Battery consistency rapid sorting method based on electrochemical impedance spectroscopy

Abstract

The invention discloses a rapid battery consistency sorting method based on an electrochemical impedance spectrum. The method comprises the following steps: S1, acquiring the electrochemical impedance spectrum of all batteries to be sorted; s2, inputting the electrochemical impedance spectrums of all the to-be-sorted batteries into a pre-trained principal component analysis conversion matrix, so that the high-dimensional impedance characteristics are reduced into one-dimensional impedance characteristics; and S3, inputting the one-dimensional impedance characteristics of all the to-be-sorted batteries into a greedy sliding window sorting algorithm to obtain a consistency sorting result of the to-be-sorted batteries. According to the method, the sorting characteristic measurement time is short, the sorting efficiency is high, the capacity utilization efficiency of the sorted battery module is higher, the service life is longer, and the method has important significance for improving the efficiency and effect of battery consistency sorting.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a method for rapid battery consistency sorting based on electrochemical impedance spectroscopy. Background Art

[0002] Battery consistency sorting technology is one of the key technologies for the cascade utilization of retired power batteries, and is of great significance for improving the charging and discharging performance, service life and safety performance of battery modules. The series module of batteries has a capacity barrel effect, and only the battery with the lowest capacity in the module can be filled or discharged, which means that the effective capacity of the battery module is equal to the capacity of the lowest battery cell, and the capacity of other batteries cannot be fully utilized, resulting in a decrease in the economic benefits of the battery module. Therefore, improving the consistency of batteries in the module is of great significance to improving the capacity utilization efficiency of the battery cells in the module and extending the service life of the battery module.

[0003] Invention patent CN115248393A discloses a battery consistency sorting method, which obtains multiple sorting variables, voltage curves and energy curves corresponding to each battery cell by performing charge and discharge tests, battery internal resistance measurements and open circuit voltage measurements on battery cells; performs multi-parameter sorting on battery cells according to multiple sorting variables to obtain multi-parameter sorting results; sorts batteries into first-category batteries and second-category batteries according to the multi-parameter sorting results; sorts second-category batteries according to voltage curves and energy curves to obtain battery sorting results. This method takes into account multiple consistency evaluation dimensions of batteries and can evaluate the consistency of batteries more comprehensively; however, it is time-consuming to perform multi-parameter measurements on batteries, and it is difficult to compare the sorting effects of different sorting schemes on the same batch of batteries to prove that this method can achieve the best sorting effect.

[0004] Similarly, invention patents CN115889245A, CN112666471A and CN107597619A respectively disclose a battery consistency sorting method, which measures battery voltage, internal resistance and discharge capacity as sorting parameters, evaluates battery consistency from multiple dimensions, and then performs consistency sorting. However, these methods also have the problems of long measurement time, complex operation, and difficulty in proving the advantages of sorting effect through rigorous comparative experiments. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a method for rapid sorting of battery consistency based on electrochemical impedance spectroscopy, which has a short time consumption for sorting feature measurement, high sorting efficiency, higher capacity utilization efficiency and longer service life of the sorted battery modules, which is of great significance for improving the efficiency and effect of battery consistency sorting. In order to achieve the above-mentioned purpose and other advantages according to the present invention, a method for rapid sorting of battery consistency based on electrochemical impedance spectroscopy is provided, comprising the following steps: S1. Obtain the electrochemical impedance spectra of all batteries to be sorted; S2, inputting the electrochemical impedance spectra of all the batteries to be sorted into a pre-trained principal component analysis conversion matrix, so that the high-dimensional impedance features are reduced to one-dimensional impedance features; S3. Input the one-dimensional impedance characteristics of all batteries to be sorted into the greedy sliding window sorting algorithm to obtain consistent sorting results for the batteries to be sorted.

[0006] Preferably, in step S1, electrochemical impedance spectra of all batteries to be sorted under a frequency set f are obtained, and the frequency range of the frequency set f is determined by a permutation feature importance method.

