequency part of the electrochemical impedance spectroscopy data based on the inductance, and fit the low-frequency part based on the inserted capacitance by the following formula:

[0076]

[0077] Where ω is the angular velocity, R0 is the ohmic internal resistance, R pol is the polarization resistance, g(τ) is the relaxation time distribution function, τ is the time constant, C in To insert the capacitor, w is.

[0078] During the fitting process, the insertion capacitance is obtained by discharging the fully charged battery with a current of 1 / 100C from a state of charge of 1 to a cut-off voltage, recording the discharge IC curve, and calculating the value of the insertion capacitance using the following formula:

[0079]

[0080] Where Cin is the inserted capacitor, Q li is the actual capacity of the battery, soc is the state of charge of the battery, ocv is the open circuit voltage of the battery, dQ is the change in battery charge, and dV is the voltage change.

[0081] The electrochemical impedance spectroscopy data of the intermediate frequency part of the electrochemical impedance spectroscopy data of the target lithium battery can be fitted by the relaxation time distribution function, and then the relaxation time distribution calculation can be performed.

[0082] Afterwards, the server can use the ridge regression regularization method to determine the initial relaxation time distribution function of the target lithium battery based on the initial regularization parameters of the preset ridge regression regularization method, the initial basis function half-maximum full width value and the electrochemical impedance spectrum data, and subsequently perform parameter optimization for the regularization parameters and the basis function half-maximum full width value.

[0083] The server mentioned in the present invention may be a server set on a business platform, or a device such as a desktop computer, a notebook computer, etc. that can execute the solution of the present invention. For the convenience of description, the following description is only based on the server as the execution subject.

[0084] S102: Determine the fitted impedance spectrum data corresponding to the initial relaxation time distribution function, take minimizing the deviation between the fitted impedance spectrum data and the electrochemical impedance spectrum data and smoothing the initial relaxation time distribution function as the optimization goal, perform parameter optimization from a preset regularization parameter search range and a preset basis function half-maximum full width value search range, and determine the preferred regularization parameter and the preferred basis function half-maximum full width value.

[0085] S103: Determine the relaxation time distribution function of the target lithium battery using a ridge regression regularization method according to the preferred regularization parameter, the preferred basis function half-maximum full width value and the electrochemical impedance spectroscopy data.

[0086] In the process of determining the initial relaxation time distribution function of the target lithium battery, the selection of regularization parameters and basis functions has a great influence on the accuracy of the final result. Based on this, in one or more embodiments of the present invention, the regularization parameters and basis functions are first optimized.

[0087] Specifically, refer to Figure 2 , Figure 2 The server may first obtain a preset regularization parameter search range. For example, assuming that the preset regularization parameter λ has a search range of 0.1 to 10 -15 , then the server can determine the minimum boundary value of the search range 10 -15 is the initial regularization parameter. Of course, how to select the initial regularization parameter can be determined according to needs, and the present invention does not limit this. Similarly, the server can obtain a preset regularization parameter search range. For example, assuming that the preset search range of the basis function half-maximum full width FWHM is 0.1Δlnτ~10Δlnτ, the server can determine the minimum boundary value of the search range 10 -15 10Δlnτ is the full width at half maximum of the initial basis function, and the basis function is determined based on this.

[0088] Furthermore, the server can also determine the search speed at the same time. The minimum search speeds of λ and FWHM are 0.1*10 -15, 0.05Δlnτ, λ and FWHM maximum search speed are 10 -15 , 0.2Δlnτ. The server can randomly determine the initial search speed of λ and FWHM within the range of the minimum search speed and the maximum search speed, and λ can be selected according to the logarithmic scale.

[0089] The server can then start DRT iteration calculation. The present invention introduces two indicators: error index R 2 opt and smoothing index Idx smth .

