CN117970159A

CN117970159A - Method, system and medium for evaluating availability of waste battery based on big data

Info

Publication number: CN117970159A
Application number: CN202410392490.8A
Authority: CN
Inventors: 邹康迪; 董杨; 申皓月; 谭添; 张志文; 周娅婷; 温玉柱; 陶青云
Original assignee: Shenzhen Shenshan Special Cooperation Zone Qiantai Technology Co ltd
Current assignee: Shenzhen Shenshan Special Cooperation Zone Qiantai Technology Co ltd
Priority date: 2024-04-02
Filing date: 2024-04-02
Publication date: 2024-05-03
Anticipated expiration: 2044-04-02
Also published as: CN117970159B

Abstract

The embodiment of the application provides a method, a system and a medium for evaluating the usability of waste batteries based on big data. The method comprises the following steps: obtaining appearance and characteristic information of retired battery cells, screening and classifying the battery cells, carrying out consistency detection according to performance test result data of single battery cells to obtain a compliant battery cell group, carrying out cycle performance detection and processing on a rebuilt battery pack to obtain a battery pack performance steady state evaluation index, carrying out echelon classification on the rebuilt battery pack according to a threshold comparison range to obtain working performance parameters of the rebuilt battery pack in a scene, processing to obtain a working performance data set, and then processing by combining a temperature difference change coefficient of the scene to obtain a performance health evaluation index, and carrying out evaluation acceptance on the scene working performance of the battery pack; therefore, the battery cells are subjected to consistency screening, grouping and performance echelon classification through the big data of the battery cells, and the performance effect of the battery pack in the echelon classification scene is evaluated, so that the usability evaluation of the waste batteries is realized.

Description

Method, system and medium for evaluating availability of waste battery based on big data

Technical Field

The application relates to the technical field of waste battery recovery, in particular to a waste battery availability evaluation method, a waste battery availability evaluation system and a waste battery availability evaluation medium based on big data.

Background

With the rapid development of new energy industry, the scale of waste power batteries is increased unprecedentedly, the recycling problem of waste batteries becomes more and more interesting, and currently, performance detection sorting and service life estimation are generally adopted for retired power batteries, and most of the retired power batteries adopt experience estimation guided by battery parameters, so that systematic, accurate and rapid estimation means are not provided, and methods for classifying, reorganizing and echelon using battery cells according to the battery performance parameters and checking the echelon utilization effect of the batteries are lacking, so that the technology for analyzing and checking the application method and effect of recycling the waste batteries cannot be formed at present.

In view of the above problems, an effective technical solution is currently needed.

Disclosure of Invention

The embodiment of the application aims to provide a method, a system and a medium for evaluating the availability of waste batteries based on big data, which can screen and group battery cells according to the big data of the battery cells and grade the battery cells in a gradient manner, evaluate the performance effect of a battery pack used in a gradient grading scene and realize the availability evaluation of the waste batteries.

The embodiment of the application also provides a method for evaluating the usability of the waste battery based on big data, which comprises the following steps:

Obtaining electric core appearance information and electric core characteristic information of the retired power battery electric core, comparing and screening the electric core meeting the preset comparison requirement according to the electric core appearance information, and classifying the electric core according to the electric core characteristic information to obtain a classified electric core;

Performing performance test on single cells in the classified cells to obtain performance test result data, performing consistency detection on the performance test result data and the performance standard data of the cells of the same type to obtain consistency detection indexes of the single cells, marking the cells with the consistency detection indexes not smaller than a preset consistency detection threshold value in the single cells as classified compliant cells, and performing cell re-grouping on the classified compliant cells according to a corresponding cell grouping and sorting method to obtain one or more re-assembled battery packs;

Performing cycle performance detection on the reprogrammed battery pack according to a preset performance detection method pack to obtain battery pack performance detection data;

Processing according to the battery performance detection data of the re-assembled battery to obtain a battery performance steady state evaluation index, comparing the battery performance steady state evaluation index with a preset battery performance gradient threshold value, grading the re-assembled battery in gradient according to the gradient grade corresponding to the threshold value comparison range, and obtaining a battery gradient grade;

performing working performance test on the reprogrammed battery pack in a plurality of adaptive application scenes corresponding to the echelon level of the battery pack to obtain working performance parameters of the battery pack, and processing and synthesizing the working performance parameters of the battery pack into a working performance data set of the battery pack according to the working performance parameters of the battery pack;

and according to the temperature difference change coefficients of the reprogrammed battery pack tested in the plurality of adaptation application scenes, processing the data of the battery pack working performance data set to obtain a battery pack performance health evaluation index, and comparing the battery pack performance evaluation index with a preset battery pack performance evaluation threshold value to evaluate the performance acceptance condition of the reprogrammed battery pack in the echelon adaptation application environment.

