CN112651431A - Clustering sorting method for retired power batteries - Google Patents

Clustering sorting method for retired power batteries Download PDF

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CN112651431A
CN112651431A CN202011479510.3A CN202011479510A CN112651431A CN 112651431 A CN112651431 A CN 112651431A CN 202011479510 A CN202011479510 A CN 202011479510A CN 112651431 A CN112651431 A CN 112651431A
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马速良
李建林
李金林
王力
李穷
李雅欣
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Beijing Lianzhi Huineng Technology Co ltd
Xinyuan Zhichu Energy Development Beijing Co ltd
North China University of Technology
Anhui Lvwo Recycling Energy Technology Co Ltd
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North China University of Technology
Anhui Lvwo Recycling Energy Technology Co Ltd
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Abstract

The invention relates to a clustering sorting method for retired power batteries. Measuring voltage data of n disassembled retired power battery unit samples, extracting m characteristic variables, per unit, calculating the distance d between characteristic vectors of the samples, and forming a similarity matrix A; defining the coding length and coding bit value for sorting according to the number n of samples and the number K of clustering clusters, selecting high-quality sorting codes according to the energy of clustering cluster groups, and forming K clustering cluster groups of a large number of retired battery monomers by using genetic evolution operations such as crossing, variation, reinsertion and the like; calculating the center of each cluster group and the maximum deviation of the samples in the cluster group to form a confidence domain; and finishing sorting and sorting reliability judgment based on the relation between the characteristic vector of the single power battery to be detected for retirement and the central distance and confidence domain of each cluster group. The invention integrates the genetic optimization thought into the clustering process, ensures the optimization direction of the polymerization process, and improves the optimization level of the sorting and clustering process of the retired power battery.

Description

Clustering sorting method for retired power batteries
The technical field is as follows:
the invention relates to a power battery detection technology, and further relates to a clustering sorting method for retired power batteries.
Background art:
the electric automobile has very important significance for relieving energy and environmental stress. The performance requirement of the electric automobile on the power battery is high, and when the capacity of the power battery is reduced to a value which does not meet the requirement of the endurance mileage, the battery needs to be replaced. The power battery retired from the electric automobile usually has residual capacity more than 70% of the initial capacity, has a certain service life, can be used in other application fields with relatively good operation working conditions and low requirements on battery performance through re-detection analysis, screening and battery monomer pairing and grouping, and bears tasks such as smooth distributed power supply power fluctuation and user side demand response in a micro-grid. Through the echelon utilization of the power battery, the pressure of the large-batch batteries entering the recovery stage can be relieved.
The energy characteristics and the power characteristics of the retired electric automobile power battery are attenuated, the difference of performance parameters among battery monomers is large, the maximization of the application value of the battery with different performances is realized, the reliability and the safety of the battery when the battery is applied again are ensured, the battery is required to be screened, and the graded echelon application of the battery is realized.
The invention content is as follows:
aiming at the problems of long testing time, unclear evaluation standard, high sorting cost and the like faced by the echelon utilization and sorting of large-batch retired power batteries, on the premise of meeting the sorting precision and speed requirements, a sorting primary screening standard is established, and a sorting detection method, a parameter set and an evaluation method which can be used for rapid sorting are provided by using methods such as parameter sensitivity analysis, state space estimation, multi-objective optimization and historical data characteristic analysis. High-compatibility accurate detection technology and expert database online learning are fused, a battery module rapid sorting device is utilized in a research and development manner, an application production line is established, and the problem of large-scale rapid sorting of retired power batteries is solved. The invention integrates the concept of a genetic optimization algorithm into the clustering analysis process of the retired power battery by using the idea of the genetic optimization algorithm, realizes directional clustering optimization of the retired power battery monomers, and is a tamping foundation for automatically, accurately and reliably completing the screening of the retired power battery and deep and high-level echelon utilization. The specific technical scheme is as follows:
a clustering sorting method for retired power batteries comprises the following steps:
step 1: measuring voltage data of n disassembled retired power battery unit samples, extracting m characteristic variables, per unit, calculating the distance d between characteristic vectors of the samples, and forming a similarity matrix A;
step 2: defining the coding length and coding bit value for sorting according to the number n of samples and the number K of clustering clusters, selecting high-quality sorting codes according to the energy of clustering cluster groups, and forming K clustering cluster groups of a large number of retired battery monomers by using genetic evolution operations such as crossing, variation, reinsertion and the like;
and step 3: calculating the center of each cluster group and the maximum deviation of the samples in the cluster group to form a confidence domain;
and 4, step 4: and finishing sorting and sorting reliability judgment based on the relation between the characteristic vector of the single power battery to be detected for retirement and the central distance and confidence domain of each cluster group.
