Reverse tuning battery cell grouping method and system based on electrical logging big data

Abstract

The invention provides a reverse tuning battery cell matching method and system based on electrical logging big data. The method comprises the following steps: S1, determining sample extraction parameters; extracting to obtain applicable parameters; an algorithm model library is built, a BS system is adopted to process applicable parameters, and a graph is obtained and output according to the discharge efficiency and the discharge platform voltage calculation result; extracting to obtain sample minimum discharge efficiency and minimum platform voltage data, and processing the sample minimum discharge efficiency and the minimum platform voltage data to obtain target matching capacity; according to the target matching capacity, the minimum platform voltage data and the sample minimum discharge efficiency, processing to obtain an applicable matching capacity, performing reverse tuning on the original matching capacity, outputting and applying the adjusted matching parameters to the battery pack; and according to the applicable matching capacity, screening out an optimal matching battery cell capacity value, and optimizing a battery cell capacity grading process for the capacity grading equipment. According to the method, the technical problems that the optimization is incomplete, the data application and analysis means are limited, and a continuous optimization battery cell matching process supported by an electrical measurement result is lacked in the existing matching technology are solved.

Description

Technical Field

[0001] The present invention relates to the field of electric vehicle energy storage systems, and in particular to a method and system for reverse tuning of battery cell grouping based on electrical measurement big data. Background Art

[0002] With the rapid development of electric vehicles, energy storage systems and other fields, the market demand for lithium battery modules is increasing. However, as a key link in the production of lithium battery modules, the assembly process of battery cells directly affects the quality, performance and safety of the modules. At present, the lithium battery industry faces many challenges in the battery cell assembly process, such as the lack of assembly standards and the difficulty in intercepting defective products in advance.

[0003] The existing invention patent application document "A method for improving the grouping rate of battery cells" with publication number CN114649558A, the existing method includes: classifying battery cell parameters into mandatory parameters and flexible parameters; and dividing the battery cells into gears according to the mandatory parameters, and setting the flexible parameter range; dividing the total gears according to the gears of the mandatory parameters, the total gears are taken out and separated separately; according to the battery cell list under each total gear, the flexible parameter range in each total gear, that is, the total parameter range, is calculated; the total parameter range refers to the difference between the minimum and maximum values ​​of the flexible parameters in the battery cells under the total gear; for each total gear, the ratio of the total parameter range to the grouping range is obtained, and the ratios are sorted from large to small to obtain B1-B2,...Bn; for the battery cells under each total gear, the grouping is performed in sequence according to the sorting parameters. However, the aforementioned existing solutions improve the grouping rate through the battery cell gear parameters, but lack in-depth analysis based on battery pack electrical measurement data, which limits the optimization and adjustment of the battery cell gear parameters, and the grouping utilization rate needs to be further improved.

[0004] The existing invention patent application document "A method for improving the grouping rate of battery cells" with publication number CN117175034A, the existing method includes: determining the number and requirements of battery cells for grouping according to capacity, voltage, and internal group; dividing the battery cells into M rows and N columns, and selecting one of the battery cells as the basis for grouping selection, and determining the battery cell standards that meet the requirements; the battery cells enter the grouping machine for scanning, the grouping machine extracts the battery cell parameters, and marks the battery cells that meet the requirements; when the number of marked battery cells meets the number of grouping, the battery cells that meet the requirements are selected for overall grouping; the subsequent battery cells fill the vacancies of the selected battery cells in turn, and the last battery cell is supplemented; repeat the above steps until all the battery cells that meet the requirements are selected for grouping. However, the above existing scheme is to change the battery cell grouping logic, from the conventional fixed parameter grading and same-grade grouping, to selecting other battery cells by laying out the parameters of one battery cell. Although efficiency has been improved by changing the pairing logic, the analysis is based only on single-cell data and lacks a closed-loop optimization mechanism guided by module electrical measurement data, which limits further improvements in module consistency and reliability.