[0007] Preferably, the permutation feature importance method relies on a multivariate nonlinear regression model such as XGBoost or Gaussian process regression, and the specific steps of the permutation feature importance method are as follows: S11. For a data set characterized by the electrochemical impedance spectrum of a battery and labeled by the remaining available discharge capacity of the battery, randomly divide it into a training set and a test set according to a certain ratio; S12, training the selected multivariate nonlinear regression model through the training set to obtain a trained model; S13, using the original electrochemical impedance spectroscopy feature data in the test set as the model input and the battery remaining available discharge capacity label data in the test set as the true value to calculate the performance index of the model; S14. For features in the test set All the corresponding feature data are randomly combined with another feature Perform data exchange, i.e. replace features; S15, after completing the replacement feature, using the electrochemical impedance spectroscopy feature data in the test set as the model input, and using the battery remaining available discharge capacity label data in the test set as the true value, to calculate the performance index of the model; S16, repeat steps S14 to S15 several times, so that the feature Exchange data with multiple different features to obtain features The average performance index of the model after multiple permutations of features is calculated, and the difference in the model performance index corresponding to step S13 is calculated, and the difference is used as the feature The permutation feature importance index of ; S17. Repeat steps S14 to S16, calculate the permutation feature importance index of all features, and sort them according to the size of the index to obtain the permutation feature importance sorting results of all features, and determine the features with stronger correlation with the label.

[0008] Preferably, the remaining available discharge capacity of the battery in step S11 is defined as the amount of electricity that can be discharged when the battery is charged and discharged under a specific operating condition starting from the current state until the preset end-of-life state.

[0009] Preferably, the steps of training the principal component analysis conversion matrix in step S2 are as follows: S21, obtaining electrochemical impedance spectroscopy data of several batteries of the same model under a frequency set f; S22, combining the electrochemical impedance data of all batteries into a two-dimensional matrix and inputting it into a principal component analysis model; S23. According to the input data distribution, the principal component analysis conversion matrix is ​​calculated.

[0010] Preferably, the greedy sliding window sorting algorithm in step S3 includes the following steps: S31, sorting the one-dimensional impedance characteristics of the batteries to be sorted; S32, linearly scanning the one-dimensional impedance characteristic data list with a preset sliding window length, calculating the range of the one-dimensional impedance characteristic in each sliding window, and updating the minimum value of the range and the corresponding sliding window; S33, after the linear scan is completed, the battery in the sliding window corresponding to the minimum value of the one-dimensional impedance characteristic range is taken out from the battery to be sorted, and added to the sorting result list; S34, repeating steps S32 to S33 until the number of batteries to be sorted is less than the sliding window size, or the minimum value of the one-dimensional impedance characteristic range does not meet the optimization constraint; outputting a sorting result list as a consistency sorting result.

[0011] Compared with the prior art, the present invention has the following beneficial effects: the fast battery consistency sorting method based on electrochemical impedance spectroscopy proposed in the present invention uses the battery broadband impedance as the feature of battery consistency sorting, and due to the different frequency set f scale and electrochemical impedance spectroscopy measurement method, the characteristic measurement time of a battery can be controlled to less than 1 minute at the shortest; the computational efficiency of the greedy sliding window sorting algorithm is very high, and the sorting of 1,000 battery data can be completed within 1 minute.

[0012] The battery consistency rapid sorting method based on electrochemical impedance spectroscopy proposed in the present invention has been scientifically demonstrated and can give a reasonable quantitative definition of battery aging consistency based on the remaining available discharge capacity of the battery. By designing experiments that can compare the consistency sorting effects, it is proved that compared with the traditional consistency sorting based on capacity calibration values, the battery modules sorted by the present invention have the advantages of higher capacity utilization efficiency and longer service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flowchart of a method for rapid battery consistency sorting based on electrochemical impedance spectroscopy according to the present invention; Figure 2 A diagram showing the importance ranking of substitution features in an embodiment of a method for rapid battery consistency sorting based on electrochemical impedance spectroscopy according to the present invention; Figure 3 A schematic diagram of calculating the remaining available discharge capacity of a battery according to the battery consistency rapid sorting method based on electrochemical impedance spectroscopy of the present invention; Figure 4 A comparison diagram of electrochemical impedance spectra of the same battery measured under different ambient temperatures and battery terminal voltage conditions according to the battery consistency rapid sorting method based on electrochemical impedance spectroscopy of the present invention; Figure 5 This is a graph showing the electrochemical impedance spectrum characteristic matching result of a 18650 ternary lithium-ion battery according to the battery consistency rapid sorting method based on electrochemical impedance spectroscopy of the present invention; Figure 6 A battery capacity estimation result diagram of a battery consistency rapid sorting method based on electrochemical impedance spectroscopy according to the present invention, using feature-matched electrochemical impedance spectroscopy data as input; Figure 7 The figure is a relationship diagram between the one-dimensional impedance characteristics of the battery consistency rapid sorting method based on electrochemical impedance spectroscopy according to the present invention and the remaining available discharge capacity of the battery. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention. Example 1