[0090] Specifically, the error index of the initial relaxation time distribution function can be determined according to the fitted impedance spectrum data and the electrochemical impedance spectrum data by the following formula:

[0091]

[0092] In the formula, is the error index of the relaxation time distribution function, Z' exp (f n ) is the real part of the electrochemical impedance spectroscopy data, Z” exp (f n ) is the imaginary part of the electrochemical impedance spectroscopy data, Z' fit (f n ) is the real part of the fitted impedance spectrum data, Z” fit (f n ) is the imaginary part of the fitted impedance spectrum data.

[0093] The error index can well reflect the deviation between the fitting result and the true result. Considering both the real part and the imaginary part can avoid the situation where the deviation of a single real part or imaginary part is too large.

[0094] And the smoothing index of the initial relaxation time distribution function is determined by the following formula:

[0095] Idx smth =Sch*100*Var(K(τ))

[0096]

[0097] In the formula, K i is the curvature of the fitted data, S ch is the number of peaks in the relaxation time distribution, Var() is the variance, Idx smth is the smoothness index, τ is the time constant, and g(τ) is the relaxation time distribution function. By introducing the number of peaks and the curvature variance, the oscillation phenomenon and the appearance of non-smooth pseudo peaks can be avoided.

[0098] Taking the error index and smoothness index into consideration, the optimal Pareto solution can be obtained using an improved multi-objective genetic optimization algorithm.

[0099] Specifically, the server can randomly select parameters from a preset regularization parameter search range and a preset basis function half-maximum full width value search range to create an initial population, determine the error index and smoothness index of the relaxation time distribution of each individual in the population, and obtain the offspring population through selection, crossover and mutation among individuals in the initial population.

[0100] The elite retention strategy is used to merge the initial population and the offspring population to obtain a merged population; the dominance relationship between individuals in the merged population is determined, and the non-dominated solutions and mutually non-dominated solutions are taken as the initial optimal solution set.

[0101] The initial optimal solution set is quickly non-dominated sorted and the crowding distance of each individual is calculated. According to the crowding distance, individuals are selected through targeted optimization method to form the updated parent population.

[0102] An updated offspring population is obtained by selection, crossover and mutation among individuals in the updated parent population, and an error index and a smoothness index of the relaxation time distribution of each individual in the updated offspring population are determined.

[0103] Through multiple rounds of iterations until the updated optimal solution set obtained according to the updated parent population and the updated child population converges, the converged optimal solution set is used as the non-dominated solution set of the regularization parameter and the half-maximum full width of the basis function.

[0104] Furthermore, in one or more embodiments of the present invention, in order to speed up the iteration speed of this method, E set The preset parameters are further improved. set It is a convergence preset value, usually set to 0.1. After each iteration, the error index R 2 opt Individuals with >0.1 are excluded from the optimal solution set to speed up the iteration process. The search speed of local and global optimal solutions is accelerated by converging the preset value. This value can be changed dynamically. If the final result is not less than E set The solution is E set It will dynamically increase by 0.01 and perform iterative search again.

[0105] After completing the parameter optimization of the regularization parameter and the half-maximum full width of the basis function and obtaining the Pareto solution set, the server can grid the Pareto solution set. For example, the horizontal axis can be set to λ, each log10 is a unit, and the vertical axis can be set to μ, μ=2 is a unit, which is obtained by the following formula:

[0106]

[0107] The above formula is the relationship between μ and the full width at half maximum FWHM. When the basis function is a Gaussian function, the relationship between μ and the width of the basis function is that it is easier to bring μ into the calculation, so μ is used to substitute into the basis function for fitting calculation.

[0108] Then, refer to Figure 2 The server may first use the minimum error index corresponding to each solution in the non-dominated solution set as a reference, and select the first grid containing the solution within the preset error index floating range from each grid in the grid coordinate system. For example, the first grid S1 containing the solution whose accuracy error index is within 15% of the minimum value is selected.

[0109] Then, the grid density and grid position corresponding to each solution in the first grid are calculated, and the unreasonable edge solutions whose grid density is less than a preset value or is at the edge of the grid are determined.

[0110] Secondly, based on the minimum smoothness index corresponding to each solution in the non-dominated solution set, a second grid containing the solution within the preset smoothness index floating range is selected from the first grid. For example, the minimum smoothness index Idx is calculated from the first grid S1. min , then select smoothing index <5*Idx min The solution is located in the second grid S2.

