Lithium battery echelon utilization sorting method based on incremental capacity change

Abstract

The invention discloses a lithium battery echelon utilization sorting method based on increment capacity change, and relates to the technical field of battery health management, and the method comprises the steps: determining an increment capacity curve and increment capacity characteristics of a lithium battery according to charging test data of the lithium battery; randomly selecting a reference battery, determining incremental capacity change between the rest lithium batteries and the reference battery, and obtaining an 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 adopting a principal component analysis method, and determining principal component indexes; calculating a comprehensive sorting index value of the lithium battery according to each characteristic parameter and the principal component index of the lithium battery; and clustering the plurality of lithium batteries by adopting a K-means algorithm according to the comprehensive sorting index value of each lithium battery so as to realize echelon utilization sorting of the plurality of lithium batteries. According to the method, the efficiency and accuracy of gradient utilization and sorting of the lithium batteries are improved.

Description

Technical Field

[0001] The present application relates to the field of battery health management technology, and in particular to a lithium battery recycling sorting method based on incremental capacity changes. Background Art

[0002] With the rapid development of the new energy industry, the production and use of lithium batteries has increased year by year, and the safe use and effective management of batteries have received widespread attention. The cascade utilization of lithium batteries refers to the process of reorganizing waste lithium batteries into cascade products so that they can be applied to other fields. Efficient cascade utilization sorting methods can extend the service life of lithium batteries, give full play to their residual value, promote the consumption of new energy, and drive the development of the new energy vehicle industry.

[0003] The basis for the sorting of lithium battery recycling is mainly static parameters and dynamic parameters. Static parameters include no-load voltage, static capacity, etc., and their feature extraction is relatively simple, but it is difficult to fully reflect the inconsistency of the battery operation state. Therefore, in practical applications, dynamic parameters are usually selected as the basis for battery classification or health state estimation. The dynamic parameters of the battery refer to the parameters that change during the charging and discharging process, such as battery temperature, charge and discharge characteristic curve, and electrochemical impedance. At present, the battery sorting method is usually based on the incremental capacity (IC) curve. The IC curve has unique peak, shape, position and other characteristic information, which can reflect the state changes of the battery operation process. There are often large differences in the IC curves of lithium batteries in different states. The offset of the peak or valley point makes it difficult to calculate the slope and area characteristics, which is difficult to apply to large-scale lithium battery recycling sorting. Therefore, how to conveniently and quickly calculate effective classification features is one of the key issues to improve the efficiency of lithium battery recycling.

[0004] In addition, IC curves usually have rich multi-feature information, and there is a strong correlation between different features, which causes multicollinearity problems and increases the amount of useless calculations, resulting in inaccurate battery recycling sorting results.

[0005] Therefore, it is necessary to design a new lithium battery recycling sorting method to address the shortcomings of the existing technology in order to solve or alleviate the above problems. Summary of the invention

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

[0007] To achieve the above objectives, this application provides the following solutions:

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

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

[0010] Acquire charging test data of multiple lithium batteries, and determine the incremental capacity curve and incremental capacity characteristics corresponding to each lithium battery according to the charging test data of each lithium battery;

[0011] Randomly selecting a lithium battery from a plurality of lithium batteries as a reference battery, and determining incremental capacity changes between the remaining lithium batteries and the reference battery according to the incremental capacity curve of each lithium battery to obtain an incremental capacity change curve of each lithium battery;

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

[0013] According to multiple characteristic parameters of each lithium battery, the principal component analysis method is used to perform correlation analysis on multiple characteristic parameters to determine the principal component index;

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

[0015] According to the comprehensive sorting index value of each lithium battery, the K-means algorithm is used to cluster multiple lithium batteries to achieve cascade utilization sorting of multiple lithium batteries.

[0016] Optionally, according to the incremental capacity change curve and incremental capacity characteristics of the lithium battery, a plurality of characteristic parameters of the lithium battery are determined, specifically including:

[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 a peak value, a left slope, a right slope and an area of ​​each incremental capacity characteristic;

[0018] The preliminary characteristic parameters are subjected to data dimension reduction to determine multiple characteristic parameters of each lithium battery.

