Ex-service battery echelon utilization management method

Abstract

The invention relates to the technical field of battery management, and discloses an ex-service battery echelon utilization management method, which comprises the following steps of: acquiring a voltage set V0, a temperature set D0, a capacity set C0 and a resistor set R0 of an ex-service battery, and preprocessing the acquired voltage set V0, the temperature set D0, the capacity set C0 and the resistor set R0 into a voltage set V6, a temperature set D6, a capacity set C6 and a resistor set R6; compared with a traditional ex-service battery echelon utilization management technology, the described method has remarkable advantages in precision, real-time performance and comprehensive evaluation capability, the method can more accurately evaluate the state of the battery through multi-index fusion, real-time monitoring and dynamic adjustment, safe and efficient operation of a battery system is ensured, and meanwhile, the method has a good application prospect. The intelligent management means reduces the risk of manual operation, and further improves the economy and reliability of the cascade utilization of the battery.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a method for managing the recycling of retired batteries. Background Art

[0002] When some single cells in the battery pack have problems or the battery pack can no longer make the electric vehicle achieve the corresponding performance, it will be eliminated. However, these retired batteries still have high available capacity and practical value. Their cascade utilization can reduce the cost of the power battery throughout its life cycle, improve the available value of the power battery, save resources, and protect the environment. However, the aging of the battery during the cascade utilization process will cause changes in the internal state and output performance of the battery, bringing uncertainty to the effective management of the battery, which in turn affects the smooth implementation of cascade utilization. Therefore, it is very necessary to conduct research on key technical issues of battery management in the cascade utilization of retired power batteries.

[0003] At present, the mainstream intelligent management technologies for the cascade utilization of retired batteries include state assessment methods based on static data and prediction methods based on empirical models. The state assessment methods based on static data rely on the static parameters of the battery, such as open circuit voltage, internal resistance, etc., to evaluate the remaining life and health status of the battery. However, due to the inability to monitor the dynamic behavior of the battery in real time, it is difficult to accurately reflect the actual state of the battery under different working conditions and to identify potential failures of the battery. The prediction methods based on empirical models use historical data and empirical models, such as equivalent circuit models, to predict the remaining service life and performance of the battery. However, their model generalization ability is weak and a large amount of historical data is required for model training. However, these data may not always be available or reliable. Therefore, a management method for the cascade utilization of retired batteries is proposed to solve the above problems. Summary of the invention

[0004] 1. Technical issues to be resolved In view of the shortcomings of the prior art, the present invention provides a method for managing the cascade utilization of retired batteries, which has the advantages of maximizing the energy utilization efficiency of retired batteries and optimizing intelligent management, and solves the problems mentioned in the above background technology.

[0005] (II) Technical solution To achieve the above object, the present invention provides the following technical solution: a method for managing the cascade utilization of retired batteries, comprising the following steps: S1: Collect the voltage set V0, temperature set D0, capacity set C0 and resistance set R0 of the retired battery; S2: pre-process the collected voltage set V0, temperature set D0, capacity set C0 and resistance set R0 into voltage set V6, temperature set D6, capacity set C6 and resistance set R 6，Calculate the covariance matrix Z of the standardized matrix X and the standardized data, construct the eigenvector matrix G, and project the covariance matrix Z into the principal component space. Use the data matrix F as training data and the real SOH to train the WLS-SVM estimation model, and predict the estimated value to monitor the degradation degree and aging trajectory of the battery. S3: Calculate the deviation between each value in the voltage set V4, temperature set D4, capacity set C4, and resistance set R4 and the average value in the set to obtain the consistency measure α v , α D , α C , α R , α v , α D , α C , α R Set the corresponding weights to calculate the consistency evaluation coefficient β 1， Determine whether the consistency evaluation coefficient β1 passes the Granger causality test. If yes, then end. If not, retain the characteristic parameters with causal relationship and analyze the corresponding weights again, calculate the consistency evaluation coefficient β1, and again determine whether the consistency evaluation coefficient β1 passes the Granger causality test until it passes. Finally, based on the comprehensive consistency evaluation coefficients β1 of the whole package, determine whether the consistency of the whole package after dynamic replacement is improved, which serves as the basis for performance optimization after dynamic configuration. S4: Calculate the total electrical performance score S1, the total safety performance score S2, and the total economic score S3 of the retired battery, set corresponding weights for the total electrical performance score S1, the total safety performance score S2, and the total economic score S3 of the battery, and calculate the matching degree M between the retired battery recycling system and the application scenario.

