A method, device and equipment for rapid screening of retired power battery gradient utilization

Abstract

The application provides a kind of retired power battery gradient utilization quick screening method, device and equipment, obtain battery test data, and the test data is used as the feature parameter of battery and constructs vector matrix, wherein the test data includes but is not limited to battery open circuit voltage, capacity, internal resistance;The vector matrix is pretreated, and the combined weight of the feature parameter is calculated by calling hierarchical entropy weight method;The battery is screened and recombined using genetic simulated annealing-K means (GASA-Kmeans, GSK) algorithm, the vector matrix after preprocessing and the combined weight are used to cluster battery individuals, and the clustering result of battery individuals is generated, the problem that traditional K-means algorithm is sensitive to initial clustering center is solved, the cost and operation difficulty of screening are reduced, and the parameter consistency performance of recombination retired power battery pack is improved.

Description

Technical Field

[0001] This invention relates to the field of retired power battery cascade utilization technology, and in particular to a rapid screening method, apparatus and equipment for the cascade utilization of retired power batteries. Background Technology

[0002] Second-hand utilization is a method for disposing of retired batteries that aligns with both economic efficiency and environmental protection principles. As a crucial aspect of this approach, research on screening and recombination methods for retired batteries has become a hot topic. When the remaining capacity of a power battery pack falls below 80% of its rated capacity, the battery pack needs to be recycled. If retired power batteries are recombined arbitrarily without screening, it will lead to low parameter consistency, affecting the operational performance of the battery pack and causing a rapid decline in its remaining lifespan. Screening retired batteries can improve the parameter consistency of the recombined battery pack, effectively extending its lifespan and ensuring the rational utilization of resources.

[0003] Early screening and recombination methods for retired batteries mainly relied on direct screening based on internal resistance or capacity, resulting in low efficiency and inconsistent battery grouping. Existing screening methods often employed multi-parameter algorithms. Patent CN115121507 A proposed a low-testing-cost method for sorting retired power batteries. Based on prior voltage interval testing and data collection, it used the K-means algorithm to sort and recombine retired power batteries, which could improve the consistency of recombined batteries to some extent. However, the traditional K-means algorithm it used was sensitive to the initial cluster centers. Patent CN 114429050 A proposed a sorting method for the tiered utilization of retired power batteries, using the LOF (Local Outlier Factor) algorithm to evaluate multiple battery aging parameters. While this method could achieve multi-level screening of retired power batteries by setting different LOFs, it did not consider the bias of battery characteristics under different application scenarios.

[0004] In short, existing methods for sorting retired power batteries using traditional algorithms have several problems. For example, traditional K-means and FCM (Fuzzy C-Means) algorithms are sensitive to initial cluster centers; traditional density-based clustering algorithms perform poorly when population density is not clearly distinguishable; neural networks and multi-class support vector machines have high initial training costs; and most existing sorting methods do not consider the specific requirements of battery characteristics for post-recombination applications.

[0005] In view of the above, this application is hereby submitted. Summary of the Invention

[0006] This invention discloses a rapid screening method, device, equipment, and medium for the cascade utilization of retired power batteries, aiming to improve the parameter consistency performance of recombined retired power battery packs and extend the service life of the battery packs.

[0007] The first embodiment of the present invention provides a rapid screening method for the secondary utilization of retired power batteries, comprising:

[0008] Acquire battery test data and construct a vector matrix using the test data as the battery's feature parameters, wherein the test data includes, but is not limited to, battery open-circuit voltage, capacity, and internal resistance;

[0009] The vector matrix is ​​preprocessed, and the combined weights of the feature parameters are calculated using the hierarchical entropy weighting method.

[0010] Based on the preprocessed vector matrix and the combined weights, the GSK algorithm is used to cluster individual batteries and generate clustering results for each battery.

[0011] Preferably, the preprocessing of the vector matrix specifically involves:

[0012] The vector matrix is ​​reduced in dimensionality using PCA and then normalized to generate a three-dimensional matrix, where the matrix dimension is the number of feature parameters and the normalization range is 0-1.

