Intelligent detection system and method based on battery pack performance big data consistency

Abstract

The present invention provides an intelligent detection system and method based on the consistency of battery pack electrical performance big data, which belongs to the field of battery pack electrical performance testing and analysis, including a data access module, a standard configuration module, and an intelligent algorithm library module including a standard algorithm library, an electrical performance algorithm model library, and a chart analysis algorithm model library. The standard algorithm library is configured to integrate multiple algorithm models, the electrical performance algorithm model library is configured to match electrical performance detection items to corresponding algorithms from the standard algorithm library based on historical electrical measurement data and electrical measurement production lines, processes, and equipment, and the chart analysis algorithm model library is configured to match electrical performance detection items to different analysis chart types based on historical electrical measurement data and electrical measurement production lines, processes, and equipment; an electrical performance detection module is used to configure the data names to be extracted for different projects, equipment manufacturers, and electrical measurement steps, and highlight them; a data analysis module processes huge electrical measurement data to improve the efficiency and accuracy of electrical performance consistency detection.

Description

Technical Field

[0001] The present invention relates to the technical field of battery pack electrical performance testing and analysis, and in particular to an intelligent detection system and method based on the consistency of battery pack electrical performance big data. Background Art

[0002] Currently, battery pack electrical performance consistency analysis primarily relies on offline manual analysis of massive electrical test files. This involves manually obtaining the device-side electrical test files, and then performing offline data analysis through a cumbersome file export process. Each battery pack generates up to 20 million pieces of electrical test data, making the manual screening process extremely time-consuming and consuming significant labor costs. More critically, manual screening of electrical performance carries a significant risk of missed detections and misjudgments, which can result in potentially defective battery packs being mistakenly used for vehicle installation, posing a potential safety hazard. Furthermore, current offline electrical performance test results are presented only in pure data form, lacking intuitive graphical displays, making early warning difficult. Finally, manually uploaded data is often not fully displayed in shipping reports, further reducing the comprehensiveness and accuracy of the analysis.

[0003] In the prior art, the Chinese invention patent application with publication number CN118410224A, "Intelligent Comprehensive Computation Method, System, Equipment and Medium for Lithium Battery Test Data", includes: S1. Extracting multi-category data from the server database, filtering and processing to obtain result data; S2. Integrating the result data and the data in the associated system to obtain integrated data, and storing it in the clickhouse database and Mysql database for unified management to obtain integrated data; S3. Analyzing and processing the integrated data, adapting the corresponding task parameters, forming work task modules of different data analysis methods, and obtaining work task analysis data results; S4. Based on the analysis data results of different work tasks and combined with the application environment factors of lithium batteries, building a Pack electrical performance simulation model; S5. Visualizing the analysis data results; S6. Storing the analysis data results and providing query data download. In the above method, the steps of data extraction, data budgeting, and data analysis adopt simple conceptual descriptions, without specific technical analysis descriptions and steps. At the same time, the method does not provide how to perform consistency analysis for different types of electrical performance data, which is not conducive to improving the efficiency of battery pack electrical performance consistency detection. Summary of the Invention

[0004] The technical problem to be solved by the present invention is how to improve the efficiency and accuracy of battery pack electrical performance consistency detection.

[0005] The present invention solves the above technical problems through the following technical solutions: an intelligent detection system based on the consistency of battery pack electrical performance big data, comprising:

[0006] A data access module is used to extract historical electrical measurement data and configuration parameters. The historical electrical measurement data includes dynamic temperature difference, charge and discharge dynamic pressure difference, charge and discharge static pressure difference, charge and discharge capacity, charge and discharge energy, charge and discharge DCR, current accuracy, SOC, ICC, discharge temperature rise, process module temperature difference, and pre-measurement temperature.

