A method and device for rapid sorting of batteries for cascade utilization, and a storage medium

Abstract

The application discloses a quick sorting method and device for cascade utilization batteries and a storage medium, and relates to the technical field of cascade utilization batteries. In order to solve the defects of the existing retired battery sorting method, such as inaccurate sorting, low efficiency, high equipment requirement and incapability of large-scale batch sorting, the battery sample is charged to a preset cut-off voltage and is subjected to static test; a static voltage sequence is recorded; feature extraction is performed on the collected static voltage sequence, and a sorting feature vector required for battery sorting is constructed; the feature vector obtained through wavelet decomposition and the cut-off voltage are subjected to dynamic modulation and gated weighted fusion; the performance grade of the battery is classified according to SOH; model training is performed based on the modulation fusion feature and the performance grade, the feature vector and the cut-off voltage of the cascade utilization battery to be sorted are acquired, the trained battery sorting model is input, and the performance grade prediction result of the battery is obtained. The application is mainly used for quick sorting of large-scale retired batteries.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cascade utilization battery, and particularly relates to a rapid sorting method and device for cascade utilization battery and a storage medium. BACKGROUND

[0002] After a certain period of use, the capacity of lithium batteries decreases, the internal resistance increases, and the safety decreases, which is difficult to meet the use requirements of the power system, and must be replaced and retired. The types of batteries entering the retirement stage are complex, and the performance differences are significant. If they are directly scrapped, not only resources are wasted, but also environmental pollution and safety risks exist. A considerable part of the retired batteries still has high residual capacity and cycle performance, which can be used for energy storage, communication base stations, electric bicycles and other cascade utilization scenarios. In order to maximize resource recovery and green recycling, it is necessary to efficiently and accurately sort the performance of retired batteries, and classify them according to their state of health (SOH) and remaining useful life (RuL) for cascade utilization. This not only relates to the safety and stability of the cascade battery application system, but also directly affects the economic benefits and recycling value.

[0003] Due to the complex sources and numerous types of retired batteries, the original data in their operation process are often missing or not in a unified format, making it difficult to build a standardized state evaluation system. At the same time, the state of the battery near the retirement stage is highly unstable, and the capacity or SOH estimated by the vehicle-mounted BMS (Battery Management System) has a large error, which affects the accuracy and reliability of the screening and grading. Therefore, the evaluation method relying on complete historical data or embedded sensing system has limited adaptability in actual cascade utilization scenarios, and an accurate grading method for retired batteries under the condition of no complete data is urgently needed.

[0004] The current retired battery sorting method mainly includes manual measurement method, cycle test method, model estimation method, etc. The manual experience method relies on manual visual inspection, voltage test and other means for rough judgment, but cannot effectively identify the internal health state of the battery, has poor accuracy and low efficiency, and is easy to cause sorting misjudgment; the capacity cycle test method: through complete charge and discharge test to measure the residual capacity and efficiency of the battery, is one of the more accurate sorting methods at present, but this method is time-consuming and requires high equipment, and is not suitable for large-scale rapid sorting. Model prediction method: based on equivalent circuit model, electrochemical model or data-driven model to model the battery state, and then sort according to the capacity, but the existing method also needs a long period of pre-test, and still cannot break through the low efficiency problem.

[0005] Although there are some technical solutions in the prior art for grouping batteries according to the state of charge of the batteries, they can only be applied to grouping of unretired power batteries and cannot be applied to large-scale gradient performance classification and classification. Some existing technologies use multiple charge and discharge and standing to sort the battery pack, but this sorting method takes a long time to test. Only using a single voltage static value has the defects of limited feature extraction dimension and inability to accurately reflect capacity differences, making it difficult to cope with the performance differentiation and complex aging state of the battery cell, and the accuracy and generalization ability of capacity sorting are limited. Some existing technologies disclose that the ohmic resistance and impedance characteristics of the retired battery are extracted by electrochemical impedance spectroscopy testing and relaxation time distribution analysis, the capacity is estimated by combining a black box model, and soft clustering sorting is realized by inputting multi-dimensional parameters into a Gaussian mixture model, which has certain rapid screening capability. However, this method has high requirements for test equipment and operating conditions, and the electrochemical impedance spectroscopy measurement process is complex and time-consuming, which is difficult to adapt to the rapid screening needs of large quantities of retired batteries. Moreover, this relaxation time distribution analysis is an analytical method for impedance spectroscopy, and electrochemical impedance spectroscopy testing requires applying a low-frequency to high-frequency excitation signal to the battery, which requires professional test equipment.