The preferred scheme is as follows: a clustering sorting method for retired power batteries comprises the following steps:
step 1: measuring voltage data of a plurality of retired power battery monomers, extracting key characteristic variables of the retired power battery monomers, per-unit calculating the key characteristic variables, and calculating the Euclidean distance between samples to form a similar matrix; the method specifically comprises the following steps:
step 1.1: measuring voltage data of a large number of disassembled power battery single body samples in the charging and discharging process;
step 1.2: defining key characteristic variables of the voltage data obtained in the step 1.1, and obtaining characteristic vectors by per unit of each characteristic value;
step 1.3: calculating the Euclidean distance between the samples based on the feature vectors after the per unit in the step 1.2 to form a similar matrix;
step 2: forming a clustering process of retired power battery monomers under genetic evolution; the method specifically comprises the following steps:
step 2.1: initializing selection rate, cross rate, variation rate and maximum iteration number parameters, setting the number of the retired power battery monomer quasi-cluster clusters, and defining the form and number of coding strings, wherein the value of each coding bit is any integer from zero to the number K-1 of the cluster clusters;
step 2.2: selecting corresponding sample clusters according to the coding bit values in the step 2.1, and calculating energy values of clustering results under each coding string;
step 2.3: sequencing the energy value sequences under the obtained coding strings, and selecting a plurality of maximum values and corresponding coding strings;
step 2.4: combining the coding strings selected in the step 2.3 in pairs randomly, and performing cross operation to form new coding strings with the same quantity;
step 2.5: carrying out mutation operation on the crossed new coding string obtained in the step 2.4, and updating the coding string;
step 2.6: combining the new coding string obtained by crossing and mutation in the step 2.4 with the remaining coding strings with the minimum energy values which are not crossed and mutated and are remained in the step 2.3 to form a child coding string;
step 2.7: selecting corresponding sample clusters according to the coding bit values on the filial generation coding strings obtained in the step 2.6, and calculating the energy values of the clustering results under the coding strings;
step 2.8: calculating the minimum value of the energy value sequence under the filial generation coding string obtained in the step 2.7, recording the coding string corresponding to the minimum value energy value, judging whether to continue evolution optimization, and if so, returning to the step 2.3; if not, entering step 3;
and step 3: sorting the retired power battery monomers into clusters according to the optimal coding strings, and calculating cluster centers and confidence domains; the method specifically comprises the following steps:
step 3.1: traversing and inquiring each coding bit value in the coding string with the maximum iteration times according to the coding string corresponding to the minimum energy value recorded in the second step, counting retired power battery monomers corresponding to the same value, recording the retired power battery monomers as the same cluster, completing sorting and clustering of the existing retired power battery monomers, and sorting the retired power battery monomers into a type which can be used for subsequent matching and forming;
step 3.2: calculating the feature centers of all cluster groups, wherein the feature direction center after the retired power battery monomer in each cluster group is per unit is the average value of the retired power battery monomers in the same cluster under the same feature value;
step 3.3: calculating confidence domains of judging reliability of feature centers of all cluster groups, wherein the maximum deviation amount of each feature value in each cluster group is the maximum value of the absolute difference value between the feature value and the center of each retired power battery single body sample of the cluster group under the feature;
and 4, step 4: identifying the category of the new retired power battery monomer to be tested and calculating the confidence of identification; the method specifically comprises the following steps:
step 4.1: measuring voltage data of the newly added retired power battery monomer to be tested in the charging and discharging process, and extracting corresponding characteristic vectors based on the step 1.2;
step 4.2: calculating the Euclidean distance between the feature vector of the newly increased retired power battery monomer to be tested and the feature centers of all cluster groups, and judging that the newly increased retired power battery monomer to be tested belongs to the cluster group with the minimum Euclidean distance;
step 4.3: calculating the deviation of each feature vector of the newly added retired power battery monomer to be tested and the feature center of the cluster family judged in the step 4.2, and defining the ratio of the deviation to the cluster family confidence domain obtained in the step 3.3, wherein the specific gravity of each feature is less than 1, and if the category judgment of the newly added retired power battery monomer to be tested is effective and reliable, otherwise, the judgment is unreliable.