[0007] Standard configuration module, used to configure and manage electrical test items, test item ranges, test steps, and display chart types;

[0008] An intelligent algorithm library module is used to adapt to the analysis of electrical performance test data for multiple types of products, equipment, and electrical test files. The intelligent algorithm library module includes a standard algorithm library, an electrical performance algorithm model library, and a chart analysis algorithm model library. The standard algorithm library is configured to integrate multiple algorithm models. The electrical performance algorithm model library is configured to match corresponding algorithms from the standard algorithm library for electrical performance test items based on historical electrical test data and electrical test production lines, processes, and equipment. The chart analysis algorithm model library is configured to match different analysis chart types for electrical performance test items based on historical electrical test data and electrical test production lines, processes, and equipment.

[0009] The electrical performance testing module is used to configure the data names to be extracted for different projects, equipment manufacturers, and electrical testing steps, and select the corresponding chart types. The background automatically highlights the maximum, minimum, and next-to-minimum values ​​of each chart.

[0010] The data analysis module is used to query the analysis results of historical electrical measurement data.

[0011] Beneficial effects: The present invention constructs a specific data structure and a matching efficient parsing algorithm in the intelligent algorithm library module. The standard algorithm library is configured to integrate multiple algorithm models, which can perform consistency analysis on different types of electrical measurement data. The electrical performance algorithm model library is configured to match the electrical performance detection items to the corresponding algorithms from the standard algorithm library based on historical electrical measurement data and electrical measurement production lines, processes, and equipment. It can process huge electrical measurement data files, solving the problems of huge data, time-consuming manual screening, high labor costs, and insufficient consistency and accuracy in traditional methods.

[0012] Preferably, the standard algorithm library includes a slice-type difference algorithm, a slice-type accumulation algorithm, data extraction, interval maximum value divide-and-conquer calculation, interval minimum value divide-and-conquer calculation, interval extreme value summation calculation, interval extreme value difference calculation, horizontal derivative difference calculation, vertical derivative difference calculation, slice-type difference-quotient operation, row threshold positioning difference calculation, inter-module voltage trend algorithm, and linear regression.

[0013] Beneficial effects: By integrating multiple efficient algorithms into the standard algorithm library, a unified statistical algorithm is constructed for each small table data (the same group), which can process each small table in a loop and calculate the maximum and minimum values ​​in the vertical direction. The concept of horizontal difference calculation is introduced. By calculating the difference of all columns, the maximum and minimum values ​​of the horizontal difference are calculated, achieving more comprehensive and in-depth data analysis.

[0014] Preferably, the calculation process of the slice difference algorithm is: take the value of step A at the Tth second as X AT and the value of the T+n(n>0) second is X A(T+n) , calculate the difference X i =X AT -X A(T+n) , extract X i The value is displayed and graphed; the calculation process of the slice accumulation algorithm is: take the value of step A at the Tth second as X AT , take the value of the Tth second of the A+n (n≥0) step as X (A+n)T , do cumulative calculation value Extract X i The values ​​are displayed and graphed.

[0015] Beneficial effects: By configuring a slicing difference algorithm, the battery pack-related pressure difference data can be extracted and analyzed, and the pressure difference performance of the battery pack during charging and discharging can be controlled according to the process threshold. By configuring a slicing accumulation algorithm, the battery discharge energy index can be extracted and analyzed, and the energy and capacity performance of the battery pack during charging and discharging can be controlled according to the process threshold.

[0016] Preferably, the process of dividing and conquering the interval maximum value is: take the interval [T, T+n] seconds of step A and the interval [α1, α2, ..., α i ] Maximum value α Max The process of calculating the minimum value of the interval by dividing and conquering is: take the interval [T,t+n] seconds of the A step corresponding to the interval [Y,y+n] columns [α1,α2,...,α i ]Minimum value α Min , where T represents time and Y represents the number of columns.

[0017] Beneficial effects: By configuring the interval maximum value divide-and-conquer calculation and the interval minimum value divide-and-conquer calculation, the data extraction and analysis of the maximum and minimum values ​​of the pressure difference indicator during the charging and discharging process are realized, and the differences between the data are intuitively displayed, providing data support for the pressure difference performance evaluation of the battery pack.