[0006] Therefore, there is a need for a rapid sorting method for gradient utilization batteries that can sort in large batches, has high sorting accuracy, sorts quickly, and is cost-effective. SUMMARY

[0007] The present application is to solve the defects of the existing retired battery sorting method, such as inaccurate sorting, low efficiency, high equipment requirements, and inability to sort in large batches, and provides a rapid sorting method for gradient utilization batteries that can sort in large batches, has high sorting accuracy, sorts quickly, and is cost-effective.

[0008] The rapid sorting method for gradient utilization batteries according to the present application comprises the following steps:

[0009] S1, safety inspection of the gradient utilization batteries to be sorted;

[0010] S2, charging the battery samples that pass the safety inspection to a preset cutoff voltage s under the same conditions and performing a standing test, and recording a standing voltage sequence during the standing process;

[0011] S3, feature extraction of the collected standing voltage sequence to construct a sorting feature vector required for battery sorting; the sorting feature vector includes the energy, energy entropy, mean, and variance of the low-frequency approximate component;

[0012] S4, dynamic modulation and gated weighted fusion of the feature vector F obtained by wavelet decomposition and the cutoff voltage s;

[0013] S5, select part of the battery samples for capacity test, obtain the SOH value, and classify the performance grade of the battery according to the SOH by using the cascade utilization battery sorting model;

[0014] S6, training the cascade utilization battery sorting model based on the extracted modulation fusion features and performance grade, obtaining the trained cascade utilization battery sorting model;

[0015] S7, obtaining the sorting feature vector F of the cascade utilization battery to be sorted and the preset cut-off voltage s, and inputting the trained battery sorting model to obtain the performance grade prediction result of the battery.

[0016] Further, the sorting feature vector is the approximate component energy entropy based on discrete wavelet transform; the approximate component energy entropy based on discrete wavelet transform is constructed as follows:

[0017] S31, decomposing the static voltage sequence by using discrete wavelet transform to obtain a low-frequency approximate component;

[0018] S32, calculating the energy entropy value.

[0019] Further, the energy of the low-frequency approximate component is calculated as follows:

[0020] ;

[0021] In the formula, E is the energy feature of the Lth layer low-frequency approximate component, is the Lth layer low-frequency approximate component obtained after wavelet decomposition, M is the sampling number, and i is the sampling point.

[0022] Further, the energy entropy value is calculated as follows:

[0023] ;

[0024] In the formula, H is the energy entropy value, E is the energy feature of the Lth layer low-frequency approximate component, M is the sampling number, and i is the sampling point.

[0025] Further, in S4, the mean value of the low-frequency approximate component is calculated as follows:

[0026] ;

[0027] In the formula, is the mean value of the low-frequency approximate component; is the Lth layer low-frequency approximate component obtained after wavelet decomposition, M is the sampling number, i is the sampling point, and i=1, 2,..., M.

[0028] Further, in S4, the variance of the low-frequency approximate component is calculated as follows:

[0029] ;

[0030] wherein, is the low-frequency approximation component variance; is the Lth layer low-frequency approximation component obtained after wavelet decomposition, M is the number of samples, i is the sampling point, i = 1, 2, …, M.