[0005] In summary, the existing battery cell matching technology has technical problems such as imperfect optimization, limited data application and analysis methods, and lack of continuous optimization of the battery cell matching process based on electrical measurement results. Summary of the invention

[0006] The technical problem to be solved by the present invention is: how to solve the technical problems of imperfect optimization of existing grouping technology, limited data application and analysis methods, and lack of continuous optimization of battery cell grouping process supported by electrical measurement results.

[0007] The present invention adopts the following technical solutions to solve the above technical problems: a method for reverse tuning of battery cell grouping based on electrical measurement big data includes:

[0008] S1. Determine sample extraction parameters, where the sample extraction parameters include: battery cell parameters, assembly process parameters, and battery pack performance evaluation data;

[0009] S2. Extract sample extraction parameters from the MES system and the electrical performance big data system, clean and store the sample extraction parameters, and obtain applicable parameters;

[0010] S3. Build an algorithm model library, use the BS system, process applicable parameters with preset logic, obtain and store the calculation results of discharge efficiency and discharge platform voltage, and output graphics according to the calculation results of discharge efficiency and discharge platform voltage;

[0011] S4, extracting the calculation results of discharge efficiency and discharge platform voltage to obtain sample minimum discharge efficiency and minimum platform voltage data, and processing them to obtain the target group capacity;

[0012] S5. According to the target group capacity, the minimum platform voltage data and the minimum discharge efficiency of the sample, the applicable group capacity is obtained, the original group capacity is reversely tuned, and the adjusted group parameters are output and applied to the battery pack;

[0013] S6. According to the applicable matching capacity, select the optimal matching cell capacity value, and optimize the cell capacity division process for the capacity division equipment accordingly.

[0014] The present invention applies electrical measurement big data to the optimization of the battery cell matching process for the first time, and realizes the precise adjustment of the battery cell matching parameters through the reverse tuning method. This method breaks through the limitations of the traditional matching process and improves the matching accuracy and efficiency. The present invention reversely optimizes the battery cell matching process through big data analysis, improves the consistency and reliability of the module, reduces the defective rate, and meets market demand.

[0015] In a more specific technical solution, in S3, the preset logic in the algorithm model library includes: interval maximum value divide-and-conquer algorithm, standard deviation calculation method, normal distribution algorithm, discharge efficiency algorithm, discharge platform voltage algorithm, theoretical group capacity algorithm and battery cell utilization algorithm.

[0016] The present invention builds an algorithm model library and uses the algorithm therein to process and analyze data, thereby realizing in-depth mining of potential problems and improvement space in the battery cell assembly process. This data analysis method based on the algorithm model improves the accuracy and efficiency of data analysis and provides strong support for process optimization.

[0017] In a more specific technical solution, the interval maximum value divide-and-conquer algorithm is used to take the maximum value of the capacity value corresponding to the number of strings of each battery pack to form a maximum value set:

[0018] {MAX[A11,A22,A33,...,Aii],MAX[B11,B22,B33,...,Bii]...}

[0019] Using the following interval minimum calculation logic, take the minimum value of the capacity value corresponding to the number of strings of each battery pack to form a minimum value set:

[0020] {MIN[A11,A22,A33,...,Aii],MAX[B11,B22,B33,...,Bii]...}

[0021] Use the following interval average calculation logic to take the average capacity value of each battery pack corresponding to the number of strings:

[0022]

[0023] In the formula, the subscript U represents the number of modules, n represents the number of cells in each module, and the subscript X represents the number and position of cells in the module. i Represents the indicator value of the battery cell under this module, U n Represents the average value of all battery cells in this module;

[0024] According to the maximum value set and the minimum value set, the extreme value average value set is obtained:

[0025] {AVG[A11,A22,A33,...,Aii],AVG[B11,B22,B33,...,Bii]...}.