[0019] Optionally, the number of incremental capacity characteristics of each lithium battery is 4, namely, a first characteristic, a second characteristic, a third characteristic and a fourth characteristic;

[0020] The multiple characteristic parameters of each lithium battery are respectively 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.

[0021] Optionally, the following formula is used to calculate multiple preliminary characteristic parameters of each lithium battery:

[0022]

[0023] Among them, 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, It 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 multiple characteristic parameters of each lithium battery, a principal component analysis method is used to perform correlation analysis on the multiple characteristic parameters to determine the principal component index, specifically including:

[0025] Normalizing each characteristic parameter of each lithium battery to obtain multiple normalized characteristic parameters of each lithium battery;

[0026] According to multiple 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;

[0027] Calculating multiple eigenvalues ​​of the correlation coefficient matrix and eigenvectors corresponding to each eigenvalue;

[0028] The eigenvectors corresponding to eigenvalues ​​greater than 1 are taken as principal component indices.

[0029] Optionally, the correlation coefficient between the normalized feature parameter xc and the normalized feature parameter yc is calculated using the following formula:

[0030]

[0031] Among them, r c (xc, yc) is the correlation coefficient between the normalized feature parameter xc and the normalized feature parameter yc, xc i is the normalized characteristic parameter xc, yc of the ith lithium battery i is the normalized characteristic parameter yc of the ith battery, μxc is the average value of the normalized feature parameter xc, μ yc is the average value of the normalized characteristic parameter yc, and n is the number of lithium batteries.

[0032] Optionally, the principal component index includes an index value of each characteristic parameter;

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

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

[0035] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0036] The present application provides a lithium battery recycling sorting method based on incremental capacity change, randomly selecting a reference battery, and using the incremental capacity change curve between the remaining lithium batteries and the reference battery as the basis for extracting classification features, which can easily and quickly calculate feature 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 comprehensive sorting index values ​​are used to replace the original multi-feature parameters to avoid multi-collinearity problems caused by multiple features, thereby improving the accuracy of lithium battery recycling sorting. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 A flow chart of the lithium battery recycling sorting method based on incremental capacity change provided in this application;

[0039] Figure 2 It is the incremental capacity curve of lithium battery under different operating conditions;

[0040] Figure 3 It is a curve diagram of the incremental capacity change of lithium battery under different operating conditions;

[0041] Figure 4 A graph showing the correlation between the area of ​​the second feature and the area of ​​the third feature and the battery capacity in different operating states;

[0042] Figure 5The left slope of the second feature and the right slope of the third feature are correlated with the battery capacity in different operating states;

[0043] Figure 6 A graph showing the correlation between the peak value of the first feature and the peak value of the fourth feature and the battery capacity in different operating states;

[0044] Figure 7 A graph showing the correlation between the peak value of the second feature and the peak value of the third feature and the battery capacity in different operating states;

[0045] Figure 8 The effect diagram of using K-means algorithm to sort 24 lithium batteries for cascade utilization;

[0046] Fig. 9 The effect diagram of using PCA / K-means algorithm to sort 24 lithium batteries for cascade utilization;

[0047] Fig.10 The effect diagram of using K-means algorithm to sort 39 lithium batteries for cascade utilization;

[0048] Fig.11 The effect diagram of 39 lithium batteries sorted by PCA / K-means algorithm for cascade utilization;

[0049] Fig.12 The effect diagram of using K-means algorithm to sort 45 lithium batteries for cascade utilization;

[0050] Fig.13 The effect diagram of 45 lithium batteries being sorted for cascade utilization using the PCA / K-means algorithm. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] The purpose of this application is to provide a lithium battery recycling sorting method based on incremental capacity changes, to solve the problems in the prior art such as cumbersome and complex calculation based on incremental capacity curve characteristics, multicollinearity caused by multiple characteristic parameters, and engineering implementation difficulties, so as to achieve accurate lithium battery recycling sorting.