[0111] Next, the unreasonable edge solutions are removed from the second grid to obtain the third grid S3, and the remaining solutions are selected as the multiple optimal solutions. The mean of the regularization parameter and the mean of the half-maximum full width of the basis function are calculated according to each optimal solution, such as averaging all the solutions in the third grid S3 to obtain the optimal λ and μ.

[0112] Finally, the server can determine the relaxation time distribution function of the target lithium battery using the Tikhonov regularization method based on the optimal λ, μ basis function half-maximum full width values ​​and electrochemical impedance spectroscopy data.

[0113] Table 1 Optimizer results corresponding to different frequency points

[0114] R2opt Idxsmth λ μ FWHM ppds=30 0.0115 2.1164e-05 10.8611 9.53926 1.75806△lnτ ppds=20 0.0021 2.80128e-04 10.38825 7.514933 2.0856△lnτ ppds=10 0.0033 4.8824e-04 9.742448 5.185193 2.8659△lnτ ppds=5 0.0092 9.83369e-12 6.38971 1.77413 1.962△lnτ

[0115] Table 1 shows the optimal solution of electrochemical impedance spectroscopy at different frequency points in the frequency range of 0.1 Hz to 3000 Hz obtained by implementing this scheme, as well as the error index and smoothness index corresponding to the optimal solution. Figure 3 It is a schematic diagram of the final DRT result obtained under the optimal value of the impedance spectrum in the range of 0.1 Hz to 3000 Hz under different SOCs in the present invention.

[0116] S104: Determine the relaxation time distribution function of the same type of target lithium battery under different charge states, and fit it by the least squares method; determine the characteristic values ​​of each characteristic peak of the relaxation time distribution function of the same type of battery under different charge states according to the fitting function, and determine the target characteristic peak that is most correlated with the change in charge state of the target lithium battery according to the Pearson correlation coefficient of the characteristic value of each characteristic peak and the corresponding charge state.

[0117] S105: determining a characteristic value of a target characteristic peak in the relaxation time distribution function of the target lithium battery, and determining the state of charge of the target lithium battery according to a correlation between the characteristic value of the target characteristic peak in the relaxation time distribution function of the target lithium battery and the state of charge.

[0118] After determining the relaxation time distribution function of the target lithium battery, the server may further analyze the charge state of the target lithium battery based on the characteristic peak of the relaxation time.

[0119] Based on this, in one or more embodiments of the present invention, the server can pre-determine the relaxation time distribution function of the same type of target lithium battery under different charge states, and use the characteristic peak rapid extraction technology to first determine the relaxation time distribution function under each charge state preset for the same type of target lithium battery, and extract the highest peak value and the corresponding center frequency of the characteristic peak in the relaxation time distribution function of the same type of target lithium battery under this charge state, and then search for the half-maximum full width value of each peak in turn, so as to obtain the three parameters of each characteristic peak: peak height, center frequency, and half-maximum full width.

[0120] Afterwards, the server can fit the characteristic peak height, center frequency and half-maximum full width in the relaxation time distribution function under different charge states by using the Gaussian function as the basis function through the least squares method according to the following formula to obtain multiple Gaussian functions corresponding to each characteristic peak:

[0121] According to the characteristic peak height, center frequency and half-maximum full width in the relaxation time distribution function under the charge state, a Gaussian function is used as the basis function to perform fitting by the least squares method according to the following formula to obtain multiple Gaussian functions corresponding to each characteristic peak:

[0122]

[0123] In the formula, G m (x) is the Gaussian function corresponding to the mth characteristic peak, f m is the center frequency corresponding to the mth characteristic peak, A m is the peak height corresponding to the mth characteristic peak, Hf m is the full width at half maximum corresponding to the mth characteristic peak.

