Lithium battery step utilization sorting method based on incremental capacity change

Abstract

The application discloses a lithium battery step utilization sorting method based on incremental capacity change, and relates to the technical field of battery health management. The method comprises the following steps: determining the incremental capacity curve and the incremental capacity characteristics of a lithium battery according to the charging test data of the lithium battery; randomly selecting a reference battery, determining the incremental capacity change between the remaining lithium batteries and the reference battery, and obtaining the incremental capacity change curve of each lithium battery; determining a plurality of characteristic parameters according to the incremental capacity change curve and the incremental capacity characteristics of the lithium battery; performing correlation analysis on the plurality of characteristic parameters by using a principal component analysis method, and determining a principal component index; calculating the comprehensive sorting index value of the lithium battery according to each characteristic parameter and the principal component index of the lithium battery; and performing clustering on a plurality of lithium batteries by using a K-means algorithm according to the comprehensive sorting index value of each lithium battery, so as to realize step utilization sorting of the plurality of lithium batteries. The application improves the efficiency and accuracy of lithium battery step utilization sorting.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of battery health management, in particular to a lithium battery step utilization sorting method based on incremental capacity change. BACKGROUND

[0002] With the rapid development of the new energy industry, the production and use scale of lithium batteries are increasing year by year, and the safe use and effective management of batteries have attracted widespread attention. The step utilization of lithium batteries refers to the process of recombining waste lithium batteries into step products so that they can be applied to other fields. An efficient step utilization sorting method can prolong the service life of lithium batteries, fully utilize their residual value, promote new energy consumption, and drive the development of the new energy vehicle industry.

[0003] The main basis for lithium battery step utilization sorting is static parameters and dynamic parameters. Static parameters include no-load voltage, static capacity, etc., and the feature extraction is relatively simple, but it is difficult to fully reflect the inconsistency of the battery operating state. Therefore, in actual application, dynamic parameters are usually selected as the basis for battery classification or health state estimation. The dynamic parameters of the battery refer to the changes in the battery temperature, charge-discharge characteristic curve and electrochemical impedance during the charge-discharge process. At present, the battery sorting method is usually based on the incremental capacity (IC) curve. The IC curve has unique characteristics such as peak value, shape and position, and can reflect the state changes in the battery operation process. The IC curves of lithium batteries in different states often have great differences, and the shift of the peak or valley points causes difficulty in calculating the slope and area characteristics, which is difficult to apply to large-scale lithium battery step utilization sorting. Therefore, how to conveniently and quickly calculate effective classification characteristics is one of the key problems to improve the efficiency of lithium battery step utilization.

[0004] In addition, the IC curve usually has rich multi-feature information, and there is strong correlation between different features, causing the problem of multicollinearity, increasing the useless calculation amount, and thus leading to inaccurate battery step utilization sorting results.

[0005] Therefore, it is necessary to design a new lithium battery step utilization sorting method to overcome the shortcomings of the prior art and solve or alleviate the above problems. SUMMARY

[0006] The purpose of the present application is to provide a lithium battery step utilization sorting method based on incremental capacity change, which can improve the efficiency and accuracy of lithium battery step utilization sorting.

[0007] To achieve the above purpose, the present application provides the following solutions:

[0008] In a first aspect, the present application provides a subject method, comprising:

[0009] A lithium battery step utilization sorting method based on incremental capacity change, comprising:

[0010] Obtain the charging test data of a plurality of lithium batteries, and determine the incremental capacity curve and the incremental capacity characteristics of each lithium battery according to the charging test data of each lithium battery, respectively;

[0011] Randomly select one lithium battery from the plurality of lithium batteries as a reference battery, and determine the incremental capacity change between the remaining lithium batteries and the reference battery according to the incremental capacity curve of each lithium battery, to obtain the incremental capacity change curve of each lithium battery;

[0012] For any lithium battery, determine a plurality of characteristic parameters of the lithium battery according to the incremental capacity change curve and the incremental capacity characteristics of the lithium battery;

[0013] According to the plurality of characteristic parameters of each lithium battery, perform correlation analysis on the plurality of characteristic parameters by using principal component analysis method, and determine a principal component index;

[0014] For any lithium battery, calculate a comprehensive sorting index value of the lithium battery according to each characteristic parameter of the lithium battery and the principal component index;

[0015] According to the comprehensive sorting index value of each lithium battery, perform clustering on the plurality of lithium batteries by using K-means algorithm, to realize step utilization sorting of the plurality of lithium batteries.