[0013] Preferably, the step of using the hierarchical entropy weighting method to calculate the combined weights of the feature parameters specifically involves:

[0014] Obtain the application scenarios and hierarchical structure of the battery, and generate a comparison matrix based on the application scenarios and hierarchical structure;

[0015] The comparison matrix is ​​verified, and feature weights for each indicator are generated when the consistency index is lower than a preset value.

[0016] The vector matrix is ​​standardized, and the entropy weights of each index are generated based on the standardized vector matrix.

[0017] The feature weights and entropy weights of each indicator are combined to generate a combined weight.

[0018] Preferably, the step of verifying the comparison matrix and generating feature weights for each indicator when the consistency index is lower than a preset value specifically includes:

[0019] Calculate the consistency ratio:

[0020]

[0021] Wherein, CR is the consistency ratio, RI is the average random consistency index, and CI is the consistency index.

[0022] Calculate the consistency index:

[0023]

[0024] Where n is the dimension of the comparison matrix, and λmax is the maximum eigenvalue of the comparison matrix;

[0025] The CR is validated, and the feature weights of each indicator are calculated when the CR is less than a preset value:

[0026] AW c =λ max W c

[0027] Where A is the comparison matrix and Wc is the feature weight obtained after normalizing the corresponding feature vector.

[0028] Preferably, the standardization of the vector matrix and the generation of entropy weights for each index based on the standardized vector matrix specifically includes:

[0029] Through function The vector matrix is ​​standardized, where G ij For the standardized matrix, x ij The original matrix;

[0030] The entropy weights of each indicator are generated using the following formula:

[0031]

[0032]

[0033] Among them, F j Let m be the information entropy, m be the feature dimension, and W be the feature dimension. s The entropy weight is used.

[0034] Preferably, the step of combining the feature weights and entropy weights of each indicator to generate a combined weight specifically includes:

[0035] The combined weights are generated using the following function:

[0036] W = γW c +(1-γ)W s ,0≤γ≤1;

[0037] Where Wc is the feature weight obtained after normalizing the corresponding feature vector, W s γ is the entropy weight, W is the combined weighting weight, and γ is the subjective preference coefficient.

[0038] Preferably, the step of clustering individual batteries based on the preprocessed vector matrix and the combined weights, and generating clustering results for the individual batteries, specifically involves:

[0039] S1031, Obtain the optimal number of clusters, and generate an initial battery cluster center population based on the optimal number of clusters and the preprocessed vector matrix;

[0040] S1032, calculate the objective function and fitness function of the initial battery cluster center population based on the maximum number of genetic iterations, individual selection operation, and crossover mutation operation to generate the initial battery cluster centers;

[0041] S1033, Calculate the weighted Euclidean distance of the initial battery cluster centers, and calculate the new battery cluster centers based on the weighted Euclidean distance;

[0042] S1034, determine whether the new battery cluster center overlaps with the original cluster center;

[0043] If not, replace the original overlapping cluster centers with new battery cluster centers, and re-execute S1033 and S1034.

[0044] If so, output the clustering results for the individual batteries.

[0045] Preferably, the objective function is:

[0046]

[0047] Where sum is the objective function value, K is the number of cluster centers, SIM represents the similarity between an individual and each cluster center, and Z... j Let j be the cluster center;

[0048] The fitness function is:

[0049]

[0050] Where Fit is the fitness function value, t f This represents the end temperature of the SA algorithm.

[0051] The function for calculating Euclidean distance is:

[0052]

[0053] Among them, X in Let c be the nth dimension feature of the i-th individual. jn W is the nth dimension feature of the j-th cluster center. n represents the scene weight corresponding to the nth feature, where m is the total number of feature dimensions;

[0054] The expression for the j-th cluster center is:

[0055]

[0056] Where, N j Let X be the number of individuals in class j. i This represents the i-th individual in the j-th class.