[0031] Further, in S4, the specific process of dynamically modulating and gate-controlling and weighting fusion of the feature vector F and the cutoff voltage s obtained after wavelet decomposition is as follows:

[0032] First, the extracted feature vector F and the cutoff voltage s are dynamically modulated:

[0033] ;

[0034] wherein, is the modulation feature, and is the modulation parameter determined by the cutoff voltage s, which is obtained by the following mapping function:

[0035] ;

[0036] wherein, and are weight matrices, and are bias vectors;

[0037] The normalized attention weight vector is generated by the softmax gate function:

[0038] ;

[0039] wherein, is the weight matrix, is the bias vector, is the modulation feature, The function ensures that the weight satisfies ;

[0040] Then, the feature weighting fusion is performed:

[0041] ;

[0042] wherein, z is the modulation fusion feature after modulation and weighting fusion, is the ith weight vector, is the ith modulation feature, and N is the number of features.

[0043] Further, the performance level category is divided according to the scene of echelon utilization.

[0044] The computer device comprises a memory and a processor for storing a computer program on the memory and running the computer program on the processor, and the processor executes the computer program to realize the fast sorting method of the cascade utilization battery.

[0045] The computer device comprises a memory and a processor for storing a computer program on the memory and running the computer program on the processor, and the processor executes the computer program to realize the fast sorting method of the cascade utilization battery.

[0046] The computer device comprises a memory and a processor for storing a computer program on the memory and running the computer program on the processor, and the processor executes the computer program to realize the fast sorting method of the cascade utilization battery.

[0047] The computer device comprises a memory and a processor for storing a computer program on the memory and running the computer program on the processor, and the processor executes the computer program to realize the fast sorting method of the cascade utilization battery.

[0048] Specifically, the present application can determine the optimal charging cutoff point according to the initial voltage state of each battery through the cutoff voltage modulation technology, thereby reducing unnecessary charging time. At the same time, through the feature fusion algorithm, the voltage interval information and the waveform features are organically combined to enhance the accuracy of the health state evaluation. In addition, the present application only needs to measure the voltage signal, and the equipment investment cost is low, the implementation and deployment are convenient, and it is especially suitable for the batch sorting and cascade utilization of large-scale retired batteries. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 It is a fast sorting flowchart of the cascade utilization battery;

[0050] Figure 2 It is a curve diagram of the static voltage sequence of two different cascade utilization batteries;

[0051] Figure 3 It is a curve diagram of the low-frequency approximation component obtained after discrete wavelet transform;

[0052] Figure 4 It is a sorting result diagram of the cascade utilization battery. Detailed Implementation

[0053] The following are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. The embodiments described below are only for explaining the present invention and should not be construed as limiting the present invention. The scope of protection of the present invention should be determined by the scope of the claims. The embodiments of the present invention are described in detail below. In order to facilitate the description of the present invention and simplify the description, the technical terms used in the specification of the present invention should be interpreted broadly, including but not limited to conventional alternatives not mentioned in this application, and including both direct and indirect implementation methods.

[0054] Example 1

[0055] Combination Figures 1-4 This embodiment describes a rapid sorting method for batteries intended for cascade utilization, comprising the following steps:

[0056] S1. First, a safety inspection is carried out on the batteries to be sorted for reuse, including whether there are obvious faults such as swelling, leakage, short circuit, or electrode deformation. If any of the above-mentioned abnormalities are found, the batteries should be rejected and not proceed to the subsequent sorting process.

[0057] S2. Charge the battery samples that have passed the safety inspection to a preset cutoff voltage s under the same conditions and perform a static test. During the static test, record the voltage change sequence U(t) over time.

[0058] The same conditions include the same charging current rate, temperature, humidity, and pressure. The preset cutoff voltage s can be set to multiple different target voltages in practical applications; for example, ternary lithium batteries can be set to 4.0V, 4.1V, 4.2V, etc., and lithium iron phosphate batteries can be set to 3.4V, 3.5V, 3.6V, etc. Based on the initial voltage state of the battery under test, the closest target cutoff voltage can be selected for charging, thereby reducing charging time and improving overall testing efficiency.

