A method and system for quality classification screening of retired batteries

Abstract

The application discloses a kind of quality classification screening method and system of retired battery, the method includes: obtaining the identification code of each retired battery, set charging and discharging strategy, obtain the clustering sequence sample of each retired battery;Each retired battery clustering sequence sample is as an initial class, obtain clustering set;Obtain the Euclidean distance of any two initial classes and the similarity distance matrix between each retired battery and other retired battery;Based on similarity distance matrix, the two classes with minimum Euclidean distance are merged, and the merged class is added in clustering set, and the two classes with minimum Euclidean distance are deleted;Repeat the process of obtaining merged class, and adding merged class in clustering set, deleting the two classes with minimum Euclidean distance, obtain final clustering set;According to final clustering set, the quality classification of the retired battery is carried out.The quality classification screening method of retired battery disclosed in the application can improve the utilization rate of retired battery.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery secondary utilization, in particular to a quality classification screening method and system for retired batteries. BACKGROUND

[0002] Power storage battery is an important energy storage component of new energy vehicles, which is usually composed of a large number of single cells connected in series and parallel to form a module, and then connected in series to form a battery pack to provide power for vehicles. After a long time of vehicle use and retirement, the single batteries in the battery pack will have great inconsistency in maximum available capacity, ohmic resistance, polarization resistance, etc. This will directly affect the output performance of the battery pack and bring great inconvenience to the secondary utilization of the retired batteries. The retired batteries still have certain residual capacity and service life and can be further used in other fields, such as power supply for electric bicycles, sightseeing vehicles, general life lighting power supply, or power storage, including renewable energy output power smoothing, distributed power supply in remote areas, charging station energy storage, and power quality regulation. Therefore, before the secondary utilization of the retired batteries, quality classification screening of the retired batteries is needed to apply the retired batteries of different qualities in different fields.

[0003] In the prior art, patent application publication No. CN106423919A, entitled "Retired lithium battery sorting method and system", calculates the score by a specific working condition, and classifies the retired lithium batteries according to the score results to sort the retired power lithium batteries and create conditions for their gradient utilization and reasonable recycling. However, the performance of the battery is tested by using a specific charge and discharge working condition, the internal parameters of the battery are simulated and calculated according to the test results, and then the battery is classified accordingly. This kind of retired battery screening method has high efficiency, but it needs strong professional knowledge in the fields of electrochemistry and control, which is difficult to realize and not universal.

[0004] Other common retired battery screening methods are: 1) manually using professional equipment to measure the parameters of the same batch of batteries and classifying them according to the numerical value. Considering that the battery state of charge (SOC) has a great influence on various battery parameters, this method is only suitable for batteries at the same SOC and has limitations. 2) using a charge and discharge instrument to fully charge and discharge the battery, and then classifying the battery according to the capacity of the complete discharge or charge. This method takes a long time and consumes a lot of energy. SUMMARY

[0005] The technical problem to be solved by the present application is to solve the problem of classifying and screening retired batteries manually using professional equipment, which has limitations, using a charge and discharge instrument to fully charge and discharge the battery, which consumes a lot of energy, and using a specific working condition to screen the field of professional knowledge, which is not universal.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] A quality classification screening method of retired batteries, comprising the following steps:

[0008] S100, obtaining the identification code of each retired battery, and establishing a database of the retired battery and the corresponding relationship of the identification code;

[0009] S200, setting a charge-discharge strategy to perform charge-discharge test on the retired battery, and obtaining the charge-discharge data and external physical parameter data of each retired battery;

[0010] S300, obtaining a clustering sequence sample according to the charge-discharge data and external physical parameter data, and storing the clustering sequence sample in the database;

[0011] S400, based on a hierarchical clustering algorithm, taking the clustering sequence sample of each retired battery as an initial class to obtain a clustering set;

[0012] S500, obtaining the Euclidean distance of any two initial classes in the clustering set;

[0013] S600, obtaining the similarity distance matrix between each retired battery and other retired batteries according to the Euclidean distance;

[0014] S700, based on the similarity distance matrix, merging the two classes with the smallest Euclidean distance to obtain a merged class, and adding the merged class to the clustering set and deleting the two classes with the smallest Euclidean distance;

[0015] S800, repeating steps S500 and S700 until the number of classes in the clustering set reaches the number of final classes, to obtain a final clustering set;

[0016] S900, according to the final clustering set, combining the identification code and the database, and classifying the quality of the retired battery.