[0026] In a more specific technical solution, in the standard deviation calculation method, n cells are taken to calculate the overall standard deviation δ, the sample standard deviation S, and the standard error δ n :

[0027]

[0028]

[0029]

[0030] In the formula, X i Represents the capacity value of each battery cell. Where μ is the sum of all capacity values ​​divided by the number of cells n.

[0031] In a more specific technical solution, in the normal distribution algorithm, the following logic is used to perform normal distribution analysis:

[0032] Y=(X-μ) / σ~N(0,1)

[0033] Where N is the sample size.

[0034] In a more specific technical solution, in S4, the following logic is used to process and obtain the minimum discharge efficiency of the sample:

[0035] b Min =Min{β1,β2,...,βn}

[0036] In the formula, the discharge efficiency Among them, the discharge capacity C 容 Equal to the discharge capacity of a single battery pack, the minimum pack capacity is C Min =Min{α1,α2,...,α S}, {α1,α2,...,α S} indicates that a single battery contains a collection of S strings of cell capacities.

[0037] In a more specific technical solution, in S4, the following logic is used to process and obtain the minimum platform voltage data:

[0038]

[0039] Where N represents the discharge energy of a certain x battery pack, C 容 is the discharge capacity of the x battery pack, and S is the number of x battery packs in series.

[0040] In a more specific technical solution, in S5, the following logic is used to obtain the theoretical group capacity:

[0041]

[0042] Where X is the target value: theoretical group capacity, C 目 is the battery pack capacity value, including product requirements, b Min Indicates the minimum discharge efficiency, V Min Indicates the minimum platform voltage;

[0043] According to the theoretical matching capacity, use the following logic to determine the applicable matching capacity X:

[0044] X = single string capacity = battery pack capacity / minimum discharge efficiency b Min / Minimum platform voltage V Min .

[0045] The present invention continuously adjusts the matching parameters, improves the process flow, and adjusts the cell gear parameters according to the data analysis results, thus forming a closed-loop optimization mechanism. This mechanism can continuously optimize and improve the matching process according to the actual situation in the production process, thus ensuring the continuity and stability of the matching process.

[0046] In a more specific technical solution, in S6, the following logic is used to screen and obtain the optimal battery cell capacity value C 目标值 :

[0047] C 目标值 ={A[C A1 ,C A2 ,...,C An ],B[C B1 ,C B2 ,...,C Bn ],C[C C1 ,C C2 ,...,C Cn ],...,N[C N1 ,C N2 ,...,C Nn ]};

[0048] By presetting the IoT platform, the optimal battery cell capacity value C目标值 Feedback to the capacity distribution equipment.

[0049] In a more specific technical solution, the reverse optimization of the battery cell matching system based on the electrical measurement big data includes:

[0050] The sample parameter acquisition module is used to determine the sample extraction parameters, where the sample extraction parameters include: battery cell parameters, assembly process parameters and battery pack performance evaluation data;

[0051] The sample data extraction module is used to extract sample extraction parameters from the MES system and the electrical performance big data system, clean and store the sample extraction parameters, and obtain applicable parameters. The sample data extraction module is connected to the sample parameter acquisition module;

[0052] The algorithm model setting and data processing module is used to build an algorithm model library, use the BS system, process applicable parameters with preset logic, obtain and store the calculation results of discharge efficiency and discharge platform voltage, and output graphics according to the calculation results of discharge efficiency and discharge platform voltage. The algorithm model setting and data processing module is connected with the sample data extraction module;

[0053] The target capacity acquisition module is used to extract the calculation results of the discharge efficiency and the discharge platform voltage to obtain the sample minimum discharge efficiency and the minimum platform voltage data, and process them to obtain the target group capacity. The target capacity acquisition module is connected with the algorithm model setting and data processing module;

[0054] The applicable flow acquisition module is used to process and obtain the applicable matching capacity according to the target matching capacity, the minimum platform voltage data and the minimum discharge efficiency of the sample, reversely optimize the original matching capacity, output and adjust the matching parameters for the battery pack, and connect the applicable flow acquisition module with the target capacity acquisition module, the algorithm model setting and the data processing module;

[0055] The capacity division process optimization module is used to screen out the optimal matching cell capacity value according to the applicable matching capacity, and optimize the cell capacity division process for the capacity division equipment accordingly. The capacity division process optimization module is connected to the applicable flow acquisition module.