[0006] Preferably, the specific steps in step S2 are: S2.1: Use the interpolation method to supplement the missing data of the collected voltage set V0, temperature set D0, capacity set C0 and resistance set R0 to obtain the voltage set V1, temperature set D1, capacity set C1 and resistance set R1, and the expression is: ; ; Among them, V i-1 and V i+1 is the known data point before and after the missing voltage value in the voltage set V0, V1 is the voltage set after the missing data is supplemented, t i is the time corresponding to the missing voltage value; ; ; Among them, Di-1 and D i+1 is the known data point before and after the missing temperature value in the temperature set D0, D1 is the temperature set after the missing data is supplemented, t i is the time corresponding to the missing temperature value; ; ; Among them, C i-1 and C i+1 is the known data point before and after the missing capacity value in the capacity set C0, C1 is the capacity set after supplementing the missing data, t i is the time corresponding to the missing capacity value; ; ; Among them, R i-1 and R i+1 is the known data point before and after the missing resistance value in the resistance set R0, R1 is the resistance set after the missing data is supplemented, t i is the time corresponding to the missing resistance value; S2.2: Use the IQR method to detect outliers on the voltage set V1, temperature set D1, capacity set C1 and resistance set R1, and calculate the interquartile range IQR of the voltage set V1, temperature set D1, capacity set C1 and resistance set R1. The expression is: IQR = Q3-Q1; Wherein, Q1 is the first quartile of each of the voltage set V1, the temperature set D1, the capacitance set C1 and the resistance set R1; S2.3: The values ​​exceeding 1.5 times of the interquartile range IQR of the voltage set V1, temperature set D1, capacity set C1 and resistance set R1 are considered as outliers and are eliminated. At the same time, the abnormal capacity changes are identified and monitored, and the voltage set V2, temperature set D2, capacity set C2 and resistance set R2 are obtained. The expression is: ; Among them, LowerBound is the lower bound outlier value, and UpperBound is the upper bound outlier value; S2.4: Perform Min-Max normalization on the voltage set V2, temperature set D2, capacity set C2 and resistance set R2, scale the data to [0,1], and obtain the expressions of the voltage set V3, temperature set D3, capacity set C3 and resistance set R3: ; Among them, V3 is the normalized voltage set, V min is the minimum value of the voltage set, Vmax is the maximum value of the voltage set; ; Among them, D3 is the normalized temperature set, D min is the minimum value of the temperature set, D max is the maximum value of the temperature set; ; Among them, C3 is the normalized capacity set, C min is the minimum value of the capacity set, C max is the maximum value of the capacity set; ; Among them, R3 is the normalized resistance set, R min is the minimum value of the resistor set, R max is the maximum value of the resistor set; S2.5: Kalman filtering is used to reduce the noise of the voltage set V3, the temperature set D3, the capacity set C3 and the resistance set R3. The expression is: ; ; in, is the predicted state vector of the voltage set V3, temperature set D3, capacity set C3 and resistance set R3 at time t, is the predicted covariance matrix, Z t is the observation vector of voltage set V3 and temperature set D3 at time t, u t-1 is the control input of the voltage set V3, temperature set D3, capacity set C3 and resistance set R3 at time t-1, A is the state transition matrix describing the change of the voltage set V3, temperature set D3, capacity set C3 and resistance set R3 over time, γ V , γ D , γ C , γ R is the state transfer coefficient of the voltage set V3, temperature set D3, capacity set C3 and resistance set R3, B is the control input matrix, Q is the process noise matrix, K t The Kalman gain is used to utilize the current observation vector Z t Corrected prediction vector, O is the observation noise covariance matrix, H is the observation matrix, and I is the identity matrix; S2.6: Use the principal component analysis method to reduce the dimension of the voltage set V4, the temperature set D4, the capacity set C4 and the resistance set R4, and perform feature extraction to calculate the standard deviation of the voltage set V5, the temperature set D5, the capacity set C5 and the resistance set R5. The expression is: ; ; ; ; in, is the average voltage value in the voltage set V5, V i is the voltage value of the ith voltage in the voltage set V5, σ v is the standard deviation of the voltage collection V5, is the average temperature value in the temperature set D5, D i is the temperature value of the ith temperature in the temperature set D5, σ D is the standard deviation of the temperature collection D5, is the average capacity value in capacity set C5, C i is the capacity value of the ith capacity in the capacity set C5, σ C is the standard deviation of the capacity set C5, is the average resistance value in the resistor set R5, R i is the resistance value of the i-th resistor in the resistor set R5, σ R is the standard deviation of the resistor set R5, and n is the total number of data in each set; S2.7: Use standard deviation σ v The voltage set V5 is standardized to obtain the voltage set V 6， The expression is: ; S2.8: Use standard deviation σ D Standardize the temperature set D5 to obtain the temperature set D 6， The expression is: ; S2.9: Use standard deviation σ C The capacity set C5 is standardized to obtain the capacity set C6, which is expressed as: ; S2.10: Use standard deviation σ R The resistor set R5 is standardized to obtain the resistor set R6, which is expressed as: ; S2.11: Calculate the covariance matrix Z of the standardized matrix X and the standardized data, expressed as: ; ; Among them, X Tis the transposed matrix of the standardized matrix X, Z is the covariance matrix of the standardized data, and n is the number of samples in the data; S2.12: Perform eigenvalue decomposition on the standardized covariance matrix Z to obtain the eigenvalue λ and eigenvector U, expressed as: ; Among them, U is the eigenvector and λ is its eigenvalue; S2.13: Construct the eigenvector matrix G and project the covariance matrix Z into the principal component space to achieve dimensionality reduction. The expression is: ; Among them, F is the data matrix after dimension reduction; S2.14: Use the data matrix F as training data and the actual SOH to train the WLS-SVM estimation model, and use the WLS-SVM estimation model to predict the estimated SOH 1， Realize the monitoring of battery degradation degree and aging trajectory, the expression is: ; Among them, SOH1 is the estimated value output by the WLS-SVM estimation model, w T is the weight vector, w T is the feature mapping function, and b is the bias term.

