Lithium battery SOC estimation algorithm, system and device based on CSHHO-LSSVM and medium

Abstract

The invention discloses a CSHHO-LSSVM-based lithium battery SOC estimation algorithm, system and device and a medium, and relates to the field of battery state-of-charge estimation, and the algorithm comprises the steps: collecting SOC data of a lithium phosphate battery to form a sample set, carrying out the normalization, and setting each initial parameter; the population is initialized according to seven kinds of chaotic mapping, the individual fitness value is calculated, and escape energy is calculated; entering an exploration stage of the algorithm to update the population according to a position updating formula, and introducing a variable spiral position updating strategy to enter a development stage of the algorithm to update the population; and executing optimal neighborhood disturbance by adopting an optimal neighborhood disturbance strategy, judging an algorithm termination condition, outputting the obtained optimal solutions sigma and gamma when the condition is met, otherwise, recalculating escape energy, and continuing to update the population. The self-adaptive weight and variable spiral position updating strategy is utilized, the global search capability of the algorithm is enhanced, and the defect that a traditional HHO algorithm is prone to falling into a local optimal solution in the search process is effectively overcome.

Description

Technical Field

[0001] The present invention relates to the field of battery state of charge estimation, and in particular to a lithium battery SOC estimation algorithm, system, device and medium based on CSHHO-LSSVM (a least squares support vector machine hyperparameter optimization method based on chaotic multi-strategy search Harris Hawk optimization algorithm). Background Art

[0002] In a battery management system (BMS), accurate estimation of the battery state of charge (SOC) is crucial to extend battery life and ensure battery safety. Traditional SOC estimation methods, such as Kalman filtering (KF) and extended Kalman filtering (EKF) based on circuit models, usually rely on accurate battery model parameters, but these parameters have large uncertainties in practical applications. To overcome this problem, machine learning methods have gradually been introduced into SOC estimation, among which the least squares support vector machine (LSSVM) is a commonly used nonlinear regression method that can provide high estimation accuracy. However, the performance of LSSVM is highly dependent on the selection of kernel function parameters (σ) and penalty parameters (γ). The optimization problem of these two hyperparameters is usually regarded as a "black box" optimization problem, and it is difficult to obtain an accurate optimization strategy through theoretical analysis. Swarm intelligence optimization algorithms, especially the Harris Hawk optimization algorithm (HHO), are widely used in LSSVM hyperparameter optimization due to their strong global search capabilities. However, traditional HHO is prone to fall into local optimal solutions during local search, which affects the optimization effect. Therefore, improving the global search capability of HHO becomes the key to improving the accuracy of SOC estimation. Summary of the invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is: how to improve the global search capability of HHO, and thus improve the accuracy of SOC estimation.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a lithium battery SOC estimation algorithm based on CSHHO-LSSVM, which includes collecting SOC data of lithium phosphate batteries to form a sample set, dividing the sample set into two groups of training data and test data, using the training data to establish a mathematical model in simulation, and using the test data to verify the model; normalizing the sample set so that all sample data values ​​are within the range of [0, 1], and the normalized conversion function is expressed as, , in, is the normalized value, is the actual value, and Represent the maximum and minimum values ​​respectively; Set each initial parameter, including population size, maximum number of iterations and dimension; initialize the population according to seven chaotic maps, where the independent variables consist of the kernel parameter σ and regularization parameter γ of the least squares vector machine, and calculate the individual fitness value; calculate the escape energy The formula is: , in, A random number represented as (-1, 1); according to the position update formula, the algorithm enters the exploration phase to update the population, and introduces a variable spiral position update strategy to enter the development phase of the algorithm to update the population; the optimal neighborhood perturbation strategy is used to execute the optimal neighborhood perturbation, and the algorithm termination condition is judged. When the condition is met, the optimal solutions σ and γ are output, otherwise the escape energy is recalculated and the population is continued to be updated; an LSSVM model based on the optimal solutions σ and γ is established, and the SOC of the power lithium battery is estimated using the obtained optimal estimation model, and a targeted error analysis is performed based on the estimated results compared with the measured results; the optimal neighborhood perturbation strategy is expressed as, , in, and Represented as a random number uniformly generated between [0, 1], Indicates the current position. The new position generated; for the generated neighborhood position, a greedy strategy is used to decide whether to keep it. The calculation formula is expressed as: , in, Expressed as The fitness of the position.