[0053] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0054] In an exemplary embodiment, Figure 1 As shown, a lithium battery recycling sorting method based on incremental capacity change is provided, comprising the following steps:

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

[0056] Specifically, S1 includes 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] Among them, Q c is the charging capacity, I c is the charging process current, t c For charging time.

[0060] S12: Extract the voltage data of the lithium battery charging process, and numerically differentiate the charging capacity and the charging process voltage to obtain the incremental capacity:

[0061]

[0062] Where IC is the incremental capacity, V c is the charging process voltage, ΔQ c is the change in adjacent charging capacity, ΔV c is the change in adjacent charging voltage.

[0063] The incremental capacity curve of lithium batteries under different operating conditions is as follows: Figure 2 As shown, it can be found that the differences in lithium batteries are mainly reflected in the peak value of the curve and its corresponding voltage position, slope, area and other characteristics. The displacement of the peak and valley values ​​makes the feature extraction calculation difficult and inefficient.

[0064] S2: Randomly select a 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 includes the following steps:

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

[0067] ICC i =IC i -IC 基准 ;

[0068] Among them, ICC i is the incremental capacity change of the ith lithium battery, IC i is the incremental capacity of the ith lithium battery, IC 基准 is the incremental capacity of the benchmark battery. The incremental capacity change curve of lithium batteries under different operating conditions is as follows: Figure 3 shown.

[0069] S22: Draw an incremental capacity change curve.

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

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

[0072] Use the following formula to calculate multiple preliminary characteristic parameters of each lithium battery:

[0073]

[0074] Among them, 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, It 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.

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

[0076] According to NASA's battery charging experiment, the battery charging cut-off voltage is 4.2V. The first and fourth features correspond to the slow-changing stage of the ICC curve, indicating that they are respectively in the initial and final stages of the battery charging state. The battery may not be fully charged or discharged and the effective slope or area features cannot be obtained. Therefore, the slope and area of ​​the first and fourth features are not considered. The left slope and right slope of the second feature are calculated based on the peak value of the second feature and the left and right valley values ​​adjacent to the peak value. The more obvious features can be selected, and the third feature is analyzed in the same way.

[0077] like Figure 3 As shown, the multiple characteristic parameters of each lithium battery are the peak of the first characteristic, the peak of the second characteristic, the peak of the third characteristic, the peak 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.

[0078] S4: According to the multiple characteristic parameters of each lithium battery, a principal component analysis method (Principal Component Analysis, PCA) is used to perform correlation analysis on the multiple characteristic parameters to determine the principal component index.

[0079] Specifically, S4 includes the following steps:

[0080] S41: normalize each characteristic parameter of each lithium battery to obtain multiple normalized characteristic parameters of each lithium battery. The formula for normalization is:

[0081]

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

[0083] S42: Calculate the correlation coefficient between any two normalized characteristic parameters according to the multiple normalized characteristic parameters of each lithium battery, and construct a correlation coefficient matrix.

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

[0085]

[0086] Among them, rc (xc, yc) is the correlation coefficient between the normalized feature parameter xc and the normalized feature parameter yc, xc i is the normalized characteristic parameter xc, yc of the ith lithium battery i is the normalized characteristic parameter yc of the ith battery, μ xc is the average value of the normalized feature parameter xc, μ yc is the average value of the normalized characteristic parameter yc, and n is the number of lithium batteries.

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

[0088]

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

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

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

[0092]

[0093]

[0094] According to Table 1, the battery capacity is strongly correlated with all characteristic parameters, and the absolute values ​​of the Pearson correlation coefficients are all greater than 0.9. At the same time, there is also a strong correlation between different characteristic parameters, which causes the problem of multicollinearity between characteristic parameters.

[0095] S43: Calculate multiple eigenvalues ​​of the correlation coefficient matrix and the eigenvector corresponding to each eigenvalue.

