CN116742166A - Power battery repairing method and system - Google Patents
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Abstract
The invention provides a power battery repair method and a system, which relate to the technical field of battery repair, and the method comprises the following steps: obtaining K battery performance indexes of M battery units; obtaining M performance index parameter sets; obtaining K repair importance parameters; obtaining M initial repairability sets; obtaining M comprehensive repairability degrees; acquiring N battery units and N performance index parameter sets, wherein N comprehensive repairability meets preset repairability; the method comprises the steps of carrying out matching of a repairing scheme based on the N performance index parameter sets, repairing the power battery according to the matching repairing scheme, and solving the technical problems that in the prior art, the performance degradation analysis of the battery is inaccurate, and then the repairability analysis of the battery is inaccurate, so that the battery repairing effect is poor, and the technical effects of assisting workers in repairing the battery, improving the battery repairing effect and prolonging the service life of the battery are achieved.
Description
Technical Field
The invention relates to the technical field of battery repair, in particular to a power battery repair method and system.
Background
The power battery is a device which is configured and used on an automobile, can store electric energy and can be recharged and provides energy for driving the automobile to run, comprises a lithium ion power battery, a metal hydride nickel power battery, a super capacitor and the like, and is an effective means for ensuring the service life of the battery as the performance of the power battery is degraded and the battery with degraded performance is repaired in time along with the increase of the service time.
At present, the technical problem of poor battery repair effect caused by inaccurate analysis of the performance degradation of the battery and thus inaccurate analysis of the repairability of the battery exists in the prior art.
Disclosure of Invention
The invention provides a power battery repairing method and system, which are used for solving the technical problem of poor battery repairing effect caused by inaccurate analysis of battery performance degradation and thus inaccurate analysis of battery repairability in the prior art.
According to a first aspect of the present invention, there is provided a power battery repair method comprising: obtaining K battery performance indexes of M battery units in a first battery pack of a power battery to be repaired, wherein M, K is an integer greater than 1; performing performance test on the M battery units according to the K battery performance indexes to obtain M performance index parameter sets, wherein each performance index parameter set comprises K battery performance index parameters; performing battery degradation influence analysis on the K battery performance indexes to obtain K repair importance parameters; performing initial repairability evaluation on the M performance index parameter sets to obtain M initial repairability sets; respectively carrying out weighted calculation on the data in the M initial repairable degree sets according to the K repairable importance parameters to obtain M comprehensive repairable degrees; according to the M comprehensive repairability, N battery units and N performance index parameter sets, wherein the N comprehensive repairability meets the preset repairability, and N is smaller than M; and carrying out matching of a repair scheme based on the N performance index parameter sets, and repairing the power battery according to the matching repair scheme.
According to a second aspect of the present invention, there is provided a power battery repair system comprising: the performance index acquisition module is used for acquiring K battery performance indexes of M battery units in a first battery pack of the power battery to be repaired, and M, K is an integer greater than 1; the performance testing module is used for performing performance testing on the M battery units according to the K battery performance indexes to obtain M performance index parameter sets, wherein each performance index parameter set comprises K battery performance index parameters; the degradation influence analysis module is used for carrying out battery degradation influence analysis on the K battery performance indexes to obtain K repair importance parameters; the initial repair evaluation module is used for evaluating the initial repairability of the M performance index parameter sets to obtain M initial repairability sets; the weighting calculation module is used for respectively carrying out weighting calculation on the data in the M initial repairable degree sets according to the K repairable importance parameters to obtain M comprehensive repairable degrees; the repairability judging module is used for acquiring N battery units and N performance index parameter sets, wherein N is smaller than M, and N comprehensive repairability meets the preset repairability according to the M comprehensive repairability; and the repair scheme matching module is used for matching the repair scheme based on the N performance index parameter sets and repairing the power battery according to the matched repair scheme.