[0018] Preferably, the process of calculating the interval extreme value sum is: take the interval [T, T+n] seconds of the A step corresponding to the interval [Y, Y+n] columns [α1, α2, .., α i ] Maximum value αMax and the minimum value α Min , do α Max +α Min Sum calculation; the process of calculating the interval extreme value difference is: take the interval [T, T+n] seconds of step A and the interval [α1, α2, .., α i ] Maximum value α Max and the minimum value α Min , do α Max -α Min Difference calculation, T represents time, Y represents the number of columns.

[0019] Beneficial effect: By configuring the interval extreme value sum calculation and interval extreme value difference calculation, the actual pressure difference of the measured voltage data can be extracted, providing data support for the pressure difference performance evaluation of the battery pack.

[0020] Preferably, the process of calculating the lateral derivative difference is as follows: take the value of the column [i, j, ..., k, ...] at the Tth second of step A, and calculate the derivative value X Max =Max[(T ai -T aj -T ak -...),(T bi -T bj -T bk -..),(T ci -T cj -T ck -..)...], derived value X Min =Min[(T ai -T aj -T ak -...),(T bi -T bj -T bk -..),(T ci -T cj -T ck -..)...], T represents time, subscripts a, b, c... represent row numbers, and subscripts i, j, k... represent column numbers; the process of calculating the longitudinal derivative difference is: take the value of the [A1, A2, ..., An] column in any step and calculate the maximum derivative value difference Y Max =Max[(A1a-A1b-A1c...),(A2a-A2b-A2c...),...,(Ana-Anb-Anc...)], Y Min =Min[(A1a-a1b-A1c...),(A2a-A2b-A2c...),...,(Ana-Anb-Anc...)].

[0021] Beneficial effect: By configuring the calculation of the lateral and longitudinal derivative differences, the current accuracy (Hall charge and discharge current, shunt charge and discharge current) can be automatically analyzed, thereby achieving quality control of the current detection items.

[0022] Preferably, it also includes:

[0023] Data management module, used to record operation errors and warning prompts;

[0024] The data comparison module is used to compare the analysis results of historical electrical measurement data with offline manual statistical data, perform comparative analysis on battery packs of the same dimension, verify the accuracy of the data, and display summary information of system data;

[0025] The system management module includes part management, user management, and role management, and is used to authorize and group user accounts.

[0026] The present invention also provides an intelligent detection method based on battery pack electrical performance big data consistency, comprising the following steps:

[0027] Step 1: After the battery pack test is completed, the electrical test file is automatically generated, and the data access module scans the electrical test file at set intervals to obtain the electrical test file data;

[0028] Step 2: After configuring the electrical test items, test item ranges, test steps, and display chart types in the standard configuration module, the model library in the intelligent algorithm library module is called to perform real-time analysis on the electrical test file data;

[0029] Step 3: The electrical performance detection module highlights the analysis results and sends them to emails, intercepting defective batteries whose electrical performance indicators exceed the process threshold.

[0030] Beneficial Effects: By dynamically setting signal columns for electrical test files, identifying and reading the precise location of each signal column in complex tables, and applying sophisticated algorithms for intelligent segmentation and reorganization, the system ensures that every piece of data used as an algorithm input feature is accurate. Leveraging an advanced model data structure parsing engine, the system automatically and efficiently maps data to the vast system algorithm library, accurately selecting the algorithm combination most suitable for the task at hand. This system can reduce the data analysis time for a single electrical test file from 12 minutes for manual operation to 7.2 seconds for automatic system processing, significantly improving efficiency by up to 100 times. Furthermore, the system achieves a high analysis accuracy of 99.5%, ensuring the accuracy and reliability of detection and analysis.