[0006] As a preferred solution of the lithium battery SOC estimation algorithm based on CSHHO-LSSVM described in the present invention, wherein: the seven chaotic maps include Sinusoidal chaotic map, Tent chaotic map, Kent chaotic map, Cubic chaotic map, Logistic chaotic map, Gauss chaotic map and Circle chaotic map; the initialization of the population according to the seven chaotic maps includes randomly generating in D-dimensional space Harris Hawk, the formula is expressed as, , in, is represented by the Harris hawk population set, Represented as a Harris Hawk individual; each dimension of each Harris Hawk iterates M times to generate M Harris Hawks; after completing all Harris Hawk iterations, the seven chaotic maps are applied to the solution space and expressed as, , in, It is expressed as the upper bound of the d-dimensional exploration space, It is expressed as the lower bound of the d-dimensional exploration space; It is expressed as the d-dimensional coordinates of the ith Harris Hawk; Represented as the coordinates of the ith Harris Hawk in the d-dimensional exploration space.

[0007] As a preferred solution of the lithium battery SOC estimation algorithm based on CSHHO-LSSVM described in the present invention, the method for constructing the position update formula includes forming an adaptive weight according to the current number of iterations t, improving the Harris Eagle optimization algorithm according to the adaptive weight, and obtaining the position update formula; the adaptive weight is formed according to the current number of iterations t It is expressed as, , in, is expressed as the maximum number of iterations; the position update formula is expressed as, , in, , , , , Represented as a random number in the range [0, 1), is represented as the optimal value of the i-th generation, Expressed as the average value of the population in the i-th generation.

[0008] As a preferred solution of the lithium battery SOC estimation algorithm based on CSHHO-LSSVM described in the present invention, wherein: the introduction of the variable spiral position update strategy into the development stage of the algorithm to update the population includes introducing the variable spiral position update strategy into the position update formula, and updating the Harris Hawk population position as follows: , in, Expressed as control variables, designed according to the spiral mathematical model.

[0009] Another object of the present invention is to provide a lithium battery SOC estimation system based on CSHHO-LSSVM, which can find the optimal kernel function parameters of LSSVM in lithium battery SOC estimation.

[0010] To solve the above technical problems, the present invention provides the following technical solutions: a system for a lithium battery SOC estimation algorithm based on CSHHO-LSSVM, comprising: a data processing module, a parameter training module and an error analysis module; the data processing module collects the SOC data of the lithium phosphate battery to form a sample set, and normalizes the sample set data; the parameter training module optimizes the kernel parameter σ and the regularization parameter γ of the least squares support vector machine based on the chaotic multi-strategy search Harris Eagle optimization algorithm, and outputs the obtained optimal solutions σ and γ; the error analysis module establishes an LSSVM model based on the optimal solutions σ and γ, uses the obtained optimal estimation model to estimate the SOC of the power lithium battery, and performs targeted error analysis based on the estimation results compared with the measured results.

[0011] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned CSHHO-LSSVM-based lithium battery SOC estimation algorithm are implemented.

[0012] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a lithium battery SOC estimation algorithm based on CSHHO-LSSVM as described above.

[0013] The beneficial effects of the present invention are as follows: the method of the present invention enhances the global search capability of the algorithm by combining the advantages of the swarm intelligence optimization algorithm and utilizing adaptive weights and variable spiral position update strategies, thereby effectively avoiding the defect of the traditional HHO algorithm that is prone to falling into the local optimal solution during the search process. By introducing a chaotic mapping method to optimize population initialization, the present method improves the coverage capability of the algorithm in the initial solution distribution stage, thereby further accelerating the convergence process of the optimal solution. Through this method, the SOC of the lithium battery can be estimated more accurately, and the effect of the LSSVM hyperparameter optimization can be significantly improved, thereby improving the accuracy and stability of the SOC estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0015] Figure 1 This is a flow chart of a Harris Hawk optimization algorithm based on chaotic multi-strategy search in Example 1.