[0059] Figure 2is a static voltage sequence plot drawn according to the voltage change over time sequence obtained by charging two different batteries to the cut-off voltage 2.8 V and standing for 1 hour. The SOH of battery 1 is 0.768, and the SOH of battery 2 is 0.726. It can be seen that there is a significant difference in the curves of the static voltage sequences of the two batteries: the voltage of battery 1 drops more gently, and the overall change is smaller, showing higher voltage stability, reflecting that the internal polarization effect is weaker and the recovery ability is stronger; while the voltage of battery 2 drops more significantly, indicating that the internal impedance is relatively large and the health status is poor. It can be seen that the voltage response characteristics during the standing process can effectively reflect the aging degree of the battery, providing an important basis for performance grade discrimination.

[0060] S3, feature extraction is performed on the static voltage sequence U(t), and a feature vector required for battery sorting is constructed. The extracted sorting feature vector includes the energy, energy entropy, mean and variance of the low-frequency approximation component based on discrete wavelet transform.

[0061] Specifically, the sorting feature vector is constructed as follows:

[0062] First, the static voltage sequence is decomposed by discrete wavelet transform to obtain a low-frequency approximation component.

[0063] Specifically, the Daubechies-6 wavelet function is used to decompose the static voltage sequence U(t) by L-layer discrete wavelet to obtain the L-layer low-frequency approximation component , i is the sampling point.

[0064] S31, the energy of the low-frequency approximation component is calculated as follows:

[0065] ;

[0066] In the formula, E is the energy feature of the L-layer low-frequency approximation component, is the L-layer low-frequency approximation component obtained after wavelet decomposition, M is the number of samples, and i is the sampling point.

[0067] S32, the calculation formula of the energy entropy value is as follows:

[0068] ;

[0069] In the formula, H is the energy entropy value, E is the energy feature of the L-layer low-frequency approximation component, M is the number of samples, and i is the sampling point.

[0070] The mean of the low-frequency approximation component is calculated as follows:

[0071] ;

[0072] In the formula, is the mean value of the low-frequency approximation component; is the Lth layer low-frequency approximation component obtained after wavelet decomposition, M is the number of samples, i is the sampling point, i = 1, 2, …, M.

[0073] The variance of the low-frequency approximation component is calculated as follows:

[0074] ;

[0075] In the formula, is the variance of the low-frequency approximation component; is the Lth layer low-frequency approximation component obtained after wavelet decomposition, M is the number of samples, i is the sampling point, i = 1, 2, …, M.

[0076] The wavelet transform has good time-frequency localization ability, which can effectively capture the non-stationary signal changes caused by polarization relaxation, potential drift, etc. during the battery standing process, so as to more accurately reflect the internal state and aging characteristics of the battery. Based on this, in the embodiment, the energy E, energy entropy H, mean value and variance of the low-frequency approximation component are used as the feature vectors of the battery standing voltage sequence. The curve of the standing voltage sequence under the low-frequency scale energy distribution complexity quantization index. This feature can depict the energy distribution balance in the approximation component, which is closely related to the polarization recovery behavior and health status of the battery.

[0077] In the present embodiment, the number of layers L of discrete wavelet decomposition is selected as 6. This number of layers is determined on the basis of the sampling length of the battery standing voltage sequence curve, the signal change frequency range and the experimental results. The actual test shows that the low-frequency approximation component obtained by decomposing to the 6th layer can better retain the main trend characteristics of the voltage response, while filtering out high-frequency noise and random disturbance, which is conducive to the stability and accuracy of subsequent feature extraction and model identification.

[0078] It should be noted that the decomposition layer is not uniquely limited. Under different sampling frequencies or voltage platform conditions, it can also be adjusted according to the specific signal characteristics, for example, it can be selected as 5 layers or 7 layers, but generally 4-8 layers are appropriate to balance the feature expression ability and computational complexity.

[0079] S4, dynamically modulating and gating and weighting fusion are performed on the sorting feature vector F obtained by wavelet decomposition and the preset cutoff voltage s.