[0007] Preferably, the specific steps in step S3 are: S3.1: Calculate the deviation between each voltage value in the voltage set V4 and the average voltage value to obtain the voltage consistency measure α v , the expression is: ; in, is the average voltage value in the voltage set V4, V i is the voltage value of the ith voltage in the voltage set V; S3.2: Calculate the deviation between each temperature value in the temperature set D4 and the average temperature value to obtain the temperature consistency measure α D， The expression is: ; in, is the average temperature value in the temperature set D4, D i is the temperature value of the i-th temperature in the temperature set D4; S3.3: Calculate the deviation between each capacity value in the capacity set C4 and the average capacity value to obtain the capacity consistency measure α C , the expression is: ; in, is the average temperature value in capacity set C4, C i is the capacity value of the i-th capacity set C4; S3.4: Calculate the deviation between each resistance value in the resistor set R4 and the average resistance value to obtain the resistance consistency measure α R， The expression is: ; in, is the average resistance value in the resistor set R4, R i is the resistance value of the i-th resistor in the resistor set R4; S3.5: Based on the voltage consistency metric α v , temperature consistency measure α D , capacity consistency measure α C Consistency measure with resistor α R Set the corresponding weights and calculate the consistency evaluation coefficient β 1， The expression is: ; Among them, W1, W2, W3 and W4 are voltage consistency metrics α v , temperature consistency measure α D , capacity consistency measure α C Consistency measure with resistor α R The corresponding weights; S3.6: Determine whether the consistency assessment coefficient β1 passes the Granger causality test. If yes, end; if no, proceed to step S3.7; S3.7: retain the characteristic parameters with causal relationship and analyze the corresponding weights again, calculate the consistency evaluation coefficient β1, and enter step S3.8; S3.8: Determine whether the consistency evaluation coefficient β1 passes the Granger causality test. If yes, end. If not, return to step S3.7. Finally, based on the comprehensive consistency evaluation coefficients β1 of the whole package, determine whether the consistency of the whole package is improved after dynamic replacement, which serves as the basis for performance optimization after dynamic configuration.

[0008] 5. Preferably, the specific steps of step S4 are: S4.1: Calculate the voltage rating function f(V), internal resistance rating function f(R), capacity rating function f(C) and discharge current capability rating function f(I), the expressions are: ; ; ; ; Among them, V J is the actual voltage, V K is the standard voltage, C J is the actual capacity, C K is the standard capacity, R max is the maximum resistance, R J is the actual resistance, I K Standard current, I J is the actual current; S4.2: Set corresponding weights for the voltage rating function f (V), internal resistance rating function f (R), capacity rating function f (C) and discharge current capability rating function f (I), and calculate the total electrical performance score S 1， The expression is: ; Among them, W v , W r , W c , W i The weights corresponding to the voltage rating function f (V), the internal resistance rating function f (R), the capacity rating function f (C) and the discharge current capability rating function f (I); S4.3: Calculate the voltage safety rating function f1 (V), internal resistance safety rating function f1 (R), temperature safety rating function f1 (C) and health status rating function f (SOH), the expression is: ; ; ; ; Among them, R max is the maximum resistance, R min is the minimum resistance, C J is the actual capacity, C K It is the standard capacity; S4.4: Set corresponding weights for the voltage safety rating function f1 (V), the internal resistance safety rating function f1 (R), the temperature safety rating function f (D), and the health status rating function f (SOH), and calculate the total battery safety performance score S2, which is expressed as: ; Among them, W v , W r , W t , W SOH are the weights corresponding to the voltage safety rating function f1(V), the internal resistance safety rating function f1(R), the temperature safety rating function f(D) and the health status rating function f(SOH), S4.3: For the economic benefits generated by the cascade utilization scenario, the corresponding weights are set for the remaining life (RUL) of the retired battery and the power demand of the application scenario, and the total economic score S3 of the battery is calculated, which is expressed as: ; Among them, RUL is the remaining life, e1 is the sum of the benefits generated by the application scenario, such as the operating costs reduced through project implementation, the direct benefits brought by sales, and the benefits brought by long-term operation, etc., e is the cost generated in the application scenario, such as the cost of purchasing retired batteries and other materials, the manpower and material costs consumed by operating equipment, and the sum of transportation and storage costs, etc., P is the maximum power that retired batteries can provide, P1 is the power demand of the application scenario, W e , W rul , W p Set corresponding weights for the economic benefits of the battery in the scenario, the remaining life of the battery, and the power demand of the scenario; S4.4: For each application scenario, the total electrical performance score is S1, and the safety performance score is S 2， The total economic performance score S3 sets the corresponding weight, and calculates the matching degree M between the battery recycling system and the application scenario, which is expressed as: ; Among them, W1, W2, and W3 are the total electrical performance score S1, the safety performance score S 2， The total economic performance score S3 sets a corresponding weight, and M is the matching degree between the application scenario and the cascade utilization system. The higher the matching degree, the more suitable it is for the application scenario.