[0056] Compared with the prior art, the present invention has the following advantages:

[0057] The present invention applies electrical measurement big data to the optimization of the battery cell matching process for the first time, and realizes the precise adjustment of the battery cell matching parameters through the reverse tuning method. This method breaks through the limitations of the traditional matching process and improves the matching accuracy and efficiency. The present invention reversely optimizes the battery cell matching process through big data analysis, improves the consistency and reliability of the module, reduces the defective rate, and meets market demand.

[0058] The present invention builds an algorithm model library and uses the algorithm therein to process and analyze data, thereby realizing in-depth mining of potential problems and improvement space in the battery cell assembly process. This data analysis method based on the algorithm model improves the accuracy and efficiency of data analysis and provides strong support for process optimization.

[0059] The present invention continuously adjusts the matching parameters, improves the process flow, and adjusts the cell gear parameters according to the data analysis results, thus forming a closed-loop optimization mechanism. This mechanism can continuously optimize and improve the matching process according to the actual situation in the production process, thus ensuring the continuity and stability of the matching process.

[0060] The present invention solves the technical problems of the existing grouping technology, namely, imperfect optimization, limited data application and analysis means, and lack of continuous optimization of the battery cell grouping process supported by electrical measurement results. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a schematic diagram of the basic steps of the method for reverse tuning of battery cell grouping based on electrical measurement big data according to Embodiment 1 of the present invention;

[0062] 3 is a schematic diagram of normal distribution of cell capacity during a specific analysis of normal distribution according to Example 1 of the present invention;

[0063] Figure 3a This is a schematic diagram of the first analysis of extracting key data trends according to Example 1 of the present invention;

[0064] Figure 3b This is a second analysis diagram of extracting key data trends according to Example 1 of the present invention;

[0065] Figure 4 This is a schematic diagram of data flow processing of the algorithm model library of Example 1 of the present invention;

[0066] Figure 5 This is a schematic diagram of a battery pack assembly according to Embodiment 1 of the present invention;

[0067] Figure 6 Schematic diagram of calculation of battery cell utilization rate according to Example 1 of the present invention. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] Example 1

[0070] like Figure 1 As shown in the figure, the reverse optimization method of battery cell matching based on electric measurement big data includes the following basic steps:

[0071] S1. Determine the sample extraction parameters, collect various parameters of the battery cells, process parameters during assembly, and battery pack performance evaluation data;

[0072] In this embodiment, as shown in Table 1, the specific analysis process of data extraction is: take the capacity values ​​[A11, A22, A33, ...,] corresponding to the battery pack codes A, B, C, ... of the assembly process and the string numbers 1, 2, 3, ..., i.

[0073] Table 1 Data extraction original table distribution example

[0074]

[0075]

[0076] S2. Data extraction: extract sample parameters from the MES system and the electrical performance big data system, and clean and store the extracted data;

[0077] In this embodiment, data extraction is performed to extract sample parameters from the MES system and the electrical performance big data system, and the extracted data is cleaned and stored. Select a suitable storage method to store the extracted data, including but not limited to: database, file system, to facilitate data management and use. In this embodiment, the sample parameters include but are not limited to: number of series, number of parallel, cell capacity, battery pack capacity, discharge capacity and discharge energy.