Optionally, in the method for evaluating availability of waste batteries based on big data according to the embodiment of the present application, the obtaining the appearance information and the characteristic information of the cells of the retired power battery, comparing and screening the cells meeting the preset comparison requirement according to the appearance information of the cells, and classifying the characteristics of the cells according to the characteristic information of the cells to obtain classified cells includes:

obtaining cell appearance information and cell characteristic information of a retired power battery cell;

The battery cell appearance information comprises a battery cell size, a battery cell identifier and a battery cell specification color code, and the battery cell characteristic information comprises a battery cell category, a battery cell specification and a battery cell performance parameter;

Comparing the appearance information of the battery cells with preset battery cell comparison information, and screening battery cells meeting preset comparison requirements;

and classifying the characteristics of the battery cells according to the characteristic information of the battery cells to obtain classified battery cells.

Optionally, in the big data based method for evaluating availability of waste batteries according to the embodiment of the present application, performing performance test on individual cells in the classified cells to obtain performance test result data, performing consistency detection with cell performance standard data of the same type of cells to obtain a consistency detection index of the individual cells, marking cells in the individual cells having a consistency detection index not smaller than a preset consistency detection threshold as classified compliant cells, and performing cell re-grouping on the classified compliant cells according to a corresponding cell grouping and sorting method to obtain one or more re-assembled battery packs, including:

Performing performance test on single battery cells in the classified battery cells according to a preset battery cell performance test method to obtain performance test result data;

the performance test result data comprises capacity attenuation data, voltage deviation data and internal resistance change data;

Acquiring cell performance standard data of cells of the same type as the classified cells, wherein the cell performance standard data comprises capacity attenuation standard data, voltage deviation standard data and internal resistance change standard data;

Performing consistency detection according to the cell performance standard data and the performance test result data of the classified cells to obtain a consistency detection index of the single cell;

Marking the battery cells corresponding to the consistency detection indexes in the single battery cells not smaller than a preset consistency detection threshold as classified compliant battery cells;

Obtaining a corresponding battery cell grouping and sorting method through inquiring a preset battery performance monitoring database according to the battery cell characteristic information of the classified battery cells;

and carrying out cell regrouping on the classified and compliant cells according to the cell grouping and sorting method to obtain one or more reprogrammed battery packs.

Optionally, in the method for evaluating availability of waste batteries based on big data according to the embodiment of the present application, the performing cycle performance detection on the reprogrammed battery pack according to a preset performance detection method packet to obtain battery pack performance detection data includes:

Acquiring a corresponding preset performance detection method package through a preset battery performance monitoring database according to the attribute information of the re-assembled battery, wherein the detection method package comprises cycle life detection, high-low temperature charge and discharge detection, short circuit detection and impact detection;

Respectively carrying out cycle performance detection on the reprogrammed battery pack according to the cycle life detection, the high-low temperature charge-discharge detection, the short circuit detection and the impact detection to obtain battery pack performance detection data;

The battery pack performance detection data comprise cycle life durability data, high-low temperature charge and discharge stability data, thermal runaway rate data and impact explosion rate data.

Optionally, in the method for evaluating availability of waste batteries based on big data according to the embodiment of the present application, the processing according to the battery performance detection data of the reprogrammed battery to obtain a battery performance steady state evaluation index, comparing the battery performance steady state evaluation index with a preset battery performance gradient threshold, grading the reprogrammed battery in gradient according to a corresponding gradient grade of a threshold comparison range, and obtaining a battery gradient grade includes:

Processing according to the cycle life durability data, the high-low temperature charge-discharge stability data, the thermal runaway rate data and the impact explosion rate data of the re-assembled battery through a preset battery performance steady-state evaluation model to obtain a battery performance steady-state evaluation index;

comparing the steady state evaluation index of the battery pack performance with a preset battery pack performance echelon threshold value to obtain a threshold value comparison result;

obtaining a corresponding echelon level according to the range of the threshold value comparison result, and grading the reprogrammed battery pack in a echelon manner according to the echelon level to obtain a battery pack echelon level;

The calculation formula of the battery performance steady state evaluation index is as follows:

；

Wherein, For the steady state evaluation index of the performance of the battery,/>For cycle life durability data,/>Is data of high-low temperature charge and discharge stability/>Is data of thermal runaway rate,/>For impact blasting rate data,/>、/>Is a preset characteristic coefficient.