[0009] (III) Beneficial effects Compared with the prior art, the present invention provides a method for managing the cascade utilization of retired batteries, which has the following beneficial effects: Compared with traditional retired battery cascade utilization management technologies, the described method has significant advantages in accuracy, real-time and comprehensive evaluation capabilities. Through multi-indicator fusion, real-time monitoring and dynamic adjustment, this method can more accurately evaluate the battery status and ensure the safe and efficient operation of the battery system. At the same time, intelligent management methods reduce the risks of human operation and further improve the economy and reliability of battery cascade utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of a flow chart of a retired battery recycling management method proposed by the present invention; Figure 2 A flowchart for checking whether the consistency assessment coefficient β1 passes the Granger causality relationship in a retired battery recycling management method proposed by the present invention. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] See also Figure 1-2 A method for managing the recycling of retired batteries comprises the following steps: S1: Collect the voltage set V0, temperature set D0, capacity set C0 and resistance set R0 of the retired battery; S2: Preprocess the collected voltage set V0, temperature set D0, capacity set C0 and resistance set R0 into voltage set V6, temperature set D6, capacity set C6 and resistance set R6, calculate the standardized matrix X and the covariance matrix Z of the standardized data, construct the eigenvector matrix G, and project the covariance matrix Z into the principal component space, use the data matrix F as training data and the real SOH to train the WLS-SVM estimation model, and predict the estimated value to monitor the degradation degree and aging trajectory of the battery; S3: Calculate the deviation between each value in the voltage set V4, temperature set D4, capacity set C4, and resistance set R4 and the average value in the set to obtain the consistency measure α v , α D , α C , α R , α v , α D , α C , α R Set the corresponding weights to calculate the consistency evaluation coefficient β1, and determine whether the consistency evaluation coefficient β1 passes the Granger causality test. If yes, end the process. If not, retain the characteristic parameters with causal relationships and analyze the corresponding weights again, calculate the consistency evaluation coefficient β1, and determine again whether the consistency evaluation coefficient β1 passes the Granger causality test until it passes. Finally, based on the comprehensive consistency evaluation coefficients β1 of the whole package, determine whether the consistency of the whole package after dynamic replacement is improved, which serves as the basis for performance optimization after dynamic configuration. S4: Calculate the total electrical performance score S1, the total safety performance score S2, and the total economic score S3 of the retired battery, set corresponding weights for the total electrical performance score S1, the total safety performance score S2, and the total economic score S3 of the battery, and calculate the matching degree M between the retired battery recycling system and the application scenario.

[0013] The specific steps in step S2 are: S2.1: Use the interpolation method to supplement the missing data of the collected voltage set V0, temperature set D0, capacity set C0 and resistance set R0 to obtain the voltage set V1, temperature set D1, capacity set C1 and resistance set R1, and the expression is: ; ; Among them, V i-1 and V i+1 is the known data point before and after the missing voltage value in the voltage set V0, V1 is the voltage set after the missing data is supplemented, t i is the time corresponding to the missing voltage value; ; ; Among them, D i-1 and D i+1 is the known data point before and after the missing temperature value in the temperature set D0, D1 is the temperature set after the missing data is supplemented, t i is the time corresponding to the missing temperature value; ; ; Among them, C i-1 and C i+1 is the known data point before and after the missing capacity value in the capacity set C0, C1 is the capacity set after supplementing the missing data, t i is the time corresponding to the missing capacity value; ; ; Among them, R i-1 and R i+1 is the known data point before and after the missing resistance value in the resistance set R0, R1 is the resistance set after the missing data is supplemented, t i is the time corresponding to the missing resistance value; S2.2: Use the IQR method to detect outliers on the voltage set V1, temperature set D1, capacity set C1 and resistance set R1, and calculate the interquartile range IQR of the voltage set V1, temperature set D1, capacity set C1 and resistance set R1. The expression is: IQR = Q3-Q1; Wherein, Q1 is the first quartile of each of the voltage set V1, the temperature set D1, the capacitance set C1 and the resistance set R1; S2.3: The values ​​exceeding 1.5 times of the interquartile range IQR of the voltage set V1, temperature set D1, capacity set C1 and resistance set R1 are considered as outliers and are eliminated. At the same time, the abnormal capacity changes are identified and monitored, and the voltage set V2, temperature set D2, capacity set C2 and resistance set R2 are obtained. The expression is: ; Among them, LowerBound is the lower bound outlier value, and UpperBound is the upper bound outlier value; S2.4: Since the numerical variation range between the data is large, normalization is used to preprocess the data in order to eliminate the influence of different dimensions. Min-Max normalization is performed on the voltage set V2, temperature set D2, capacity set C2 and resistance set R2, and the data is scaled to [0,1] to obtain the voltage set V3, temperature set D3, capacity set C3 and resistance set R3. The expression is: ; Among them, V3 is the normalized voltage set, V min is the minimum value of the voltage set, V max is the maximum value of the voltage set; ; Among them, D3 is the normalized temperature set, D min is the minimum value of the temperature set, D max is the maximum value of the temperature set; ; Among them, C3 is the normalized capacity set, C min is the minimum value of the capacity set, C max is the maximum value of the capacity set; ; Among them, R3 is the normalized resistance set, R min is the minimum value of the resistor set, R max is the maximum value of the resistor set; S2.5: Due to the inevitable noise generated by sensor measurement errors, external interference, etc. during the data acquisition process, Kalman filtering is used to reduce the noise of the voltage set V3, temperature set D3, capacity set C3 and resistance set R3 in order to eliminate the interference of the noise. The expression is: ; ;