[0031] Preferably, the step 1 includes:

[0032] 1.1. Introduce the OpenXML library and use the C# file I / O function to open and read the electrical test file. When reading the contents of the electrical test file, use the SpreadsheetDocument class in the OpenXML library to read the CSV file content. Use the SpreadsheetDocument.Open method to open the file and obtain the WorkbookPart and WorksheetPart components.

[0033] 1.2. Traverse the Sheets element in WorkbookPart, obtain the names and IDs of all worksheets, select the worksheet to be parsed based on the user's selection or default settings, find the corresponding WorksheetPart by the worksheet ID, parse the SheetData element therein, which contains all the row and column information in the worksheet, traverse the Row element in SheetData and the Cell element in each Row, determine the row and column number of each cell, and construct the two-dimensional table structure of the CSV file;

[0034] 1.3. According to user needs, set the rules or conditions for extracting cells. When traversing the two-dimensional table structure of the CSV file, perform logical judgment on each cell, determine the cells that meet the extraction requirements according to the set rules, and mark them. Based on the position of the marked cells, extract the content of the target cells from the two-dimensional table structure, remove spaces and convert data types, and save them to the custom data structure according to the predetermined format.

[0035] Preferably, the step 2 includes:

[0036] 2.1. Enter a unique process number to identify the CSV file or data set currently being processed. The user selects the table row area to be processed and locates the table row area. A date selector and time input box are provided. The user enters or selects a date range to filter rows that meet the time conditions. A drop-down list lists all column names in the CSV file. The user can select one or more column names to determine the column area.

[0037] 2.2. Select an algorithm from the standard algorithm library in the electrical performance algorithm model library, combine the selected row area, column area, and algorithm, and construct a custom model data structure that meets your needs;

[0038] 2.3. Based on the configuration information, the backend system obtains the algorithms in the standard algorithm library and automatically maps and matches them. It extracts the table content based on the selected row and column areas and passes them to the algorithm for calculation. The calculation results are presented to the user in the form of charts and reports. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1This is an architecture diagram of an intelligent detection system for battery pack electrical performance big data consistency provided by an embodiment of the present invention;

[0040] Figure 2 This is a calculation example diagram of the intelligent detection system for battery pack electrical performance big data consistency provided by an embodiment of the present invention;

[0041] Figure 3 A diagram showing calculation results of an intelligent detection system for battery pack electrical performance big data consistency provided by an embodiment of the present invention;

[0042] Figure 4 An example diagram of calculating the lateral derivative difference in the intelligent detection system for consistency of battery pack electrical performance big data provided by an embodiment of the present invention;

[0043] Figure 5 A diagram showing the calculation results of the lateral derivative difference in the intelligent detection system for consistency of battery pack electrical performance big data provided by an embodiment of the present invention;

[0044] Figure 6 A diagram showing the calculation results of the longitudinal derivative difference in the intelligent detection system for consistency of battery pack electrical performance big data provided by an embodiment of the present invention;

[0045] Figure 7 A diagram illustrating a calculation example of a slice-based difference-quotient operation in an intelligent detection system for battery pack electrical performance big data consistency, provided by an embodiment of the present invention;

[0046] Figure 8 An example diagram of calculating the line threshold positioning difference in the intelligent detection system for consistency of battery pack electrical performance big data provided by an embodiment of the present invention;

[0047] Figure 9 This is a diagram illustrating an example of a calculation algorithm for an inter-module voltage trend in an intelligent detection system for battery pack electrical performance big data consistency, provided by an embodiment of the present invention;

[0048] Figure 10 This is an example diagram of linear regression calculation in an intelligent detection system for battery pack electrical performance big data consistency, provided by an embodiment of the present invention;

[0049] Figure 11 A schematic diagram of an electrical performance algorithm model library in an intelligent detection system for battery pack electrical performance big data consistency provided by an embodiment of the present invention;

[0050] Figure 12 A schematic diagram of a chart analysis algorithm model library in an intelligent detection system for battery pack electrical performance big data consistency provided by an embodiment of the present invention;