[0016] Figure 2This is a CSHHO optimization flow chart of a least squares support vector machine hyperparameter optimization method based on chaotic multi-strategy search Harris Hawk optimization algorithm for lithium battery SOC estimation in Example 2.

[0017] Figure 3 This is a training set data diagram of the DST (sample 1) working condition of a least squares support vector machine hyperparameter optimization method based on chaotic multi-strategy search Harris Eagle optimization algorithm for lithium battery SOC estimation in Example 2.

[0018] Figure 4 This is a training set data diagram of the FUDS (Sample 2) working condition of a least squares support vector machine hyperparameter optimization method based on chaotic multi-strategy search Harris Eagle optimization algorithm for lithium battery SOC estimation in Example 2.

[0019] Figure 5 This is a training set data diagram of the BJDST (sample 3) working condition of a least squares support vector machine hyperparameter optimization method based on chaotic multi-strategy search Harris Eagle optimization algorithm for lithium battery SOC estimation in Example 2.

[0020] Figure 6 This is a test set data diagram of the US06 (sample 4) working condition of a least squares support vector machine hyperparameter optimization method based on chaotic multi-strategy search Harris Eagle optimization algorithm for lithium battery SOC estimation in Example 2.

[0021] Figure 7 This is an SOC prediction output diagram of a least squares support vector machine hyperparameter optimization method based on chaotic multi-strategy search Harris Eagle optimization algorithm for lithium battery SOC estimation in Example 2.

[0022] Figure 8 This is a local enlarged view of the SOC prediction error of a least squares support vector machine hyperparameter optimization method based on chaotic multi-strategy search Harris Eagle optimization algorithm for lithium battery SOC estimation in Example 2. DETAILED DESCRIPTION

[0023] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0024] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0025] Example 1

[0026] Reference Figure 1 , which is the first embodiment of the present invention, and this embodiment provides a Harris Hawk optimization algorithm based on chaotic multi-strategy search, including, algorithm design ideas: In order to effectively reduce the dependence of SOC estimation on circuit model parameters, the least squares support vector machine (LSSVM) is often used in SOC estimation. However, in the process of using LSSVM to estimate the SOC of lithium batteries, there is also a problem of difficulty in finding the kernel function parameter σ and the penalty parameter γ. The influence of these two parameters on the estimation accuracy of LSSVM is difficult to find a necessary corresponding relationship in theory, so the optimization problem of the kernel function parameter σ and the penalty parameter γ can be regarded as a "black box" optimization problem. The swarm intelligence optimization algorithm can quickly obtain some feasible solutions based on the input and output relationship without giving the function expression of the optimization problem, and is an important method for solving black box optimization problems. Harris Hawk Optimization Algorithm (HHO) is a typical swarm intelligence optimization algorithm, which also has the disadvantages of relatively poor convergence and easy to fall into local optimal solutions in the problem of optimizing LSSVM hyperparameters. Therefore, the present invention focuses on improving the search capability of the HHO algorithm under different test functions, and improving the ability of the HHO algorithm to solve the "black box" optimization problem of optimizing the hyperparameters of LSSVM. The enhanced HHO algorithm searches for the hyperparameters of LSSVM and estimates the SOC of the lithium battery.

[0027] By simulating the predation process of Harris Hawk in nature, adaptive weights and variable spiral position update strategies are introduced to enhance the global search capability of the algorithm. In order to make the initial solution generated by the HHO algorithm in the population initialization stage cover the solution space as much as possible, the best chaotic mapping method is selected from seven commonly used chaotic mapping population initialization methods, and it is used as the population initialization method to improve the algorithm. In order to better find the global optimal solution of the algorithm, the optimal neighborhood perturbation strategy is introduced. Therefore, based on the above scheme, the present invention designs a chaotic multi-strategy search Harris Hawk optimization algorithm (ChaoticMulti-strategySearchHHO, CSHHO) to improve the global search capability of the HHO algorithm and increase the speed of HHO searching for the optimal solution.