According to the power battery repairing method adopted by the invention, K battery performance indexes of M battery units in a first battery pack of a power battery to be repaired are obtained, and M, K is an integer greater than 1; performing performance test on the M battery units according to the K battery performance indexes to obtain M performance index parameter sets, wherein each performance index parameter set comprises K battery performance index parameters; performing battery degradation influence analysis on the K battery performance indexes to obtain K repair importance parameters; performing initial repairability evaluation on the M performance index parameter sets to obtain M initial repairability sets; respectively carrying out weighted calculation on the data in the M initial repairable degree sets according to the K repairable importance parameters to obtain M comprehensive repairable degrees; according to the M comprehensive repairability, N battery units and N performance index parameter sets, wherein the N comprehensive repairability meets the preset repairability, and N is smaller than M; and carrying out the matching of the repair scheme based on the N performance index parameter sets, and repairing the power battery according to the matching repair scheme, so as to achieve the technical effects of assisting workers in repairing the battery, improving the battery repair effect and prolonging the service life of the battery.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following brief description will be given of the drawings used in the description of the embodiments or the prior art, it being obvious that the drawings in the description below are only exemplary and that other drawings can be obtained from the drawings provided without the inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a power battery repairing method according to an embodiment of the present invention;
FIG. 2 is a flow chart of obtaining K repair importance parameters according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of obtaining an initial repair evaluation model according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a power battery repair system according to an embodiment of the present invention.
Reference numerals illustrate: the system comprises a performance index acquisition module 11, a performance test module 12, a degradation influence analysis module 13, an initial restoration evaluation module 14, a weighted calculation module 15, a restoration degree judgment module 16 and a restoration scheme matching module 17.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In order to solve the technical problems of poor battery repair effect caused by inaccurate performance degradation analysis of the battery and inaccurate repair degree analysis of the battery in the prior art, the inventor obtains the power battery repair method and system through creative labor.
Example 1
Fig. 1 is a diagram of a power battery repair method according to an embodiment of the present invention, where the method includes:
step S100: obtaining K battery performance indexes of M battery units in a first battery pack of a power battery to be repaired, wherein M, K is an integer greater than 1;
specifically, the power battery refers to a device which is configured and used on an automobile, can store electric energy and can be recharged, and provides energy for driving the automobile to run, and comprises a lithium ion power battery, a metal hydride nickel power battery, a super capacitor and the like. The power battery is generally formed by combining a plurality of battery units, the first battery pack is formed by M battery units, M, K is an integer greater than 1, and K battery performance indexes for representing the battery performance, such as pressure difference, internal resistance, active material content, electrode plate sulfation degree and the like, are obtained based on the first battery pack, and all the indexes can cause battery aging and performance degradation.
Step S200: performing performance test on the M battery units according to the K battery performance indexes to obtain M performance index parameter sets, wherein each performance index parameter set comprises K battery performance index parameters;
specifically, before repairing the battery, the repairability of the battery needs to be evaluated, the degradation degrees of different batteries are different, and performance tests need to be performed on M battery units so as to facilitate subsequent analysis of the repairability, and basic data are provided for repairing the battery. And respectively performing performance tests on the M battery units according to the K battery performance indexes to obtain M performance index parameter sets, wherein each performance index parameter set comprises K battery performance index parameters, and the K battery performance index parameters refer to performance test values of the K battery performance indexes, such as a differential pressure value, an internal resistance value and the like.
Step S300: performing battery degradation influence analysis on the K battery performance indexes to obtain K repair importance parameters;
as shown in fig. 2, step S300 of the embodiment of the present invention further includes:
step S310: acquiring historical degradation record data of a battery of the same type of the first battery pack;
step S320: extracting a plurality of historical degradation index parameter sets with the same battery degradation capacity according to the historical degradation record data;
step S330: according to K initial index parameters of the K battery performance indexes, carrying out degradation degree analysis on the historical degradation index parameter sets to obtain a plurality of index degradation degree sets;
step S340: respectively calculating and acquiring K degradation degree average values of the K battery performance indexes based on the multiple index degradation degree sets;
step S350: and calculating a first sum of the K degradation degree average values, and taking the ratio of the K degradation degree average values to the first sum as the K repair importance parameters.