[0016] Optionally, determining the plurality of characteristic parameters of the lithium battery according to the incremental capacity change curve and the incremental capacity characteristics of the lithium battery specifically comprises:

[0017] For any lithium battery, determine a plurality of preliminary characteristic parameters of the lithium battery according to the incremental capacity change curve of the lithium battery; wherein the plurality of preliminary characteristic parameters include the peak value, the left slope, the right slope and the area of each incremental capacity characteristic;

[0018] Perform data dimension reduction on the preliminary characteristic parameters to determine the plurality of characteristic parameters of each lithium battery.

[0019] Optionally, the number of incremental capacity characteristics of each lithium battery is four, which are a first characteristic, a second characteristic, a third characteristic and a fourth characteristic, respectively;

[0020] The plurality of characteristic parameters of each lithium battery are the peak value of the first characteristic, the peak value of the second characteristic, the peak value of the third characteristic, the peak value of the fourth characteristic, the left slope of the second characteristic, the right slope of the third characteristic, the area of the second characteristic and the area of the third characteristic, respectively.

[0021] Optionally, the plurality of preliminary characteristic parameters of each lithium battery are calculated by using the following formula:

[0022]

[0023] Slope ml is the left slope of the mth incremental capacity characteristic of any lithium battery, Slope mr is the right slope of the mth incremental capacity characteristic of any lithium battery, Area m is the area of the mth incremental capacity characteristic of any lithium battery, is the peak value of the mth incremental capacity characteristic of any lithium battery, is the left valley value adjacent to the peak value of the mth incremental capacity characteristic of any lithium battery, is the right valley value adjacent to the peak value of the mth incremental capacity characteristic of any lithium battery, is the charging voltage corresponding to the peak value of the mth incremental capacity characteristic of any lithium battery, is the charging voltage corresponding to the left valley value adjacent to the peak value of the mth incremental capacity characteristic of any lithium battery, is the charging voltage corresponding to the right valley value adjacent to the peak value of the mth incremental capacity characteristic of any lithium battery.

[0024] Optionally, according to the plurality of characteristic parameters of each lithium battery, a principal component analysis method is adopted to perform correlation analysis on the plurality of characteristic parameters to determine a principal component index, specifically including:

[0025] Each characteristic parameter of each lithium battery is normalized to obtain a plurality of normalized characteristic parameters of each lithium battery;

[0026] According to the plurality of normalized characteristic parameters of each lithium battery, a correlation coefficient between any two normalized characteristic parameters is calculated, and a correlation coefficient matrix is constructed;

[0027] A plurality of eigenvalues of the correlation coefficient matrix and a characteristic vector corresponding to each eigenvalue are calculated;

[0028] The characteristic vector corresponding to an eigenvalue greater than 1 is taken as the principal component index.

[0029] Optionally, the following formula is adopted to calculate the correlation coefficient between the normalized characteristic parameter xc and the normalized characteristic parameter yc:

[0030]

[0031] wherein r c (xc,yc) is the correlation coefficient between the normalized characteristic parameter xc and the normalized characteristic parameter yc, xc i is the normalized characteristic parameter xc of the i th lithium battery, yc i is the normalized characteristic parameter yc of the i th lithium battery, μxc is an average value of the normalized characteristic parameter xc, and n is a number of lithium batteries. yc is an average value of the normalized characteristic parameter yc, and n is a number of lithium batteries.

[0032] Optionally, the principal component index comprises an index value of each characteristic parameter.