[0015] Reference Figure 1 , a battery consistency rapid sorting method based on electrochemical impedance spectroscopy, comprising the following steps: 1) Obtain the electrochemical impedance spectra of all batteries to be sorted under the frequency set f; 2) The electrochemical impedance spectra of all batteries to be sorted are input into the pre-trained principal component analysis conversion matrix to reduce the high-dimensional impedance features to one-dimensional impedance features; 3) Taking the one-dimensional impedance characteristic as the sorting parameter, the number of cells in the battery module as the length of the sliding window, and minimizing the range of the one-dimensional impedance characteristic as the optimization goal, the one-dimensional impedance characteristics of all batteries to be sorted are input into the greedy sliding window sorting algorithm to obtain consistent sorting results for the batteries to be sorted.

[0016] In step 1), the frequency range of the frequency set f is determined by the permutation feature importance method. The permutation feature importance method relies on the XGBoost model and includes the following steps: For a data set characterized by the electrochemical impedance spectrum of a battery and labeled by the remaining available discharge capacity of the battery, it is randomly divided into a training set and a test set according to a certain ratio; Use the training set to train the XGBoost model to obtain a trained model; The original electrochemical impedance spectroscopy characteristic data in the test set is used as the model input, and the battery remaining available discharge capacity label data in the test set is used as the true value to calculate the model's goodness of fit; For the features in the test set All the corresponding feature data are randomly combined with another feature Perform data exchange, i.e. replace features; The electrochemical impedance spectroscopy feature data in the test set after feature replacement is used as the model input, and the battery remaining available discharge capacity label data in the test set is used as the true value to calculate the model's goodness of fit; Order Features Exchange data with multiple different features to obtain features The average goodness of fit of the model after multiple feature substitutions is calculated, and the difference between the goodness of fit of the model before feature substitution is calculated, and the difference is used as the feature The permutation feature importance index of ; Calculate the permutation feature importance index of all features and sort them according to the size of the index to obtain the permutation feature importance ranking results of all features and determine the features with stronger correlation with the label. Figure 2 As shown, the top five features ranked by permutation feature importance are distributed in the frequency range of 10 Hz to 500 Hz, so the frequency range of the frequency set f is determined to be 10 Hz to 500 Hz.

[0017] In the above steps, the remaining available discharge capacity of the battery is defined as the amount of electricity that can be discharged from the battery starting from the current state, when the battery is charged and discharged under a specific condition until the capacity calibration value decays to 75% of the rated capacity. Figure 3 As shown, the battery Starting from the state, charge and discharge cycles are performed under typical working conditions until the capacity calibration value decays to 75% of the rated capacity, that is, reaching state, then under typical working conditions, the remaining available discharge capacity of this battery is .

[0018] In step 2), the training method of the principal component analysis conversion matrix includes the following steps: Obtain electrochemical impedance spectroscopy data of several batteries of the same model under frequency set f; The electrochemical impedance data of all batteries were combined into a two-dimensional matrix and input into the principal component analysis model; According to the input data distribution, the principal component analysis transformation matrix is ​​calculated.

[0019] In step 3), the greedy sliding window sorting algorithm includes the following steps: Sort the one-dimensional impedance characteristics of the cells to be sorted; Perform a linear scan on the one-dimensional impedance characteristic data list with a preset sliding window length, calculate the range of the one-dimensional impedance characteristic in each sliding window, and update the minimum value of the range and the corresponding sliding window; After the linear scan is completed, the battery in the sliding window corresponding to the minimum value of the one-dimensional impedance characteristic range is taken out from the battery to be sorted and added to the sorting result list; Repeat the above sorting process until the number of batteries to be sorted is less than the sliding window size, or the minimum value of the one-dimensional impedance characteristic range does not meet the optimization constraint; output the sorting result list as the consistency sorting result.

[0020] The process of the greedy sliding window sorting algorithm is as follows Figure 4 shown.

[0021] The implementation principle of the present invention is as follows: The present invention is mainly aimed at the cascade utilization scenario of retired batteries. Since the consistency of retired batteries is relatively poor, there are safety hazards in connecting batteries in parallel, so the reorganization modules in the cascade utilization scenario are mainly series modules.