[0017] Advantages: By setting a charge-discharge strategy, only the local charge-discharge curve and the change of external physical parameters during the corresponding test are obtained to obtain a feature vector, which replaces the directly measured performance parameters, avoiding full charge-discharge test and multiple charge-discharge cycle test, reducing power consumption and test time. Through the hierarchical clustering algorithm, data-driven retired single battery screening reduces the artificial classification screening work relying on professional personnel, and improves the universality of the method.

[0018] In an embodiment of the present application, the charge-discharge test of the retired battery by setting a charge-discharge strategy comprises the following steps:

[0019] constant current charging the retired battery for a certain time;

[0020] standing for a certain time;

[0021] constant current discharging the retired battery, and the discharging time is the same as the charging time, and in the constant current discharging stage, charging and discharging are performed through pulse currents of different rates;

[0022] standing for a certain time;

[0023] constant voltage charging the retired battery for a certain time;

[0024] standing for a certain time;

[0025] constant voltage discharging the retired battery, and the discharging time is the same as the charging time;

[0026] obtaining charging and discharging data and external physical parameter data of each retired battery.

[0027] In an embodiment of the present application, charging and discharging data and external physical parameter data in a time period with the same time interval and time starting point are selected, a plurality of feature vectors are obtained, the plurality of feature vectors are normalized to obtain clustering sequence samples, wherein the plurality of feature vectors include temperature change time sequences of each charging and discharging stage, voltage change time sequences of the constant voltage charging stage, and constant current stage incremental capacity.

[0028] In an embodiment of the present application, the temperature change time sequence of each charging and discharging stage is obtained by the following formula:

[0029]

[0030] In the formula, T represents the temperature change time sequence of each charging and discharging stage, and t represents the sampling time. When the temperature change time sequence of each charging and discharging stage is obtained, the value range of t is t=1, 2, …n1.

[0031] In an embodiment of the present application, the voltage change time sequence of the constant voltage charging stage is obtained by the following formula:

[0032]

[0033] In the formula, V represents the voltage change time sequence of the constant voltage charging stage, and t represents the sampling time. When the voltage change time sequence of the constant voltage charging stage is obtained, the value range of t is t=1, 2, …n2. In an embodiment of the present application, the constant current stage incremental capacity is obtained by the following formula:

[0034]

[0035] In the formula, IC represents the incremental capacity in the constant current stage, Q(t) and V(t) represent the battery capacity and terminal voltage at time t, Q(K) and V(K) represent the discrete form of the battery capacity and the discrete form of the terminal voltage, I represents the current in the constant current stage, N represents the sampling interval, T0 represents the sampling period time, Δt represents the time change, ΔV(K) represents the change of the terminal voltage at time K in the discrete form, and V(K-N) represents the terminal voltage at time K-N T0.

[0036] The clustered sequence sample is a set of normalized feature vectors, namely

[0037] S={T', V', IC'};

[0038] In the formula, S represents the clustered sequence sample, T', V' and IC' represent the temperature change time sequence of each charge and discharge stage, the voltage change time sequence of the constant voltage charging stage and the incremental capacity in the constant current stage after normalization processing, respectively.

[0039] In an embodiment of the present application, the Euclidean distance of any two initial classes is obtained by the following formula:

[0040]

[0041] In the formula, d ij represents the Euclidean distance of any two initial classes, f i represents the center of the i th retired battery initial class, f j represents the center of the j th retired battery initial class, f i (t) represents the index value of the i th retired battery at time t, and f j (t) represents the index value of the j th retired battery at time t.

[0042] In an embodiment of the present application, the similarity distance matrix between each retired battery and other retired batteries is obtained by the following formula:

[0043]

[0044] In the formula, D represents the similarity distance matrix of the i th retired battery and the i th retired battery, d ij represents the Euclidean distance of any two initial classes, and C represents the clustering set.

[0045] A quality classification and screening system for retired batteries comprises:

[0046] A code scanner is used for the identification code of each retired battery.

[0047] A charge-discharge instrument is configured to charge and discharge the retired battery according to a set charge-discharge strategy.

[0048] A data acquisition instrument is configured to acquire charge-discharge data and external physical parameter data of each retired battery.