[0033] According to each characteristic parameter of the lithium battery and the principal component index, a comprehensive sorting index value of the lithium battery is calculated, specifically comprising:

[0034] Each characteristic parameter of the lithium battery is multiplied by a corresponding index value and summed to obtain the comprehensive sorting index value of the lithium battery.

[0035] According to the specific embodiments provided in the present application, the following technical effects are disclosed:

[0036] The present application provides a lithium battery step utilization sorting method based on incremental capacity change, randomly selects a reference battery, and takes the incremental capacity change curve between the remaining lithium batteries and the reference battery as the classification feature extraction basis, which can simply and quickly calculate the characteristic parameters and improve the efficiency of lithium battery classification. At the same time, principal component analysis and K-means algorithm are used for classification, and a comprehensive sorting index value is used instead of the original multiple characteristic parameters to avoid the multicollinearity problem caused by multiple characteristics, thereby improving the accuracy of lithium battery step utilization sorting. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0038] Figure 1 The flowchart of the lithium battery step utilization sorting method based on incremental capacity change provided by the present application is shown in the figure.

[0039] Figure 2 The incremental capacity curve of the lithium battery under different operating states is shown in the figure.

[0040] Figure 3 The incremental capacity change curve of the lithium battery under different operating states is shown in the figure.

[0041] Figure 4 The figure shows the correlation change between the area of the second feature and the area of the third feature and the capacity of the battery under different operating states.

[0042] Figure 5The left slope of the second feature and the right slope of the third feature and the correlation change graph of the battery capacity in different operating states;

[0043] Figure 6 The peak of the first feature and the peak of the fourth feature and the correlation change graph of the battery capacity in different operating states;

[0044] Figure 7 The peak of the second feature and the peak of the third feature and the correlation change graph of the battery capacity in different operating states;

[0045] Figure 8 The effect diagram of using K-means algorithm for step-by-step utilization sorting of 24 lithium batteries;

[0046] Figure 9 The effect diagram of using PCA / K-means algorithm for step-by-step utilization sorting of 24 lithium batteries;

[0047] Figure 10 The effect diagram of using K-means algorithm for step-by-step utilization sorting of 39 lithium batteries;

[0048] Figure 11 The effect diagram of using PCA / K-means algorithm for step-by-step utilization sorting of 39 lithium batteries;

[0049] Figure 12 The effect diagram of using K-means algorithm for step-by-step utilization sorting of 45 lithium batteries;

[0050] Figure 13 The effect diagram of using PCA / K-means algorithm for step-by-step utilization sorting of 45 lithium batteries. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0052] The purpose of the present application is to provide a lithium battery step-by-step utilization sorting method based on incremental capacity change, to solve the problems of complex and complicated calculation based on incremental capacity curve features, multiple collinearity caused by multiple feature parameters, and engineering implementation difficulties in the prior art, and to realize accurate lithium battery step-by-step utilization sorting.

[0053] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0054] In one exemplary embodiment, as shown in Figure 1 A lithium battery step utilization sorting method based on incremental capacity change is provided, comprising the following steps:

[0055] S1: Obtain the charging test data of a plurality of lithium batteries, and determine the incremental capacity curve and the incremental capacity characteristics of each lithium battery according to the charging test data of each lithium battery, respectively.

[0056] Specifically, S1 specifically comprises the following steps:

[0057] S11: Extract the current and time data of the lithium battery charging process, and calculate the charging capacity:

[0058] Q c =∫I c dt c ;

[0059] Wherein, Q c is the charging capacity, I c is the charging current, and t c is the charging time.

[0060] S12: Extract the voltage data of the lithium battery charging process, and perform numerical differentiation on the charging capacity and the charging process voltage to obtain the incremental capacity:

[0061]

[0062] Wherein, IC is the incremental capacity, V c is the charging process voltage, ΔQ c is the change of adjacent charging capacity, and ΔV c is the change of adjacent charging voltage.

[0063] The incremental capacity curve of the lithium battery under different operating conditions is shown in Figure 2 It can be found that the difference of the lithium battery mainly reflects in the peak value of the curve and the corresponding voltage position, slope, area and other characteristics. The displacement of the peak and valley values leads to difficulty in feature extraction and calculation, and low efficiency.