[0096] Specifically, firstly, according to the correlation coefficient matrix, the eigenvector and eigenvalue of the correlation coefficient matrix are calculated by MATLAB software:

[0097] [F,λ]=pcacov(R c );

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

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

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

[0101] Then calculate the contribution rate based on the eigenvalue of the correlation coefficient matrix:

[0102]

[0103] Among them, η Rc is the contribution rate, λ j is the j-th 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 used as the principal component index.

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

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

[0109] Principal component number Eigenvalue <![CDATA[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 Tables 2 and 3, the first principal component was finally determined as the principal component index for comprehensive sorting.

[0111] In S4, the eigenvalues ​​and contribution rate indices of the principal components are calculated according to the correlation coefficient matrix, and the principal component indices that can ultimately characterize the eight characteristic parameters of each lithium battery are determined. Then, the principal component indices of each lithium battery are used as the input of the K-means algorithm to achieve effective battery sorting.

[0112] S5: 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 main component index.

[0113] Specifically, the main 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 then summed to obtain a comprehensive sorting index value of the lithium battery.

[0114] That is F t =0.3524*peak value of the first feature+0.3597*peak value of the second feature+0.3613*peak value of the third feature+0.3499*peak value of the fourth feature+0.3550*left slope of the second feature-0.3569*right slope of the third feature+0.3613*area of ​​the second feature+0.3311*area of ​​the third feature; where F t It is the comprehensive sorting index value.

[0115] This application converts the original 8 feature parameters into 1 principal component comprehensive classification index, which can avoid the collinearity problem between multiple features and improve the accuracy of lithium battery recycling sorting.

[0116] S6: Based on the comprehensive sorting index value of each lithium battery, the K-means algorithm is used to cluster multiple lithium batteries to achieve cascade 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 in combination with the K-means algorithm, so that the sum of square errors of each sample from the center point of the cluster to which it belongs is minimized, thereby achieving effective sorting of lithium battery cascade utilization. S6 specifically includes the following steps:

[0118] S61: Normalize the comprehensive sorting index value:

[0119]

[0120] Among them, X norm is the normalization method, M main is the comprehensive sorting index value, Sum the eigenvalues ​​corresponding to the first principal component index to the dth principal component index, The sum of the 1st normalized feature parameter to the Bth normalized feature parameter, where B is the number of normalized feature parameters.

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

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

[0123]

[0124] in, is the normalized comprehensive sorting index value of the ith one.

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

[0126]

[0127] in, is the kth lithium battery classification cluster center at the gth iteration, is the cluster center closest to the comprehensive sorting index value at the g-th iteration, and h is the number of comprehensive sorting index values ​​in the lithium battery classification cluster center. The first formula above is used to assign comprehensive sorting index values, and the second formula is used to recalculate cluster centers.

[0128] S65: Calculate the accuracy of lithium battery recycling sorting:

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

[0130] Among them, Class PCA / K-means The battery classification result of PCA / K-means algorithm, Class real This is the actual classification result of the battery.

[0131] Figures 8 to 13 The results of cascade utilization sorting of three groups of lithium batteries (with 24, 39, and 45 batteries) using K-means and PCA / K-means are shown respectively. The sorting accuracy is shown in Table 4.

[0132] Table 4 Sorting results of different lithium battery packs based on K-means and PCA / K-means

[0133]

[0134] As shown in Table 4, extracting sorting features based on the incremental capacity change curve can obtain better estimation results. The sorting effects of the three groups of lithium batteries based on the PCA / K-means algorithm are better than those of the K-means algorithm. For the three groups of batteries with the largest number, the sorting accuracy can reach 97.78%, which has good practicality.

[0135] This application randomly selects a reference battery, and uses the incremental capacity change curve between the remaining lithium batteries and the reference battery as the basis for extracting classification features. It can easily and quickly calculate characteristic parameters such as slope and area, thereby improving the efficiency of lithium battery classification. At the same time, considering the multicollinearity problem caused by the high correlation between different characteristic parameters, the K-means algorithm fused with principal component analysis is used for classification, and the principal component characteristic index is obtained to replace the original multi-feature parameters, which can improve the accuracy of lithium battery classification.