[0078] S3, build an algorithm model library, use the BS system to process the algorithm, store the calculation results of discharge efficiency and discharge platform voltage, and output graphics;

[0079] In this embodiment, the algorithm model library includes but is not limited to: data interval maximum value divide and conquer algorithm, interval maximum value divide and conquer algorithm, standard deviation calculation, normal distribution algorithm, discharge efficiency algorithm, discharge platform voltage algorithm, theoretical group capacity algorithm and battery cell utilization algorithm. For example, the BS system can be used for algorithm processing, and the calculation results of discharge efficiency and discharge platform voltage can be stored and output as graphs.

[0080] In this embodiment, during the specific analysis process of calculating the interval maximum value, the maximum value of the capacity value corresponding to the number of strings of each battery pack is taken to form a set:

[0081] {MAX[A11,A22,A33,...,Aii],MAX[B11,B22,B33,...,Bii]...}

[0082] In this embodiment, as shown in Table 1 above, during the specific analysis process of interval minimum value calculation, the minimum value of the capacity value corresponding to the number of strings of each battery pack is taken to form a set:

[0083] {MIN[A11,A22,A33,...,Aii],MAX[B11,B22,B33,...,Bii]...}

[0084] In this embodiment, as shown in Table 1 above, during the specific analysis process of calculating the interval average value, the average value of the capacity value corresponding to the number of strings of each battery pack is taken:

[0085]

[0086] The subscript U represents the number of modules, n represents the number of cells in each module, and the subscript X represents the number and position of cells in the module. i Represents the indicator value of the battery cell under this module, U n Represents the average value of all battery cells under this module), forming a set:

[0087] {AVG[A11, A22, A33,...,Aii],AVG[B11,B22,B33,...,Bii]...}

[0088] In this embodiment, in the specific analysis process of standard deviation calculation, n cells are taken, X i Represents the capacity value of each battery cell. μ is the sum of all capacity values ​​divided by the number of cells n. The standard deviation is calculated as follows:

[0089] Population standard deviation:

[0090] Sample standard deviation:

[0091] Standard error:

[0092] like Figure 2 As shown, in this embodiment, the specific analysis process of normal distribution is:

[0093] Y=(X-μ) / σ~N(0,1)

[0094] Wherein, N is the sample capacity, the mean is the sum of the sample capacities / sample capacity value N. Specifically, the horizontal axis is the capacity value contained in all battery cell capacity samples, and the vertical axis is the percentage of the number of each capacity in the total number.

[0095] like Figure 3aAs shown, in this embodiment, the MES system extracts the capacity values ​​of multiple strings of cells in a single battery pack to form a battery pack capacity value data set, calculates the maximum value, minimum value, and average value, stores the data, and makes a chart. Figure 3b As shown, in this embodiment, the electrical performance big data analysis system is used to extract the discharge capacity and discharge energy data actually measured by electrical measurement, and the minimum matching capacity of a single package to form a data set, calculate the maximum value, minimum value, and average value, and store the data and make a chart.

[0096] S4, extracting the result data in the above step S3, extracting the sample minimum discharge efficiency and minimum platform voltage data, and then obtaining the optimal target group capacity according to the algorithm;

[0097] In this embodiment, during the minimum discharge efficiency analysis, the minimum discharge efficiency is:

[0098] b Min =Min{β1,β2,...,βn}

[0099] Among them, the discharge efficiency Discharge capacity C 容 = Single battery pack discharge capacity, minimum pack capacity C Min =Min{α1,α2,...,α S}, {α1,α2,...,α S} indicates that a single battery contains a collection of S strings of cell capacities.

[0100] The specific analysis process of the minimum platform voltage is as follows: Minimum platform voltage V Min =Min{V1,V2,...,Vn}, N: discharge energy of a certain x battery pack, C 容 : discharge capacity of a certain x battery pack, S: number of strings of a certain x battery pack.

[0101] like Figure 4 As shown, the specific analysis process of the minimum platform voltage is as follows: take the discharge capacity, discharge energy, and number of strings of battery packs {A1, A2, ..., An} in the multi-pack set B, the platform voltage Vn of pack An = (pack An) discharge energy / (pack An) discharge capacity / (pack An) number of strings, and form a multi-pack set {V1, V2, ..., Vn} from the platform voltages of n packs in set B. The minimum platform voltage V Min =Min{V1,V2,...,Vn}.