[0028] Chaotic mapping: The traditional HHO algorithm adopts a random population initialization strategy. The limitation of this method is that it cannot ensure comprehensive coverage of the entire solution space. Chaotic sequences exhibit multiple advantages such as ergodicity, randomness, and regularity within a specific range, which means that it has the ability to thoroughly search the entire search space with a higher probability. Introducing chaotic sequences in the population initialization stage of the HHO algorithm can effectively enhance the global search capability of the algorithm, making it easier to jump out of the local optimal solution and maintain the diversity of the population. The present invention selects seven common chaotic maps, namely Sinusoidal, Tent, Kent, Cubic, Logistic, Gauss, and Circle, for population initialization of the HHO algorithm. After analyzing the results, the optimal method for the HHO algorithm was selected as the population initialization method for the improved algorithm. The following are seven chaotic mappings: Sinusoidal Chaotic Mapping (1.1) Where P is the control parameter , ; Tent Chaos Map (1.2) Kent Chaos Map (1.3) Control parameters , when using chaos mapping, the initial value Cannot be compared with system parameters Otherwise, the system will evolve into a periodic system.

[0029] Cubic Chaos Map (1.4) Where b and c are the influencing factors of the chaotic map. When b and c are different, the range of the Cubic chaotic map is also different. Take b=4 and c=3.

[0030] Logistic Chaotic Mapping (1.5) When P=4, the number of generated Logistic chaotic maps is between (0,1).

[0031] Gauss Chaos Map (1.6) Circle Chaos Map (1.7) like Figure 1As shown, the three steps of using seven chaotic maps to initialize the population of the Harris Hawk Optimization Algorithm HHO are: Step 1: Generate random Harris Hawks, namely: (1.8), in, is represented by the Harris hawk population set, Represented as a Harris Hawk individual.

[0032] Step 2: Iterate each dimension of each Harris Hawk M times to generate M Harris Hawks.

[0033] Step 3: After all Harris Hawk iterations are completed, apply the chaos maps (1.1) to (1.7) to the solution space.

[0034] (1.9), in, It is expressed as the upper bound of the d-dimensional exploration space, It is expressed as the lower bound of the d-dimensional exploration space; It is expressed as the d-dimensional coordinates of the ith Harris Hawk; Represented as the coordinates of the ith Harris Hawk in the d-dimensional exploration space.

[0035] The adaptive weight is formed according to the current number of iterations t, and the Harris Eagle optimization algorithm is improved according to the adaptive weight. The position update formula is obtained. Specifically, the adaptive weight composed of the current number of iterations t is as follows: (1.10), in, Expressed as the maximum number of iterations; this adaptive weight has the characteristic of nonlinear change between [0, 1], which is due to the cosine function in Therefore, at the beginning of the exploration phase, the weight value is small, but the change speed is slightly faster; at the end of the exploration phase, the weight value is large, but the change speed will slow down, thus fully ensuring the convergence of the algorithm. The improved HHO algorithm position update formula is: (1.11), in, , , , , Represented as a random number in the range [0, 1), is represented as the optimal value of the i-th generation, It is expressed as the average value of the population in the i-th generation. The position update after introducing the adaptive weight dynamically adjusts the weight size according to the increase in the number of iterations, so that the randomly selected Harris Hawk position and the optimal average Harris Hawk position in the population The guidance of Harris Hawk individuals is different at different times. As the number of iterations increases, the Harris Hawk population will move closer to the optimal position, and a larger weight will speed up the movement of the Harris Hawk position, thereby accelerating the convergence of the algorithm.

[0036] Introducing a variable spiral position update strategy into the position update formula to update the position of the Harris Hawk population. Specifically, in order to simulate the real process of prey searching in nature, a variable spiral position update strategy is introduced in the search phase of the HHO algorithm. In the exploration phase of the HHO algorithm, that is, in formula (1.11), the variable b is introduced to control the shape of the spiral, so that the Harris Hawk can formulate more diverse search path strategies for position updates, and the parameter b is designed to be a variable that changes with the number of iterations, dynamically adjusting the spiral shape of the Harris Hawk's exploration phase, enhancing the Harris Hawk's ability to explore unknown areas, and thus improving the algorithm's global search capability. Its definition is as follows: (1.12), in, It is represented as a control variable, and the spiral shape is dynamically adjusted by introducing the number of iterations t based on the original spiral model. The parameters are designed so that the spiral shape changes from large to small as the number of iterations increases. In the early exploration stage of the HHO algorithm, the Harris Hawk searches for the target in a larger spiral shape, and the Harris Hawk explores the global optimal solution as much as possible to improve the algorithm's global optimal search capability; in the later exploration stage of the HHO algorithm, the Harris Hawk searches for the target in a smaller spiral shape to improve the algorithm's search accuracy.