[0057] The second embodiment of the present invention provides a rapid screening device for the cascade utilization of retired power batteries, comprising:

[0058] The vector matrix construction unit acquires battery test data and uses the test data as the characteristic parameters of the battery to construct a vector matrix, wherein the test data includes the battery open-circuit voltage, capacity, and internal resistance;

[0059] The combined weight calculation unit is used to preprocess the vector matrix and call the hierarchical entropy weight method to calculate the combined weight of the feature parameters.

[0060] Clustering unit, used to cluster individual batteries based on the preprocessed vector matrix and the combined weights, and generate clustering results for individual batteries.

[0061] The third embodiment of the present invention provides a rapid screening device for the cascade utilization of retired power batteries, characterized in that it includes a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a rapid screening method for the cascade utilization of retired power batteries as described in any of the above embodiments.

[0062] The fourth embodiment of the present invention provides a computer-readable storage medium, characterized in that it stores a computer program, which can be executed by the processor of the device where the computer-readable storage medium is located, to implement a rapid screening method for the cascade utilization of retired power batteries as described in any of the above claims.

[0063] This invention provides a rapid screening method, apparatus, equipment, and medium for the cascade utilization of retired power batteries. It acquires battery test data, constructs a vector matrix based on the data, preprocesses the vector matrix, calculates the combined weights of the feature parameters using the hierarchical entropy weight method, and uses the GSK algorithm to screen and reorganize the batteries. Based on the preprocessed vector matrix and the combined weights, individual batteries are clustered, generating clustering results. This addresses the sensitivity of the traditional K-means algorithm to initial cluster centers, reduces screening costs and operational difficulty, and improves the parameter consistency of reorganized retired power battery packs. Attached Figure Description

[0064] Figure 1This is a flowchart illustrating a rapid screening method for the cascade utilization of retired power batteries provided in the first embodiment of the present invention.

[0065] Figure 2 This is the hierarchical structure diagram constructed by the hierarchical structure construction part in the hierarchical entropy weight method provided by this invention;

[0066] Figure 3 This is a graph showing the changes in inter-cluster distance and intra-cluster distance under different cluster numbers when calculating the optimal number of clusters provided by this invention;

[0067] Figure 4 This is a preprocessed battery data point set map provided by the present invention;

[0068] Figure 5 This is a diagram illustrating the effect of battery clustering and recombination in an energy storage scenario provided by the present invention.

[0069] Figure 6 This is a flowchart of the scene-specific weight calculation using the hierarchical entropy weight method provided by the present invention;

[0070] Figure 7 This is a flowchart illustrating the rapid screening method for retired power batteries provided by the present invention.

[0071] Figure 8 This is a schematic diagram of a rapid screening device for the secondary utilization of retired power batteries provided in the first embodiment of the present invention; Detailed Implementation

[0072] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0073] To better understand the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0074] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0075] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0076] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0077] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0078] The terms "first" and "second" used in the embodiments are merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permissible. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein.

[0079] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0080] This invention discloses a rapid screening method, device, equipment, and medium for the cascade utilization of retired power batteries, aiming to improve the parameter consistency performance of recombined retired power battery packs and extend the service life of the battery packs.

[0081] The first embodiment of the present invention provides a rapid screening method for the cascade utilization of retired power batteries, which can be executed by a rapid screening device for the cascade utilization of retired power batteries (hereinafter referred to as the screening device), and in particular, by one or more processors within the screening device, to at least achieve the following steps:

[0082] S101, acquire battery test data, and construct a vector matrix using the test data as the characteristic parameters of the battery, wherein the test data includes, but is not limited to, battery open-circuit voltage, capacity, and internal resistance;

[0083] In this embodiment, the evaluation device can be a desktop computer, laptop computer, server, workstation, or other terminal with data processing and analysis capabilities. The evaluation device can be equipped with a corresponding operating system and application software, and the functions required in this embodiment are realized through the combination of the operating system and application software.

[0084] It should be noted that in this embodiment, the battery open-circuit voltage, capacity, and internal resistance are used as battery characteristic parameters to construct the parameter matrix. In other embodiments, other parameters can also be used to construct the parameter matrix, and no specific limitation is made here.