Specifically, the degradation of the battery is closely related to the degradation of K battery performance indexes, on the basis of the K battery performance indexes, the importance of the K battery performance indexes on the degradation of the battery is analyzed to obtain K repair importance parameters, and the limitation of battery repair evaluation is realized, wherein the specific process is as follows:
and acquiring historical degradation record data of the same type of battery of the first battery pack, and extracting a plurality of historical degradation index parameter sets with the same battery degradation capacity according to the historical degradation record data, wherein each historical degradation index parameter set comprises K historical degradation index parameters, and the degradation degrees of the K historical degradation index parameters are different. According to the K initial index parameters of the K battery performance indexes, the K initial index parameters are standard parameters of the K battery performance indexes, namely parameters when the battery is not degraded, such as parameters when the battery leaves a factory, the ratio between the K historical degradation index parameters and the K initial index parameters is used as K index degradation degrees, and the K index degradation degrees form an index degradation degree set, so that a plurality of index degradation degree sets are obtained. And respectively calculating and acquiring K degradation degree averages of the K battery performance indexes based on the multiple index degradation degree sets, namely, any one index degradation degree set comprises K index degradation degrees, respectively extracting first index degradation degrees in the multiple index degradation degree sets, calculating and acquiring the first index degradation degree average as 1 degradation degree average, and thus, pushing to obtain the K degradation degree averages of the K battery performance indexes. And calculating a first sum of the K degradation degree average values, and taking the ratio of the K degradation degree average values to the first sum as the K repair importance parameters to realize degradation influence on each performance index and provide data support for subsequent repair evaluation.
Step S400: performing initial repairability evaluation on the M performance index parameter sets to obtain M initial repairability sets;
as shown in fig. 3, step S400 in the embodiment of the present invention includes:
step S410: constructing an initial repair evaluation model, wherein the initial repair evaluation model comprises K initial repair evaluation units which are respectively used for performing initial repairability evaluation on the K battery performance indexes;
step S420: randomly acquiring n groups of training data based on a training sample data set, wherein the training sample data set comprises a first performance index parameter sample set and a first initial repairability evaluation sample set which have corresponding relations;
step S430: initializing a weight training layer based on the n groups of training data to obtain n initial learners;
step S440: according to the output results of the n initial learners, obtaining n output error rates of the n initial learners;
step S450: according to the n output error rates, updating weight distribution of the n groups of training data, which is used for carrying out next round of iterative training, and fusing n initial learners after the iterative training is finished to obtain a first initial repair evaluation unit;
step S460: and continuously acquiring other K-1 initial repair evaluation units to obtain the initial repair evaluation model.
The step S450 of the embodiment of the present invention further includes:
step S451: according to the output results of the n initial learners, n error values of the n initial learners are obtained;
step S452: carrying out error direction analysis on the n error values to obtain a positive error rate and a negative error rate;
step S453: and carrying out weight distribution optimization on the n groups of training data according to the positive error rate and the negative error rate, wherein the optimized weight distribution formula is as follows:
wherein a is n Identifying an update weight for the nth set of training data; e, e n The output error rate of the nth set of training data is identified,ω n identifying a positive error rate or a negative error rate of the nth set of training data if the output error of the nth set of training data is a positive error value, ω n For positive error rate, if the output error of the nth set of training data is negative error value, ω n Is a negative error rate; a, a m With omega m e m Is increased by decreasing.
Specifically, the initial repairability evaluation is performed on the M performance index parameter sets, and M initial repairability sets are obtained, wherein the specific process is as follows:
firstly, an initial repair evaluation model is constructed, wherein the initial repair evaluation model comprises K initial repair evaluation units, the K initial repair evaluation units are respectively used for evaluating the initial repairability of the K battery performance indexes, and the initial repair evaluation units are formed by fusing a plurality of initial learners. The method comprises the steps of randomly acquiring n groups of training data based on a training sample data set, wherein the training sample data set comprises a first performance index parameter sample set and a first initial repairability evaluation sample set which have a corresponding relation, the n groups of training data can be acquired based on the prior art, the n groups of training data are randomly extracted from the training sample data set without replacement, n is an integer larger than 1, preferably n is an integer not smaller than 100, because the number of the training data is too small, the number of fused initial learners is small and can influence the accuracy of an initial repairability evaluation unit, the prototype of the initial learners is a neural network model in machine learning, a weight training layer is a network layer in the neural network model, each group of training data in the n groups of training data is respectively used as model training data, the n groups of training data are input into the initial learners through an input end, and after the output result, namely the accuracy reaches a preset requirement (more than 50%), the n initial learners are obtained through training.
And obtaining output results of the n initial learners, namely n initial repairability, and obtaining n output error rates of the n initial repairability, wherein the n initial repairability, the ratio of the error value between the first initial repairability evaluation samples and the first initial repairability evaluation samples can be used as the n output error rates. And according to the n output error rates, carrying out weight distribution updating on the n groups of training data, carrying out next round of iterative training, fusing n initial learners after the iterative training is finished to obtain a first initial repair evaluation unit, integrating the functions of the n initial learners by the first initial repair evaluation unit, providing technical support for evaluating the repairability of the battery, and ensuring the evaluation accuracy of the repairability. And continuously acquiring other K-1 initial repair evaluation units by adopting the same method to obtain the initial repair evaluation model.