[0064] S2: Randomly select one lithium battery from a plurality of lithium batteries as a reference battery, determine the incremental capacity change between the remaining lithium batteries and the reference battery according to the incremental capacity curve of each lithium battery, and obtain the incremental capacity change curve of each lithium battery.

[0065] Specifically, S2 specifically comprises the following steps:

[0066] S21: Randomly select a reference battery according to the incremental capacity, and calculate the incremental capacity change of the lithium battery:

[0067] ICC i = IC i - IC 基准 ;

[0068] wherein ICC i is the incremental capacity change of the i-th lithium battery, ICC i is the incremental capacity of the i-th lithium battery, and ICC 基准 is the incremental capacity of the reference battery. The incremental capacity change curves of lithium batteries under different operating conditions are shown in FIG. 2. Figure 3

[0069] S22: drawing the incremental capacity change curve.

[0070] S3: for any lithium battery, determining a plurality of characteristic parameters of the lithium battery according to the incremental capacity change curve and the incremental capacity characteristics of the lithium battery.

[0071] Specifically, for any lithium battery, a plurality of preliminary characteristic parameters of the lithium battery are determined according to the incremental capacity change curve of the lithium battery. The plurality of preliminary characteristic parameters include the peak value, the left slope, the right slope and the area of each incremental capacity characteristic. The preliminary characteristic parameters are subjected to data dimension reduction to determine the plurality of characteristic parameters of each lithium battery.

[0072] The following formula is used to calculate the plurality of preliminary characteristic parameters of each lithium battery:

[0073]

[0074] wherein Slope ml is the left slope of the m-th incremental capacity characteristic of any lithium battery, Slope mr is the right slope of the m-th incremental capacity characteristic of any lithium battery, Area m is the area of the m-th incremental capacity characteristic of any lithium battery, is the peak value of the m-th incremental capacity characteristic of any lithium battery, is the left valley value adjacent to the peak value of the m-th incremental capacity characteristic of any lithium battery, is the right valley value adjacent to the peak value of the m-th incremental capacity characteristic of any lithium battery, is the charging voltage corresponding to the peak value of the m-th incremental capacity characteristic of any lithium battery, is the charging voltage corresponding to the left valley value adjacent to the peak value of the m-th incremental capacity characteristic of any lithium battery, is the charging voltage corresponding to the right valley value adjacent to the peak value of the m-th incremental capacity characteristic of any lithium battery.

[0075] ​In this embodiment, the number of incremental capacity characteristics of each lithium battery is 4, which are respectively a first characteristic, a second characteristic, a third characteristic and a fourth characteristic.

[0076] According to the NASA battery charging experiment process, the battery charging cutoff voltage is 4.2V, the first characteristic and the fourth characteristic correspond to the slow change stage of the ICC curve, which indicates that they are respectively located at the initial and end stages of the battery charging state, and the battery may not be fully charged and fully discharged to obtain effective slope or area characteristics, so the slope and area of the first characteristic and the fourth characteristic are not considered. The left slope and the right slope of the second characteristic are calculated based on the peak value of the second characteristic and the left valley value and the right valley value adjacent to the peak value, and the more obvious characteristic can be selected, and the third characteristic is analyzed in the same way.

[0077] As shown in Figure 3 , the plurality of characteristic parameters of each lithium battery are respectively a peak value of the first characteristic, a peak value of the second characteristic, a peak value of the third characteristic, a peak value of the fourth characteristic, a left slope of the second characteristic, a right slope of the third characteristic, an area of the second characteristic and an area of the third characteristic.

[0078] S4: According to the plurality of characteristic parameters of each lithium battery, a principal component analysis method (PCA) is used to analyze the correlation of the plurality of characteristic parameters, and a principal component index is determined.