Optionally, in the method for evaluating availability of waste batteries based on big data according to the embodiment of the present application, the performing the working performance test on the reprogrammed battery pack in a plurality of adaptive application scenarios corresponding to the battery pack echelon level to obtain a battery pack working performance parameter, and processing and synthesizing the battery pack working performance parameter into a battery pack working performance data set according to the battery pack working performance parameter includes:

Acquiring a plurality of adaptation application scenes corresponding to the echelon level of the battery pack according to the preset battery performance monitoring database;

Performing working performance test on the reprogrammed battery pack in the plurality of adaptive application scenes to obtain working performance parameters of the battery pack;

The working performance parameters of the battery pack comprise the number of use cycles, available discharge multiplying power, effective discharge depth, working voltage amplitude, average working voltage, charge attenuation rate, attenuation time and internal resistance degradation degree;

and processing according to the working performance parameters of the battery pack to respectively obtain a performance degradation index, a working voltage fluctuation index, a charge duration index and an internal resistance degradation failure index, and synthesizing the performance degradation index, the working voltage fluctuation index, the charge duration index and the internal resistance degradation failure index into a battery pack working performance data set.

Optionally, in the method for evaluating availability of waste batteries based on big data according to the embodiment of the present application, the processing is performed by combining data of the battery working performance data set according to a temperature difference change coefficient of the reprogrammed battery pack tested in the multiple adapting application scenarios to obtain a battery performance health evaluation index, and then the performance acceptance condition of the reprogrammed battery pack in the echelon adapting application environment is evaluated by comparing the battery performance health evaluation index with a preset battery performance evaluation threshold, including:

Acquiring temperature change data of the reprogrammed battery pack tested in the plurality of adaptive application scenes, and processing the plurality of temperature change data to obtain a temperature difference change coefficient;

Processing through a preset battery performance health assessment model according to the performance degradation index, the working voltage fluctuation index, the charge persistence index and the internal resistance degradation failure index and the temperature difference change coefficient to obtain a battery performance health assessment index;

comparing the performance health evaluation index of the battery pack with a preset performance evaluation threshold of the battery pack;

If the performance health evaluation index of the battery pack is not smaller than the preset battery pack performance evaluation threshold, the performance acceptance of the reprogrammed battery pack in the echelon adaptation application environment is passed;

if the performance health evaluation index of the battery pack is smaller than the preset battery pack performance evaluation threshold, the performance acceptance of the reprogrammed battery pack in the echelon adaptation application environment is not passed;

The calculation formula of the battery pack performance health evaluation index is as follows:

；

Wherein, For the performance health evaluation index of the battery pack,/>Is the index of performance decay,/>Is the fluctuation index of the working voltage,/>Is charge persistence index,/>Is the internal resistance deterioration failure index,/>Is the temperature difference change coefficient,/>、/>Is a preset characteristic coefficient.

In a second aspect, an embodiment of the present application provides a big data-based worn-out battery availability evaluation system, the system including: the system comprises a memory and a processor, wherein the memory comprises a program of a big data-based waste battery availability evaluation method, and the program of the big data-based waste battery availability evaluation method realizes the following steps when being executed by the processor:

Optionally, in the big data based waste battery availability evaluation system according to the embodiment of the present application, the obtaining the appearance information and the characteristic information of the cells of the retired power battery, comparing and screening the cells meeting the preset comparison requirement according to the appearance information of the cells, and classifying the characteristics of the cells according to the characteristic information of the cells to obtain classified cells includes:

In a third aspect, an embodiment of the present application further provides a computer readable storage medium, where the computer readable storage medium includes a big data based waste battery availability evaluation method program, where the big data based waste battery availability evaluation method program, when executed by a processor, implements the steps of the big data based waste battery availability evaluation method according to any one of the above.

As can be seen from the above, the method, the system and the medium for evaluating the availability of the waste battery based on big data provided by the embodiment of the application are characterized in that the appearance and the characteristic information of the retired battery cells are obtained to screen and classify the battery cells, the compliance battery cell grouping is obtained by carrying out consistency detection according to the performance test result data of a single battery cell, the circulating performance of the rebuilt battery pack is detected and processed to obtain the steady state evaluation index of the performance of the battery pack, the rebuilt battery pack is graded in a echelon manner according to the threshold comparison range to obtain the working performance parameter of the rebuilt battery pack in a scene and processed to obtain the working performance data set, and the performance health evaluation index is obtained by combining the temperature difference change coefficient of the scene to evaluate and accept the scene working performance of the battery pack; therefore, the battery cells are subjected to consistency screening, grouping and performance echelon classification through the big data of the battery cells, and the performance effect of the battery pack in the echelon classification scene is evaluated, so that the usability evaluation of the waste batteries is realized.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and drawings.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for evaluating the availability of waste batteries based on big data provided by an embodiment of the application;

fig. 2 is a flowchart of a method for evaluating availability of waste batteries based on big data to obtain classified battery cells according to an embodiment of the present application;