[0049] A storage and operation device is configured to establish a database of the correspondence between the retired batteries and the identification codes, acquire clustering sequence samples according to the charge-discharge data and external physical parameter data, and store the clustering sequence samples in the database; acquire a clustering set by taking the clustering sequence samples of each retired battery as an initial class; acquire the Euclidean distance between any two initial classes in the clustering set; acquire a similarity distance matrix between each retired battery and other retired batteries according to the Euclidean distance; merge the two classes with the smallest Euclidean distance based on the similarity distance matrix, acquire a merged class, add the merged class to the clustering set, and delete the two classes with the smallest Euclidean distance; repeat the process of adding the merged class to the clustering set and deleting the two classes with the smallest Euclidean distance until the number of classes in the clustering set reaches the number of final classes, and acquire a final clustering set; and classify the retired batteries according to quality based on the final clustering set, the identification codes, and the database.

[0050] In an embodiment of the present application, in the charge-discharge instrument, the set charge-discharge strategy for charging and discharging the retired battery includes the following steps:

[0051] The retired battery is charged at a constant current for a certain period of time.

[0052] The retired battery is left to stand for a certain period of time.

[0053] The retired battery is discharged at a constant current, and the discharge time is the same as the charge time. In the constant current discharge stage, the retired battery is charged and discharged by different pulse currents.

[0054] The retired battery is left to stand for a certain period of time.

[0055] The retired battery is charged at a constant voltage for a certain period of time.

[0056] The retired battery is left to stand for a certain period of time.

[0057] The retired battery is discharged at a constant voltage, and the discharge time is the same as the charge time.

[0058] The charge-discharge data and external physical parameter data of each retired battery are acquired.

[0059] Compared with the prior art, the application has the beneficial effects that: by clustering sequence samples, similar retired batteries are classified, so as to determine the subsequent use of the retired batteries, and the retired batteries with similar characteristics in the same class can be uniformly arranged for subsequent use, high-precision retired battery classification is realized, the utilization rate of the retired batteries is improved, and resource waste and safety risks are avoided. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 A flowchart of a quality classification and screening method of a retired battery according to an embodiment of the application.

[0061] Figure 2 A flowchart of setting a charge-discharge strategy according to an embodiment of the application.

[0062] Figure 3 A block diagram of a quality classification and screening system of a retired battery according to an embodiment of the application. DETAILED DESCRIPTION

[0063] To facilitate those skilled in the art to understand the technical scheme of the application, the technical scheme of the application will be further described in conjunction with the drawings of the specification.

[0064] The terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0065] Referring to Figure 1 As shown in the drawings, the application provides a quality classification and screening method of a retired battery, which comprises the following steps:

[0066] S100, obtaining an identification code of each retired battery and establishing a database of the correspondence between the retired battery and the identification code.

[0067] In step S100, a code scanner is used to generate an identification code for each retired single battery, which is, for example, a two-dimensional code, a bar code or an electronic tag, etc. At the same time, a database is established, which stores the data of the correspondence between each retired single battery and the identification code in the database. The database is located in a storage and operation device, which is applied in an actual scene, and the storage and operation device is, for example, an upper computer.

[0068] S200, setting a charge-discharge strategy to perform charge-discharge test on the retired battery, and obtaining charge-discharge data and external physical parameter data of each retired battery.

[0069] In step S200, each of the retired batteries is tested by the charge-discharge instrument according to the charge-discharge strategy.

[0070] As shown in FIGS. 1-3, Figure 1 and Figure 2 In an embodiment of the present application, the setting of the charge-discharge strategy for testing the retired batteries in step S200 includes the following steps:

[0071] S210, constant current charging the retired batteries for a certain time.

[0072] S220, standing for a certain time.

[0073] S230, constant current discharging the retired batteries, and the discharging time is the same as the charging time, and in the constant current discharging stage, the charge-discharge is performed by different pulse currents of different rates.

[0074] S240, standing for a certain time.

[0075] For example, the constant current charging of the retired single battery is performed for 10 minutes or 30 minutes, and after the charging, the retired battery stands for 5 minutes, and after the discharging, the retired battery stands for 5 minutes.

[0076] S250, constant voltage charging the retired batteries for a certain time.

[0077] S260, standing for a certain time.

[0078] S270, constant voltage discharging the retired batteries, and the discharging time is the same as the charging time.

[0079] For example, the constant voltage charging and discharging of the retired single battery is performed for 10 minutes or 30 minutes, and the standing time is 5 minutes.