[0124] Among them, each Gaussian function corresponds to a characteristic peak. By integrating each Gaussian function, the area of ​​the corresponding characteristic peak at different center frequencies can be obtained. The peak area and peak height of all characteristic peaks are used as evaluation indicators. The values ​​of these two evaluation indicators are measured and calculated under different states of charge. Then, the Pearson correlation coefficients between the two evaluation indicators and the state of charge are calculated respectively. The characteristic peak corresponding to the Gaussian function that is most correlated with the change in the state of charge is selected, and the peak area and peak height of this characteristic peak in the relaxation time distribution function of the target lithium battery are used to estimate the state of charge. Figure 4 It is a schematic diagram of fitting the DRT extraction parameters using a Gaussian function in the present invention.

[0125] In one or more embodiments of the present invention, the state of charge and the peak area and peak height of the characteristic peak may be nonlinearly fitted, and the state of charge of the target lithium battery may be determined according to the correlation between the peak area and peak height of the target characteristic peak in the relaxation time distribution function of the target lithium battery and the state of charge by the following formula:

[0126] SOC=k1S+k2A+k3Asoh

[0127] Among them, SOC is the state of charge of the target lithium battery, S is the peak area of ​​the target characteristic peak, A is the peak height of the target characteristic peak, Asoh is the ratio of the first difference between the total impedance of the relaxation time distribution function of the target lithium battery and the corresponding target characteristic peak impedance to the second difference between the total impedance of the relaxation time distribution function of the same type of battery of the target lithium battery at the rated state of charge and the corresponding target characteristic peak impedance, Asoh can reflect the change relationship of the state of health (State of Health, SOH) of the target lithium battery to a certain extent, k1, k2 and k3 are coefficients obtained by fitting the characteristic value parameters of the characteristic peaks.

[0128] based on Figure 1 The method for determining the state of charge of the lithium battery shown in the figure uses inductance and inserted capacitors to achieve full-band fitting of the impedance spectrum data, which is applicable to the full-band DRT fitting method. The fast optimization method for the regularization parameter and the basis function width uses the error index and the smoothness index to avoid the overfitting and underfitting problems in the ridge regression regularization, so that the result has characteristic peaks without pseudo peaks. The multi-objective optimization automatically selects a fast convergence architecture, uses a convergence preset value to speed up the convergence speed, and dynamically adjusts the convergence preset value to give the optimal solution under the current situation.

[0129] The optimization goal is to minimize the error index and smoothness index of the relaxation time distribution function. The optimization of the regularization parameter and the basis function width is considered at the same time. The relaxation time distribution of the lithium battery is determined based on the optimal regularization parameter and basis function. The two indicators are used to avoid the problems of unsmooth pseudo peaks caused by overfitting and excessive errors caused by underfitting. The relaxation time distribution results obtained have characteristic peaks without pseudo peaks, which improves the recognition of the analytical results of the relaxation time distribution of lithium batteries and better reflects the response of the internal polarization resistance of the battery.

[0130] At the same time, the DRT distribution extraction parameter method adopted in the present invention can accurately extract the peak height, peak area and other parameters of DRT, and use the fitting regression method to obtain the characteristic peak parameters most related to SOC, and consider the influence of aging changes on the battery estimation accuracy. It combines data-driven and model recognition methods, but does not require a large amount of data for pre-calculation. While obtaining SOC, the polarization resistance parameters of the internal reaction of the battery can also be obtained, which better reflects the ability to estimate the battery state of charge and has good dynamic response.

[0131] The present invention is applicable to different states of charge (SOC=0-1) and different numbers of frequency points (ppd), and is applicable to any impedance spectrum data.

[0132] When applying the method for determining the state of charge of a lithium battery provided by the present invention, it is not necessary to Figure 1 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0133] The above is a method for determining the state of charge of a lithium battery provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding device for determining the state of charge of a lithium battery, such as Figure 5 shown.