FIG. 3 is a flow chart of obtaining a reprogrammed battery pack according to the big data based method for evaluating the availability of used batteries provided by the embodiment of the present application;

Fig. 4 is a flowchart of obtaining battery performance detection data according to the big data-based method for evaluating availability of waste batteries according to an embodiment of the present application;

fig. 5 is a flowchart of a method for evaluating availability of waste batteries based on big data to obtain a gradient level of a battery pack according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.

It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.

Referring to fig. 1, fig. 1 is a flowchart of a method for evaluating availability of waste batteries based on big data according to some embodiments of the application. The method for evaluating the usability of the waste battery based on the big data is used in terminal equipment, such as a computer, a mobile phone terminal and the like. The method for evaluating the usability of the waste battery based on the big data comprises the following steps:

S11, obtaining cell appearance information and cell characteristic information of retired power battery cells, comparing and screening the cells meeting preset comparison requirements according to the cell appearance information, and classifying the cells according to the cell characteristic information to obtain classified cells;

S12, performing performance test on single cells in the classified cells to obtain performance test result data, performing consistency detection on the performance test result data and the performance standard data of the cells of the same type to obtain consistency detection indexes of the single cells, marking the cells with the consistency detection indexes not smaller than a preset consistency detection threshold value in the single cells as classified compliant cells, and performing cell re-grouping on the classified compliant cells according to a corresponding cell grouping and sorting method to obtain one or more re-assembled battery packs;

S13, performing cycle performance detection on the reprogrammed battery pack according to a preset performance detection method package to obtain battery pack performance detection data;

S14, processing according to the battery performance detection data of the reprogrammed battery to obtain a battery performance steady state evaluation index, comparing the battery performance steady state evaluation index with a preset battery performance gradient threshold value, grading the reprogrammed battery gradient according to the corresponding gradient grade of the threshold value comparison range, and obtaining a battery gradient grade;

S15, performing working performance test on the reprogrammed battery pack in a plurality of adaptive application scenes corresponding to the echelon level of the battery pack to obtain working performance parameters of the battery pack, and processing and synthesizing the working performance parameters of the battery pack into a working performance data set of the battery pack;

S16, according to the temperature difference change coefficients of the reprogrammed battery pack tested in the plurality of adaptation application scenes, processing the data of the battery pack working performance data set to obtain a battery pack performance health evaluation index, and comparing the battery pack performance health evaluation index with a preset battery pack performance evaluation threshold value to evaluate the performance acceptance condition of the reprogrammed battery pack in the echelon adaptation application environment.

The method comprises the steps of screening and classifying battery cells of a retired power battery to realize the evaluation of recombination and echelon application of the battery cells of the waste power battery, screening out the qualified battery cells according to the consistency detection of the test performance of the classified battery cells, regrouping the qualified battery cells, performing cyclic performance detection on the regrouped battery pack to obtain multiple performance detection data, performing performance steady-state evaluation according to the multiple performance detection data, correspondingly selecting the echelon grade of the battery pack according to a comparison range, performing working in an application scene corresponding to the echelon grade of the battery pack, performing performance test to obtain a data set of performance test, calculating according to the data of the performance data set and the temperature coefficient of the scene to obtain the performance health evaluation index of the battery pack in the echelon scene, and finally evaluating the working performance condition of the regrouped battery pack in the echelon application environment according to the threshold comparison result of the evaluation index to realize the echelon application evaluation and practical effectiveness verification of the recovered recombinant battery.

Referring to fig. 2, fig. 2 is a flowchart of a method for evaluating availability of waste batteries based on big data to obtain classified battery cells according to some embodiments of the application. According to the embodiment of the application, the battery core appearance information and the battery core characteristic information of the retired power battery core are obtained, the battery core meeting the preset comparison requirement is compared and screened according to the battery core appearance information, and the battery core is subjected to characteristic classification according to the battery core characteristic information to obtain the classified battery core, which is specifically as follows:

s21, obtaining cell appearance information and cell characteristic information of retired power battery cells;

s22, the appearance information of the battery cell comprises a battery cell size, a battery cell identifier and a battery cell specification color code, and the characteristic information of the battery cell comprises a battery cell type, a battery cell specification and a battery cell performance parameter;

S23, comparing the appearance information of the battery cell with preset battery cell comparison information, and screening battery cells meeting preset comparison requirements;

And S24, classifying the characteristics of the battery cells according to the characteristic information of the battery cells to obtain classified battery cells.