[0037] Introducing the optimal neighborhood perturbation strategy to find the global optimal solution of the algorithm Specifically, in order to improve the search efficiency of the HHO algorithm and avoid falling into the local optimum too early, the optimal neighborhood perturbation strategy is introduced in HHO. This random search not only increases the global search capability of the algorithm, but also prevents the algorithm from being premature to a certain extent. Specifically, in each iteration, a random perturbation is applied to the optimal position so that it can explore in the neighborhood. In this way, even if the current optimal position is close to the local optimum, the algorithm still has the opportunity to find a better global solution through neighborhood search. Neighborhood perturbation can be expressed as: (1.13), in, and, Represented as a random number uniformly generated between [0, 1], Indicates the current position. The new position generated. The size and direction of the disturbance are randomly determined to ensure the randomness and extensiveness of the search. This method can improve the convergence speed of the HHO algorithm and enhance its performance in global optimization problems.

[0038] For the generated neighborhood positions, a greedy strategy is used to decide whether to keep them. The calculation formula is: (1.14), in, yes The fitness of the position. If the generated position is better than the original position, it will replace the original position and make it the global optimal position. Otherwise, the optimal position remains unchanged.

[0039] Impact of Chaos Strategy on HHO: In order to test and verify the performance of CSHHO, we designed 2 sets of experiments. Based on the classic 23 benchmark functions and the standard IEEECEC2017. All experiments are as follows: Experiment 1: First, 7 chaotic mappings were used as population initialization methods for HHO and tested respectively. Secondly, 7 sets of data were analyzed and the optimal chaotic mapping method was selected as the population initialization method for the improved algorithm.

[0040] Experiment 2: First, based on Experiment 1, the adaptive weight mechanism, variable spiral position update, and optimal neighborhood perturbation mechanism are added to the HHO algorithm and tested. Secondly, the CSHHO algorithm is analyzed and compared with the HHO, WOA, SCA, and CSO algorithms. Thirdly, the CSHHO algorithm is analyzed and compared with the developed advanced variants, such as IDSCA–HHOGCHHO, DEPSOASS, IGSA, and DGOBLFOA. Finally, based on the previous step, the algorithm-based accuracy scalability test is performed using IEEE CEC 2017, with the test dimensions of D=50 and D=100.

[0041] To ensure the fairness of the experiment, the same parameters were used for evaluation. All population sizes N were set to 30 and dimensions D was set to 30. Each algorithm was run 50 times independently on each test function. In each run, the function error value ,in is the average value of the test function over all iterations, is the best value of the recorded test function. In order to compare the performance of various algorithms, the mean error (Mean) and standard deviation (Std) of the function error value are used to evaluate the performance of the algorithm in all tests. Rank is also used to sort the average values ​​of all algorithms in the simulation experiment from low to high. The lower the rank, the better the algorithm is compared with other algorithms; conversely, the higher the rank, the worse the algorithm is compared with other algorithms. Best records the best value in 50 experiments, and Worst records the worst value in 50 experiments. In addition, two non-parametric tests are designed, namely: the Wilcoxon signed rank test after Bonferroni-Holm correction is used to detect whether there is a significant performance difference between all algorithms; the Friedman test is used to rank the advantages and disadvantages of all algorithms, the test value is recorded in the ARV row, and Rank is its ranking.