[0014] in, is the predicted state vector of the voltage set V3, temperature set D3, capacity set C3 and resistance set R3 at time t, is the predicted covariance matrix, Z t is the observation vector of voltage set V3 and temperature set D3 at time t, u t-1 is the control input of the voltage set V3, temperature set D3, capacity set C3 and resistance set R3 at time t-1, A is the state transition matrix describing the change of the voltage set V3, temperature set D3, capacity set C3 and resistance set R3 over time, γ V , γ D , γ C , γ R is the state transfer coefficient of the voltage set V3, temperature set D3, capacity set C3 and resistance set R3, B is the control input matrix, Q is the process noise matrix, K t The Kalman gain is used to utilize the current observation vector Z t Corrected prediction vector, O is the observation noise covariance matrix, H is the observation matrix, and I is the identity matrix; S2.6: Use the principal component analysis method to reduce the dimension of the voltage set V4, the temperature set D4, the capacity set C4 and the resistance set R4, and perform feature extraction to calculate the standard deviation of the voltage set V5, the temperature set D5, the capacity set C5 and the resistance set R5. The expression is: ; ; ; ; in, is the average voltage value in the voltage set V5, V i is the voltage value of the ith voltage in the voltage set V5, σ v is the standard deviation of the voltage collection V5, is the average temperature value in the temperature set D5, D i is the temperature value of the ith temperature in the temperature set D5, σ D is the standard deviation of the temperature collection D5, is the average capacity value in capacity set C5, C i is the capacity value of the ith capacity in the capacity set C5, σ C is the standard deviation of the capacity set C5, is the average resistance value in the resistor set R5, R i is the resistance value of the i-th resistor in the resistor set R5, σ R is the standard deviation of the resistor set R5, and n is the total number of data in each set; S2.7: To better interpret the results of principal component analysis, standardize the data so that each feature contributes the same to the principal component, using the standard deviation σv The voltage set V5 is standardized to obtain the voltage set V6, which is expressed as: ; S2.8: Use standard deviation σ D Standardize the temperature set D5 to obtain the temperature set D 6， The expression is: ; S2.9: Use standard deviation σ C Standardize the capacity set C5 to obtain the capacity set C 6， The expression is: ; S2.10: Use standard deviation σ R The resistor set R5 is standardized to obtain the resistor set R6, which is expressed as: ; S2.11: Calculate the covariance matrix Z of the standardized matrix X and the standardized data. The covariance matrix is ​​used to calculate the principal components of the data. The principal components are the projections of the original data in the new coordinate system. These new coordinate axes, i.e., the principal components, are the eigenvectors of the covariance matrix, expressed as: ; ; Among them, X T is the transposed matrix of the standardized matrix X, Z is the covariance matrix of the standardized data, and n is the number of samples in the data; S2.12: Perform eigenvalue decomposition on the standardized covariance matrix Z to obtain the eigenvalue λ and eigenvector U, expressed as: ; Among them, U is the eigenvector and λ is its eigenvalue; S2.13: Construct the eigenvector matrix G and project the covariance matrix Z into the principal component space to achieve dimensionality reduction. The expression is: ; Among them, F is the data matrix after dimension reduction; S2.14: Use the data matrix F as training data and the real SOH to train the WLS-SVM estimation model, and use the WLS-SVM estimation model to predict and estimate SOH1 to monitor the degradation degree and aging trajectory of the battery. The expression is: ; Among them, SOH1 is the estimated value output by the WLS-SVM estimation model, w Tis the weight vector, w T is the feature mapping function, and b is the bias term.

[0015] The specific steps in step S3 are: S3.1: In order to achieve consistency evaluation of multi-level voltage, temperature, capacity and internal resistance, calculate the deviation between each voltage value in the voltage set V4 and the average voltage value to obtain the voltage consistency metric α v , the expression is: ; in, is the average voltage value in the voltage set V4, V i is the voltage value of the ith voltage in the voltage set V; S3.2: Calculate the deviation between each temperature value in the temperature set D4 and the average temperature value to obtain the temperature consistency measure α D , the expression is: ; in, is the average temperature value in the temperature set D4, D i is the temperature value of the ith temperature in the temperature set D4; S3.3: Calculate the deviation between each capacity value in the capacity set C4 and the average capacity value to obtain the capacity consistency measure α C , the expression is: ; in, is the average temperature value in capacity set C4, C i is the capacity value of the i-th capacity set C4; S3.4: Calculate the deviation between each resistance value in the resistor set R4 and the average resistance value to obtain the resistance consistency measure α R , the expression is: ; in, is the average resistance value in the resistor set R4, R i is the resistance value of the i-th resistor in the resistor set R4; S3.5: Based on the voltage consistency metric α v , temperature consistency measure α D , capacity consistency measure α C Consistency measure with resistor α R Set the corresponding weights and calculate the consistency evaluation coefficient β1, which is expressed as: ; Among them, W1, W2, W3 and W4 are voltage consistency metrics αv , temperature consistency measure α D , capacity consistency measure α C Consistency measure with resistor α R The corresponding weights; S3.6: Determine whether the consistency assessment coefficient β1 passes the Granger causality test. If yes, end; if no, proceed to step S3.7; S3.7: Retain the characteristic parameters with causal relationships and analyze the corresponding weights again to calculate the consistency assessment coefficient β 1， Go to step S3.8; S3.8: Determine whether the consistency evaluation coefficient β1 passes the Granger causality test. If yes, end. If not, return to step S3.7. Finally, based on the comprehensive consistency evaluation coefficients β1 of the whole package, determine whether the consistency of the whole package is improved after dynamic replacement, which serves as the basis for performance optimization after dynamic configuration.

[0016] Granger causality test is a statistical test method in time series analysis, which is used to determine whether a variable in a time series can be used to predict the change of another time series variable. In a battery system, there are multiple parameters that may affect each other. Through the Granger causality test, it is possible to identify which historical changes in parameters have a significant impact on the future changes of other parameters, thereby helping to determine the key influencing factors in consistency assessment. By judging the causal relationship between different parameters, only variables with actual causal relationships can be included in the consistency assessment model to avoid interference from irrelevant variables. This helps to simplify the model structure and enhance the predictive ability and accuracy of the model.