[0102] like Figure 4 As shown, in this embodiment, during the specific analysis process of the minimum discharge efficiency calculation, the set A contains S series of cells forming a capacity set {α1, α2, ..., α S}, the minimum matching capacity value of set A CMin=Min{α1,α2,...,α S}, single pack set A discharge efficiency β = discharge capacity / C Min , the discharge efficiency of multiple packets forms a set B, and the minimum discharge efficiency of set B is b Min =Min{β1,β2,...,βn}.

[0103] In this embodiment, Figure 4 As shown, the specific analysis process of the minimum platform voltage is as follows: take the discharge capacity, discharge energy, and number of strings of battery packs {A1, A2, ..., An} in the multi-pack set B, the platform voltage Vn of pack An = (pack An) discharge energy / (pack An) discharge capacity / (pack An) number of strings, and form a multi-pack set {V1, V2, ..., Vn} from the platform voltages of n packs in set B. The minimum platform voltage V Min =Min{V1,V2,...,Vn}.

[0104] S5. Reversely adjust the original group capacity according to the target group capacity;

[0105] In this embodiment, the theoretical matching capacity algorithm analysis process is:

[0106]

[0107] Where X is the target value: theoretical group capacity, C 目 is the battery pack capacity value, including product requirements, b Min Indicates the minimum discharge efficiency, V Min Indicates the minimum platform voltage.

[0108] In this embodiment, the original matching capacity is reversely optimized according to the target matching capacity, the matching parameters are adjusted and the process flow is improved to form a closed-loop optimization mechanism.

[0109] like Figure 5 As shown, X (target value) is the optimal group capacity, X (target value) = single string group capacity = battery pack capacity value (product requirements) / minimum discharge efficiency b Min / Minimum platform voltage V Min According to the matching process, the Sth string of the module X (target value) = 150Ah has P = 3 groups of cells connected in parallel, and the capacity of a single cell is C 单 =X(target value) / P,C 单 =150Ah / 3=50Ah, the current module S string uses a cell capacity of C 单 =50Ah is the best for parallel connection.

[0110] S6. Optimize the cell capacity division process according to the target group capacity to improve the cell utilization rate.

[0111] In this embodiment, during the specific analysis of the battery cell utilization, the battery cell utilization Y=n1 / N, where N is the sample size and n1 is the number of optimized values.

[0112] According to the above steps, filter out the optimal cell capacity value C for {A, B, C...N} product types 目标值 , Through the Internet of Things platform, C 目标值 Feedback to the capacity division device, the capacity division device according to the current C 目标值 The capacity is divided according to the value, which greatly improves the utilization rate of the battery cells.

[0113] like Figure 6 As shown, in this embodiment, the battery utilization rate of the battery cells before grouping in different projects is predicted: sample size N, the number of single battery cells in project 1 with a capacity ≥ the optimized value is n1, and the battery utilization rate = n1 / N. Sample size N, the number of single battery cells in project 2 with a capacity ≥ the optimized value is n1, and the battery utilization rate = n2 / N.

[0114] The present invention applies electrical measurement big data to the optimization of the battery cell grouping process for the first time, and uses electrical measurement data analysis to reversely support the continuous optimization of the battery cell grouping process, thereby achieving precise adjustment of the battery cell grouping process and reducing fluctuations and uncertainties in production. This method breaks through the limitations of traditional grouping processes, improves grouping accuracy and efficiency, thereby improving the overall product quality and enhancing market competitiveness.

[0115] In summary, the present invention applies electrical measurement big data to the optimization of the battery cell matching process for the first time, and realizes the precise adjustment of the battery cell matching parameters through the reverse tuning method. This method breaks through the limitations of the traditional matching process and improves the matching accuracy and efficiency. The present invention reversely optimizes the battery cell matching process through big data analysis, improves the consistency and reliability of the module, reduces the defective rate, and meets market demand.