[0051] Figure 13A discrete graph showing analysis of static discharge data based on an intelligent battery pack electrical performance big data consistency detection system according to an embodiment of the present invention;

[0052] Figure 14 A line chart showing DCR data analysis based on the intelligent battery pack electrical performance big data consistency detection system provided by an embodiment of the present invention;

[0053] Figure 15 A combined discrete graph for analyzing dynamic voltage data of charge and discharge based on an intelligent detection system for consistency of battery pack electrical performance big data provided by an embodiment of the present invention;

[0054] Figure 16 A flowchart of an intelligent detection method for consistency of battery pack electrical performance big data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the following describes the technical solutions of the present invention clearly and completely with reference to specific embodiments and the accompanying drawings. It is obvious that the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0056] like Figure 1 As shown, this embodiment provides an intelligent detection system for consistency based on battery pack electrical performance big data, including:

[0057] The data access module is used to extract historical electrical measurement data and configuration parameters of the electrical measurement production line, process, equipment, etc., and verify the work step number of the electrical measurement file; the historical electrical measurement data includes key indicators such as dynamic temperature difference, charge and discharge dynamic pressure difference, charge and discharge static pressure difference, charge and discharge capacity, charge and discharge energy, charge and discharge DCR, current accuracy, SOC, ICC, discharge temperature rise, process module temperature difference, and pre-measurement temperature;

[0058] Standard configuration module, used to configure and manage electrical test items, test item ranges, test steps, and display chart types;

[0059] The intelligent algorithm library module is used to adapt to the electrical performance test data analysis of various types of products, equipment and electrical measurement files, including a standard algorithm library, an electrical performance algorithm model library, and a chart analysis algorithm model library. The standard algorithm library is configured to integrate multiple algorithm models, including slicing difference algorithm, slicing accumulation algorithm, data extraction, interval maximum value divide-and-conquer calculation, interval minimum value divide-and-conquer calculation, interval extreme value sum calculation, interval extreme value difference calculation, horizontal derivative difference calculation, vertical derivative difference calculation, slicing difference-quotient operation, row threshold positioning difference calculation, inter-module voltage trend algorithm, linear regression, and recursive algorithm. It can perform consistency analysis on different types of electrical measurement data.

[0060] The calculation process of the slice difference algorithm is as follows: take the value of step A at the Tth second as X AT and the value of the T+n(n>0) second is X A(T+n) , calculate the difference X i =X AT -X A(T+n) , extract X i The values ​​can be displayed and graphed for the extraction and automated analysis of historical electrical measurement data, and the electrical performance data can be controlled according to process thresholds.

[0061] like Figure 2 As shown, taking voltage as an example, the value of the first column in the last second of step 11 is A1, and the value of the first second of step 12 is A2. The difference of the voltage in the first column is X1=A1-A2. Similarly, the difference of the voltage in the second column, the third column, ..., the i-th column is calculated as X2, X3, ..., X i , take [X1,...,X i ] values ​​to draw slice difference line chart and scatter plot. Figure 3 As shown in the figure, the slice difference algorithm is used to extract and automatically analyze the pressure difference data related to the battery pack, and control the pressure difference performance of the battery pack during the charging and discharging process according to the process threshold.

[0062] The calculation process of the slice accumulation algorithm is: take the value of step A at the Tth second as X AT , take the value of the Tth second of the A+n (n≥0) step as X (A+n)t , do cumulative calculation value Extract X i The values ​​are displayed and graphed.

[0063] like Figure 2 As shown, taking voltage as an example, the value of the first column in the last second of step 11 is A1, and the value of the first second of step 12 is A2, and the accumulation of voltage 1 is X1=A1+A2; take [X1,...,X i ] value to draw slice-type cumulative line chart and scatter plot. Figure 3As shown in the figure, the slice difference algorithm is used to extract and automatically analyze the pressure difference data related to the battery pack, and control the pressure difference performance of the battery pack during the charging and discharging process according to the process threshold.