The process of updating the weight distribution of the n groups of training data is as follows: and sequencing the n output error rates according to the order from small to large, setting an important coefficient according to the output error rate, wherein the initial learner with smaller output error rate has larger weight, and the initial learner with larger output error rate has smaller weight, so as to obtain the n weights of the n initial learners.
Further, according to the output results of the n initial learners, obtaining that n error values (error values between n initial repairability and first initial repairability evaluation samples) of the n initial learners are positive and negative, performing error direction analysis on the n error values, obtaining a positive error number and a negative error number, respectively taking the ratio of the positive error number, the negative error number and n as a positive error rate and a negative error rate, and performing weight distribution optimization on the n sets of training data according to the positive error rate and the negative error rate, wherein the optimized weight distribution formula is as follows:
wherein a is n Identifying an update weight for the nth set of training data; e, e n The output error rate of the nth set of training data is identified,ω n identifying a positive error rate or a negative error rate of the nth set of training data if the output error of the nth set of training data is a positive error value, ω n For positive error rate, if the output error of the nth set of training data is negative error value, ω n Is a negative error rate; a, a m With omega m e m Is increased by decreasing.
And carrying out iterative training update on the n initial learners based on the weight distribution formula until the output error rates of the n initial learners are smaller than a preset error rate (self-setting, such as 10%), and finishing the iterative circulation of the n initial learners to provide support for constructing the initial repair evaluation model.
Step S500: respectively carrying out weighted calculation on the data in the M initial repairable degree sets according to the K repairable importance parameters to obtain M comprehensive repairable degrees;
specifically, the K repair importance parameters represent the importance degree of the K battery performance indexes for the degradation repair of the power battery, any one of the M initial repairable degree sets includes K initial repairable degrees corresponding to the K battery performance indexes, the K repair importance parameters are used as weight values of the K initial repairable degrees, the K initial repairable degrees are weighted, and a corresponding comprehensive repairable degree in the initial repairable degree set can be obtained, based on the weighted calculation, the M comprehensive repairable degrees are obtained, and the comprehensive repairable degree represents the repairable degree of the power battery, for example, the battery performance can be improved to 80% of the initial performance, because the battery cannot be repaired to be identical to the performance in factory, only the degradation degree can be reduced, and the service life can be prolonged.
Step S600: according to the M comprehensive repairability, N battery units and N performance index parameter sets, wherein the N comprehensive repairability meets the preset repairability, and N is smaller than M;
specifically, the preset repair degree is obtained, and the preset repair degree is set by the person skilled in the art, that is, if the comprehensive repairability of the battery is too low, for example, the performance after repair can only reach 20% of the initial performance, a new battery unit can be directly replaced without repair necessity, based on the preset repair degree, whether the M comprehensive repairability meets the preset repair degree is judged, N battery units and N performance index parameter sets with N comprehensive repairability meeting the preset repair degree are extracted, and N is smaller than M.
Step S700: and carrying out matching of a repair scheme based on the N performance index parameter sets, and repairing the power battery according to the matching repair scheme.
The step S700 of the embodiment of the present invention includes:
step S710: obtaining a plurality of historical performance index parameter sets based on repair record data of power battery repair in past time;
step S720: constructing a plurality of index element sets according to the plurality of historical performance index parameter sets;
step S730: obtaining a history restoration scheme set according to the history restoration schemes of the plurality of history performance index parameter sets;
step S740: constructing a plurality of data elements according to the history restoration scheme set;
step S750: constructing a battery repair scheme database based on the index relationships of the index element sets and the data elements;
step S760: inputting the N performance index parameter sets into the battery repair scheme database for indexing, and obtaining a plurality of corresponding historical repair schemes as a plurality of primary repair schemes;
step S770: and carrying out restoration effect evaluation on the plurality of primary restoration schemes to obtain the optimal primary restoration scheme as the matched restoration scheme.
The step S770 of the embodiment of the present invention includes:
step S771: performing repair performance evaluation on the plurality of primary repair schemes to obtain a plurality of performance evaluation results:
step S772: performing repair efficiency evaluation on the plurality of primary repair schemes to obtain a plurality of efficiency evaluation results;
step S773: weighting calculation is carried out on the performance evaluation results and the efficiency evaluation results, and a plurality of comprehensive evaluation results are obtained;
step S774: and selecting a first selected repair scheme corresponding to the maximum comprehensive evaluation result in the plurality of comprehensive evaluation results as the matching repair scheme.