[0042] In order to select the best and most effective chaotic mapping method among the classical chaotic mapping methods, so that the algorithm can obtain the best initial solution position and accelerate the convergence of the Harris Hawk algorithm population, Sinusoidal chaotic mapping, Tent chaotic mapping, Kent chaotic mapping, Cubic chaotic mapping, Logistic chaotic mapping, Gauss chaotic mapping, and Circle chaotic mapping are selected and initialized as the population of the HHO algorithm respectively, forming Sinusoidal–HHO, Tent–HHO, Kent–HHO, Cubic–HHO, Logistic–HHO, Gauss–HHO, and Circle–HHO, and the numerical performance of these seven algorithms is compared. Table 1 lists the results of these seven algorithms in 23 classical test functions. Table 2 shows the results of two non-parametric tests of these seven algorithms. The symbol "+\=\-" represents the number of algorithms whose results obtained by Gauss-HHO are larger\similar\lower than those of other algorithms. The ARV row in Table 2 is the result of the Friedman test of the HHO algorithm after the seven chaotic mappings, and the Rank row is its ranking.

[0043] Table 1 Comparison results of seven chaotic maps , , , , Table 2 Results of Wilcoxon rank sum test and Friedman test after Bonferroni–Holm correction , , By analyzing the experimental results, it is concluded that under the UM function (F1–F7), the Sinusoidal chaotic map achieved the best results in the F3, F5, and F7 test functions, and the Circle chaotic map achieved the best results in the F1 and F4 test functions. The Sinusoidal chaotic map has the greatest impact on the HHO algorithm, followed by the Gauss chaotic map and the Circle chaotic map. Under the MM function: (F8–F23), the Gauss chaotic map has the greatest impact on the HHO algorithm. The Circle chaotic map, the Sinusoidal chaotic map, the Tent chaotic map, and the Kent chaotic map achieved the best results in the F21, F15, F20, F13, and F23 test functions, respectively; the results of the seven chaotic maps were compared in 23 test functions, and the Gauss chaotic map obtained the optimal solution.

[0044] Table 2 shows that among the 23 tested functions, the results based on Gauss–HHO are better; its ARV row gives the Friedman test results. From the average ranking, Gauss–HHO obtains the best results, which shows that for the HHO optimization algorithm, the population initialization of the HHO optimization algorithm using Gauss chaotic mapping is also random, ergodic, and sensitive to initial values. Using Gaussian chaotic mapping to initialize the population of the HHO optimization algorithm can obtain better optimization accuracy.

[0045] Example 2 Reference Figure 2-Figure 8 , which is the second embodiment of the present invention, and which is different from the first embodiment in that: a least squares support vector machine hyperparameter optimization method based on chaotic multi-strategy search Harris Eagle optimization algorithm for lithium battery SOC estimation includes: In the proposed SOC estimation based on CSHHO–LSSVM model, CSHHO is used to optimize the parameters of LSSVM algorithm. The construction of the model includes the following parts, such as Figure 2 As shown: (1) Sample collection. Collect the SOC data of lithium phosphate batteries to form a sample set as the training set for the SOC estimation model.

[0046] (2) Sample division. The samples are divided into two groups: training data and test data. In the simulation, the training data is used to establish the mathematical model, and the test data is used to verify the model.

[0047] (3) Sample normalization. After normalization, all sample data values ​​are in the range of [0, 1], which makes the model calculation faster. The normalized conversion function is, (2.1), in, is the normalized value, is the actual value, and Represent the maximum and minimum values ​​respectively.

[0048] 4) Model construction. The steps of the SOC estimation algorithm based on the CSHHO–LSSVM model are as follows: Step 21: Set each initial parameter, including population size, maximum number of iterations, and dimension.

[0049] Step 22: Initialize the population according to formula (1.1-1.9), where the independent variable x consists of the kernel parameter σ and the regularization parameter γ of the least squares vector machine.

[0050] Step 23: Calculate individual fitness values.

[0051] Step 24: The formula for calculating the escape energy E is expressed as, (2.2), in, A random number represented as (-1, 1).

[0052] Step 25: Use formulas (1.10-1.11) to enter the exploration phase of the algorithm to update the population.

[0053] Step 26: Use formula (1.12) to enter the development phase of the algorithm to update the population.

[0054] Step 27: Use formula (1.13-1.14) to perform optimal neighborhood perturbation. Determine the termination condition of the algorithm. When the condition is met, output the optimal solutions σ and γ, otherwise continue to return to step 24.

[0055] Step 28: Establish an LSSVM model based on σ and γ. Use the obtained optimal estimation model to estimate the SOC of the power lithium battery, compare the estimated results with the measured results, and conduct targeted error analysis.