Compared with the prior art, the invention has the beneficial effects that: in the technical scheme of the invention, the global optimization process is combined with the clustering problem by using coding, selecting, crossing and variation ideas in the genetic evolution process for reference. The external description of the multi-class aggregation can be realized through n-system coding (n is equivalent to the number of the cluster families to be clustered in the patent), and the aim of optimizing the clustering form is realized through the iterative process of selection, intersection and variation. And finally, by defining the cluster center and the confidence domain, the effective and reliable judgment on the type of the newly added retired power battery to be tested can be realized. Compared with the existing clustering method, the clustering method has the advantages that the genetic optimization idea is integrated into the clustering process, the optimization direction of the aggregation process is ensured, the intra-class distance is minimized, the inter-class distance is maximized, the optimization level of the sorting and clustering process of the retired power batteries is improved, and the consistency of the subsequent retired power batteries after grouping is favorably ensured.
Description of the drawings:
FIG. 1 is a flow chart of the cluster sorting method of the present invention.
FIG. 2 is a flow chart of the genetic clustering sorting method of the present invention.
Fig. 3 is a schematic diagram of the encoding process of the present invention.
FIG. 4 is a schematic diagram of the genetic evolution single-point crossover process of the present invention.
FIG. 5 is a schematic diagram of the genetic evolution mutation process of the present invention.
FIG. 6 is a schematic diagram of the k-th cluster family center and confidence domain of the present invention with two features as examples.
The specific implementation mode is as follows:
example (b):
the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
The invention provides a clustering sorting method for retired power batteries, and FIG. 1 shows the implementation process of the method in the embodiment; the method comprises the following steps:
step 1: measuring voltage data of n retired power battery cells, wherein the voltage data comprises the following steps: extracting m key characteristic variables, per unit, calculating the Euclidean distance between samples to form a similar matrix A;
step 1.1: measuring voltage data of a large number of disassembled n retired power battery single body samples in the charging and discharging process, wherein Ui(t) represents the voltage value of the ith sample at the time t, i is 1,2, …, n;
step 1.2: defining key characteristic variable x of voltage data obtained in step 1.1jJ is 1,2, …, m, and the component eigenvector X is X1 x2…xm]And calculating the characteristic value of the ith sample:
X(i)＝[x1(i) x2(i)…xm(i)]and obtaining a characteristic vector Y ═ Y by each characteristic value per unit1 y2…ym]And the s characteristic value of the i sample is per unit as follows:
Figure RE-GDA0002971708360000041
step 1.3: calculating the Euclidean distance between the samples based on the feature vector Y after the per unit in the step 1.2, wherein the Euclidean distance between the ith sample and the jth sample
Figure RE-GDA0002971708360000042
Forming a similarity matrix
Figure RE-GDA0002971708360000051
Wherein
Figure RE-GDA0002971708360000052
Step 2: defining links such as a coding form, cluster family energy and coding variation for clustering and sorting retired power battery monomers to form a clustering process of retired power battery monomers under genetic evolution, wherein a specific flow is shown in fig. 2, a coding process is shown in fig. 3, a cross process is shown in fig. 4, and a variation process is shown in fig. 5;
step 2.1: defining the selection ratio PsCross over ratio PcThe rate of variation PmAnd the maximum iteration number is G, the iteration number G is made to be 0, the number K of all the retired power battery monomer quasi-cluster clusters obtained in the step 1 is set, coding strings with the same length as the number of the samples are defined, and in the G-th iteration, the coding strings are
Figure RE-GDA0002971708360000053
The length of the code string being equal to the number n of code bits, each code bit
Figure RE-GDA0002971708360000054
Values of any integer from zero to the number of cluster groups K-1, i.e.