[0134] Figure 5 A schematic diagram of a device for determining the state of charge of a lithium battery provided by the present invention includes:

[0135] The acquisition module 201 is used to acquire the electrochemical impedance spectroscopy data of the target lithium battery; according to the initial regularization parameter of the preset ridge regression regularization method, the initial basis function half-maximum full width value and the electrochemical impedance spectroscopy data, the ridge regression regularization method is used to determine the initial relaxation time distribution function of the target lithium battery;

[0136] An optimization module 202 is used to determine the fitting impedance spectrum data corresponding to the initial relaxation time distribution function, with the minimum deviation between the fitting impedance spectrum data and the electrochemical impedance spectrum data and the smoothness of the initial relaxation time distribution function as the optimization goal, and to perform parameter optimization from a preset regularization parameter search range and a preset basis function half-maximum full width value search range to determine the preferred regularization parameter and the preferred basis function half-maximum full width value;

[0137] A fitting module 203 is used to determine the relaxation time distribution function of the target lithium battery by using a ridge regression regularization method according to the preferred regularization parameter, the preferred basis function half-maximum full width value and the electrochemical impedance spectroscopy data;

[0138] A selection module 204 is used to determine the relaxation time distribution function of the same type of target lithium battery at different states of charge, and fit it by the least square method; determine the characteristic value of each characteristic peak of the relaxation time distribution function of the same type of battery at different states of charge according to the fitting function, and determine the target characteristic peak that is most correlated with the change of the state of charge of the target lithium battery according to the characteristic value of each characteristic peak and the Pearson correlation coefficient of the corresponding state of charge;

[0139] The determination module 205 is used to determine the characteristic value of the target characteristic peak in the relaxation time distribution function of the target lithium battery, and determine the state of charge of the target lithium battery according to the correlation between the characteristic value of the target characteristic peak in the relaxation time distribution function of the target lithium battery and the state of charge.

[0140] For the specific definition of the device for determining the state of charge of a lithium battery, please refer to the definition of the method for determining the state of charge of a lithium battery in the above text, which will not be repeated here. Each module in the above-mentioned device for determining the state of charge of a lithium battery can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0141] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A method for determining the state of charge of a lithium battery is provided.

[0142] The present invention also provides Figure 6 The structural diagram of the computer device shown in FIG. Figure 6 As shown in the figure, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1A method for determining the state of charge of a lithium battery is provided.

[0143] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0144] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. A method for determining the state of charge of a lithium battery, characterized in that: include: Obtain electrochemical impedance spectroscopy data of the target lithium battery; According to the preset initial regularization parameters of the ridge regression regularization method, the initial basis function half-maximum full width value and the electrochemical impedance spectroscopy data, the ridge regression regularization method is used to determine the initial relaxation time distribution function of the target lithium battery; Determine the fitted impedance spectrum data corresponding to the initial relaxation time distribution function, take the minimum deviation between the fitted impedance spectrum data and the electrochemical impedance spectrum data and the smoothness of the initial relaxation time distribution function as the optimization goal, perform parameter optimization from a preset regularization parameter search range and a preset basis function half-maximum full width value search range, and determine the preferred regularization parameter and the preferred basis function half-maximum full width value; According to the preferred regularization parameter, the preferred basis function half-maximum full width value and electrochemical impedance spectroscopy data, a ridge regression regularization method is used to determine the relaxation time distribution function of the target lithium battery; Determine the relaxation time distribution function of the same type of target lithium battery at different states of charge, and fit it by the least square method; determine the characteristic values ​​of each characteristic peak of the relaxation time distribution function of the same type of battery at different states of charge according to the fitting function, and determine the target characteristic peak that is most relevant to the change of the state of charge of the target lithium battery according to the Pearson correlation coefficient of the characteristic value of each characteristic peak and the corresponding state of charge; Determine the characteristic value of the target characteristic peak in the relaxation time distribution function of the target lithium battery, and determine the state of charge of the target lithium battery based on the correlation between the characteristic value of the target characteristic peak in the relaxation time distribution function of the target lithium battery and the state of charge.