The step S771 of the embodiment of the present invention includes:
step S7711: performing battery capacity recovery degree evaluation on the plurality of primary repair schemes to obtain a plurality of first evaluation results;
step S7712: predicting the performance of the repaired multiple primary selected repair schemes in a preset time to obtain multiple performance degradation prediction results;
step S7713: correcting and compensating the plurality of first evaluation results according to the plurality of performance degradation prediction results;
step S7714: and taking the corrected and compensated first evaluation results as the performance evaluation results.
Specifically, the repair scheme is matched based on the N performance index parameter sets, and the power battery is repaired according to the matched repair scheme, and the specific process is as follows:
specifically, a plurality of historical performance index parameter sets are obtained from repair record data of power battery repair in the past time (such as the past month), and a historical repair scheme set is obtained according to a historical repair scheme of the plurality of historical performance index parameter sets, wherein the historical repair scheme is generally formulated by professional power battery repair personnel. And then, correspondingly constructing fixed index elements for the plurality of historical performance index parameter sets, namely, carrying out one-layer index for each historical performance index parameter set, and simultaneously, after summarizing the corresponding historical repair schemes formulated for the plurality of historical performance index parameter sets, constructing data elements for each historical repair scheme, wherein the plurality of index element sets are indexes of the plurality of data elements, carrying out multi-layer index query for the plurality of data elements through the plurality of index element sets, and completing construction of a battery repair scheme database on the basis. And inputting the N performance index parameter sets into the battery repair scheme database for indexing to obtain a plurality of corresponding historical repair schemes, evaluating the repair effect of the plurality of primary repair schemes as a plurality of primary repair schemes, and obtaining the most preferred repair scheme as the matching repair scheme to realize the matching acquisition of the repair schemes and ensure the repair efficiency of the battery.
Specifically, the repair performance of the plurality of primary repair schemes is evaluated, and a plurality of performance evaluation results are obtained, wherein the process is as follows: and evaluating the recovery degree of the battery capacity of the plurality of primary repair schemes to obtain a plurality of first evaluation results, namely determining that the power battery is repaired by adopting the plurality of primary repair schemes respectively according to the prior art, and then, improving the battery capacity by 80% for example to obtain a plurality of first evaluation results. However, a situation may occur after the battery is repaired, that is, after a period of time after the battery is repaired, the battery capacity is quickly reduced to a value before the repair, such repair is meaningless, so performance prediction after repair is required to be performed on the plurality of primary selected repair schemes within a preset time, specifically, performance change data after repair of the power battery repaired by using the plurality of primary selected repair schemes may be collected, a plurality of performance change data after repair corresponding to the plurality of primary selected repair schemes may be obtained, a plurality of markov chain prediction models are trained by the plurality of performance change data after repair, the markov chain prediction models are constructed by a common technical means in the art, a plurality of performance reduction prediction results are obtained by the plurality of markov chain models, the performance reduction prediction results include performance reduction degrees within the preset time, the performance reduction speed is calculated based on the performance reduction prediction results, that is, if the performance reduction speed is too fast, the effect of the primary selected repair scheme is poor, the performance reduction speed is determined, the first performance compensation result is corrected based on the determined, the first performance compensation result is properly evaluated, and the performance is properly evaluated by the first evaluation result.
And further performing repair efficiency evaluation on the plurality of primary repair schemes, wherein the repair efficiency evaluation can be specifically determined according to the repair required time, and the longer the required time is, the lower the corresponding efficiency evaluation result is, so that a plurality of efficiency evaluation results are obtained. And further, the performance evaluation results and the efficiency evaluation results are weighted and calculated to obtain a plurality of comprehensive evaluation results, and the weight values occupied by the performance evaluation results and the efficiency evaluation results can be set by the user, so that the performance evaluation results and the efficiency evaluation results can be determined according to the importance degree of the user on the performance evaluation results and the efficiency evaluation results by combining with the actual requirements. And finally, selecting a primary selected repair scheme corresponding to the maximum comprehensive evaluation result in the plurality of comprehensive evaluation results as the matching repair scheme, repairing the power battery according to the matching repair scheme, and prolonging the service life of the power battery.