[0080] S280, obtaining the charge-discharge data and the external physical parameter data of each of the retired batteries.

[0081] Through the above-mentioned different pulse currents of different discharging rates or the simplified incomplete charge-discharge experimental conditions, since the complete charge-discharge is not required, the required test time is short, the consumed electric energy is small, and the electric power cost is low. Through the setting of the incomplete charge-discharge experimental conditions, the internal parameters of the battery can be more easily and clearly obtained.

[0082] In the process of the charge-discharge of the retired single battery, the charge-discharge data and the external physical parameter data of the retired single battery are collected by the data acquisition instrument, wherein the charge-discharge data is, for example, current, voltage, and time, and the external physical parameter data is, for example, temperature change and size change.

[0083] The data collected by the data acquisition instrument is stored in the database corresponding to the identification code of the retired battery.

[0084] S300, according to the charge-discharge data and external physical parameter data, obtaining a clustering sequence sample, and storing the clustering sequence sample in the database.

[0085] The charge-discharge data and external physical parameter data in the time period with the same time interval and time starting point are selected, a plurality of feature vectors are obtained, the plurality of feature vectors are normalized to obtain a clustering sequence sample, wherein the plurality of feature vectors include temperature change time sequence of each charge-discharge stage, voltage change time sequence of constant voltage charging stage and incremental capacity of constant current stage. And the clustering sequence sample and the retired battery and identification code corresponding relationship data are stored in the database.

[0086] Wherein, the temperature change time sequence of each charge-discharge stage is obtained by the following formula:

[0087]

[0088] In the formula, T represents the temperature change time sequence of each charge-discharge stage, t represents the sampling time, and when obtaining the temperature change time sequence of each charge-discharge stage, the value range of t is t=1, 2, …n1.

[0089] The voltage change time sequence of constant voltage charging stage is obtained by the following formula:

[0090]

[0091] In the formula, V represents the voltage change time sequence of constant voltage charging stage, t represents the sampling time, and when obtaining the voltage change time sequence of constant voltage charging stage, the value range of t is t=1, 2, …n2.

[0092] The incremental capacity of battery is defined as the ratio of capacity change to terminal voltage change, that is, dQ / dV, and the IC curve is the incremental capacity curve. The change curve is drawn with dQ / dV value as ordinate and corresponding terminal voltage V value as abscissa. The value of dQ / dV can be obtained as follows:

[0093]

[0094] Further, the incremental capacity of constant current stage is obtained by the following formula:

[0095]

[0096] In the formula, IC represents the incremental capacity in the constant current stage, Q(t) and V(t) represent the battery capacity and terminal voltage at time t, Q(K) and V(K) represent the discrete form of the battery capacity and the discrete form of the terminal voltage, I represents the current in the constant current stage, N represents the sampling interval, T0 represents the sampling period time, Δt represents the time change, ΔV(K) represents the change of the terminal voltage at time K in the discrete form, and V(K-N) represents the terminal voltage at time K-N T0.

[0097] The clustering sequence sample is a set of normalized feature vectors, that is

[0098] S={T', V', IC'};

[0099] In the formula, S represents the clustering sequence sample, T', V', and IC' represent the temperature change time sequence of each charge and discharge stage, the voltage change time sequence of the constant voltage charging stage, and the incremental capacity in the constant current stage after normalization processing, respectively.

[0100] Because different feature vectors have different scales due to different dimensions and units, the clustering weights are distorted. The clustering weights are distorted because the clustering algorithm based on distance as a measure is biased towards indicators with larger values. For example, the capacity and internal resistance of the battery are used as clustering indicators, where the capacity is in mAh and the internal resistance is in Ω. The aging degrees of the batteries with 2000 mAh and 1980 mAh are not much different, while the batteries with internal resistances of 0.1 Ω and 0.3 Ω may be the difference between new batteries and scrap batteries. However, the values of 2000 and 1980 differ by 20, and the values of 0.1 and 0.3 differ by 0.2. The former value is much larger than the latter value, and when calculating the Euclidean distance, the distance value is mainly contributed by the former, making the clustering result biased towards the distribution of indicators with larger values, and the distribution of the latter which is also important is almost ignored. This is the clustering weight distortion, so the individual feature vectors need to be normalized.

[0101] S400, based on the hierarchical clustering algorithm, the clustering sequence sample of each retired battery is taken as an initial class to obtain a clustering set.