In order to realize recycling of the battery, firstly, the battery cells of the retired waste battery are screened and classified to primarily screen qualified battery cells and effectively classify the battery cells, the battery cells meeting the requirements are screened by comparing the information of the appearance size, the model identifier and the specification color code of the battery cells, and then the battery cells are classified according to the types, the specifications and the performance parameters of the battery cells, such as voltage, capacity, charge-discharge multiplying power and the like, so as to obtain the classified battery cells.

Referring to fig. 3, fig. 3 is a flowchart of a method for obtaining a reprogrammed battery pack based on big data for estimating the availability of a spent battery according to some embodiments of the application. According to the embodiment of the application, performance test is performed on single cells in the classified cells to obtain performance test result data, and consistency detection is performed on the performance test result data and the performance test result data of the cells of the same type with the performance test result data of the cells to obtain consistency detection indexes of the single cells, the cells with the consistency detection indexes not smaller than a preset consistency detection threshold value in the single cells are marked as classified compliant cells, and the classified compliant cells are subjected to cell re-grouping according to a corresponding cell grouping and sorting method to obtain one or more re-assembled battery packs, wherein the method comprises the following steps:

S31, performing performance test on single battery cells in the classified battery cells according to a preset battery cell performance test method to obtain performance test result data;

S32, the performance test result data comprise capacity attenuation data, voltage deviation data and internal resistance change data;

S33, acquiring cell performance standard data of cells of the same type as the classified cells, wherein the cell performance standard data comprises capacity attenuation standard data, voltage deviation standard data and internal resistance change standard data;

s34, carrying out consistency detection according to the cell performance standard data and the performance test result data of the classified cells to obtain a consistency detection index of the single cell;

s35, marking the battery cells corresponding to the consistency detection indexes which are not smaller than a preset consistency detection threshold value in the single battery cell as classified compliant battery cells;

S36, obtaining a corresponding battery cell grouping and sorting method through inquiring a preset battery performance monitoring database according to the battery cell characteristic information of the classified battery cells;

S37, carrying out cell regrouping on the classified and compliant cells according to the corresponding cell grouping and sorting method to obtain one or more re-assembled battery packs.

After screening out the cells of the compliance class, performing performance screening on the classified cells to remove the cells which do not meet the compliance, obtaining the cells with reliable performance through the compliance detection, using the cells for further cell grouping, obtaining a cell performance test result through the performance test of the single classified cells, wherein the cell performance test method is a performance test method which is obtained through presetting a battery performance monitoring database and is matched with the cells, the performance test result data comprises capacity attenuation data, voltage deviation data and internal resistance variation data of the cells, performing the compliance detection calculation by combining the standard data of the cell performance through a formula to obtain a compliance detection index, screening out the compliance cells according to the threshold comparison result of the compliance detection index, and performing grouping on the cells according to the corresponding cell grouping method queried by the database to obtain a re-grouping battery pack, thereby realizing the performance screening and re-grouping of the cells, wherein the calculation formula of the compliance detection index is as follows:

；

Wherein, For consistency detection index,/>、/>、/>Respectively capacity attenuation data, voltage deviation data and internal resistance change data,/>、/>、/>Respectively capacity attenuation standard data, voltage deviation standard data and internal resistance change standard data,/>、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained through the query of a preset battery performance monitoring database platform).

Referring to fig. 4, fig. 4 is a flowchart of a method for obtaining battery performance detection data according to a big data-based method for evaluating the availability of waste batteries according to some embodiments of the application. According to the embodiment of the application, the method for detecting the cycle performance of the reprogrammed battery pack according to the preset performance detection method comprises the following steps of:

S41, acquiring a corresponding preset performance detection method package through a preset battery performance monitoring database according to the attribute information of the re-assembled battery, wherein the detection method package comprises cycle life detection, high-low temperature charge and discharge detection, short circuit detection and impact detection;

s42, respectively carrying out cycle performance detection on the reprogrammed battery pack according to the cycle life detection, the high-low temperature charge-discharge detection, the short circuit detection and the impact detection to obtain battery pack performance detection data;

And S43, the battery pack performance detection data comprise cycle life durability data, high-low temperature charge and discharge stability data, thermal runaway rate data and impact explosion rate data.