[0079] Specifically, S4 specifically includes the following steps:

[0080] S41: Each characteristic parameter of each lithium battery is normalized to obtain a plurality of normalized characteristic parameters of each lithium battery. The formula of the normalization processing is:

[0081]

[0082] Wherein, X norm is the normalized characteristic parameter, X is the characteristic parameter before normalization, X max is the maximum value of the characteristic parameter before normalization, and X min is the minimum value of the characteristic parameter before normalization.

[0083] S42: According to the plurality of normalized characteristic parameters of each lithium battery, the correlation coefficient between any two normalized characteristic parameters is calculated, and a correlation coefficient matrix is constructed.

[0084] Specifically, the following formula is used to calculate the correlation coefficient between the normalized characteristic parameter xc and the normalized characteristic parameter yc, that is, the Pearson correlation coefficient:

[0085]

[0086] Wherein, rc (xc,yc) is a correlation coefficient between the normalized characteristic parameter xc and the normalized characteristic parameter yc, xc i is the normalized characteristic parameter xc of the i-th lithium battery, yc i is the normalized characteristic parameter yc of the i-th battery, μ xc is the average value of the normalized characteristic parameter xc, μ yc is the average value of the normalized characteristic parameter yc, n is the number of lithium batteries.

[0087] The correlation coefficient matrix constructed in the present application is:

[0088]

[0089] Wherein, R c is the correlation coefficient matrix, p1 is the peak value of the first characteristic, p2 is the peak value of the second characteristic, p3 is the peak value of the third characteristic, p4 is the peak value of the fourth characteristic, k1 is the left slope of the second characteristic, k2 is the right slope of the third characteristic, a1 is the area of the second characteristic, and a2 is the area of the third characteristic.

[0090] Figures 4 to 7 The correlation changes of the peak value of the first characteristic, the peak value of the second characteristic, the peak value of the third characteristic, the peak value of the fourth characteristic, the left slope of the second characteristic, the right slope of the third characteristic, the area of the second characteristic and the area of the third characteristic extracted based on the ICC curve are shown, and it can be found that the peak value of the first characteristic, the peak value of the second characteristic, the peak value of the third characteristic, the peak value of the fourth characteristic, the area of the second characteristic, the area of the third characteristic and the left slope of the second characteristic are positively correlated with the direction of battery capacity attenuation, while the right slope of the third characteristic is negatively correlated with the direction of battery capacity attenuation. The correlation coefficients between the above characteristic parameters and battery capacity and between the characteristic parameters are shown in Table 1.

[0091] Table 1 Correlation coefficients between characteristic parameters and battery capacity and between characteristic parameters

[0092]

[0093]

[0094] According to Table 1, the battery capacity and all characteristic parameters show strong correlation, and the absolute values of the Pearson correlation coefficients are all greater than 0.9, and there is also strong correlation between different characteristic parameters, thereby causing the problem of multicollinearity between characteristic parameters.

[0095] S43: Calculate a plurality of eigenvalues of the correlation coefficient matrix and a characteristic vector corresponding to each eigenvalue.

[0096] Specifically, first, the eigenvectors and eigenvalues of the correlation coefficient matrix are calculated by MATLAB software according to the correlation coefficient matrix:

[0097] [F, λ] = pcacov(R c );

[0098] wherein F is the eigenvector of the correlation coefficient matrix (the dimension is consistent with that of the correlation coefficient matrix), λ is the eigenvalue of the correlation coefficient matrix, and pcacov(·) represents the principal component analysis function of the MATLAB software.

[0099] Then, according to the eigenvalues greater than 1 in the calculation result of λ, the principal component index in F finally used for battery sorting calculation is determined:

[0100] λ (which > 1) → F (final principal component). As shown in Table 3, only the first principal component has an eigenvalue greater than 1, therefore, the first principal component (eigenvector F1 in Table 2) is selected as the final principal component index.

[0101] Then, according to the eigenvalues of the correlation coefficient matrix, the contribution rate is calculated:

[0102]

[0103] wherein η Rc is the contribution rate, λ j is the jth eigenvalue of the correlation coefficient matrix, and d represents the number of eigenvalues greater than 1.

[0104] S44: The eigenvector corresponding to the eigenvalue greater than 1 is taken as the principal component index.