[0080] First, the extracted sorting feature vector F and the preset cutoff voltage s are dynamically modulated:

[0081] ;

[0082] In the formula, is the modulation feature, and are modulation parameters determined by the cut-off voltage s, which are obtained by the following mapping functions:

[0083] ;

[0084] wherein, and are weight matrices, and are bias vectors. This transformation enables the model to adaptively adjust the feature distribution according to the cut-off voltage s, effectively reducing the distribution difference between data from different sources.

[0085] The normalized attention weight vector is generated by the softmax gating function:

[0086] ;

[0087] wherein, is a weight matrix, is a bias vector, is a modulated feature, The function ensures that the weights satisfy .

[0088] Then the feature weighting fusion is performed:

[0089] ;

[0090] wherein z is the modulated and weighted fusion feature, is the i-th weight vector, is the i-th modulated feature, and N is the number of features.

[0091] The modulation mechanism can adaptively correct and align the resting feature vector according to the initial voltage of the battery and the charging cut-off condition. The low-frequency approximation component of the resting voltage sequence reflects the polarization effect and capacity degradation characteristics inside the battery, while different initial voltages and charging conditions will cause the shift of feature amplitude and distribution. By applying modulation and attention weight fusion to the feature vector, the model can effectively reduce the feature difference under different test conditions, maintain the consistency and stability of feature expression, and thus improve the universality and robustness of the sorting model under cross-batch batteries and diversified application scenarios.

[0092] Secondly, in the model application stage, the battery does not need to be strictly charged to a fixed cut-off voltage, but only needs to be charged to any voltage within the preset interval to complete feature acquisition. This strategy significantly reduces the charging time consumption, improves the overall sorting efficiency, while ensuring the sorting accuracy, and avoids the constraints of harsh test conditions on large-scale retired battery batch sorting.

[0093] S5, randomly select part of the battery samples from the batteries that pass the safety inspection for capacity test, obtain the SOH value, and classify the performance level of the battery according to the SOH.

[0094] The SOH value is the ratio of the test capacity to the rated capacity, which is used to represent the health degree of the battery. The performance level category can be divided according to the scene of gradient utilization, for example, SOH belongs to [0.8, 0.85) is one category, belongs to [0.75-0.8) is two categories, and belongs to [0.7-0.75) is three categories. A large number of literature and industry standards (such as T / CASE 240-2021) take SOH≈0.8 as the typical inflection point of the battery from the "power battery stage" to the "gradient utilization stage". Due to the inconsistency between batteries, there is a large difference in the capacity of retired batteries. Interval division principle: SOH belongs to [0.8, 0.85): the capacity of the battery in this range decreases less, and the output power and cycle stability are still good. After consistency screening and grouping, it can meet the demand of grid-side energy storage for service life and safety. SOH belongs to [0.75, 0.8): the capacity decreases, but it can still guarantee a long time standby power supply. The base station and other scenes mainly run in the intermittent mode of "standby-discharge-charge", and the continuous power requirement is not high, so the battery in this interval is suitable for base station standby power supply. SOH belongs to [0.7, 0.75): the capacity of the battery in this interval further decreases, and the cycle life and consistency are insufficient to support high-power or long-time application, but it can still meet the demand of low-speed electric vehicles or light energy storage devices. When SOH<0.7, the performance of the battery degrades obviously, the internal resistance increases, and the safety risk increases, which is not suitable for gradient utilization battery, and generally enters the material disassembly and recycling link. The above description is a typical application scene, which can be set according to the specific scene in practice.

[0095] S6, based on the obtained modulation fusion features and SOH performance classification, a gradient utilization battery sorting model is constructed and pre-trained.

[0096] Optionally, the gradient utilization battery sorting model is constructed based on a machine learning method. In this embodiment, the battery sorting model is trained by random forest. The sorting model constructed based on the random forest model is trained, and the training data includes the fusion features z obtained from steps S1-S4 after modulation and weighted fusion, and the performance level classification label. The classification label can be set according to the capacity distribution of the gradient utilization battery and the specific use scene. The fusion features z and the performance level classification label are input into the random forest model to train the sorting model, and a gradient utilization battery sorting model based on the relaxation voltage fusion features is obtained.