[0017] The specific steps of step S4 are: S4.1: In order to achieve accurate matching of the cascade system, the electrical performance rating of the cascade system is used to match the usage scenario, calculate the voltage rating function f (V), internal resistance rating function f (R), capacity rating function f (C) and discharge current capability rating function f (I), the expression is: ; ; ; ; Among them, V J is the actual voltage, V K is the standard voltage, C J is the actual capacity, C K is the standard capacity, R max is the maximum resistance, R J is the actual resistance, I K Standard current, IJ is the actual current; S4.2: Set corresponding weights for the voltage rating function f (V), internal resistance rating function f (R), capacity rating function f (C) and discharge current capability rating function f (I), and calculate the total electrical performance score S1, the expression is: ; Among them, W v , W r , W c , W i The weights corresponding to the voltage rating function f (V), the internal resistance rating function f (R), the capacity rating function f (C) and the discharge current capability rating function f (I); S4.3: In order to achieve accurate matching of the cascade system, the safety performance rating of the cascade system is rated to match the usage scenario, and the voltage safety rating function f1 (V), internal resistance safety rating function f1 (R), temperature safety rating function f1 (C) and health status rating function f (SOH) are calculated. The expression is: ; ; ; ; Among them, R max is the maximum resistance, R min is the minimum resistance, C J is the actual capacity, C K It is the standard capacity; S4.4: Set corresponding weights for the voltage safety rating function f1 (V), the internal resistance safety rating function f1 (R), the temperature safety rating function f (D) and the health status rating function f (SOH), and calculate the total battery safety performance score S 2， The expression is: ; Among them, W v , W r , W t , W SOH The weights corresponding to the voltage safety rating function f1 (V), the internal resistance safety rating function f1 (R), the temperature safety rating function f (D) and the health status rating function f (SOH); S4.3: In order to achieve accurate matching of the cascade system, the economic performance rating of the cascade system is used to match the usage scenario, generate economic benefits for the cascade utilization scenario, set corresponding weights for the remaining life RUL of the retired battery and the power demand of the application scenario, and calculate the total economic score S of the battery 3， The expression is: ; Among them, RUL is the remaining life, e1 is the sum of the benefits generated by the application scenario, such as the operating costs reduced through project implementation, the direct benefits brought by sales, and the benefits brought by long-term operation, etc., e is the cost generated in the application scenario, such as the cost of purchasing retired batteries and other materials, the manpower and material costs consumed by operating equipment, and the sum of transportation and storage costs, etc., P is the maximum power that retired batteries can provide, P1 is the power demand of the application scenario, W e , W rul , W p Set corresponding weights for the economic benefits of the battery in the scenario, the remaining life of the battery, and the power demand of the scenario; S4.4: For each application scenario, set corresponding weights for the total electrical performance score S1, the safety performance score S2, and the total economic performance score S3, and calculate the matching degree M between the battery recycling system and the application scenario. The expression is: ; Among them, W1, W2, and W3 are the total electrical performance score S1, the safety performance score S2, and the total economic performance score S3, respectively, and corresponding weights are set. M is the matching degree between the application scenario and the cascade utilization system. The higher the matching degree, the more suitable it is for the application scenario.

[0018] In summary, compared with traditional retired battery cascade utilization management technologies, the described method has significant advantages in accuracy, real-time and comprehensive evaluation capabilities. This method can more accurately evaluate the battery status through multi-indicator fusion, real-time monitoring and dynamic adjustment, ensuring the safe and efficient operation of the battery system. At the same time, intelligent management methods reduce the risks of human operation and further improve the economy and reliability of battery cascade utilization.

[0019] It should be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0020] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for managing the recycling of retired batteries, characterized in that: The following steps are involved: S1: Collect the voltage set V0, temperature set D0, capacity set C0 and resistance set R0 of the retired battery; S2: Preprocess the collected voltage set V0, temperature set D0, capacity set C0 and resistance set R0 into voltage set V6, temperature set D6, capacity set C6 and resistance set R6, calculate the standardized matrix X and the covariance matrix Z of the standardized data, construct the eigenvector matrix G, and project the covariance matrix Z into the principal component space, use the data matrix F as training data and the real SOH to train the WLS-SVM estimation model, and predict the estimated value to monitor the degradation degree and aging trajectory of the battery; S3: Calculate the deviation between each value in the voltage set V4, temperature set D4, capacity set C4, and resistance set R4 and the average value in the set to obtain the consistency measure α v , α D , α C , α R , and α v , α D , α C , α R Set the corresponding weights to calculate the consistency evaluation coefficient β1, and determine whether the consistency evaluation coefficient β1 passes the Granger causality test. If yes, end the process. If not, retain the characteristic parameters with causal relationships and analyze the corresponding weights again, calculate the consistency evaluation coefficient β1, and determine again whether the consistency evaluation coefficient β1 passes the Granger causality test until it passes. Finally, based on the comprehensive consistency evaluation coefficients β1 of the whole package, determine whether the consistency of the whole package after dynamic replacement is improved, which serves as the basis for performance optimization after dynamic configuration. S4: Calculate the total electrical performance score S1, the total safety performance score S2, and the total economic score S3 of the retired battery, set corresponding weights for the total electrical performance score S1, the total safety performance score S2, and the total economic score S3 of the battery, and calculate the matching degree M between the retired battery recycling system and the application scenario.