[0136] In an exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned lithium battery recycling sorting method based on incremental capacity change.

[0137] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned lithium battery recycling sorting method based on incremental capacity change is implemented.

[0138] In an exemplary embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the above-mentioned lithium battery recycling sorting method based on incremental capacity change.

[0139] In an exemplary embodiment, a computer device is provided, which may be a database. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used 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 the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store pending transactions. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above-mentioned lithium battery ladder utilization sorting method based on incremental capacity change is realized.

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

[0141] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the 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 memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0142] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0143] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0144] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A lithium battery recycling sorting method based on incremental capacity change, characterized in that: The lithium battery recycling sorting method based on incremental capacity change includes: Acquire charging test data of multiple lithium batteries, and determine the incremental capacity curve and incremental capacity characteristics corresponding to each lithium battery according to the charging test data of each lithium battery; Randomly select a lithium battery from a 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, determining a plurality of characteristic parameters of the lithium battery according to the incremental capacity change curve and incremental capacity characteristics of the lithium battery; According to multiple characteristic parameters of each lithium battery, the principal component analysis method is used to perform correlation analysis on multiple characteristic parameters to determine the principal component index; For any lithium battery, according to each characteristic parameter of the lithium battery and the main component index, calculate the comprehensive sorting index value of the lithium battery; According to the comprehensive sorting index value of each lithium battery, the K-means algorithm is used to cluster multiple lithium batteries to achieve cascade utilization sorting of multiple lithium batteries.

2. The lithium battery recycling sorting method based on incremental capacity change according to claim 1 is characterized in that: According to the incremental capacity change curve and incremental capacity characteristics of the lithium battery, multiple characteristic parameters of the lithium battery are determined, specifically including: 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 a peak value, a left slope, a right slope and an area of ​​each incremental capacity characteristic; The preliminary characteristic parameters are subjected to data dimension reduction to determine multiple characteristic parameters of each lithium battery.

3. The lithium battery recycling sorting method based on incremental capacity change according to claim 2 is characterized in that: The number of incremental capacity characteristics of each lithium battery is 4, namely the first characteristic, the second characteristic, the third characteristic and the fourth characteristic; The multiple characteristic parameters of each lithium battery are respectively 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.

4. The lithium battery recycling sorting method based on incremental capacity change according to claim 2, characterized in that: Use the following formula to calculate multiple preliminary characteristic parameters of each lithium battery: Among them, 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, It 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.

5. The lithium battery recycling sorting method based on incremental capacity change according to claim 1, characterized in that: According to the multiple characteristic parameters of each lithium battery, the principal component analysis method is used to perform correlation analysis on the multiple characteristic parameters to determine the principal component index, including: Normalizing each characteristic parameter of each lithium battery to obtain multiple normalized characteristic parameters of each lithium battery; According to multiple 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; Calculating multiple eigenvalues ​​of the correlation coefficient matrix and eigenvectors corresponding to each eigenvalue; The eigenvectors corresponding to eigenvalues ​​greater than 1 are taken as principal component indices.

6. The lithium battery recycling sorting method based on incremental capacity change according to claim 5, characterized in that: The correlation coefficient between the normalized feature parameter xc and the normalized feature parameter yc is calculated using the following formula: Among them, r c (xc, yc) is the correlation coefficient between the normalized feature parameter xc and the normalized feature parameter yc, xc i is the normalized characteristic parameter xc, yc of the ith lithium battery i is the normalized characteristic parameter yc of the ith battery, μ xc is the average value of the normalized feature parameter xc, μ yc is the average value of the normalized characteristic parameter yc, and n is the number of lithium batteries.

7. The lithium battery recycling sorting method based on incremental capacity change according to claim 1, characterized in that: The principal component index includes the index value of each characteristic parameter; According to each characteristic parameter of the lithium battery and the main component index, a comprehensive sorting index value of the lithium battery is calculated, specifically including: Each characteristic parameter of the lithium battery is multiplied by the corresponding index value and then summed to obtain a comprehensive sorting index value of the lithium battery.

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