The method comprises the steps of obtaining corresponding battery pack performance detection packages through inquiring a preset database according to attribute information such as power parameters, specification parameters, capacity setting and charge and discharge multiple setting of a re-assembled battery pack, wherein the battery pack performance detection packages comprise four detection methods, respectively carrying out cycle performance detection on the battery pack according to the detection methods to obtain performance detection data of the re-assembled battery pack, wherein in a link of carrying out the cycle performance detection on the re-assembled battery pack according to the performance detection methods, the parameters are processed on the basis that the obtained battery pack detection parameters comprise the current charge quantity, the total charge and discharge times, the total service time, the high-low temperature difference value, the available discharge multiple and the charge and discharge average efficiency of the battery pack to obtain cycle life durability data and high-low temperature charge and discharge stability data, and the thermal runaway rate data and the impact explosion rate data are obtained by carrying out short circuit test and impact explosion test on the re-assembled battery pack, wherein the calculation formulas of the cycle life durability data and the high-low temperature charge and discharge stability data are respectively:

；

Wherein, For cycle life durability data,/>Is data of high-low temperature charge and discharge stability/>、/>、/>Respectively the current charge quantity, the total charge and discharge times and the total service time of the battery pack,/>、/>、/>Respectively high and low temperature difference, available discharge multiple, average charge and discharge efficiency,/>、/>、/>、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained through the query of a preset battery performance monitoring database platform).

Referring to fig. 5, fig. 5 is a flowchart of a method for obtaining a gradient level of a battery pack according to a big data-based method for evaluating availability of a used battery according to some embodiments of the present application. According to the embodiment of the application, the battery performance steady state evaluation index is obtained by processing the battery performance detection data of the reprogrammed battery, and is compared with a preset battery performance echelon threshold value, and the reloaded battery is subjected to echelon classification according to the corresponding echelon level of the threshold value comparison range, so as to obtain the battery echelon level, which is specifically as follows:

S51, processing according to the cycle life durability data, the high-low temperature charge-discharge stability data, the thermal runaway rate data and the impact explosion rate data of the re-assembled battery through a preset battery performance steady-state evaluation model to obtain a battery performance steady-state evaluation index;

S52, comparing the battery pack performance steady state evaluation index with a preset battery pack performance echelon threshold value to obtain a threshold value comparison result;

S53, obtaining a corresponding echelon grade according to the range of the threshold comparison result, and grading the reprogrammed battery pack according to the echelon grade to obtain a battery pack echelon grade;

；

Wherein, For the steady state evaluation index of the performance of the battery,/>For cycle life durability data,/>Is data of high-low temperature charge and discharge stability/>Is data of thermal runaway rate,/>For impact blasting rate data,/>、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained through the query of a preset battery performance monitoring database platform).

After obtaining the performance test of the rebuilt battery pack, performing steady-state evaluation on the rebuilt battery pack according to the performance test result to obtain a grading of the battery pack for echelon application, namely grading the battery pack according to the evaluation result of the performance test data of the battery pack, calculating an obtained steady-state evaluation index of the battery pack performance according to a calculation formula of a steady-state evaluation model of the preset battery pack, comparing the obtained steady-state evaluation index of the battery pack performance with a echelon threshold, obtaining a corresponding echelon grade according to the threshold range of the evaluation index, namely realizing the echelon grading of the performance condition of the rebuilt battery pack, wherein the preset battery pack performance echelon threshold can be graded according to the echelon grading requirement of the category of the rebuilt battery pack, such as grading the preset battery pack performance echelon threshold grade into one to five grades, wherein the corresponding threshold range is [0,0.22 ], [0.22,0.4 ], [0.4,0.68 ], [0.68,0.83 ], [0.83,1.0], and if the threshold comparison result of the steady-state evaluation index of the battery pack performance of the rebuilt battery pack of a certain category is 0.63, the third grade range is obtained.

According to the embodiment of the invention, the method for testing the working performance of the reprogrammed battery pack in a plurality of adaptive application scenes corresponding to the echelon level of the battery pack, obtaining the working performance parameters of the battery pack, and processing and synthesizing the working performance parameters of the battery pack into the working performance data set of the battery pack according to the working performance parameters of the battery pack, specifically comprises the following steps:

In order to evaluate the practical effect of the reprogrammed battery pack in the application scenario of echelon grading, the reprogrammed battery pack is required to be put into the adaptation application scenario corresponding to the echelon grade for working application, then performance tests are carried out on the battery pack in the working and use process to obtain performance parameters, the performance parameters comprise the use cycle times, the practical available discharge multiplying power, the effective discharge depth, the fluctuation amplitude of working voltage, the working average voltage, the attenuation rate of charge, the total attenuation time and the degradation degree condition of the internal resistance of the battery pack, and then the working performance parameters are respectively calculated to obtain a performance degradation index, a working voltage fluctuation index, a charge duration index and an internal resistance degradation failure index, namely the failure evaluation index reflecting the performance degradation condition, the working voltage fluctuation condition, the charge duration effect and the internal resistance degradation of the reprogrammed battery pack, and the four indexes are synthesized into a battery pack working performance data set, wherein the calculation formulas of the performance degradation index, the working voltage fluctuation index, the charge duration index and the internal resistance degradation failure index are respectively:

；

Wherein, Is the index of performance decay,/>Is the fluctuation index of the working voltage,/>Is charge persistence index,/>Is the internal resistance deterioration failure index,/>、/>、/>The number of the using cycles, the available discharge multiplying power and the effective discharge depth are respectively/>、/>Respectively the working voltage amplitude and the average working voltage,/>、/>Respectively the charge attenuation rate and the attenuation time,/>Is the degradation degree of internal resistance,/>、/>、/>、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained through the query of a preset battery performance monitoring database platform).

According to the embodiment of the invention, according to the temperature difference change coefficients of the reprogrammed battery pack tested in the plurality of adaptation application scenes, the battery pack performance health evaluation index is obtained by processing the data of the battery pack working performance data set, and then the battery pack performance health evaluation index is compared with a preset battery pack performance evaluation threshold value, so as to evaluate the performance acceptance condition of the reprogrammed battery pack in the echelon adaptation application environment, specifically:

；

Wherein, For the performance health evaluation index of the battery pack,/>Is the index of performance decay,/>Is the fluctuation index of the working voltage,/>Is charge persistence index,/>Is the internal resistance deterioration failure index,/>Is the temperature difference change coefficient,/>、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained through the query of a preset battery performance monitoring database platform).

And finally, accurately evaluating the performance achievement of the re-assembled battery in the echelon grading application scene, wherein the temperature change condition of the battery in a plurality of adaptation application scenes is required to be considered and corrected to obtain an accurate performance evaluation result of the battery, the temperature difference change coefficient obtained by calculating according to the temperature change data of the plurality of scenes is combined with four indexes of the working performance data set of the battery, the calculation formula of the performance health evaluation model of the battery is preset to calculate, and the performance health evaluation index of the battery is obtained, namely the use evaluation result of the re-assembled battery in the echelon grading scene is obtained, and then the acceptance condition of the use performance of the re-assembled battery in the echelon adaptation application environment is judged according to the comparison condition of the evaluation index and a threshold value, wherein the calculation formula of the temperature difference change coefficient is as follows:

；

Wherein, Is the temperature difference change coefficient,/>For the temperature change data in the ith adaptive application scene, n is the number of adaptive application scenes,/>Overall temperature change data for all adaptation application scenarios,/>、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained through the query of a preset battery performance monitoring database platform).

The invention also discloses a big data-based waste battery availability evaluation system, which comprises a memory and a processor, wherein the memory comprises a big data-based waste battery availability evaluation method program, and the big data-based waste battery availability evaluation method program realizes the following steps when the processor executes the sign abnormal correction data:

According to the embodiment of the invention, the battery core appearance information and the battery core characteristic information of the retired power battery core are obtained, the battery core meeting the preset comparison requirement is compared and screened according to the battery core appearance information, and the battery core is subjected to characteristic classification according to the battery core characteristic information to obtain the classified battery core, which is specifically as follows:

According to the embodiment of the invention, performance test is performed on single cells in the classified cells to obtain performance test result data, and consistency detection is performed on the performance test result data and the performance test result data of the cells of the same type with the performance test result data of the cells to obtain consistency detection indexes of the single cells, the cells with the consistency detection indexes not smaller than a preset consistency detection threshold value in the single cells are marked as classified compliant cells, and the classified compliant cells are subjected to cell re-grouping according to a corresponding cell grouping and sorting method to obtain one or more re-assembled battery packs, wherein the method comprises the following steps:

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According to the embodiment of the invention, the method for detecting the cycle performance of the reprogrammed battery pack according to the preset performance detection method comprises the following steps of:

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According to the embodiment of the invention, the battery performance steady state evaluation index is obtained by processing the battery performance detection data of the reprogrammed battery, and is compared with a preset battery performance echelon threshold value, and the reloaded battery is subjected to echelon classification according to the corresponding echelon level of the threshold value comparison range, so as to obtain the battery echelon level, which is specifically as follows:

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A third aspect of the present invention provides a computer-readable storage medium having embodied therein a big data based waste battery availability evaluation method program which, when executed by a processor, implements the steps of the big data based waste battery availability evaluation method as described in any one of the above.