[0056] Performance indicators: In order to compare the performance differences between different models, this paper uses four evaluation indicators, namely root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and determination coefficient (R-squared, R2). The calculation formulas of the four evaluation indicators are: (2.3), (2.4), (2.5), (2.6), in, is the SOC prediction value; is the true value of SOC; is the average value of SOC; n is the number of predicted values. RMSE, MAE, and MAPE represent the fitting effect between the true value and the predicted value. The smaller the value, the more accurate the prediction result. It represents the degree of fit of the model to the data. The closer its value is to 1, the more accurate the prediction result is.

[0057] Experimental analysis: This simulation experiment uses a university 18650 battery data set to verify the proposed model, where DST (sample 1), FUDS (sample 2), BJDST (sample 3) operating condition data are selected as training sets, and US06 (sample 4) operating conditions are selected as test sets. Figure 3-Figure 6 shown.

[0058] analyze Figure 7 It can be seen that under the same working conditions, when using CSHHO-LSSVM (a least squares support vector machine hyperparameter optimization method based on the chaotic multi-strategy search Harris Hawk optimization algorithm), GCHHO-LSSVM (a least squares support vector machine hyperparameter optimization method based on the Harris Hawk optimization algorithm with the introduction of Gaussian mutation and dimensional decision logic) and WOA-LSSVM (a least squares support vector machine hyperparameter optimization method based on the whale optimization algorithm) to predict SOC, the prediction output curve of CSHHO-LSSVM is closer to the expected value than the other methods, indicating that the prediction accuracy of CSHHO-LSSVM is higher than that of GCHHO-LSSVM and WOA-LSSVM. Figure 8 It can be seen that under the US06 working condition, the prediction errors of the proposed algorithm are basically kept within 2%, while the prediction errors of the other algorithms are basically kept within 16%. The training error shows that the SOC prediction result of CSHHO-LSSVM is more ideal and the prediction error is smaller.

[0059] Table 3 Comparison of SOC estimation results , To further verify the accuracy of the prediction model, the prediction results of CSHHO-LSSVM are compared with those of GCHHO-LSSVM and WOA-LSSVM. The evaluation indicators of the three models for the battery SOC estimation results are shown in Table 3. It can be seen that the RMSE of the CSHHO-LSSVM algorithm of the present invention is reduced by about 0.13 and 0.43, the MAPE% is reduced by about 0.30 and 5.11, and the MAE is reduced by about 0.21 and 0.42, respectively, compared with the GCHHO-LSSVM algorithm and the WOA-LSSVM algorithm, and the R2 is increased by about 0.008 and 0.013, respectively, reflecting that the prediction error of the algorithm of the present invention is smaller. In summary, the CSHHO-LSSVM model has higher accuracy and better regression fitting performance.

[0060] Embodiment 3 is the third embodiment of the present invention, which is different from the previous two embodiments in that: a system for a least squares support vector machine hyperparameter optimization method based on a chaotic multi-strategy search Harris Hawk optimization algorithm for lithium battery SOC estimation, comprising a data processing module, a parameter training module and an error analysis module; the data processing module collects the SOC data of the lithium phosphate battery to form a sample set, and normalizes the sample set data; the parameter training module optimizes the kernel parameter σ and the regularization parameter γ of the least squares support vector machine based on the chaotic multi-strategy search Harris Hawk optimization algorithm, and outputs the optimal solutions σ and γ; the error analysis module establishes an LSSVM model based on the optimal solutions σ and γ, uses the obtained optimal estimation model to estimate the SOC of the power lithium battery, and performs targeted error analysis based on the estimation results compared with the measured results.