Based on the above analysis, the present invention provides a power battery repair method, in this embodiment, K battery performance indexes of M battery units in a first battery pack of a power battery to be repaired are obtained, and M, K is an integer greater than 1; performing performance test on the M battery units according to the K battery performance indexes to obtain M performance index parameter sets, wherein each performance index parameter set comprises K battery performance index parameters; performing battery degradation influence analysis on the K battery performance indexes to obtain K repair importance parameters; performing initial repairability evaluation on the M performance index parameter sets to obtain M initial repairability sets; respectively carrying out weighted calculation on the data in the M initial repairable degree sets according to the K repairable importance parameters to obtain M comprehensive repairable degrees; according to the M comprehensive repairability, N battery units and N performance index parameter sets, wherein the N comprehensive repairability meets the preset repairability, and N is smaller than M; and carrying out the matching of the repair scheme based on the N performance index parameter sets, and repairing the power battery according to the matching repair scheme, so as to achieve the technical effects of assisting workers in repairing the battery, improving the battery repair effect and prolonging the service life of the battery.
Example two
Based on the same inventive concept as the power battery repair method in the foregoing embodiment, as shown in fig. 4, the present invention further provides a power battery repair system, which includes:
a performance index obtaining module 11, where the performance index obtaining module 11 is configured to obtain K battery performance indexes of M battery units in a first battery pack of a power battery to be repaired, and M, K is an integer greater than 1;
the performance test module 12 is configured to perform performance test on the M battery units according to the K battery performance indexes to obtain M performance index parameter sets, where each performance index parameter set includes K battery performance index parameters;
the degradation influence analysis module 13 is used for carrying out battery degradation influence analysis on the K battery performance indexes to obtain K repair importance parameters;
the initial repair evaluation module 14, wherein the initial repair evaluation module 14 is configured to perform initial repairability evaluation on the M performance index parameter sets, and obtain M initial repairability sets;
the weighting calculation module 15 is configured to perform weighting calculation on the data in the M initial repairable degree sets according to the K repair importance parameters, so as to obtain M comprehensive repairable degrees;
the repairability judging module 16, wherein the repairability judging module 16 is configured to obtain N battery units and N performance index parameter sets, where N is smaller than M, of which N comprehensive repairability meets a preset repairability according to the M comprehensive repairability;
and the repair scheme matching module 17 is used for matching the repair scheme based on the N performance index parameter sets, and repairing the power battery according to the matched repair scheme.
Further, the degradation impact analysis module 13 is further configured to:
acquiring historical degradation record data of a battery of the same type of the first battery pack;
extracting a plurality of historical degradation index parameter sets with the same battery degradation capacity according to the historical degradation record data;
according to K initial index parameters of the K battery performance indexes, carrying out degradation degree analysis on the historical degradation index parameter sets to obtain a plurality of index degradation degree sets;
respectively calculating and acquiring K degradation degree average values of the K battery performance indexes based on the multiple index degradation degree sets;
and calculating a first sum of the K degradation degree average values, and taking the ratio of the K degradation degree average values to the first sum as the K repair importance parameters.
Further, the initial repair evaluation module 14 is further configured to:
constructing an initial repair evaluation model, wherein the initial repair evaluation model comprises K initial repair evaluation units which are respectively used for performing initial repairability evaluation on the K battery performance indexes;
randomly acquiring n groups of training data based on a training sample data set, wherein the training sample data set comprises a first performance index parameter sample set and a first initial repairability evaluation sample set which have corresponding relations;
initializing a weight training layer based on the n groups of training data to obtain n initial learners;
according to the output results of the n initial learners, obtaining n output error rates of the n initial learners;
according to the n output error rates, updating weight distribution of the n groups of training data, which is used for carrying out next round of iterative training, and fusing n initial learners after the iterative training is finished to obtain a first initial repair evaluation unit;
and continuously acquiring other K-1 initial repair evaluation units to obtain the initial repair evaluation model.
Further, the initial repair evaluation module 14 is further configured to:
according to the output results of the n initial learners, n error values of the n initial learners are obtained;
carrying out error direction analysis on the n error values to obtain a positive error rate and a negative error rate;
and carrying out weight distribution optimization on the n groups of training data according to the positive error rate and the negative error rate, wherein the optimized weight distribution formula is as follows:
wherein a is n Identifying an update weight for the nth set of training data; e, e n The output error rate of the nth set of training data is identified,ω n identifying a positive error rate or a negative error rate of the nth set of training data if the output error of the nth set of training data is a positive error value, ω n For positive error rate, if the output error of the nth set of training data is negative error value, ω n Is a negative error rate; a, a m With omega m e m Is increased by decreasing.