[0102] The functional median-based hierarchical clustering algorithm is executed. It is assumed that there are m single retired batteries to be clustered, which are to be divided into K classes, that is, the clustering mainly puts similar single retired batteries into a group.

[0103] The clustering set is expressed by the following formula:

[0104] C={C1, C2,..., CK}; m}={S1, S2,..., SK}; i};

[0105] wherein C represents a cluster set, C m represents the mth cluster, S i represents the cluster sequence sample of the ith retired battery, i.e. each cluster sequence sample of each retired battery is a cluster.

[0106] S500, obtaining the Euclidean distance of any two initial clusters in the cluster set.

[0107] wherein the Euclidean distance of the any two initial clusters is obtained by the following formula:

[0108]

[0109] wherein d ij represents the Euclidean distance of the any two initial clusters, f i represents the center of the initial cluster of the ith retired battery, f j represents the center of the initial cluster of the jth retired battery, f i (t) represents the index value of the ith retired battery at time t, f j (t) represents the index value of the jth retired battery at time t.

[0110] wherein f i represents the center of the initial cluster of the ith retired battery, it can be understood that, because each cluster sequence sample of each retired battery is a cluster of the cluster set, i.e. each cluster is also composed of the temperature change time sequence of each charging and discharging stage, the voltage change time sequence of the constant voltage charging stage and the incremental capacity of the constant current stage. The number of points of the temperature change time sequence of each charging and discharging stage is multiple, and the center of these number of points, i.e. the temperature change time sequence data of the closest center point, is the center point of the temperature change time sequence, and the voltage change time sequence of the constant voltage charging stage and the incremental capacity of the constant current stage. Similarly, the data composed of the center point of each feature vector, i.e. the center of the initial cluster of the retired battery.

[0111] S600, obtaining the similarity distance matrix between each retired battery and other retired batteries according to the Euclidean distance.

[0112] The similarity distance matrix between each retired battery and other retired batteries is obtained by the following formula:

[0113]

[0114] wherein D represents the similarity distance matrix between the ith retired battery and the ith retired battery, d ij represents the Euclidean distance of the any two initial clusters, C represents a cluster set.

[0115] S700, merging the two classes with the minimum Euclidean distance based on the similarity distance matrix, obtaining a merged class, and adding the merged class to the cluster set and deleting the two classes with the minimum Euclidean distance.

[0116] In the embodiment, the two classes with the minimum Euclidean distance are, for example, C p and C q , and the merged class is C pq =C p ∪C q , the cluster set is added with the merged class and deleted with the two classes with the minimum Euclidean distance, which can be expressed as C={C\{C P ,C q}, C pq}, and at this time, the number of classes in the cluster set is reduced by 1.

[0117] S800, repeating step S500 and step S700 until the number of classes in the cluster set reaches the final number of classes, and obtaining a final cluster set.

[0118] The final number of classes can be determined according to industry experience or by using Calinski-Harabasz index and elbow method.

[0119] S900, classifying the retired batteries according to the final cluster set in combination with the identification code and the database.

[0120] Taking each single retired battery as a clustering object, the classes in the final cluster set are the classification results, and according to the classification results, the corresponding identification code is found in combination with the data stored in the database, and then each retired battery is classified, and the retired batteries of the same class are grouped to achieve good usability. In the embodiment, for example, the final goal is to divide the quality of the retired batteries into four classes, and then the retired batteries can be divided into four classes of excellent, good, medium and poor according to the remaining capacity of the retired batteries. Specifically, the retired batteries with a remaining capacity greater than 75% are excellent, the retired batteries with a remaining capacity greater than 50% and less than 75% are good, the retired batteries with a remaining capacity greater than 25% and less than 50% are medium, and the retired batteries with a remaining capacity less than 25% are poor.