2. A method for managing the recycling of retired batteries according to claim 1, characterized in that: The specific steps in step S2 are: S2.1: Use the interpolation method to supplement the missing data of the collected voltage set V0, temperature set D0, capacity set C0 and resistance set R0 to obtain the voltage set V1, temperature set D1, capacity set C1 and resistance set R1, and the expression is: ； ； Among them, V i-1 and V i+1 is the known data point before and after the missing voltage value in the voltage set V0, V1 is the voltage set after the missing data is supplemented, t i is the time corresponding to the missing voltage value; ； ； Among them, D i-1 and D i+1 is the known data point before and after the missing temperature value in the temperature set D0, D1 is the temperature set after the missing data is supplemented, t i is the time corresponding to the missing temperature value; ； ； Among them, C i-1 and C i+1 is the known data point before and after the missing capacity value in the capacity set C0, C1 is the capacity set after supplementing the missing data, t i is the time corresponding to the missing capacity value; ； ； Among them, R i-1 and R i+1 is the known data point before and after the missing resistance value in the resistance set R0, R1 is the resistance set after the missing data is supplemented, t i is the time corresponding to the missing resistance value; S2.2: Use the IQR method to detect outliers on the voltage set V1, temperature set D1, capacity set C1 and resistance set R1, and calculate the interquartile range IQR of the voltage set V1, temperature set D1, capacity set C1 and resistance set R1. The expression is: IQR = Q3-Q1; Wherein, Q1 is the first quartile of each of the voltage set V1, the temperature set D1, the capacitance set C1 and the resistance set R1; S2.3: Any value that exceeds 1.5 times the interquartile range IQR of the voltage set V1, temperature set D1, capacity set C1, and resistance set R1 is considered an outlier and is removed. At the same time, the abnormal capacity changes are identified and monitored, and the voltage set V2, temperature set D2, capacity set C2, and resistance set R1 are obtained. 2， The expression is: ； Among them, LowerBound is the lower bound outlier value, and UpperBound is the upper bound outlier value; S2.4: Perform Min-Max normalization on the voltage set V2, temperature set D2, capacity set C2 and resistance set R2, scale the data to [0,1], and obtain the voltage set V3, temperature set D3, capacity set C3 and resistance set R3. The expression is: ； Among them, V3 is the normalized voltage set, V min is the minimum value of the voltage set, V max is the maximum value of the voltage set; ； Among them, D3 is the normalized temperature set, D min is the minimum value of the temperature set, D max is the maximum value of the temperature set; ； Among them, C3 is the normalized capacity set, C min is the minimum value of the capacity set, C max is the maximum value of the capacity set; ； Among them, R3 is the normalized resistance set, R min is the minimum value of the resistor set, R max is the maximum value of the resistor set; S2.5: Kalman filtering is used to reduce the noise of the voltage set V3, the temperature set D3, the capacity set C3 and the resistance set R3. The expression is: ； ； in, is the predicted state vector of the voltage set V3, temperature set D3, capacity set C3 and resistance set R3 at time t, is the predicted covariance matrix, Z t is the observation vector of voltage set V3 and temperature set D3 at time t, u t-1 is the control input of the voltage set V3, temperature set D3, capacity set C3 and resistance set R3 at time t-1, A is the state transition matrix describing the change of the voltage set V3, temperature set D3, capacity set C3 and resistance set R3 over time, γ V , γ D , γ C , γ R is the state transfer coefficient of the voltage set V3, temperature set D3, capacity set C3 and resistance set R3, B is the control input matrix, Q is the process noise matrix, K t The Kalman gain is used to utilize the current observation vector Z t Corrected prediction vector, O is the observation noise covariance matrix, H is the observation matrix, and I is the identity matrix; S2.6: Use the principal component analysis method to reduce the dimension of the voltage set V4, the temperature set D4, the capacity set C4 and the resistance set R4, and perform feature extraction to calculate the standard deviation of the voltage set V5, the temperature set D5, the capacity set C5 and the resistance set R5. The expression is: ； ； ； ； in, is the average voltage value in the voltage set V5, V i is the voltage value of the ith voltage in the voltage set V5, σ v is the standard deviation of the voltage collection V5, is the average temperature value in the temperature set D5, D i is the temperature value of the ith temperature in the temperature set D5, σ D is the standard deviation of the temperature collection D5, is the average capacity value in capacity set C5, C i is the capacity value of the ith capacity in the capacity set C5, σ C is the standard deviation of the capacity set C5, is the average resistance value in the resistor set R5, R i is the resistance value of the i-th resistor in the resistor set R5, σ R is the standard deviation of the resistor set R5, and n is the total number of data in each set; S2.7: Use standard deviation σ v The voltage set V5 is standardized to obtain the voltage set V 6， The expression is: ； S2.8: Use standard deviation σ D Standardize the temperature set D5 to obtain the temperature set D 6， The expression is: ； S2.9: Use standard deviation σ C Standardize the capacity set C5 to obtain the capacity set C 6， The expression is: ； S2.10: Use standard deviation σ R The resistor set R5 is standardized to obtain the resistor set R 6， The expression is: ； S2.11: Calculate the covariance matrix Z of the standardized matrix X and the standardized data, expressed as: ； ； Among them, X T is the transposed matrix of the standardized matrix X, Z is the covariance matrix of the standardized data, and n is the number of samples in the data; S2.12: Perform eigenvalue decomposition on the standardized covariance matrix Z to obtain the eigenvalue λ and eigenvector U, expressed as: ； Among them, U is the eigenvector and λ is its eigenvalue; S2.13: Construct the eigenvector matrix G and project the covariance matrix Z into the principal component space to achieve dimensionality reduction. The expression is: ； Among them, F is the data matrix after dimension reduction; S2.14: Use the data matrix F as training data and the actual SOH to train the WLS-SVM estimation model, and use the WLS-SVM estimation model to predict the estimated SOH 1， Realize the monitoring of battery degradation degree and aging trajectory, the expression is: ； Among them, SOH1 is the estimated value output by the WLS-SVM estimation model, w T is the weight vector, w T is the feature mapping function, and b is the bias term.