[0105] The results of the multi-feature PCA based on the ICC curve are shown in Table 2 and Table 3.

[0106] Table 2 Eigenvectors of principal component analysis of the correlation coefficient matrix

[0107] F1 F2 F3 F4 F5 F6 F7 F8 0.3524 -0.3555 0.4973 0.0097 -0.6199 0.3146 0.1322 -0.0358 0.3597 -0.2167 0.0448 0.0311 0.3997 0.2296 -0.1787 0.7592 0.3613 0.0321 -0.2117 -0.2675 0.0822 -0.2057 0.8349 0.0769 0.3499 0.3929 -0.5444 0.4336 -0.2499 0.4166 -0.0345 -0.0418 0.3550 -0.3200 -0.0182 0.5882 0.0486 -0.6259 -0.1033 -0.1432 -0.3569 0.1910 0.4025 0.6141 0.2005 0.1770 0.4584 0.1235 0.3613 -0.0155 0.2129 -0.0615 0.5823 0.3387 -0.0397 -0.6040 0.3311 0.7294 0.4493 -0.1119 -0.0713 -0.3076 -0.1730 0.1193

[0108] Table 3 Eigenvalues and contribution rates of principal component analysis of the correlation coefficient matrix

[0109] Principal component number Eigenvalue Contribution rate (η Rc )%]] Cumulative contribution rate % 1 7.6100 95.1253 95.1253 2 0.2865 3.5808 98.7061 3 0.0598 0.7470 99.4531 4 0.0249 0.3119 99.7650 5 0.0087 0.1085 99.8735 6 0.0079 0.0991 99.9726 7 0.0019 0.0237 99.9963 8 0.0003 0.0037 100

[0110] According to Table 2 and Table 3, it is finally determined that the first principal component is the principal component index for comprehensive sorting.

[0111] In S4, the eigenvalues and contribution rate indexes of the principal components are calculated according to the correlation coefficient matrix, the principal component index capable of representing the 8 characteristic parameters of each lithium battery is determined, and then the principal component index of each lithium battery is taken as the input of the K-means algorithm, so as to realize the effective sorting of the batteries.

[0112] S5: For any lithium battery, according to each characteristic parameter of the lithium battery and the principal component index, a comprehensive sorting index value of the lithium battery is calculated.

[0113] Specifically, the principal component index includes an index value of each characteristic parameter. Each characteristic parameter of the lithium battery is multiplied by the corresponding index value and summed to obtain the comprehensive sorting index value of the lithium battery.

[0114] i.e. F t = 0.3524*peak value of the first characteristic + 0.3597*peak value of the second characteristic + 0.3613*peak value of the third characteristic + 0.3499*peak value of the fourth characteristic + 0.3550*left slope of the second characteristic - 0.3569*right slope of the third characteristic + 0.3613*area of the second characteristic + 0.3311*area of the third characteristic; wherein F t is the comprehensive sorting index value.

[0115] The present application converts the original 8 characteristic parameters into 1 principal component comprehensive classification index, which can avoid the multicollinearity problem between multiple characteristics and improve the accuracy of lithium battery step utilization sorting.

[0116] S6: According to the comprehensive sorting index value of each lithium battery, a K-means algorithm is used to cluster multiple lithium batteries to realize step utilization sorting of multiple lithium batteries.

[0117] Specifically, the principal component index is used as the classification basis, and the distance from the sample to the cluster center is calculated by combining the K-means algorithm, so that the sum of squared errors of each sample to the cluster center point is minimized, thereby realizing effective sorting of lithium battery step utilization. S6 specifically includes the following steps:

[0118] S61: The comprehensive sorting index value is normalized:

[0119]

[0120] Wherein, X norm is a normalization processing method, M main is the comprehensive sorting index value, is the sum of eigenvalues corresponding to the first principal component index to the dth principal component index, is the sum of the first normalized characteristic parameter to the Bth normalized characteristic parameter, and B is the number of normalized characteristic parameters.