[0061] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0062] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0063] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0064] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0065] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A lithium battery SOC estimation algorithm based on CSHHO-LSSVM, characterized by: include, Collect the SOC data of lithium phosphate batteries to form a sample set, divide the sample set into two groups: training data and test data. Use the training data to establish a mathematical model in the simulation, and use the test data to verify the model. Normalize the sample set so that all sample data values ​​are in the range of [0, 1]. The normalized conversion function is expressed as: ， in, is the normalized value, is the actual value, and Represent the maximum and minimum values ​​respectively; Set each initial parameter, including population size, maximum number of iterations, and dimension; Initialize the population according to seven chaotic maps, where the independent variables consist of the kernel parameter σ and the regularization parameter γ of the least squares vector machine, and calculate the individual fitness value; Calculating the escape energy The formula is expressed as, ， in, A random number represented as (-1, 1); According to the position update formula, enter the exploration phase of the algorithm to update the population, and introduce the variable spiral position update strategy to enter the development phase of the algorithm to update the population; The optimal neighborhood perturbation strategy is used to perform the optimal neighborhood perturbation and determine the termination condition of the algorithm. When the condition is met, the optimal solutions σ and γ are output. Otherwise, the escape energy is recalculated and the population is continued to be updated. Establish an LSSVM model based on the optimal solution σ and γ, use the obtained optimal estimation model to estimate the SOC of the power lithium battery, and conduct targeted error analysis based on the comparison of the estimated results with the measured results; The optimal neighborhood perturbation strategy is expressed as: ， in, and Represented as a random number uniformly generated between [0, 1], Indicates the current position. The new position generated; For the generated neighborhood positions, a greedy strategy is used to decide whether to keep them. The calculation formula is expressed as: ， in, Expressed as The fitness of the position.

2. The lithium battery SOC estimation algorithm based on CSHHO-LSSVM as claimed in claim 1, characterized in that: The seven chaotic maps include Sinusoidal chaotic map, Tent chaotic map, Kent chaotic map, Cubic chaotic map, Logistic chaotic map, Gauss chaotic map and Circle chaotic map; The initialization of the population according to the seven chaotic maps includes randomly generating Harris Hawk, the formula is expressed as, ， in, is represented as the Harris hawk population set, Represented as Harris Hawk individuals; Each dimension of each Harris Hawk is iterated M times, generating M Harris Hawks; After completing all Harris Hawk iterations, seven types of chaos mapping are applied to the solution space expressed as, ， in, It is expressed as the upper bound of the d-dimensional exploration space, It is expressed as the lower bound of the d-dimensional exploration space; It is expressed as the d-dimensional coordinates of the ith Harris Hawk; Represented as the coordinates of the ith Harris Hawk in the d-dimensional exploration space.

3. The lithium battery SOC estimation algorithm based on CSHHO-LSSVM as claimed in claim 2, characterized in that: The method for constructing the position update formula includes forming an adaptive weight according to the current number of iterations t, improving the Harris Eagle optimization algorithm according to the adaptive weight, and obtaining the position update formula; The adaptive weight is formed according to the current number of iterations t It is expressed as, ， in, Expressed as the maximum number of iterations; The position update formula is expressed as: ， in, , , , , Represented as a random number in the range [0, 1), is represented as the optimal value of the i-th generation, Expressed as the average value of the population in the i-th generation.

4. The lithium battery SOC estimation algorithm based on CSHHO-LSSVM as claimed in claim 2, characterized in that: The introduction of the variable spiral position update strategy into the development phase of the algorithm to update the population includes introducing the variable spiral position update strategy into the position update formula, and updating the Harris Hawk population position as follows: ， in, Expressed as control variables, designed according to the spiral mathematical model.

5. A system using a lithium battery SOC estimation algorithm based on CSHHO-LSSVM as claimed in claim 4, characterized in that: It includes data processing module, parameter training module and error analysis module; The data processing module collects the SOC data of the lithium phosphate battery to form a sample set, and normalizes the sample set data; The parameter training module optimizes the kernel parameter σ and the regularization parameter γ of the least squares support vector machine based on the chaotic multi-strategy search Harris Eagle optimization algorithm, and outputs the obtained optimal solutions σ and γ; The error analysis module establishes an LSSVM model based on the optimal solutions σ and γ, uses the obtained optimal estimation model to estimate the SOC of the power lithium battery, and performs targeted error analysis based on the comparison of the estimation results with the actual measurement results.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a lithium battery SOC estimation algorithm based on CSHHO-LSSVM described in claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a lithium battery SOC estimation algorithm based on CSHHO-LSSVM described in claims 1 to 4 are implemented.

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