[0097] As an ensemble learning method, random forest builds multiple decision trees with differences, and in the prediction stage, the judgment results of each sub-model are voted and fused to realize the classification output of the overall model, improve the stability and accuracy of the overall model. This method is not sensitive to noise, has strong nonlinear modeling ability and good anti-overfitting performance, and can effectively process complex data scenes with high feature dimension and uneven sample distribution.

[0098] In the classification model of the random forest algorithm, a commonly used method is to directly count the prediction results of multiple decision trees and determine the final output classification label by majority voting. That is, each decision tree independently judges the battery performance grade, and the final result is determined by the voting results of all decision trees. This mechanism can form complementarity between different decision trees, effectively alleviate the bias that a single model may bring, and significantly improve the accuracy and robustness of the sorting result.

[0099] Specifically, each decision tree gives a predicted classification label for the input sample, and the final prediction result of the random forest model is determined by the following formula:

[0100] ;

[0101] Wherein, is the final sorting category, B is the total number of decision trees, is the function that returns the most frequently occurring category in all prediction results, is the category of the first decision tree, is the category of the second decision tree, is the category of the Bth decision tree.

[0102] S7, obtain the feature vector of the step utilization battery to be sorted and the cut-off voltage, and input the trained battery sorting model to obtain the performance grade prediction result of the battery, and realize fast sorting.

[0103] After the sorting model training in step S6 is completed, only the feature vector of the step utilization to be sorted and the corresponding cut-off voltage input to the trained sorting model through steps S2 and S3 can obtain the SOH performance category of the battery sample.

[0104] As Figure 3Fig. 6 shows a verification result of the fast sorting method for the used batteries according to the present application. Steps S1-S6 were performed on 130 used batteries. In order to verify the sorting effect, capacity tests were performed on all batteries to determine their true SOH values. In this embodiment, the batteries were divided into three categories according to SOH, i.e., SOH belonging to [0.8, 0.85) was classified as the first category, SOH belonging to [0.75-0.8) was classified as the second category, and SOH belonging to [0.7-0.75) was classified as the third category. 104 batteries were randomly selected from the 130 used batteries as training samples. According to step S2, the cutoff voltages were set to 3.4 V and 3.6 V. The batteries were charged to the nearest cutoff voltage and rested for 1 hour, and the voltage sampling frequency was 1 Hz. According to steps S3 and S4, the features were extracted and modulated and fused. The modulated and fused features of the 104 batteries and the corresponding SOH performance classification were trained using a random forest. According to steps S2 and S3, the remaining 26 batteries were charged to any voltage value within the set 3.4 V-3.6 V cutoff voltage interval according to the initial voltage state of the batteries, and the feature vectors and corresponding cutoff voltages were obtained. According to step S6, the 26 test battery samples were input into the sorting model trained using the 104 batteries, and the SOH grades of the 26 test battery samples were obtained. Figure 4 As can be seen, the model sorting result is highly consistent with the actual label, and only deviates at individual boundary samples. The overall sorting accuracy is high, which verifies the effectiveness and reliability of the method for sorting used batteries. In practical applications, the initial voltage of the battery to be sorted can be selected to charge the battery to the nearest preset cutoff voltage interval, and the sorting feature vector input and the cutoff voltage can be input into the trained model to achieve fast sorting of the performance grade of the used battery.

[0105] Embodiment 2

[0106] A computer device, comprising: a memory, a processor to store a computer program on the memory and executable on the processor, and the processor executes the computer program to implement the fast sorting method for the used batteries.

[0107] Embodiment 3

[0108] A computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the fast sorting method for the used batteries.