[0085] S102, preprocess the vector matrix and call the hierarchical entropy weight method to calculate the combined weights of the feature parameters. For details, please refer to [link to documentation]. Figure 6 ;

[0086] In this embodiment, the vector matrix can be preprocessed by using PCA dimensionality reduction and normalization to reduce the dimensionality of the vector matrix to a three-dimensional matrix, where the matrix dimension is the number of feature parameters and the normalization range is 0-1.

[0087] It should be noted that in this embodiment, battery application scenarios can be categorized into three types: energy storage, low-speed electric vehicles, and backup power, and a hierarchical structure can be constructed, consisting of three layers: the target layer, the criterion layer, and the solution layer. For details, please refer to [link / reference]. Figure 2 For energy storage, capacity and internal resistance are the primary considerations, followed by open-circuit voltage; for low-speed electric vehicles, internal resistance and open-circuit voltage are the primary considerations, followed by capacity; for backup power supplies, open-circuit voltage and capacity are the primary considerations, followed by internal resistance.

[0088] Construct a comparison matrix for each scenario. The comparison matrix represents the importance of each element in the criterion layer compared to other elements in the current application scenario. The scale is shown in Table 1. The completed comparison matrix is ​​a 3×3 matrix, named a, a ij This represents an element in matrix a. Taking an energy storage scenario as an example, such as a... i Represents internal resistance, a j If a represents capacity, then ij A value of 1 indicates that, in energy storage scenarios, internal resistance and capacity characteristics are equally important. The final comparison matrix is ​​shown in Table 2, which is divided into comparison matrices for three different scenarios: energy storage, low-speed electric vehicles, and backup energy.

[0089] Table 1

[0090]

[0091] Table 2

[0092]

[0093] Where ① represents the open-circuit voltage; ② represents the internal resistance; and ③ represents the capacitance.

[0094] Specifically, in this embodiment,

[0095] Obtain the application scenarios and hierarchical structure of the battery, and generate a comparison matrix based on the application scenarios and hierarchical structure, specifically including:

[0096] Calculate the consistency ratio:

[0097]

[0098] Wherein, CR is the consistency ratio, RI is the average random consistency index, CI is the consistency index, RI is 0.52 according to Table 3, and n is the dimension of the comparison matrix in Table 3.

[0099] Calculate the consistency index:

[0100]

[0101] Where n is the dimension of the comparison matrix, and λmax is the maximum eigenvalue of the comparison matrix;

[0102] The CR is validated. When the CR is less than a preset value (e.g., less than 0.1), the feature weights of each indicator are calculated. The calculation results are shown in Table 4.

[0103] AW c =λ max W c

[0104] Where A is the comparison matrix and Wc is the feature weight obtained after normalizing the corresponding feature vector.

[0105] Table 3

[0106]

[0107] Table 4

[0108]

[0109] The comparison matrix is ​​verified, and feature weights for each indicator are generated when the consistency index is lower than a preset value.

[0110] The vector matrix is ​​standardized, and entropy weights for each index are generated based on the standardized vector matrix. Specifically:

[0111] Through function The vector matrix is ​​standardized, where G ij For the standardized matrix, x ij The original matrix;

[0112] The entropy weights of each indicator are generated using the following formula:

[0113]

[0114]

[0115] Among them, F j Let m be the information entropy, m be the feature dimension, and W be the feature dimension. s The entropy weight is used.

[0116] The feature weights and entropy weights of each indicator are combined to generate a combined weight. Specifically, the combined weight is generated using the following function:

[0117] W = γW c +(1-γ)W s ,0≤γ≤1;

[0118] Where Wc is the feature weight obtained after normalizing the corresponding feature vector, W s γ is the entropy weight, W is the combined weighting weight, and γ is the subjective preference coefficient, which is usually taken as 0.6.

[0119] S103, cluster the individual batteries according to the preprocessed vector matrix and the combined weights, and generate the clustering results of the individual batteries.