[0064] The process of data extraction is: take the Tth second of step A Column values ​​are displayed and graphed.

[0065] like Figure 2 In the configuration shown, the value of the voltage in the first column at the last second of step 12 is A1, and the value of the voltage in the second column at the last second of step 12 is A2. The values ​​of [A1, A2, ..., Ai] are used to draw the data extraction line graph and scatter graph. Figure 3 As shown, by configuring data extraction, it is used for data extraction and automated analysis of battery SOC indicators, and the SOC performance of the battery pack charge and discharge process is controlled according to the process threshold.

[0066] The process of calculating the interval maximum value by dividing and conquering is as follows: take the interval [T,T+n] seconds of step A and the interval [α1,α2,...,α i ] Maximum value α Max . Where T represents time and Y represents the number of columns, such as Figure 3 As shown in the figure, by dividing and conquering the maximum values ​​between configurations, the maximum value of the pressure difference indicator during the charging and discharging process is used for data extraction and automatic analysis, which intuitively displays the differences between the data and provides data support for the pressure difference performance evaluation of the battery pack.

[0067] The process of calculating the minimum value of the interval by dividing and conquering is as follows: take the interval [T,T+n] seconds of the A step corresponding to the interval [Y,Y+n] columns [α1,α2,...,α i ]Minimum value α Min . Where T represents time and Y represents the number of columns, such as Figure 3 As shown in the figure, by dividing and conquering the minimum values ​​between configurations, the minimum value of the pressure difference indicator during the charging and discharging process is used for data extraction and automatic analysis, which intuitively displays the differences between the data and provides data support for the pressure difference performance evaluation of the battery pack.

[0068] The process of calculating the interval extreme value sum is as follows: take the interval [T, T+n] seconds of step A and the interval [α1, α2, .., α i ] Maximum value α Max and the minimum value α Min , do α Max +α Min Sum calculation. (T represents time, Y represents the number of columns). Figure 2As shown, the value corresponding to the voltage in the first column in the last second of step 11 is α1, and the value corresponding to the voltage in the second column is α2. The value corresponding to the voltage in the first column in the first second of step 12 is α3, and the value corresponding to the voltage in the second column is α4. The maximum value among [α1, α2, α3, α4] is α Max , take the minimum value of [α1,α2,α3,α4] as α Min , α Max +α Min The value of is the interval extreme value and.

[0069] The process of calculating the interval extreme value difference is as follows: take the interval [T, T+n] seconds of step A and the interval [α1, α2, .., α i ] Maximum value α Max and the minimum value α Min , do α Max -α Min Calculate the difference. (T represents time, Y represents the number of columns). Figure 2 As shown, the value corresponding to the voltage in the first column in the last second of step 11 is α1, and the value corresponding to the voltage in the second column is α2. The value corresponding to the voltage in the first column in the first second of step 12 is α3, and the value corresponding to the voltage in the second column is α4. The maximum value among [α1, α2, α3, α4] is α Max , take the minimum value of [α1,α2,α3,α4] as α Min , α Max -α Min The value of is the interval extreme difference.

[0070] By configuring the algorithm of interval extreme value summation and interval extreme value difference calculation, the measured voltage data is extracted, and the actual pressure difference data is extracted from the interval extreme value calculation, providing data support for the pressure difference performance evaluation of the battery pack.

[0071] The process of calculating the lateral derivative difference is as follows: take the value of the [i, j, ..., k, ...] column at the Tth second of step A and calculate the derivative value X Max =Max[(T ai -T aj -T ak -...),(T bi -T bj -T bk -..),(T ci -T cj -T ck -..)...], derived value X Min =Min[(T ai -T aj -T ak -...),(T bi -T bj -T bk-..),(T ci -T cj -T ck -..)...], T represents time, subscripts a, b, c... represent row numbers, and subscripts i, j, k... represent column numbers. Figure 4 As shown, the value of column i at the first second in step 12 is A1, the value of column j is B2, and a column is expanded to |A1-B2|=α1. The value of column A at the nth second in step 12 is An, the value of column B is Bn, and a column is expanded to |An-Bn|=αn. All values ​​of the difference column [α1,..,αn] are derived, and the maximum value Max[α1,...,αn] or Min[α1,...,αn] is taken. Figure 5 As shown, the current accuracy (Hall charge and discharge current, shunt charge and discharge current) is automatically analyzed through lateral derivative difference calculation, and the quality control of the current detection items is carried out according to the process threshold.