[0121] S62: Randomly select k lithium battery cluster centers, defined as μ c1 , μ c2 ,..., μ ck .

[0122] S63: The error sum of squares of the normalized comprehensive sorting index value distance from the cluster center is constructed as a value function J(c i , μ k ), and the clustering result is the minimum value function:

[0123]

[0124] wherein, is the i-th normalized comprehensive sorting index value.

[0125] S64: Set the maximum number of iterations G, and update the following formula in each iteration process until the value function converges or the number of iterations reaches the maximum number of iterations:

[0126]

[0127] wherein, is the k-th lithium battery classification cluster center in the g-th iteration, is the cluster center closest to the comprehensive sorting index value in the g-th iteration, and h is the number of comprehensive sorting index values in the lithium battery classification cluster center. The first formula is used to allocate the comprehensive sorting index value, and the second formula is used to recalculate the cluster center.

[0128] S65: Calculate the accuracy of lithium battery step utilization sorting Accuracy:

[0129] Accuracy = Class PCA / K-means / Class real ;

[0130] wherein, Class PCA / K-means is the battery classification result of the PCA / K-means algorithm, and Class real is the real battery classification result.

[0131] Figures 8 to 13 Three groups of lithium batteries (battery number: 24, 39, 45) are respectively shown in the K-means and PCA / K-means step utilization sorting results, and the sorting accuracy is shown in Table 4.

[0132] Table 4: Step utilization sorting results of different lithium battery groups based on K-means and PCA / K-means

[0133]

[0134] According to Table 4, the sorting features based on the incremental capacity change curve can obtain a better estimation effect, the sorting effect of the three groups of lithium batteries based on the PCA / K-means algorithm is better than that of the K-means algorithm, and the sorting accuracy of the most numerous battery 3 group can reach 97.78%, which has good practicability.

[0135] The application randomly selects a reference battery, takes the incremental capacity change curve between the remaining lithium batteries and the reference battery as the classification feature extraction basis, can simply and quickly calculate the slope and area and other characteristic parameters, and improves the classification efficiency of the lithium batteries. Meanwhile, considering the multicollinearity problem caused by the high correlation between different characteristic parameters, the K-means algorithm based on principal component analysis is adopted for classification, the principal component feature index is obtained instead of the original multiple characteristic parameters, and the classification accuracy of the lithium batteries can be improved.

[0136] In an exemplary embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the computer program to implement the above-mentioned lithium battery step utilization sorting method based on incremental capacity change.

[0137] In an exemplary embodiment, a computer readable storage medium is provided, which stores a computer program, the computer program being executed by a processor to implement the above-mentioned lithium battery step utilization sorting method based on incremental capacity change.

[0138] In an exemplary embodiment, a computer program product is provided, comprising a computer program, the computer program being executed by a processor to implement the above-mentioned lithium battery step utilization sorting method based on incremental capacity change.

[0139] In an exemplary embodiment, a computer device, which can be a database, is provided. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store to-be-processed transactions. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement the above-mentioned lithium battery echelon utilization sorting method based on incremental capacity change.

[0140] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0141] 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. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application 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, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0142] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0143] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist, they should be considered as the scope of the present application.