Figure RE-GDA0002971708360000055
Is an integer and
Figure RE-GDA0002971708360000056
and randomly generating N code strings
Figure RE-GDA0002971708360000057
Step 2.2: selecting corresponding sample clusters according to the coding bit values in the step 2.1, and calculating energy values of clustering results under each coding string;
step 2.2.1: let i equal to 1, k equal to 0, and energy value e (i) equal to 0;
step 2.2.2: traversing and inquiring elements with each coding bit equal to k in the ith coding string, and recording the serial numbers of the coding bits to form a set INDEXk(i)＝{Index1,k(i),Index2,k(i),…,Indexr,k(i) If INDEXk(i) If the element is empty or single element set, then E is equal to E, otherwise, the order is given
Figure RE-GDA0002971708360000058
Step 2.2.3: judging whether K is larger than or equal to the number K-1 of the cluster groups to be clustered or not; if yes, making k equal to 0, and entering step 2.2.4; if not, k is equal to k +1, and the step 2.2.2 is returned;
step 2.2.4: judging whether i is more than or equal to the number N of the coding strings; if yes, entering step 2.3; if not, i is equal to i +1, and the step 2.2.2 is returned;
step 2.3 obtaining the energy value sequence [ E (1), E (2), …, E (N) under N coding strings]Sorting, selecting the largest
Figure RE-GDA0002971708360000059
An E (si) value and a corresponding code string
Figure RE-GDA00029717083600000510
Represents a rounded-down symbol;
step 2.4: randomly pairwise combined selected in step 2.3
Figure RE-GDA00029717083600000511
A code string
Figure RE-GDA00029717083600000512
Figure RE-GDA00029717083600000513
Performing a crossover operation to form
Figure RE-GDA00029717083600000514
New code string
Figure RE-GDA00029717083600000515
Step 2.4.1: if it is
Figure RE-GDA0002971708360000061
If the number is even, the number selected in step 2.3 is randomly selected
Figure RE-GDA0002971708360000062
A code string
Figure RE-GDA0002971708360000063
Composition of
Figure RE-GDA0002971708360000064
For the coding string; if it is
Figure RE-GDA0002971708360000065
If the number of the codes is odd, a code string is randomly selected
Figure RE-GDA0002971708360000066
Do not carry out pairing, the rest
Figure RE-GDA0002971708360000067
A code string is composed of
Figure RE-GDA0002971708360000068
For the coding string, making h equal to 1;
step 2.4.2: produce a [0,1 ]]If p is the random number of>PcThen go to step 2.4.3; if p is<PcIf so, cross-coding at the random coding bit of the h-th pair of coding strings;
step 2.4.3: judging whether H is smaller than H, if so, returning to the step 2.4.2 if H is H + 1; if not, obtaining
Figure RE-GDA0002971708360000069
New code string
Figure RE-GDA00029717083600000610
Step 2.5 is entered
Step 2.5: for the crossed one obtained in step 2.4
Figure RE-GDA00029717083600000611
A code string
Figure RE-GDA00029717083600000612
Is subjected to a mutation operation, and the mutation operation,
Figure RE-GDA00029717083600000613
updating
Figure RE-GDA00029717083600000614
A code string
Figure RE-GDA00029717083600000615
Step 2.5.1: let si be 1 and bj be 1;
step 2.5.2: produce a [0,1 ]]If p is the random number of>PmThen go to step 2.5.3; if p is<PmThen, for the si-th encoding string
Figure RE-GDA00029717083600000616
Bj th coded bit of
Figure RE-GDA00029717083600000617
Performing a mutation operation to change to one in [0, K-1 ]]Integers within the range that are not themselves,
Figure RE-GDA00029717083600000618
step 2.5.3: judging whether bj is smaller than n, if so, determining bj as bj +1 and returning to the step 2.5.2; if not, the si-th coding string is mutated and updated
Figure RE-GDA00029717083600000619
Entering step 2.5.4;
step 2.5.4: judging whether si is less than
Figure RE-GDA00029717083600000620
If so, si is si +1, bj is 1 and the step 2.5.2 is returned; if not, the mutation is completely updated
Figure RE-GDA00029717083600000621
Entering a step 2.6 by each coding string;
step 2.6: let g be g +1, the result of the crossover and 2.5 mutation in step 2.4
Figure RE-GDA00029717083600000622
Individual code string and stepStep 2.3 Retention
Figure RE-GDA00029717083600000623
Encoding strings of minimum energy values to form new N encoding strings
Figure RE-GDA00029717083600000624
Step 2.7: new N code strings obtained according to step 2.6
Figure RE-GDA00029717083600000625
Coding bit values, selecting corresponding sample clusters, and calculating the energy value of the clustering result under each coding string;
step 2.7.1: let i equal to 1, k equal to 0, and energy value e (i) equal to 0;
step 2.7.2: traversing and inquiring elements with each coding bit equal to k in the ith coding string, and recording the serial numbers of the coding bits to form a set INDEXk(i)＝{Index1,k(i),Index2,k(i),…,Indexr,k(i) }; if INDEXk(i) If the element is empty or single element set, then E is equal to E, otherwise, the order is given