Further, the repair scheme matching module 17 is further configured to:
obtaining a plurality of historical performance index parameter sets based on repair record data of power battery repair in past time;
constructing a plurality of index element sets according to the plurality of historical performance index parameter sets;
obtaining a history restoration scheme set according to the history restoration schemes of the plurality of history performance index parameter sets;
constructing a plurality of data elements according to the history restoration scheme set;
constructing a battery repair scheme database based on the index relationships of the index element sets and the data elements;
inputting the N performance index parameter sets into the battery repair scheme database for indexing, and obtaining a plurality of corresponding historical repair schemes as a plurality of primary repair schemes;
and carrying out restoration effect evaluation on the plurality of primary restoration schemes to obtain the optimal primary restoration scheme as the matched restoration scheme.
Further, the repair scheme matching module 17 is further configured to:
performing repair performance evaluation on the plurality of primary repair schemes to obtain a plurality of performance evaluation results:
performing repair benefit evaluation on the plurality of primary repair schemes to obtain a plurality of benefit evaluation results;
weighting calculation is carried out on the performance evaluation results and the benefit evaluation results, and a plurality of comprehensive evaluation results are obtained;
and selecting a first selected repair scheme corresponding to the maximum comprehensive evaluation result in the plurality of comprehensive evaluation results as the matching repair scheme.
Further, the repair scheme matching module 17 is further configured to:
performing battery capacity recovery degree evaluation on the plurality of primary repair schemes to obtain a plurality of first evaluation results;
predicting the performance of the repaired multiple primary selected repair schemes in a preset time to obtain multiple performance degradation prediction results;
correcting and compensating the plurality of first evaluation results according to the plurality of performance degradation prediction results;
and taking the corrected and compensated first evaluation results as the performance evaluation results.
A specific example of a power battery repair method according to the first embodiment is also applicable to a power battery repair system according to the present embodiment, and a person skilled in the art will be aware of the power battery repair system according to the present embodiment through the foregoing detailed description of the power battery repair method, so that details thereof will not be described herein for brevity.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and are not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (8)
1. A method of repairing a power cell, the method comprising:
obtaining K battery performance indexes of M battery units in a first battery pack of a power battery to be repaired, wherein M, K is an integer greater than 1;
performing performance test on the M battery units according to the K battery performance indexes to obtain M performance index parameter sets, wherein each performance index parameter set comprises K battery performance index parameters;
performing battery degradation influence analysis on the K battery performance indexes to obtain K repair importance parameters;
performing initial repairability evaluation on the M performance index parameter sets to obtain M initial repairability sets;
respectively carrying out weighted calculation on the data in the M initial repairable degree sets according to the K repairable importance parameters to obtain M comprehensive repairable degrees;
according to the M comprehensive repairability, N battery units and N performance index parameter sets, wherein the N comprehensive repairability meets the preset repairability, and N is smaller than M;
and carrying out matching of a repair scheme based on the N performance index parameter sets, and repairing the power battery according to the matching repair scheme.
2. The method of claim 1, wherein performing a battery degradation impact analysis on the K battery performance metrics to obtain K repair importance parameters comprises:
acquiring historical degradation record data of a battery of the same type of the first battery pack;
extracting a plurality of historical degradation index parameter sets with the same battery degradation capacity according to the historical degradation record data;
according to K initial index parameters of the K battery performance indexes, carrying out degradation degree analysis on the historical degradation index parameter sets to obtain a plurality of index degradation degree sets;
respectively calculating and acquiring K degradation degree average values of the K battery performance indexes based on the multiple index degradation degree sets;
and calculating a first sum of the K degradation degree average values, and taking the ratio of the K degradation degree average values to the first sum as the K repair importance parameters.