[0120] Specifically, in this embodiment, please refer to Figure 7 ,

[0121] S1031, Obtain the optimal number of clusters, and generate an initial battery cluster center population based on the optimal number of clusters and the preprocessed vector matrix;

[0122] It should be noted that the optimal K value is the one that results in the largest decrease in intra-class distance and the fastest increase in inter-class distance. For graphs showing the changes in intra-class and inter-class distances under different K values, please refer to [link to graph]. Figure 3 ;

[0123] If the maximum magnitudes of the decrease in intra-class distance and the increase in inter-class distance occur at different points in the K-value variation, then the K-value with the largest change in the ratio of intra-class distance to inter-class distance is selected as the optimal K-value. According to... Figure 3 When K changes from 2 to 3, Din (intra-class distance) decreases the fastest, reaching 0.0367; when K changes from 3 to 4, Dot (inter-class distance) increases the fastest, reaching 0.0387. Comparing the two cases of K=3 and K=4 using Dot / Din as the index, the index is 0.6 when K=3 and 1.05 when K=4. The optimal K value is selected as 4.

[0124] Furthermore, define initial parameters such as scene category number, number of clusters, preference coefficient, crossover probability, mutation probability, initial temperature, ending temperature, and temperature decrease ratio;

[0125] S1032, calculate the objective function and fitness function of the initial battery cluster center population based on the maximum number of genetic iterations, individual selection operation, and crossover mutation operation to generate the initial battery cluster centers;

[0126] In this embodiment, an initial cluster center population is generated, and the objective function and fitness function are calculated:

[0127]

[0128] Where sum is the objective function value, K is the number of cluster centers, SIM represents the similarity between an individual and each cluster center, and Z... j Let j be the cluster center;

[0129] The fitness function is:

[0130]

[0131] Where Fit is the fitness function value, t f This represents the end temperature of the SA algorithm.

[0132] S1033, Calculate the weighted Euclidean distance of the initial battery cluster centers, and calculate the new battery cluster centers based on the weighted Euclidean distance;

[0133] The function for calculating Euclidean distance is:

[0134]

[0135] Among them, X in Let c be the nth dimension feature of the i-th individual. jn W is the nth dimension feature of the j-th cluster center. n represents the scene weight corresponding to the nth feature, where m is the total number of feature dimensions;

[0136] The expression for the j-th cluster center is:

[0137]

[0138] Where, N j Let X be the number of individuals in class j. i This represents the i-th individual in the j-th class.

[0139] S1034, determine whether the new battery cluster center overlaps with the original cluster center;

[0140] If not, replace the original overlapping cluster centers with new battery cluster centers, and re-execute S1033 and S1034.

[0141] If so, output the clustering results for the individual batteries.

[0142] In this embodiment, the clustering results can be visualized. Taking an energy storage scenario as an example, the clustering effect of the instances can be displayed as follows: Figure 5 , Figure 6 As shown, where U oc R is the open-circuit voltage. a Let Q be the internal resistance and Q be the capacitance.

[0143] In this embodiment, the standard deviation coefficient can also be used as a test index to test the parameter consistency of the recombined battery pack. The standard deviation coefficient is the ratio of the standard deviation to the mean. Here, the energy storage scenario is used as an example to show the parameter consistency results. Please refer to Table 5. It can be seen that after adopting the GSK algorithm, the consistency of the internal resistance and capacity parameters that are of key concern in the energy storage scenario is improved.

[0144] Table 5

[0145]

[0146]

[0147] The second embodiment of the present invention provides a rapid screening device for the cascade utilization of retired power batteries, comprising:

[0148] The vector matrix construction unit acquires battery test data and uses the test data as the characteristic parameters of the battery to construct a vector matrix, wherein the test data includes the battery open-circuit voltage, capacity, and internal resistance;

[0149] The combined weight calculation unit is used to preprocess the vector matrix and call the hierarchical entropy weight method to calculate the combined weight of the feature parameters.