Figure RE-GDA0002971708360000071
Step 2.7.3: judging whether K is larger than or equal to the number K-1 of the cluster groups to be clustered or not; if yes, let k equal to 0, and go to step 2.7.4; if not, k is equal to k +1, and the step 2.7.2 is returned;
step 2.7.4: judging whether i is more than or equal to the number N of the coding strings; if yes, entering step 2.8; if not, i is equal to i +1, and the step 2.7.2 is returned;
step 2.8: calculating the energy value sequences [ E (1), E (2), …, E (N) ] under the N coding strings obtained in the step 2.7]Record the code string corresponding to the minimum energy value, and record as mCH(g)(ii) a Judging whether G is smaller than G, if so, returning to the step 2.3; if not, entering step 3;
and step 3: sorting the retired power battery single cells into clusters according to the optimal coding strings, and calculating cluster group centers and confidence domains, wherein the k-th cluster group center and confidence domain, taking two characteristics as examples, are schematically shown in FIG. 6;
step 3.1: according to the encoding string mCH corresponding to the minimum energy value recorded in the step 2(g)Traversing and querying coding string mCH with maximum iteration number(G)Recording a set of encoded bit sequence numbers INDEX for elements in which each encoded bit is equal to kk＝{Index1,k,Index2,k,…,Indexr,kThe sequence numbers belong to INDEXkMarking the retired power battery monomer of the medium element as a kth cluster group, and sorting the retired power battery monomer into a type which can be used for subsequent assembly of a package;
step 3.2: calculating the feature centers of all K cluster groups, wherein the central point of Y after X is marked as per unit in the K cluster group
Figure RE-GDA0002971708360000072
Wherein the ith feature is centered on
Figure RE-GDA0002971708360000073
Step 3.3: calculating confidence domains of judging reliability of all K cluster group feature centers, wherein the ith feature maximum deviation value of all retired power battery single body samples in the kth cluster group is
Figure RE-GDA0002971708360000074
The confidence domain of the reliability of the characteristic center judgment of the kth cluster family is epsilonk＝[ε1,k,ε2,k,...,εm,k]；
And 4, step 4: identifying the category of the new retired power battery monomer to be tested and calculating the confidence of identification;
step 4.1: voltage data U for measuring charge-discharge process of newly-added retired power battery monomer to be testedtestExtracting corresponding feature vectors based on step 1.2
Figure RE-GDA0002971708360000075
Step 4.2: computing a feature vector YtestEuclidean distance from feature centers of all K cluster families, wherein the distance from the center of the kth cluster family is
Figure RE-GDA0002971708360000081
Judging that the newly-added retired power battery monomer to be tested belongs to the Lth cluster group, wherein L is arg (min (D)k,testI K ═ 0,1,. K-1)) ∈ {0,1,. K-1}, where arg (min (·)) represents the index that the minimum represents;
step 4.3: computing a feature vector YtestWith center of features of cluster L
Figure RE-GDA0002971708360000082
Ratio of deviation of (a) to confidence domain
Figure RE-GDA0002971708360000083
Definition of all in m-dimensional featuresJudging the category of the new retired power battery monomer to be tested to be effective and reliable, otherwise, judging to be unreliable.
Finally, it should be noted that: the described embodiments are only some embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Claims (6)

1. A clustering sorting method for retired power batteries is characterized by comprising the following steps:
step 1: measuring voltage data of n disassembled retired power battery unit samples, extracting m characteristic variables, per unit, calculating the distance d between characteristic vectors of the samples, and forming a similarity matrix A;
step 2: defining the coding length and coding bit value for sorting according to the number n of samples and the number K of clustering clusters, selecting high-quality sorting codes according to the energy of clustering cluster groups, and forming K clustering cluster groups of a large number of retired battery monomers by using genetic evolution operations such as crossing, variation, reinsertion and the like;
and step 3: calculating the center of each cluster group and the maximum deviation of the samples in the cluster group to form a confidence domain;
and 4, step 4: and finishing sorting and sorting reliability judgment based on the relation between the characteristic vector of the single power battery to be detected for retirement and the central distance and confidence domain of each cluster group.