[0121] Please refer to Figure 3As shown, the application also provides a quality classification screening system for retired batteries, comprising a code scanner 100, a charge-discharge instrument 200, a data acquisition instrument 300, a storage and operation device 400, and a retired battery 500. The code scanner 100 is used to obtain the identification code of each retired battery 500. The charge-discharge instrument 200 is used to perform charge-discharge testing on the retired battery 500 according to a set charge-discharge strategy. The data acquisition instrument 300 is used to obtain the charge-discharge data and external physical parameter data of each retired battery 500. The storage and operation device 400 is used to establish a database of the correspondence between the retired battery 500 and the identification code, obtain clustering sequence samples according to the charge-discharge data and external physical parameter data, and store the clustering sequence samples in the database. The clustering sequence samples of each retired battery 500 are taken as an initial class, a clustering set is obtained, the Euclidean distance between any two initial classes in the clustering set is obtained, the similarity distance matrix between each retired battery 500 and other retired batteries is obtained according to the Euclidean distance, the two classes with the smallest Euclidean distance are merged based on the similarity distance matrix, a merged class is obtained, the merged class is added to the clustering set, the two classes with the smallest Euclidean distance are deleted, and the process of adding the merged class to the clustering set and deleting the two classes with the smallest Euclidean distance is repeated until the number of classes in the clustering set reaches the final number of classes, a final clustering set is obtained, and the retired batteries 500 are classified according to quality based on the final clustering set, the identification code, and the database. In the charge-discharge instrument 200, the set charge-discharge strategy for the retired battery includes the following steps:

[0122] S210, the retired battery is charged at a constant current for a certain time.

[0123] S220, standing for a certain time.

[0124] S230, the retired battery is discharged at a constant current, and the discharge time is the same as the charging time. In the constant current discharge stage, different pulse currents are used for charge-discharge.

[0125] S240, standing for a certain time.

[0126] S250, the retired battery is charged at a constant voltage for a certain time.

[0127] S260, standing for a certain time.

[0128] S270, the retired battery is discharged at a constant voltage, and the discharge time is the same as the charging time.

[0129] S280, obtaining the charge-discharge data and external physical parameter data of each retired battery.

[0130] It is apparent for a person skilled in the art that the present application is not limited to the details of the above-described exemplary embodiments, but that it can be implemented in other concrete forms without departing from the spirit or essential characteristics of the application. Therefore, the embodiments should be considered in all respects as illustrative and not restrictive, the scope of the application being defined by the appended claims rather than the above description, and all changes coming within the meaning and range of equivalency of the claims are therefore intended to be embraced therein, no reference signs in the claims being regarded as limiting the claims concerned.

[0131] The above-described embodiments are merely exemplary and are not intended to limit the scope of the present application, and it is apparent for a person skilled in the art that various modifications and improvements can be made thereto without departing from the spirit of the present application, and such modifications and improvements are intended to be included in the scope of the present application.

Claims

1. A method of quality classification screening of retired batteries, characterized by, The method comprises the following steps: S100, obtaining the identification code of each retired battery and establishing a database of the correspondence between the retired battery and the identification code; S200, setting a charging and discharging strategy to perform an incomplete charging and discharging test on the retired battery, and obtaining the charging and discharging data and external physical parameter data of each retired battery; comprising: constant current charging the retired battery for a certain time; resting for a certain time; constant current discharging the retired battery, and the discharging time is the same as the charging time, and in the constant current discharging stage, charging and discharging are performed through different pulse currents; resting for a certain time; constant voltage charging the retired battery for a certain time; resting for a certain time; constant voltage discharging the retired battery, and the discharging time is the same as the charging time; obtaining the charging and discharging data and external physical parameter data of each retired battery; S300, obtaining a clustering sequence sample according to the charging and discharging data and external physical parameter data, and storing the clustering sequence sample in the database; selecting the charging and discharging data and external physical parameter data in a time period with the same time interval and time starting point, obtaining a plurality of feature vectors, normalizing the plurality of feature vectors to obtain a clustering sequence sample, wherein the plurality of feature vectors include temperature change time series of each charging and discharging stage, voltage change time series of the constant voltage charging stage, and incremental capacity of the constant current stage; S400, based on a hierarchical clustering algorithm, taking the clustering sequence sample of each retired battery as an initial class to obtain a clustering set; S500, obtaining the Euclidean distance of any two initial classes in the clustering set; S600, according to the Euclidean distance, obtaining a similarity distance matrix between each retired battery and other retired batteries; S700, based on the similarity distance matrix, merging the two classes with the smallest Euclidean distance to obtain a merged class, and adding the merged class to the clustering set and deleting the two classes with the smallest Euclidean distance; S800, repeating steps S500 and S700 until the number of classes in the clustering set reaches the final number of classes, to obtain a final clustering set; S900, according to the final clustering set, combining the identification code and the database, and classifying the retired batteries according to quality.