Claims

1. A rapid sorting method of cascade utilization battery, characterized in that, The method comprises the following steps: S1, performing safety inspection on the to-be-sorted cascade utilization battery; S2, charging the battery sample that passes the safety inspection to a preset cut-off voltage s under the same condition and performing a standing test, and recording a standing voltage sequence during the standing process; S3, performing feature extraction on the standing voltage sequence to construct a sorting feature vector required for battery sorting; the sorting feature vector comprises energy, energy entropy, mean value, and variance of the low-frequency approximation component; S4, performing dynamic modulation and gated weighted fusion on the sorting feature vector F obtained through wavelet decomposition and the preset cut-off voltage s; S5, selecting part of the battery sample to perform capacity test to obtain an SOH value, and classifying the performance grade of the battery according to the SOH by using the cascade utilization battery sorting model; S6, training the cascade utilization battery sorting model based on the extracted modulation fusion feature and the performance grade to obtain the trained cascade utilization battery sorting model; S7, obtaining the sorting feature vector F of the to-be-sorted cascade utilization battery and the preset cut-off voltage s, and inputting the trained battery sorting model to obtain the performance grade prediction result of the battery.

2. The method of claim 1, wherein the method is characterized by: The sorting feature vector is an energy entropy of an approximation component based on discrete wavelet transform; the energy entropy of the approximation component based on the discrete wavelet transform is constructed as follows: S31, decomposing the standing voltage sequence by using the discrete wavelet transform to obtain a low-frequency approximation component; S32, calculating an energy entropy value.

3. The method of claim 2, wherein the step of determining the state of charge of each battery is performed by measuring the voltage of each battery. The energy of the low-frequency approximation component is calculated as follows: ； In the formula, E is an energy feature of the Lth layer low-frequency approximation component, is the Lth layer low-frequency approximation component obtained after wavelet decomposition, M is the number of samples, and i is a sampling point.

4. The method of claim 2, wherein the method is characterized by: The calculation formula of the energy entropy value is as follows: ； In the formula, H is the energy entropy value, E is the energy feature of the Lth layer low-frequency approximation component, M is the sampling number, and i is a sampling point.

5. The method of claim 1, wherein the method is characterized by: In S4, the mean value of the low-frequency approximation component is calculated as follows: ； wherein is the mean of the low frequency approximation component; is the Lth level low frequency approximation component after wavelet decomposition, M is the number of samples, i is the sampling point, i = 1, 2,..., M.

6. The method of claim 1, wherein the method is characterized by: In S4, the variance of the low-frequency approximation component is calculated as follows: ； wherein is the low frequency approximation component variance; is the Lth low frequency approximation component after wavelet decomposition, M is the number of samples, i is the sampling point, i = 1, 2,..., M.

7. The method of claim 1, wherein the method is characterized by: In S4, the specific process of performing dynamic modulation and gated weighted fusion on the sorting feature vector F obtained through wavelet decomposition and the cut-off voltage s is as follows: First, the extracted feature vector F and the cut-off voltage s are dynamically modulated: ； wherein is a modulation feature, and are modulation parameters determined by the cut-off voltage s, respectively, which are obtained from the following mapping function: ； wherein, and are weight matrices, and are bias vectors, respectively. A normalized attention weight vector is generated by using a softmax gating function: ； wherein is a weight matrix, is a bias vector, is a modulation feature, the function ensures that the weights satisfy ; Then, feature weighted fusion is performed: ； In the formula, z is a modulation fusion feature after modulation and weighted fusion, is the i th weight vector, is the i th modulation feature, and N is the number of features.

8. The method of claim 1, wherein the method is a method of rapid sorting of batteries for use in a cascade. The category of the performance grade is divided according to the scene of cascade utilization.

9. A computer apparatus, comprising: It comprises: a memory and a processor to store a computer program on the memory and run the computer program on the processor, and the processor executes the computer program to implement the fast sorting method of the cascade utilization battery according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer program is stored on the memory and is executed by the processor to implement the fast sorting method of the cascade utilization battery according to any one of claims 1-8.

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