2. The clustering sorting method for retired power batteries according to claim 1, comprising the following steps:
step 1: measuring voltage data of a plurality of retired power battery monomers, extracting key characteristic variables of the retired power battery monomers, per-unit calculating the key characteristic variables, and calculating the Euclidean distance between samples to form a similar matrix; the method specifically comprises the following steps:
step 1.1: measuring voltage data of a large number of disassembled power battery single body samples in the charging and discharging process;
step 1.2: defining key characteristic variables of the voltage data obtained in the step 1.1, and obtaining characteristic vectors by per unit of each characteristic value;
step 1.3: calculating the Euclidean distance between the samples based on the feature vectors after the per unit in the step 1.2 to form a similar matrix;
step 2: forming a clustering process of retired power battery monomers under genetic evolution; the method specifically comprises the following steps:
step 2.1: initializing selection rate, cross rate, variation rate and maximum iteration number parameters, setting the number of the retired power battery monomer quasi-cluster clusters, and defining the form and number of coding strings, wherein the value of each coding bit is any integer from zero to the number K-1 of the cluster clusters;
step 2.2: selecting corresponding sample clusters according to the coding bit values in the step 2.1, and calculating energy values of clustering results under each coding string;
step 2.3: sequencing the energy value sequences under the obtained coding strings, and selecting a plurality of maximum values and corresponding coding strings;
step 2.4: combining the coding strings selected in the step 2.3 in pairs randomly, and performing cross operation to form new coding strings with the same quantity;
step 2.5: carrying out mutation operation on the crossed new coding string obtained in the step 2.4, and updating the coding string;
step 2.6: combining the new coding string obtained by crossing and mutation in the step 2.4 with the remaining coding strings with the minimum energy values which are not crossed and mutated and are remained in the step 2.3 to form a child coding string;
step 2.7: selecting corresponding sample clusters according to the coding bit values on the filial generation coding strings obtained in the step 2.6, and calculating the energy values of the clustering results under the coding strings;
step 2.8: calculating the minimum value of the energy value sequence under the filial generation coding string obtained in the step 2.7, recording the coding string corresponding to the minimum value energy value, judging whether to continue evolution optimization, and if so, returning to the step 2.3; if not, entering step 3;
and step 3: sorting the retired power battery monomers into clusters according to the optimal coding strings, and calculating cluster centers and confidence domains; the method specifically comprises the following steps:
step 3.1: traversing and inquiring each coding bit value in the coding string with the maximum iteration times according to the coding string corresponding to the minimum energy value recorded in the second step, counting retired power battery monomers corresponding to the same value, recording the retired power battery monomers as the same cluster, completing sorting and clustering of the existing retired power battery monomers, and sorting the retired power battery monomers into a type which can be used for subsequent matching and forming;
step 3.2: calculating the feature centers of all cluster groups, wherein the feature direction center after the retired power battery monomer in each cluster group is per unit is the average value of the retired power battery monomers in the same cluster under the same feature value;
step 3.3: calculating confidence domains of judging reliability of feature centers of all cluster groups, wherein the maximum deviation amount of each feature value in each cluster group is the maximum value of the absolute difference value between the feature value and the center of each retired power battery single body sample of the cluster group under the feature;
and 4, step 4: identifying the category of the new retired power battery monomer to be tested and calculating the confidence of identification; the method specifically comprises the following steps:
step 4.1: measuring voltage data of the newly added retired power battery monomer to be tested in the charging and discharging process, and extracting corresponding characteristic vectors based on the step 1.2;
step 4.2: calculating the Euclidean distance between the feature vector of the newly increased retired power battery monomer to be tested and the feature centers of all cluster groups, and judging that the newly increased retired power battery monomer to be tested belongs to the cluster group with the minimum Euclidean distance;
step 4.3: calculating the deviation of each feature vector of the newly added retired power battery monomer to be tested and the feature center of the cluster family judged in the step 4.2, and defining the ratio of the deviation to the cluster family confidence domain obtained in the step 3.3, wherein the specific gravity of each feature is less than 1, and if the category judgment of the newly added retired power battery monomer to be tested is effective and reliable, otherwise, the judgment is unreliable.