2. The method of claim 1, wherein the method further comprises: The temperature change time series of each charging and discharging stage is obtained by the following formula: In the formula, T represents the temperature change time series of each charging and discharging stage, t represents the sampling time, and when obtaining the temperature change time series of each charging and discharging stage, t takes the value range t=1, 2, …n1.

3. The method of claim 2, wherein the method further comprises: The voltage change time series of the constant voltage charging stage is obtained by the following formula: In the formula, V represents the voltage change time series of the constant voltage charging stage, t represents the sampling time, and when obtaining the voltage change time series of the constant voltage charging stage, t takes the value range t=1, 2, …n2.

4. The method of claim 3, wherein the step of determining the quality of the retired battery is performed by a method comprising: The incremental capacity of the constant current stage is obtained by the following formula: In the formula, IC represents the incremental capacity in the constant current stage, Q(t) and V(t) represent the battery capacity and terminal voltage at time t, Q(K) and V(K) represent the discrete form of the battery capacity and the discrete form of the terminal voltage, I represents the current in the constant current stage, N represents the sampling interval, T0 represents the sampling period time, Δt represents the time change, ΔV(K) represents the change of the terminal voltage at time K in the discrete form, and V(K-N) represents the terminal voltage at time K-N T0. The clustered sequence sample is a set of normalized feature vectors, namely S={T', V', IC'}; In the formula, S represents the clustered sequence sample, T', V', and IC' represent the temperature change time sequence of each charge and discharge stage, the voltage change time sequence of the constant voltage charging stage, and the incremental capacity in the constant current stage after normalization processing, respectively.

5. The method of claim 1, wherein the method further comprises: The Euclidean distance of any two initial classes is obtained by the following formula: where d ij is the Euclidean distance between the arbitrary two initial classes, f i is the center of the i-th initial class of retired batteries, f j is the center of the j-th initial class of retired batteries, f i (t) is the index value of the i-th retired battery at time t, f j (t) is the index value of the j-th retired battery at time t.

6. The method of claim 5, wherein the step of determining the quality of the retired battery is performed by a method comprising: The similarity distance matrix between each retired battery and other retired batteries is obtained by the following formula: where D represents the similarity distance matrix of the i-th retired battery and the i-th retired battery, d ij represents the Euclidean distance of the arbitrary two initial classes, and C represents the clustering set.

7. A quality sorting system for retired batteries, comprising: It comprises: A code scanner is configured to obtain the identification code of each retired battery; A charge and discharge instrument is configured to perform incomplete charge and discharge test on the retired battery according to a set charge and discharge strategy; A data acquisition instrument is configured to obtain the charge and discharge data and external physical parameter data of each retired battery; A storage and operation device is configured to establish a database of the correspondence between the retired batteries and the identification codes, obtain clustered sequence samples according to the charge and discharge data and external physical parameter data, and store the clustered sequence samples in the database, wherein the charge and discharge data and external physical parameter data in a time period with the same time interval and time starting point are selected, a plurality of feature vectors are obtained, the plurality of feature vectors are normalized to obtain the clustered sequence samples, the plurality of feature vectors include the temperature change time sequence of each charge and discharge stage, the voltage change time sequence of the constant voltage charging stage, and the incremental capacity in the constant current stage; the clustered sequence sample of each retired battery is taken as an initial class to obtain a clustered set; the Euclidean distance of any two initial classes in the clustered set is obtained; the similarity distance matrix between each retired battery and other retired batteries is obtained according to the Euclidean distance; based on the similarity distance matrix, the two classes with the smallest Euclidean distance are merged to obtain a merged class, and the merged class is added to the clustered set and the two classes with the smallest Euclidean distance are deleted; the process of adding the merged class to the clustered set and deleting the two classes with the smallest Euclidean distance is repeated until the number of classes in the clustered set reaches the number of final classes, and a final clustered set is obtained; the retired batteries are classified according to quality based on the final clustered set, the identification code, and the database; The incomplete charge and discharge test comprises: The retired battery is charged at a constant current for a certain time; It is left for a certain time; The retired battery is discharged at a constant current, and the discharge time is the same as the charge time. In the constant current discharge stage, different pulse currents are used for charge and discharge; It is left for a certain time; The retired battery is charged at a constant voltage for a certain time; Rest for a certain time; The retired battery is discharged at a constant voltage, and the discharge time is the same as the charging time; Obtain the charge-discharge data and external physical parameter data of each retired battery.

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