[0150] Clustering unit, used to cluster individual batteries based on the preprocessed vector matrix and the combined weights, and generate clustering results for individual batteries.

[0151] The third embodiment of the present invention provides a rapid screening device for the cascade utilization of retired power batteries, characterized in that it includes a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a rapid screening method for the cascade utilization of retired power batteries as described in any of the above embodiments.

[0152] The fourth embodiment of the present invention provides a computer-readable storage medium, characterized in that it stores a computer program, which can be executed by the processor of the device where the computer-readable storage medium is located, to implement a rapid screening method for the cascade utilization of retired power batteries as described in any of the above claims.

[0153] This invention provides a rapid screening method, apparatus, equipment, and medium for the cascade utilization of retired power batteries. It acquires battery test data, constructs a vector matrix based on the data, preprocesses the vector matrix, and calculates the combined weights of the feature parameters using the hierarchical entropy weight method. Based on the preprocessed vector matrix and the combined weights, individual batteries are clustered, generating clustering results. This addresses the sensitivity of the traditional K-means algorithm to initial cluster centers and utilizes the GSK algorithm for battery screening and recombination, reducing screening costs and operational difficulty, and improving the parameter consistency performance of recombined retired power battery packs.

[0154] Exemplary examples show that the computer program described in the third and fourth embodiments of the present invention can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in implementing a rapid screening device for the cascade utilization of retired power batteries. For example, the apparatus described in the second embodiment of the present invention.

[0155] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the aforementioned rapid screening method for the cascade utilization of retired power batteries, connecting various parts of the method through various interfaces and lines.

[0156] The memory can be used to store the computer program and / or modules. The processor, by running or executing the computer program and / or modules stored in the memory, and by calling the data stored in the memory, implements various functions of a rapid screening method for the cascade utilization of retired power batteries. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, text conversion function, etc.), etc.; the data storage area may store data created based on the use of the mobile phone (such as audio data, text message data, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0157] If the implemented module is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0158] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0159] The above description is merely a preferred embodiment 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 technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for rapid screening of retired power battery for stepwise utilization, characterized in that, The method comprises the following steps: acquiring battery test data and constructing a vector matrix as a characteristic parameter of the battery; preprocessing the vector matrix and calculating the combined weight of the characteristic parameter by using a hierarchical entropy weight method; wherein the combined weight of the characteristic parameter is calculated by the hierarchical entropy weight method as follows: acquiring the application scenario and hierarchical structure of the battery, and generating a comparison matrix according to the application scenario and hierarchical structure; checking the comparison matrix, and generating the characteristic weight of each index when the consistency index is lower than a preset value; normalizing the vector matrix, and generating the entropy weight value of each index according to the normalized vector matrix; combining the characteristic weight of each index and the entropy weight value of each index to generate the combined weight; normalizing the vector matrix, and generating the entropy weight value of each index according to the normalized vector matrix, specifically including: generating the entropy weight value of each index through a function normalizing the vector matrix, wherein, G ij for the normalized matrix, x ij is the original matrix; and generating the entropy weight value of each index through the following formula: ； ； wherein, F j is the information entropy, m is the feature dimension, W s is the entropy weight value; clustering the battery individuals according to the preprocessed vector matrix and the combined weight, and generating the clustering result of the battery individuals.

2. The quick screening method for stepwise utilization of retired power batteries according to claim 1, characterized in that, The preprocessing of the vector matrix comprises the following steps: dimension reduction of the vector matrix by using a PCA dimension reduction method, and normalization of the vector matrix to generate a three-dimensional matrix, wherein the matrix dimension is the number of characteristic parameters, and the normalization range is 0-1.

3. The quick screening method for stepwise utilization of retired power batteries according to claim 1, characterized in that, The checking of the comparison matrix and the generation of the characteristic weight of each index when the consistency index is lower than a preset value comprise the following steps: calculating the consistency ratio: wherein CR is the consistency ratio, RI is the average random consistency index, and CI is the consistency index; calculating the consistency index: wherein n is the dimension of the comparison matrix, and λmax is the maximum eigenvalue of the comparison matrix; checking CR, and calculating the characteristic weight of each index when CR is less than a preset value: wherein A is the comparison matrix, and Wc is the characteristic weight obtained by normalizing the corresponding characteristic vector.