2. The method for determining the state of charge of a lithium battery according to claim 1, wherein: The initial relaxation time distribution function of the target lithium battery is determined by the ridge regression regularization method using the following formula: Where N is the frequency of impedance spectrum data, Z fit (f m ) is the frequency f m The fitted impedance under n is the vector coefficient of the relaxation time distribution function, φ n (τ) is the basis function, f m is the frequency corresponding to each data point of the electrochemical impedance spectroscopy data, τ is the time constant, i is the imaginary part, e z (f) is the fitting error, Z exp ' is the real part of the electrochemical impedance spectroscopy data, Z exp ” is the imaginary part of the electrochemical impedance spectroscopy data, A is the fitting coefficient matrix, λ is the regularization parameter, and L is the second-order derivative matrix.

3. The method for determining the state of charge of a lithium battery according to claim 1, wherein: The optimization goal is to minimize the deviation between the fitted impedance spectrum data and the electrochemical impedance spectrum data and smooth the initial relaxation time distribution function, perform parameter optimization from a preset regularization parameter search range and a preset basis function half-maximum full width value search range, and determine the preferred regularization parameter and the preferred basis function half-maximum full width value, specifically including: Determining an error index of an initial relaxation time distribution function according to a deviation between the fitted impedance spectrum data and the electrochemical impedance spectrum data; Determining a smoothing index of the initial relaxation time distribution function according to the number of peaks and the curvature variance of the initial relaxation time distribution function; Taking the minimum error index and smoothness index of the relaxation time distribution function as the optimization goal, a multi-objective genetic optimization algorithm is used to optimize the parameters from the preset regularization parameter search range and the preset basis function half-maximum full width value search range to determine the non-dominated solution set of the regularization parameter and the half-maximum full width value of the basis function; A plurality of preferred solutions are selected from the non-dominated solution set, and a mean of a regularization parameter is calculated according to each preferred solution as a preferred regularization parameter, and a mean of a basis function half-maximum full width value is calculated as a preferred basis function half-maximum full width value.

4. The method for determining the state of charge of a lithium battery according to claim 3, wherein: The error index of the initial relaxation time distribution function is determined from the fitted impedance spectroscopy data and the electrochemical impedance spectroscopy data by the following formula: in, is the error index of the relaxation time distribution function, Z' exp (f n ) is the real part of the electrochemical impedance spectroscopy data, Z” exp (f n ) is the imaginary part of the electrochemical impedance spectroscopy data, Z' fit (f n ) is the real part of the fitted impedance spectrum data, Z” fit (f n ) is the imaginary part of the fitted impedance spectrum data.

5. The method for determining the state of charge of a lithium battery as claimed in claim 3, wherein: The smoothing exponent of the initial relaxation time distribution function is determined by: Idx smth =Sch*100*Var(K(τ)), Among them, K i is the curvature of the fitted data, S ch is the number of peaks in the relaxation time distribution, Var() is the variance, Idx smth is the smoothing index, τ is the time constant, and g(τ) is the relaxation time distribution function.

6. The method for determining the state of charge of a lithium battery as claimed in claim 3, characterized in that: The method takes the minimum error index and smoothness index of the relaxation time distribution function as the optimization goal, uses a multi-objective genetic optimization algorithm to perform parameter optimization from a preset regularization parameter search range and a preset basis function half-maximum full width value search range, and determines a non-dominated solution set of the regularization parameter and the basis function half-maximum full width value, specifically including: An initial population is created by randomly selecting parameters from a preset regularization parameter search range and a preset basis function half-maximum full width value search range, an error index and a smoothness index of the relaxation time distribution of each individual in the population are determined, and a progeny population is obtained through selection, crossover and mutation among individuals in the initial population; The elite retention strategy is used to merge the initial population and the offspring population to obtain a merged population; the dominance relationship between individuals in the merged population is determined, and the non-dominated solutions and mutually non-dominated solutions are taken as the initial optimal solution set; Perform fast non-dominated sorting on the initial optimal solution set and calculate the crowding distance of each individual. According to the crowding distance, individuals are selected through targeted optimization method to form the updated parent population. An updated offspring population is obtained through selection, crossover and mutation among individuals in the updated parent population, and an error index and a smoothness index of the relaxation time distribution of each individual in the updated offspring population are determined; Through multiple rounds of iterations until the updated optimal solution set obtained according to the updated parent population and the updated child population converges, the converged optimal solution set is used as the non-dominated solution set of the regularization parameter and the half-maximum full width of the basis function.