[0116] The present invention builds an algorithm model library and uses the algorithm therein to process and analyze data, thereby realizing in-depth mining of potential problems and improvement space in the battery cell assembly process. This data analysis method based on the algorithm model improves the accuracy and efficiency of data analysis and provides strong support for process optimization.

[0117] The present invention continuously adjusts the matching parameters, improves the process flow, and adjusts the cell gear parameters according to the data analysis results, thus forming a closed-loop optimization mechanism. This mechanism can continuously optimize and improve the matching process according to the actual situation in the production process, thus ensuring the continuity and stability of the matching process.

[0118] The present invention solves the technical problems of the existing grouping technology, namely, imperfect optimization, limited data application and analysis means, and lack of continuous optimization of the battery cell grouping process supported by electrical measurement results.

[0119] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for reverse tuning of battery cell grouping based on electrical measurement big data, characterized in that: The method comprises: S1. Determine sample extraction parameters, wherein the sample extraction parameters include: battery cell parameters, assembly process parameters, and battery pack performance evaluation data; S2. Extract sample extraction parameters from the MES system and the electrical performance big data system, clean and store the sample extraction parameters, and obtain applicable parameters; S3, building an algorithm model library, using the BS system, processing the applicable parameters with preset logic, obtaining and storing the calculation results of the discharge efficiency and the discharge platform voltage, and outputting a graph according to the calculation results of the discharge efficiency and the discharge platform voltage; S4, extracting the calculation results of the discharge efficiency and the discharge platform voltage to obtain sample minimum discharge efficiency and minimum platform voltage data, and processing them to obtain the target group capacity; S5. According to the target matching capacity, the minimum platform voltage data and the sample minimum discharge efficiency, obtain the applicable matching capacity, reversely optimize the original matching capacity, output and apply the adjusted matching parameters to the battery pack; S6. Filter out the optimal cell capacity value according to the applicable group capacity, and optimize the cell capacity division process for the capacity division equipment accordingly.

2. The method for reverse tuning battery cell grouping based on electrical measurement big data according to claim 1 is characterized in that: In S3, the preset logic in the algorithm model library includes: interval maximum value divide-and-conquer algorithm, standard deviation calculation method, normal distribution algorithm, discharge efficiency algorithm, discharge platform voltage algorithm, theoretical group capacity algorithm and battery cell utilization algorithm.

3. The method for reverse tuning battery cell grouping based on electrical measurement big data according to claim 2 is characterized in that: Using the interval maximum value divide-and-conquer algorithm, take the maximum value of the capacity value corresponding to the number of strings of each battery pack to form a maximum value set: {MAX[A11,A22,A33,...,Aii],MAX[B11,B22,B33,...,Bii]...} Using the following interval minimum value calculation logic, take the minimum value of the capacity value corresponding to the number of strings of each battery pack to form a minimum value set: {MIN[A11,A22,A33,...,Aii],MAX[B11,B22,B33,...,Bii]...} The following interval average value calculation logic is used to take the average value of the capacity value of each battery pack corresponding to the number of strings: In the formula, the subscript U represents the number of modules, n represents the number of cells in each module, and the subscript X represents the number and position of cells in the module. i Represents the indicator value of the battery cell under this module, U n Represents the average value of all battery cells in this module; According to the maximum value set and the minimum value set, the extreme value average value set is obtained by processing: {AVG[A11,A22,A33,...,Aii],AVG[B11,B22,B33,...,Bii]...}.

4. The method for reverse tuning battery cell grouping based on electrical measurement big data according to claim 2, characterized in that: In the standard deviation calculation method, n cells are taken to calculate the overall standard deviation δ, sample standard deviation S, and standard error δ n : Where, X i Represents the capacity value of each battery cell. Where μ is the sum of all capacity values ​​divided by the number of cells n.