3. A method for managing the recycling of retired batteries according to claim 2, characterized in that: The specific steps in step S3 are: S3.1: Calculate the deviation between each voltage value in the voltage set V4 and the average voltage value to obtain the voltage consistency measure α v， The expression is: ； in, is the average voltage value in the voltage set V4, V i is the voltage value of the ith voltage in the voltage set V; S3.2: Calculate the deviation between each temperature value in the temperature set D4 and the average temperature value to obtain the temperature consistency measure α D , the expression is: ； in, is the average temperature value in the temperature set D4, D i is the temperature value of the ith temperature in the temperature set D4; S3.3: Calculate the deviation between each capacity value in the capacity set C4 and the average capacity value to obtain the capacity consistency measure α C , the expression is: ； in, is the average temperature value in capacity set C4, C i is the capacity value of the i-th capacity set C4; S3.4: Calculate the deviation between each resistance value in the resistor set R4 and the average resistance value to obtain the resistance consistency measure α R， The expression is: ； in, is the average resistance value in the resistor set R4, R i is the resistance value of the i-th resistor in the resistor set R4; S3.5: Based on the voltage consistency metric α v , temperature consistency measure α D , capacity consistency measure α C Consistency measure with resistor α R Set the corresponding weights and calculate the consistency evaluation coefficient β 1， The expression is: ； Among them, W1, W2, W3 and W4 are voltage consistency metrics α v , temperature consistency measure α D , capacity consistency measure α C Consistency measure with resistor α R The corresponding weights; S3.6: Determine whether the consistency assessment coefficient β1 passes the Granger causality test. If yes, end; if no, proceed to step S3.7; S3.7: retain the characteristic parameters with causal relationship and analyze the corresponding weights again, calculate the consistency evaluation coefficient β1, and enter step S3.8; S3.8: Determine whether the consistency assessment coefficient β1 passes the Granger causality test. If yes, end the process. If not, return to step S3.

7. S3.9: Finally, based on the comprehensive consistency evaluation coefficients β1 of the whole package, it is determined whether the consistency of the whole package is improved after dynamic replacement, which serves as the basis for performance optimization after dynamic configuration.

4. A method for managing the recycling of retired batteries according to claim 1, characterized in that: The specific steps of step S4 are: S4.1: Calculate the voltage rating function f(V), internal resistance rating function f(R), capacity rating function f(C) and discharge current capability rating function f(I), the expressions are: ； ； ； ； Among them, V J is the actual voltage, V K is the standard voltage, C J is the actual capacity, C K is the standard capacity, R max is the maximum resistance, R J is the actual resistance, I K Standard current, I J is the actual current; S4.2: Set corresponding weights for the voltage rating function f (V), internal resistance rating function f (R), capacity rating function f (C) and discharge current capability rating function f (I), and calculate the total electrical performance score S 1， The expression is: ； Among them, W v , W r , W c , W i The weights corresponding to the voltage rating function f (V), the internal resistance rating function f (R), the capacity rating function f (C) and the discharge current capability rating function f (I); S4.3: Calculate the voltage safety rating function f1 (V), internal resistance safety rating function f1 (R), temperature safety rating function f1 (C) and health status rating function f (SOH), the expression is: ； ； ； ； Among them, R max is the maximum resistance, R min is the minimum resistance, C J is the actual capacity, C K It is the standard capacity; S4.4: Set corresponding weights for the voltage safety rating function f1 (V), the internal resistance safety rating function f1 (R), the temperature safety rating function f (D) and the health status rating function f (SOH), and calculate the total battery safety performance score S 2， The expression is: ； Among them, W v , W r , W t , W SOH The weights corresponding to the voltage safety rating function f1 (V), the internal resistance safety rating function f1 (R), the temperature safety rating function f (D) and the health status rating function f (SOH); S4.3: For the economic benefits generated by the cascade utilization scenario, the corresponding weights are set for the remaining life (RUL) of the retired battery and the power demand of the application scenario, and the total economic score S of the battery is calculated. 3， The expression is: ； Among them, RUL is the remaining life, e1 is the sum of the benefits generated by the application scenario, such as the operating costs reduced through project implementation, the direct benefits brought by sales, and the benefits brought by long-term operation, etc., e is the cost generated in the application scenario, such as the cost of purchasing retired batteries and other materials, the manpower and material costs consumed by operating equipment, and the sum of transportation and storage costs, etc., P is the maximum power that retired batteries can provide, P1 is the power demand of the application scenario, W e , W rul , W p Set corresponding weights for the economic benefits of the battery in the scenario, the remaining life of the battery, and the power demand of the scenario; S4.4: For each application scenario, set corresponding weights for the total electrical performance score S1, the safety performance score S2, and the total economic performance score S3, and calculate the matching degree M between the battery recycling system and the application scenario. The expression is: ； Among them, W1, W2, and W3 are the total electrical performance scores S 1， Safety performance score S 2， The total economic performance score S3 sets a corresponding weight, and M is the matching degree between the application scenario and the cascade utilization system. The higher the matching degree, the more suitable it is for the application scenario.

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