7. The method for determining the state of charge of a lithium battery according to claim 1, wherein: The determination of the relaxation time distribution function of the target lithium battery under different charge states of the same type of battery and fitting by the least square method specifically includes: For each charge state preset for the same type of target lithium battery, determine the relaxation time distribution function under the charge state, and extract the characteristic peak height, center frequency and half-maximum full width in the relaxation time distribution function under the charge state; According to the characteristic peak height, center frequency and half-maximum full width in the relaxation time distribution function under the charge state, a Gaussian function is used as the basis function to perform fitting by the least squares method according to the following formula to obtain multiple Gaussian functions corresponding to each characteristic peak: Among them, G m (x) is the Gaussian function corresponding to the mth characteristic peak, f m is the center frequency corresponding to the mth characteristic peak, A m is the peak height corresponding to the mth characteristic peak, Hf m is the full width at half maximum corresponding to the mth characteristic peak.

8. The method for determining the state of charge of a lithium battery as claimed in claim 1, wherein: The characteristic values ​​of each characteristic peak are the peak area and peak height of each characteristic peak; The step of determining the state of charge of the target lithium battery according to the correlation between the characteristic value of the target characteristic peak in the relaxation time distribution function of the target lithium battery and the state of charge specifically includes: The state of charge of the target lithium battery is determined by the following formula based on the correlation between the peak area and peak height of the target characteristic peak in the relaxation time distribution function of the target lithium battery and the state of charge: SOC=k1S+k2A+k3Asoh; Among them, SOC is the state of charge of the target lithium battery, S is the peak area of ​​the target characteristic peak, A is the peak height of the target characteristic peak, Asoh is the ratio of the first difference between the total impedance of the relaxation time distribution function of the target lithium battery and the corresponding target characteristic peak impedance to the second difference between the total impedance of the relaxation time distribution function of the same type of battery of the target lithium battery at the rated state of charge and the corresponding target characteristic peak impedance, k1, k2 and k3 are coefficients obtained by fitting the characteristic value parameters of the characteristic peak.

9. A device for determining the state of charge of a lithium battery, characterized in that: include: An acquisition module, used to acquire electrochemical impedance spectroscopy data of a target lithium battery; According to the preset initial regularization parameters of the ridge regression regularization method, the initial basis function half-maximum full width value and the electrochemical impedance spectroscopy data, the ridge regression regularization method is used to determine the initial relaxation time distribution function of the target lithium battery; An optimization module is used to determine the fitting impedance spectrum data corresponding to the initial relaxation time distribution function, with the minimum deviation between the fitting impedance spectrum data and the electrochemical impedance spectrum data and the smoothness of the initial relaxation time distribution function as the optimization goal, and to perform parameter optimization from a preset regularization parameter search range and a preset basis function half-maximum full width value search range to determine the preferred regularization parameter and the preferred basis function half-maximum full width value; A fitting module, for determining a relaxation time distribution function of a target lithium battery by using a ridge regression regularization method according to a preferred regularization parameter, a preferred basis function half-maximum full width value and electrochemical impedance spectroscopy data; A selection module is used to determine the relaxation time distribution function of the same type of target lithium battery at different charge states, and fit it by the least square method; according to the fitting function, the characteristic value of each characteristic peak of the relaxation time distribution function of the same type of battery at different charge states is determined, and according to the characteristic value of each characteristic peak and the Pearson correlation coefficient of the corresponding charge state, the target characteristic peak most correlated with the charge state change of the target lithium battery is determined; The determination module is used to determine the characteristic value of the target characteristic peak in the relaxation time distribution function of the target lithium battery, and determine the charge state of the target lithium battery according to the correlation between the characteristic value of the target characteristic peak in the relaxation time distribution function of the target lithium battery and the charge state.

10. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any one of claims 1 to 8 is implemented.

Images