[0022] Considering the behavior of battery cells in a series module, it is found that due to the series relationship constraint, the current passing through all battery cells in the series module is equal at all times, which means: 1) The working conditions experienced by each battery cell are exactly the same; 2) From the moment the battery enters the series module, the cumulative discharge of each battery cell is equal at all times.

[0023] According to the above conclusions, without reorganizing the battery cells to be sorted, each battery cell can be subjected to a single-cell cycle aging test under a typical working condition until the battery cell capacity decays to the end of its life. Since the available capacity of the battery module is always equal to the capacity of the lowest battery cell, by analyzing the cumulative discharge capacity-capacity curve of each battery cell, the cumulative discharge capacity-capacity curve of the battery module can be calculated when these cells are grouped in different combinations. Figure 5 As shown, C1, C2 and C3 represent the cumulative discharge capacity-capacity curves of three different battery cells respectively; if these three battery cells are used to form a battery module when the cumulative discharge capacity is 0Ah, then the cumulative discharge capacity-capacity curve of the module can be represented by M; when the cumulative discharge capacity is 0Ah, the remaining available discharge capacity of the battery module has been marked in the figure.

[0024] Note that the remaining available discharge capacity of the module is the minimum value of the remaining available discharge capacity of all battery cells. This means that when the battery module reaches the end of its life, the remaining available discharge capacity of the battery cells in the module, except for the battery cell with the lowest capacity, is more or less wasted. The remaining available discharge capacity of the battery cell is , the remaining available discharge capacity of the battery module is , then for the For a single battery, the remaining available discharge capacity utilization rate is Right now ; The capacity utilization of the battery module can be obtained by taking the average of the remaining available discharge capacity utilization of all battery cells. , and this index is used as a quantitative index to evaluate the aging consistency of battery cells in the module. Obviously, the closer the remaining available discharge capacity of the battery cells is, the higher the capacity utilization rate of the battery module is.

[0025] Since the remaining available discharge capacity of the battery to be sorted under a certain working condition is unknown. Therefore, it is hoped that a battery state characterization quantity related to the remaining available discharge capacity of the battery can be found, and the remaining available discharge capacity under the working condition can be estimated by the state characterization quantity, or the consistency of the remaining available discharge capacity can be approximated by the consistency of the state characterization quantity. After trying, it was found that the relationship between the electrochemical impedance spectrum and the remaining available discharge capacity of the battery can be established through the multivariate nonlinear regression model of XGBoost. Figure 6As shown in the figure, the remaining available discharge capacity of the battery is estimated by electrochemical impedance characteristics, and the goodness of fit of the model can reach 0.919, showing a strong correlation; however, the average absolute percentage error of the model reaches 17.5%, indicating that the description of the remaining available discharge capacity of the battery using electrochemical impedance characteristics is not accurate enough; the larger the actual remaining available discharge capacity, the lower the estimation accuracy.

[0026] It is reasonable for the above two types of data to present such a relationship, because the greater the remaining available discharge capacity, the farther the distance from the current state to the end of the battery life, and the greater the uncertainty. In order to improve the estimation accuracy, consider using the permutation feature importance method to find the frequency range in the electrochemical impedance spectrum that has the strongest correlation with the remaining available discharge capacity, and then use the principal component analysis method to reduce the feature dimension to better analyze the relationship between battery impedance and the remaining available discharge capacity. After the above steps, it is found that there is an exponential correlation between the one-dimensional impedance feature after dimensionality reduction and the remaining available discharge capacity of the battery. Figure 7 As shown in the figure, the smaller the one-dimensional impedance feature is, the greater the absolute value of the slope of the remaining available discharge capacity of the battery to the one-dimensional impedance feature is. This results in that when the actual value of the remaining available discharge capacity of the battery is large, the accuracy of the electrochemical impedance feature in describing the remaining available discharge capacity of the battery will inevitably decrease. This finding is consistent with Figure 6 The phenomena shown are exactly the same.

[0027] Nevertheless, it is still feasible to approximate the consistency of the remaining available discharge capacity of the battery through the consistency of the battery's one-dimensional impedance characteristics. In the experimental data set, the one-dimensional impedance characteristics of the battery are used as the sorting parameter. When the number of battery modules is 4 and 7, the average battery module capacity utilization rate in the sorting results is 87.50% and 79.08%, respectively.