The invention discloses a big data-based waste battery availability evaluation method, a big data-based waste battery availability evaluation system and a big data-based waste battery availability evaluation medium, wherein appearance and characteristic information of retired battery cells are obtained for screening and classifying the battery cells, compliance battery cell grouping is obtained by consistency detection according to performance test result data of single battery cells, a re-assembled battery pack is subjected to cycle performance detection and treatment to obtain a battery pack performance steady state evaluation index, the re-assembled battery pack is subjected to echelon classification according to a threshold comparison range to obtain working performance parameters of the re-assembled battery pack in a scene and treatment to obtain a working performance data set, and then the working performance of the battery pack is evaluated and accepted by combining with a temperature difference change coefficient of the scene to obtain a performance health evaluation index; therefore, the battery cells are subjected to consistency screening, grouping and performance echelon classification through the big data of the battery cells, and the performance effect of the battery pack in the echelon classification scene is evaluated, so that the usability evaluation of the waste batteries is realized.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.

The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or optical disk, or the like, which can store program codes.

Or the above-described integrated units of the invention may be stored in a readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.

Claims

1. The method for evaluating the usability of the waste battery based on the big data is characterized by comprising the following steps:

2. The method for evaluating the availability of the waste battery based on big data according to claim 1, wherein the steps of obtaining the appearance information and the characteristic information of the cells of the retired power battery, comparing and screening the cells meeting the preset comparison requirement according to the appearance information of the cells, classifying the characteristics of the cells according to the characteristic information of the cells to obtain classified cells comprise:

3. The big data-based waste battery availability evaluation method according to claim 2, wherein the performance test is performed on the single battery cells in the classified battery cells to obtain performance test result data, and then consistency detection is performed on the performance test result data and the battery cell performance standard data of the same type of battery cells to obtain a consistency detection index of the single battery cells, the battery cells with the consistency detection index not smaller than a preset consistency detection threshold value in the single battery cells are marked as classified compliance battery cells, and the classified compliance battery cells are subjected to battery cell re-grouping according to a corresponding battery cell grouping and sorting method to obtain one or more re-assembled battery packs, and the method comprises the following steps:

4. The big data based waste battery availability assessment method of claim 3, wherein the performing the cycle performance detection on the reprogrammed battery pack according to a preset performance detection method package, to obtain battery pack performance detection data, comprises:

5. The method for evaluating the availability of waste batteries based on big data according to claim 4, wherein the step of processing the battery performance detection data of the reprogrammed battery to obtain a battery performance steady state evaluation index, comparing the battery performance steady state evaluation index with a preset battery performance step threshold, and grading the reloaded battery according to the corresponding step level of the threshold comparison range to obtain a battery step level comprises the steps of:

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6. The big data-based waste battery availability evaluation method according to claim 5, wherein the performing the working performance test on the reprogrammed battery in the plurality of adaptation application scenarios corresponding to the battery echelon level to obtain the battery working performance parameter, and processing and synthesizing the battery working performance parameter into the battery working performance data set according to the battery working performance parameter comprises:

7. The big data-based waste battery availability assessment method according to claim 6, wherein the step of performing processing according to the temperature difference change coefficients of the reprogrammed battery pack tested in the plurality of adaptation application scenarios in combination with the data of the battery pack working performance data set to obtain a battery pack performance health assessment index, and performing threshold comparison with a preset battery pack performance assessment threshold value to assess the performance acceptance condition of the reprogrammed battery pack in the echelon adaptation application environment comprises:

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Wherein, For the performance health evaluation index of the battery pack,/>Is the index of performance decay,/>In order to be an index of the fluctuation of the operating voltage,Is charge persistence index,/>Is the internal resistance deterioration failure index,/>Is the temperature difference change coefficient,/>、/>Is a preset characteristic coefficient.

8. Waste battery availability evaluation system based on big data, characterized in that it comprises: the system comprises a memory and a processor, wherein the memory comprises a program of a big data-based waste battery availability evaluation method, and the program of the big data-based waste battery availability evaluation method realizes the following steps when being executed by the processor:

9. The big data-based junk battery availability evaluation system of claim 8, wherein the obtaining the appearance information and the characteristic information of the cells of the retired power battery, comparing and screening the cells meeting the preset comparison requirement according to the appearance information of the cells, classifying the cells according to the characteristic information of the cells to obtain classified cells, comprises:

10. A computer-readable storage medium, characterized in that the computer-readable storage medium includes therein a big data based waste battery availability evaluation method program, which when executed by a processor, implements the steps of the big data based waste battery availability evaluation method according to any one of claims 1 to 7.