5. The method for reverse tuning battery cell grouping based on electrical measurement big data according to claim 2, characterized in that: In the normal distribution algorithm, the following logic is used to perform normal distribution analysis: Y=(X-μ) / σ~N(0,1) Where N is the sample size.

6. The method for reverse tuning battery cell grouping based on electrical measurement big data according to claim 1, characterized in that: In S4, the following logic is used to process and obtain the minimum discharge efficiency of the sample: b Min =Min{β1,β2,...,βn} In the formula, the discharge efficiency Among them, the discharge capacity C 容 Equal to the discharge capacity of a single battery pack, the minimum pack capacity is C Min =Min{α1,α2,...,α S }, {α1,α2,...,α S } indicates that a single battery contains a collection of S strings of cell capacities.

7. The method for reverse tuning battery cell grouping based on electrical measurement big data according to claim 1, characterized in that: In S4, the minimum platform voltage data is obtained by processing using the following logic: V Min =Min{V1,V2,...,Vn}, Where N represents the discharge energy of a certain x battery pack, C 容 is the discharge capacity of the x battery pack, and S is the number of x battery packs in series.

8. The method for reverse tuning battery cell grouping based on electrical measurement big data according to claim 1, characterized in that: In S5, the following logic is used to obtain the theoretical group capacity: Where X is the target value: theoretical group capacity, C 目 is the battery pack capacity value, including product requirements, b Min Indicates the minimum discharge efficiency, V Min Indicates the minimum platform voltage; According to the theoretical matching capacity, the applicable matching capacity X is determined using the following logic: X = single string capacity = battery pack capacity / minimum discharge efficiency b Min / Minimum platform voltage V Min .

9. The method for reverse tuning battery cell grouping based on electrical measurement big data according to claim 1, characterized in that: In S6, the following logic is used to screen and obtain the optimal battery cell capacity value C 目标值 : C 目标值 ={A[C A1 ,C A2 ,...,C An ],B[C B1 ,C B2 ,...,C Bn ],C[C C1 ,C C2 ,...,C Cn ],...,N[C N1 ,C N2 ,...,C Nn ]}; By presetting the IoT platform, the optimal battery cell capacity value C 目标值 Feedback to the capacity division device.

10. Reverse tuning of the battery cell matching system based on electrical measurement big data, characterized in that: The system comprises: A sample parameter acquisition module is used to determine sample extraction parameters, wherein the sample extraction parameters include: battery cell parameters, assembly process parameters and battery pack performance evaluation data; A sample data extraction module is used to extract sample extraction parameters from the MES system and the electrical performance big data system, clean and store the sample extraction parameters, and obtain applicable parameters. The sample data extraction module is connected to the sample parameter acquisition module; An algorithm model setting and data processing module is used to build an algorithm model library, use a BS system, process the applicable parameters with preset logic, obtain and store the calculation results of discharge efficiency and discharge platform voltage, and output a graph according to the calculation results of the discharge efficiency and the discharge platform voltage. The algorithm model setting and data processing module is connected to the sample data extraction module; A target capacity acquisition module is used to extract the calculation results of the discharge efficiency and the discharge platform voltage to obtain the sample minimum discharge efficiency and minimum platform voltage data, and process them to obtain the target group capacity. The target capacity acquisition module is connected to the algorithm model setting and data processing module; An applicable flow acquisition module is used to process and obtain applicable matching capacity according to the target matching capacity, the minimum platform voltage data and the sample minimum discharge efficiency, reversely optimize the original matching capacity, and output and adjust matching parameters for the battery pack. The applicable flow acquisition module is connected to the target capacity acquisition module and the algorithm model setting and data processing module; The capacity division process optimization module is used to screen out the optimal matching cell capacity value according to the applicable matching capacity, and optimize the cell capacity division process for the capacity division equipment accordingly. The capacity division process optimization module is connected to the applicable flow acquisition module.

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