[0028] For reference, when the grouping rate is exactly the same, if the battery capacity calibration value is used as the sorting parameter, when the number of battery modules is 4 and 7 respectively, the average battery module capacity utilization rate in the sorting results is 83.07% and 80.25% respectively. From the above reference indicators, compared with the traditional consistency sorting method that uses capacity calibration value as the sorting parameter, the consistency sorting method that uses one-dimensional impedance characteristics as the sorting parameter has obvious advantages in sorting performance and sorting efficiency.

[0029] The number of devices and processing scales described here are used to simplify the description of the present invention, and the application, modification and variation of the present invention will be obvious to those skilled in the art.

[0030] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A method for rapid battery consistency sorting based on electrochemical impedance spectroscopy, characterized in that: The following steps are involved: S1. Obtain the electrochemical impedance spectra of all batteries to be sorted; S2, inputting the electrochemical impedance spectra of all the batteries to be sorted into a pre-trained principal component analysis conversion matrix, so that the high-dimensional impedance features are reduced to one-dimensional impedance features; S3. Input the one-dimensional impedance characteristics of all batteries to be sorted into the greedy sliding window sorting algorithm to obtain consistent sorting results for the batteries to be sorted.

2. A method for rapid battery consistency sorting based on electrochemical impedance spectroscopy as claimed in claim 1, characterized in that: In the step S1, the electrochemical impedance spectra of all batteries to be sorted under the frequency set f are obtained, and the frequency range of the frequency set f is determined by the permutation feature importance method.

3. A method for rapid battery consistency sorting based on electrochemical impedance spectroscopy as claimed in claim 2, characterized in that: The permutation feature importance method relies on a multivariate nonlinear regression model such as XGBoost or Gaussian process regression. The specific steps of the permutation feature importance method are as follows: S11. For a data set characterized by the electrochemical impedance spectrum of a battery and labeled by the remaining available discharge capacity of the battery, randomly divide it into a training set and a test set according to a certain ratio; S12, training the selected multivariate nonlinear regression model through the training set to obtain a trained model; S13, using the original electrochemical impedance spectroscopy feature data in the test set as the model input and the battery remaining available discharge capacity label data in the test set as the true value to calculate the performance index of the model; S14. For features in the test set All the corresponding feature data are randomly combined with another feature Perform data exchange, i.e. replace features; S15, after completing the replacement feature, using the electrochemical impedance spectroscopy feature data in the test set as the model input, and using the battery remaining available discharge capacity label data in the test set as the true value, to calculate the performance index of the model; S16, repeat steps S14 to S15 several times, so that the feature Exchange data with multiple different features to obtain features The average performance index of the model after multiple permutations of features is calculated, and the difference in the model performance index corresponding to step S13 is calculated, and the difference is used as the feature The permutation feature importance index of ; S17. Repeat steps S14 to S16, calculate the permutation feature importance index of all features, and sort them according to the size of the index to obtain the permutation feature importance sorting results of all features, and determine the features with stronger correlation with the label.

4. A method for rapid battery consistency sorting based on electrochemical impedance spectroscopy as claimed in claim 3, characterized in that: The remaining available discharge capacity of the battery in step S11 is defined as the amount of electricity that can be discharged when the battery is charged and discharged under a certain condition starting from the current state until the preset end-of-life state.

5. A method for rapid battery consistency sorting based on electrochemical impedance spectroscopy as claimed in claim 1, characterized in that: The steps of training the principal component analysis conversion matrix in step S2 are as follows: S21. Obtain the frequency set of several batteries of the same model f Electrochemical impedance spectroscopy data under; S22, combining the electrochemical impedance data of all batteries into a two-dimensional matrix and inputting it into a principal component analysis model; S23. According to the input data distribution, the principal component analysis conversion matrix is ​​calculated.

6. A method for rapid battery consistency sorting based on electrochemical impedance spectroscopy as claimed in claim 1, characterized in that: The greedy sliding window sorting algorithm in step S3 includes the following steps: S31, sorting the one-dimensional impedance characteristics of the batteries to be sorted; S32, linearly scanning the one-dimensional impedance characteristic data list with a preset sliding window length, calculating the range of the one-dimensional impedance characteristic in each sliding window, and updating the minimum value of the range and the corresponding sliding window; S33, after the linear scan is completed, the battery in the sliding window corresponding to the minimum value of the one-dimensional impedance characteristic range is taken out from the battery to be sorted, and added to the sorting result list; S34, repeating steps S32 to S33 until the number of batteries to be sorted is less than the sliding window size, or the minimum value of the one-dimensional impedance characteristic range does not meet the optimization constraint; outputting a sorting result list as a consistency sorting result.

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