[0144] The principles and implementation modes of the present application are described by applying specific examples herein. The above description of the embodiments is only used to help understand the method and its core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range can be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A lithium battery step utilization sorting method based on incremental capacity change, characterized in that, The lithium battery step utilization sorting method based on incremental capacity change comprises: Obtain charging test data of a plurality of lithium batteries, and determine the incremental capacity curve and the incremental capacity characteristics of each lithium battery according to the charging test data of each lithium battery, respectively; Randomly select one lithium battery from the plurality of lithium batteries as a reference battery, and determine the incremental capacity change between the remaining lithium batteries and the reference battery according to the incremental capacity curve of each lithium battery, to obtain the incremental capacity change curve of each lithium battery; For any lithium battery, determine a plurality of characteristic parameters of the lithium battery according to the incremental capacity change curve and the incremental capacity characteristics of the lithium battery, specifically comprising: for any lithium battery, determine a plurality of preliminary characteristic parameters of the lithium battery according to the incremental capacity change curve of the lithium battery; wherein the plurality of preliminary characteristic parameters include the peak value, left slope, right slope and area of each incremental capacity characteristic; perform data dimension reduction on the preliminary characteristic parameters to determine the plurality of characteristic parameters of each lithium battery; The following formula is used to calculate the plurality of preliminary characteristic parameters of each lithium battery: ； in, Slope ml For any lithium battery m The left slope of each incremental capacity feature, For any lithium battery m The right slope of each incremental capacity feature, Area m For any lithium battery m The area of ​​each incremental capacity feature For any lithium battery m The peak value of each incremental capacity characteristic, For any lithium battery m The left valley value adjacent to the peak of each incremental capacity feature. For any lithium battery m The right valley value adjacent to the peak of each incremental capacity feature. For any lithium battery m The charging voltage corresponding to the peak value of each incremental capacity characteristic. For any lithium battery m The charging voltage corresponding to the left trough value adjacent to the peak value of each incremental capacity characteristic. For any lithium battery m The charging voltage corresponding to the right valley value adjacent to the peak value of each incremental capacity feature; According to the plurality of characteristic parameters of each lithium battery, perform correlation analysis on the plurality of characteristic parameters by using principal component analysis method to determine the principal component index; For any lithium battery, calculate the comprehensive sorting index value of the lithium battery according to each characteristic parameter of the lithium battery and the principal component index; According to the comprehensive sorting index value of each lithium battery, perform clustering on the plurality of lithium batteries by using K-means algorithm to realize step utilization sorting of the plurality of lithium batteries.

2. The lithium battery step-ladder utilization sorting method based on incremental capacity change according to claim 1, wherein, The number of incremental capacity characteristics of each lithium battery is four, which are first characteristic, second characteristic, third characteristic and fourth characteristic, respectively; The plurality of characteristic parameters of each lithium battery are the peak value of the first characteristic, the peak value of the second characteristic, the peak value of the third characteristic, the peak value of the fourth characteristic, the left slope of the second characteristic, the right slope of the third characteristic, the area of the second characteristic and the area of the third characteristic, respectively.

3. The lithium battery step-ladder utilization sorting method based on incremental capacity change according to claim 1, wherein, According to the plurality of characteristic parameters of each lithium battery, perform correlation analysis on the plurality of characteristic parameters by using principal component analysis method to determine the principal component index, specifically comprising: Perform normalization processing on each characteristic parameter of each lithium battery to obtain a plurality of normalized characteristic parameters of each lithium battery; According to the plurality of normalized characteristic parameters of each lithium battery, calculate the correlation coefficient between any two normalized characteristic parameters, and construct a correlation coefficient matrix; Calculate a plurality of eigenvalues of the correlation coefficient matrix and a characteristic vector corresponding to each eigenvalue; The characteristic vector corresponding to the eigenvalue greater than 1 is taken as the principal component index.

4. The lithium battery step-ladder utilization sorting method based on incremental capacity change according to claim 3, characterized in that, The normalized characteristic parameter is calculated using the following equation xc The correlation coefficient between the normalized characteristic parameter yc and the normalized characteristic parameter ； wherein, is a normalized characteristic parameter xc is a normalized characteristic parameter yc is a correlation coefficient between the normalized characteristic parameters is a normalized characteristic parameter i of the i-th lithium battery xc , is a normalized characteristic parameter i of the i-th battery yc , is an average value of the normalized characteristic parameters xc , is an average value of the normalized characteristic parameters yc , n is the number of lithium batteries.

5. The lithium battery step-ladder utilization sorting method based on incremental capacity change according to claim 1, wherein, The principal component index includes the index value of each characteristic parameter; According to each characteristic parameter of the lithium battery and the principal component index, calculate the comprehensive sorting index value of the lithium battery, specifically comprising: Multiply each characteristic parameter of the lithium battery by the corresponding index value and sum to obtain the comprehensive sorting index value of the lithium battery.

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