3. The method of claim 1, wherein the performing initial repairability assessment on the M performance index parameter sets to obtain M initial repairability sets comprises:
constructing an initial repair evaluation model, wherein the initial repair evaluation model comprises K initial repair evaluation units which are respectively used for performing initial repairability evaluation on the K battery performance indexes;
randomly acquiring n groups of training data based on a training sample data set, wherein the training sample data set comprises a first performance index parameter sample set and a first initial repairability evaluation sample set which have corresponding relations;
initializing a weight training layer based on the n groups of training data to obtain n initial learners;
according to the output results of the n initial learners, obtaining n output error rates of the n initial learners;
according to the n output error rates, updating weight distribution of the n groups of training data, which is used for carrying out next round of iterative training, and fusing n initial learners after the iterative training is finished to obtain a first initial repair evaluation unit;
and continuously acquiring other K-1 initial repair evaluation units to obtain the initial repair evaluation model.
4. A method as claimed in claim 3, wherein the method further comprises:
according to the output results of the n initial learners, n error values of the n initial learners are obtained;
carrying out error direction analysis on the n error values to obtain a positive error rate and a negative error rate;
and carrying out weight distribution optimization on the n groups of training data according to the positive error rate and the negative error rate, wherein the optimized weight distribution formula is as follows:
wherein a is n Identifying an update weight for the nth set of training data; e, e n The output error rate of the nth set of training data is identified,ω n identifying a positive error rate or a negative error rate of the nth set of training data if the output error of the nth set of training data is a positive error value, ω n For positive error rate, if the output error of the nth set of training data is negative error value, ω n Is a negative error rate; a, a m With omega m e m Is increased by decreasing.
5. The method of claim 1, wherein the matching of the repair scheme based on the N performance index parameter sets comprises:
obtaining a plurality of historical performance index parameter sets based on repair record data of power battery repair in past time;
constructing a plurality of index element sets according to the plurality of historical performance index parameter sets;
obtaining a history restoration scheme set according to the history restoration schemes of the plurality of history performance index parameter sets;
constructing a plurality of data elements according to the history restoration scheme set;
constructing a battery repair scheme database based on the index relationships of the index element sets and the data elements;
inputting the N performance index parameter sets into the battery repair scheme database for indexing, and obtaining a plurality of corresponding historical repair schemes as a plurality of primary repair schemes;
and carrying out restoration effect evaluation on the plurality of primary restoration schemes to obtain the optimal primary restoration scheme as the matched restoration scheme.
6. The method of claim 5, wherein the evaluating the repair effect for the plurality of primary repair solutions to obtain an optimal primary repair solution as the matching repair solution comprises:
performing repair performance evaluation on the plurality of primary repair schemes to obtain a plurality of performance evaluation results:
performing repair efficiency evaluation on the plurality of primary repair schemes to obtain a plurality of efficiency evaluation results;
weighting calculation is carried out on the performance evaluation results and the efficiency evaluation results, and a plurality of comprehensive evaluation results are obtained;
and selecting a first selected repair scheme corresponding to the maximum comprehensive evaluation result in the plurality of comprehensive evaluation results as the matching repair scheme.
7. The method of claim 6, wherein performing repair performance evaluation on the plurality of initially selected repair scenarios to obtain a plurality of performance evaluation results comprises:
performing battery capacity recovery degree evaluation on the plurality of primary repair schemes to obtain a plurality of first evaluation results;
predicting the performance of the repaired multiple primary selected repair schemes in a preset time to obtain multiple performance degradation prediction results;
correcting and compensating the plurality of first evaluation results according to the plurality of performance degradation prediction results;
and taking the corrected and compensated first evaluation results as the performance evaluation results.
8. A power cell repair system, the system comprising:
the performance index acquisition module is used for acquiring K battery performance indexes of M battery units in a first battery pack of the power battery to be repaired, and M, K is an integer greater than 1;
the performance testing module is used for performing performance testing on the M battery units according to the K battery performance indexes to obtain M performance index parameter sets, wherein each performance index parameter set comprises K battery performance index parameters;
the degradation influence analysis module is used for carrying out battery degradation influence analysis on the K battery performance indexes to obtain K repair importance parameters;
the initial repair evaluation module is used for evaluating the initial repairability of the M performance index parameter sets to obtain M initial repairability sets;
the weighting calculation module is used for respectively carrying out weighting calculation on the data in the M initial repairable degree sets according to the K repairable importance parameters to obtain M comprehensive repairable degrees;
the repairability judging module is used for acquiring N battery units and N performance index parameter sets, wherein N is smaller than M, and N comprehensive repairability meets the preset repairability according to the M comprehensive repairability;
and the repair scheme matching module is used for matching the repair scheme based on the N performance index parameter sets and repairing the power battery according to the matched repair scheme.
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