3. The clustering sorting method for the retired power battery according to claim 2, characterized by comprising the following steps:
step 1: measuring voltage data of n retired power battery single cells, extracting m key characteristic variables, per unit, calculating the Euclidean distance between samples, and forming a similar matrix A;
step 1.1: measuring voltage data of a large number of disassembled n retired power battery single body samples in the charging and discharging process, wherein Ui(t) represents the voltage value of the ith sample at the time t, i is 1,2, …, n;
step 1.2: defining m key characteristic variables of the voltage data obtained in step 1.1, and forming a characteristic vector X ═ X1 x2…xm]Calculating a characteristic value x (i) ═ x of the ith sample1(i) x2(i)…xm(i)]And obtaining a characteristic vector Y ═ Y by each characteristic value per unit1 y2…ym]And the s characteristic value of the i sample is per unit as follows:
Figure FDA0002837946780000031
step 1.3: calculating the Euclidean distance between the samples based on the feature vector Y after the per unit in the step 1.2, wherein the Euclidean distance between the ith sample and the jth sampleDistance of formula
Figure FDA0002837946780000032
Forming a similar matrix:
Figure FDA0002837946780000033
step 2: defining a coding form, clustering cluster family energy and coding variation links for clustering and sorting retired power battery monomers to form a clustering process of the retired power battery monomers under genetic evolution;
step 2.1: defining the selection ratio PsCross over ratio PcThe rate of variation PmAnd the maximum iteration number is G, the iteration number G is made to be 0, the number K of all the retired power battery monomer quasi-cluster clusters obtained in the step 1 is set, coding strings with the same length as the number of the samples are defined, and in the G-th iteration, the coding strings are
Figure FDA0002837946780000034
Each coded bit
Figure FDA0002837946780000035
Taking any integer from zero to the cluster family number K-1 and randomly generating N coding strings
Figure FDA0002837946780000036
Step 2.2: selecting corresponding sample clusters according to the coding bit values in the step 2.1, and calculating energy values of clustering results under each coding string;
step 2.2.1: let i equal to 1, k equal to 0, and energy value e (i) equal to 0;
step 2.2.2: traversing and inquiring elements with each coding bit equal to k in the ith coding string, and recording the serial numbers of the coding bits to form a set INDEXk(i)＝{Index1,k(i),Index2,k(i),…,Indexr,k(i) }; if INDEXk(i) If the element is empty or single element set, E is equal to E, and if not, E is equal to EThen order:
Figure FDA0002837946780000041
step 2.2.3: judging whether K is larger than or equal to the number K-1 of the cluster groups to be clustered or not; if yes, making k equal to 0, and entering step 2.2.4; if not, k is equal to k +1, and the step 2.2.2 is returned;
step 2.2.4: judging whether i is more than or equal to the number N of the coding strings; if yes, entering step 2.3; if not, i is equal to i +1, and the step 2.2.2 is returned;
step 2.3: for the obtained energy value sequences under N coding strings [ E (1), E (2), …, E (N)]Sorting, selecting the largest
Figure FDA0002837946780000042
Individual energy values and corresponding code strings
Figure FDA0002837946780000043
Figure FDA0002837946780000044
Represents a rounded-down symbol;
step 2.4: randomly pairwise combined selected in step 2.3
Figure FDA0002837946780000045
A code string
Figure FDA0002837946780000046
Figure FDA0002837946780000047
Performing a crossover operation to form
Figure FDA0002837946780000048
New code string
Figure FDA0002837946780000049
Step 2.5: for the crossed one obtained in step 2.4
Figure FDA00028379467800000410
A code string
Figure FDA00028379467800000411
Is subjected to a mutation operation, and the mutation operation,
Figure FDA00028379467800000412
updating
Figure FDA00028379467800000413
A code string
Figure FDA00028379467800000414
Step 2.6: let g be g +1, the result of the crossover and 2.5 mutation in step 2.4
Figure FDA00028379467800000415
Individual code strings and those reserved in step 2.3
Figure FDA00028379467800000416
Encoding strings of minimum energy values to form new N encoding strings
Figure FDA00028379467800000417
Step 2.7: new N code strings obtained according to step 2.6
Figure FDA00028379467800000418
Coding the bit value, selecting a corresponding sample cluster, and calculating the energy value of the clustering result under each coding string similar to the calculation process of the step 2.2;
step 2.8: calculating N code strings from step 2.7Recording the minimum value of the energy value sequence, recording the code string corresponding to the minimum value energy value, and recording as mCH(g)(ii) a Judging whether G is smaller than G, if so, returning to the step 2.3; if not, entering step 3;
and step 3: sorting the retired power battery monomers into clusters according to the optimal coding strings, and calculating cluster centers and confidence domains; the method specifically comprises the following steps:
step 3.1: according to the encoding string mCH corresponding to the minimum energy value recorded in the step 2(g)Traversing and querying coding string mCH with maximum iteration number(G)Recording a set of encoded bit sequence numbers INDEX for elements in which each encoded bit is equal to kk＝{Index1,k,Index2,k,…,Indexr,kThe sequence numbers belong to INDEXkMarking the retired power battery monomer of the medium element as a kth cluster group, and sorting the retired power battery monomer into a type which can be used for subsequent assembly of a package;
step 3.2: calculating the feature centers of all K cluster groups, wherein the central point of Y after X is marked as per unit in the K cluster group
Figure FDA0002837946780000051
Wherein the ith feature is centered on
Figure FDA0002837946780000052
Step 3.3: calculating confidence domains of judging reliability of all K cluster group feature centers, wherein the ith feature maximum deviation value of all retired power battery single body samples in the kth cluster group is
Figure FDA0002837946780000053
The confidence domain of the reliability of the characteristic center judgment of the kth cluster family is epsilonk＝[ε1,k,ε2,k,...,εm,k]；
And 4, step 4: identifying the category of the new retired power battery monomer to be tested and calculating the confidence of identification; the method specifically comprises the following steps:
step 4.1: measuring new retired power battery monomer charge to be measuredVoltage data of discharge process, extracting corresponding characteristic vector Y based on step 1.2test；
Step 4.2: computing a feature vector YtestEuclidean distance from feature centers of all K cluster families, wherein the distance from the center of the kth cluster family is
Figure FDA0002837946780000054
Judging that the newly-added retired power battery monomer to be tested belongs to the Lth cluster group, wherein L is arg (min (D)k,testI K ═ 0,1,. K-1)) ∈ {0,1,. K-1}, where arg (min (·)) represents the index that the minimum represents;
step 4.3: computing a feature vector YtestWith center of features of cluster L
Figure FDA0002837946780000055
Ratio of deviation to confidence domain of (c):
Figure FDA0002837946780000056
definition of all in m-dimensional features
Figure FDA0002837946780000057
Judging the category of the new retired power battery monomer to be tested to be effective and reliable, otherwise, judging to be unreliable.
4. The cluster sorting method for retired power batteries according to claim 3, wherein step 2.4 comprises the following processes:
step 2.4.1: if it is
Figure FDA0002837946780000061
If the number is even, the number selected in step 2.3 is randomly selected
Figure FDA0002837946780000062
Code ofString
Figure FDA0002837946780000063
Composition of
Figure FDA0002837946780000064
For the coding string; if it is
Figure FDA0002837946780000065
If the number of the codes is odd, a code string is randomly selected
Figure FDA0002837946780000066
Do not carry out pairing, the rest
Figure FDA0002837946780000067
A code string is composed of
Figure FDA0002837946780000068
For the coding string, making h equal to 1;
step 2.4.2: produce a [0,1 ]]If p is the random number of>PcThen go to step 2.4.3; if p is<PcIf so, cross-coding at the random coding bit of the h-th pair of coding strings;
step 2.4.3: judging whether H is smaller than H, if so, returning to the step 2.4.2 if H is H + 1; if not, obtaining
Figure FDA0002837946780000069
New code string
Figure FDA00028379467800000610
Go to step 2.5.
5. The cluster sorting method for retired power batteries according to claim 3, wherein the method comprises the following steps in step 2.5:
step 2.5.1 let si be 1 and bj be 1;
step 2.5.2 producing a [ alpha ]0,1]If p is the random number of>PmThen go to step 2.5.3; if p is<PmThen, for the si-th encoding string
Figure FDA00028379467800000611
Bj th coded bit of
Figure FDA00028379467800000612
Performing a mutation operation to change to one in [0, K-1 ]]Integers within the range that are not themselves,
Figure FDA00028379467800000613
step 2.5.3, determining whether bj is less than n, if so, determining bj as bj +1, and returning to step 2.5.2; if not, the si-th coding string is mutated and updated
Figure FDA00028379467800000614
Entering step 2.5.4;
step 2.5.4 judging if si is less than
Figure FDA00028379467800000615
If so, si is si +1, bj is 1 and the step 2.5.2 is returned; if not, the mutation is completely updated
Figure FDA00028379467800000616
The code string proceeds to step 2.6.
6. The cluster sorting method for retired power batteries according to claim 3, wherein step 2.7 comprises the following processes:
step 2.7.1 let i equal 1, k equal 0, and energy value e (i) equal 0;
step 2.7.2, the element with each coding bit equal to k in the ith coding string is searched in a traversing way, and the sequence number of the coding bit is recorded to form a set INDEXk(i)＝{Index1,k(i),Index2,k(i),…,Indexr,k(i) }; if INDEXk(i) If the element is empty or single element set, then E is equal to E, otherwise, the order is given
Figure FDA00028379467800000617
Step 2.7.3, judging whether K is larger than or equal to the number K-1 of the quasi-clustering cluster groups; if yes, let k equal to 0, and go to step 2.7.4; if not, k is equal to k +1, and the step 2.7.2 is returned;
step 2.7.4 determining whether i is greater than or equal to the number of encoding strings N; if yes, entering step 2.8; if not, i is equal to i +1, and the procedure returns to step 2.7.2.
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