[0072] The process of calculating the longitudinal derivative difference is as follows: take the value of the column [A1, A2, ..., An] in any step and calculate the maximum value Y of the derivative difference Max =Max[(A1a-A1b-A1c...),(A2a-A2b-A2c...),...,(Ana-Anb-Anc...)], Y Min =Min[(A1a-A1b-A1c...),(A2a-A2b-A2c...),...,(Ana-Anb-Anc...)], where a, b, c... represent row numbers. Figure 6 As shown, in step H, the value in row a of column A1 is A1a, and the value in row b is A1b. This creates a new row: |A1a - A1b| = Y1. In step H, the value in row a of column An is Ana, and the value in row b is Anb. This creates a new row: |Ana - Anb| = Yn. Derive all values ​​in the difference column [Y1, ..., Yn] and take the maximum value (Max[Y1, ..., Yn]) or the minimum value (Min[Y1, ..., Yn].

[0073] The process of slice difference quotient operation is: take the value of any step at the Tth second as A, and the value of the step at the T+nth (n>0) second as B, slice difference quotient value X=(AB) / C (C is a dynamic value), extract the X value and display it in a chart. Figure 7 As shown, by configuring the slice-type difference-quotient algorithm, ICC is automatically analyzed and quality control of the current detection items is performed according to the process threshold.

[0074] The process of calculating the row threshold positioning difference is: Min{|Xi-C|}, where C is a dynamic variable and Xi is the value in a certain column at a certain step. Filter the row where the value Xi reaches 2.85V or is closest to 2.85V under the "lowest single cell voltage" column, and grab the "single cell voltage difference" column of that row. Figure 8 As shown in the figure, by configuring this algorithm, all data in any process step can be compared with the standard, and the voltage difference of the battery pack can be determined according to the process regulations to determine whether it meets the specifications, thus solving quality problems caused by non-compliant voltage differences and large differences in battery cells.

[0075] The calculation process of the inter-module voltage trend algorithm is as follows:

[0076] The average value of all cell values ​​in the module is calculated using the following formula:

[0077] Where n represents the number of modules, i represents the number and position of cells under the module, and X i Indicates the indicator value of the battery cell under this module, U n Indicates the average value of all battery cells in this module.

[0078] Then, the average values ​​of all battery cell values ​​under adjacent modules are arranged and combined to obtain the difference to obtain the voltage trend between modules.

[0079] Permutations and combinations:

[0080]

[0081] When m=n:

[0082]

[0083] A represents the arrangement, n represents the number of modules, and m represents the number of modules to be compared. C: Combination: n represents the number of modules, and m represents the number of modules to be compared. For a battery pack with multiple modules, the cells in the distributed points are divided according to the number of cells bound to the modules, and the average value U of all the cell values ​​under each module is calculated. n , take the average value of adjacent modules, perform permutations and combinations to find the difference, such as Figure 9 shown.

[0084] The calculation process of linear regression is:

[0085] y=a+bx

[0086] Among them, y represents the index value of the battery cell, x represents the position of the battery cell in the battery pack, b represents the slope, and a is the parameter.

[0087]

[0088] Where n represents the number of cells in the battery pack. Indicates the average number of battery cells, Indicates the average value of battery cell indicators.