4. The quick screening method for stepwise utilization of retired power batteries according to claim 1, characterized in that, The combination of the characteristic weight of each index and the entropy weight value of each index to generate the combined weight comprises the following steps: generating the combined weight by using the following function: ； wherein Wc is a feature weight obtained after normalization of the corresponding feature vector, W s is an entropy weight value, W is a combined weighting weight, The clustering of the battery individuals by using the GSK algorithm according to the preprocessed vector matrix and the combined weight, and the generation of the clustering result of the battery individuals comprise the following steps: is a subjective preference coefficient.

5. The quick screening method for stepwise utilization of retired power batteries according to claim 1, characterized in that, S1031, acquiring the optimal clustering number, and generating an initial battery clustering center population based on the optimal clustering number and the preprocessed vector matrix; S1032, calculating the objective function and the fitness function of the initial battery clustering center population according to the maximum genetic iteration number, individual selection operation and crossover mutation operation, and generating the initial battery clustering center; S1033, calculating the weighted Euclidean distance of the initial battery clustering center, and calculating the new battery clustering center based on the weighted Euclidean distance; S1034, judging whether the new battery clustering center coincides with the original clustering center; if not, replacing the original clustering center with the new battery clustering center, and re-executing S1033 and S1034; if yes, outputting the clustering result of the battery individuals. The objective function is:

6. The quick screening method for stepwise utilization of retired power batteries according to claim 5, characterized in that, The fitness function is: wherein sum is the objective function value, K is the number of cluster centers, SIM represents the similarity of an individual to each cluster center, Z j is the jth cluster center; Fit wherein, The calculation function of the Euclidean distance is: is the fitness function value, t f is the end temperature of the SA algorithm; The expression of the jth clustering center is: wherein X in is the nth-dimensional feature of the ith individual, c jn is the nth-dimensional feature of the jth cluster center, W n is the scene weight corresponding to the nth-dimensional feature, and m is the total number of feature dimensions. The method comprises the following steps: where N j is the number of individuals in the jth class, X i represents the ith individual in the jth class.

7. A device for rapid screening of retired power battery gradient utilization, characterized in that, ​ The vector matrix construction unit acquires battery test data and constructs a vector matrix as characteristic parameters of the battery from the test data, wherein the test data includes open-circuit voltage, capacity, and internal resistance of the battery. The combined weight calculation unit is configured to preprocess the vector matrix and calculate combined weights of the characteristic parameters by using a hierarchical entropy weight method. Specifically, the combined weights of the characteristic parameters are calculated by obtaining an application scenario and a hierarchical structure of the battery, generating a comparison matrix according to the application scenario and the hierarchical structure, checking the comparison matrix and generating characteristic weights of each index when a consistency index is lower than a preset value, normalizing the vector matrix and generating entropy weight values of each index according to the normalized vector matrix, and combining the characteristic weights of each index and the entropy weight values of each index to generate the combined weights. The vector matrix is normalized, and the entropy weight value of each index is generated according to the normalized vector matrix, specifically including: generating the entropy weight value of each index through a function The vector matrix is normalized, and the entropy weight value of each index is generated according to the normalized vector matrix, specifically including: generating the entropy weight value of each index through a function G ij The normalized matrix is x ij The original matrix; and the entropy weight value of each index is generated through the following formula: ； ； wherein, F j is the information entropy, m is the feature dimension, W s is the entropy weight value; The clustering unit is configured to cluster battery individuals according to the preprocessed vector matrix and the combined weights and generate a clustering result of the battery individuals.

8. A retired power battery cascade utilization rapid screening device, characterized in that, The computer program stored in the memory can be executed by the processor to implement the method for quickly screening retired power battery gradient utilization according to any one of claims 1 to 5.

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