[0089] Through linear regression, the index value of any battery cell can be predicted, and the degree of dispersion between the index value and the target value can be displayed, so that the change trend of the battery cell voltage can be predicted, such as Figure 10 As shown, potential voltage anomalies, such as overcharging and over-discharging, can be discovered in a timely manner, so that measures can be taken in advance to intervene and avoid damage to battery performance caused by voltage anomalies.

[0090] The recursive algorithm can realize batch processing of files. The execution process of the algorithm includes:

[0091] (1) Design a recursive function: Write a recursive function that can traverse the specified folder and all its subfolders, collect all qualified files to be processed, and for each folder, first check whether it contains subfolders. If so, recursively call the function for each subfolder, and then check whether the current folder contains the file to be processed. If so, add it to the file list.

[0092] (2) File filtering: During the recursive traversal process, the files to be processed are filtered based on their extensions, name patterns, or other features.

[0093] (3) Unified processing: After collecting all pending files, the batch processing module processes them one by one. The processing process includes reading the file content, applying a regular expression matching algorithm to determine the position of the header column, and performing vertical segmentation operations. The recursive algorithm can automatically retrieve all pending files in the folder and perform unified processing, greatly improving analysis efficiency.

[0094] Before executing each algorithm, the present invention uses a regular expression to determine the header position. The specific process includes:

[0095] (1) Matching header features: Use regular expressions to match specific patterns or keywords in the header. For example, the header may contain specific tags, formats, or content, and regular expressions can accurately identify these features.

[0096] (2) Locating the header column: Once the header feature is matched, the position of the header column in the table can be determined. By parsing the regular expression matching results, the starting and ending positions of the header column can be determined.

[0097] (3) Identify delimiters: Regular expressions identify delimiters in tables, such as blank lines, specific characters, or strings, which are used to identify the boundaries between different parts or rows.

[0098] (4) Define segmentation rules: Define segmentation rules based on the position and characteristics of the delimiter. For example, when encountering multiple consecutive blank lines or specific strings, the table is segmented into multiple small tables.

[0099] (5) Perform splitting operation: After using regular expressions to match the delimiter, perform splitting operation according to the splitting rules, traverse and split the table content, and generate multiple small table data.

[0100] (6) Data preprocessing: Preprocess the tabular data using regular expressions to remove irrelevant information and standardize the format, providing a clean and accurate data basis for subsequent statistical analysis.

[0101] (7) Automated processing: Split and preprocess table data using regular expressions.

[0102] (8) After splitting the large table into multiple small tables, conducting more detailed and in-depth analysis on different small tables will help discover patterns and trends in the data and improve the accuracy and efficiency of statistical analysis.

[0103] Use regular expression matching algorithms to automatically determine the position information of the header column and implement precise intelligent automatic vertical segmentation, splitting large tables into multiple small tables, thereby greatly improving the efficiency of subsequent statistical analysis.

[0104] The electrical performance algorithm model library is configured to match the electrical performance test items to the corresponding algorithms from the standard algorithm library based on historical electrical test data and electrical test production lines, processes, and equipment, including adding modules, editing modules, deleting modules, and page display area modules, such as Figure 11 As shown, in the newly added module, you can set configuration items such as project name, equipment manufacturer, electrical test step, file name, line number, test item, title name, algorithm, unit conversion, title name, inspection item order, advanced mode, etc., which are used to configure modules and algorithms for newly added electrical performance test items. The specific method is as follows:

[0105] Project name, equipment manufacturer, electrical measurement step selection test item configuration; File name: step layer electrical measurement file is step layer, record layer electrical measurement file is record layer; Row number: configure the data position of the record layer electrical measurement parameters, fill in "1" in the first row and "-1" in the last row; Test item: configure standard test item and determine the upper and lower limit range values; Title name: electrical performance analysis parameter name; Algorithm: electrical performance analysis source file containing multiple parameter values, configure standard algorithm library model; Unit conversion: if the value parameter unit is inconsistent with the unit of the electrical performance analysis parameter formed later, perform unit conversion; Inspection item order: the order of electrical performance analysis parameters in the subsequent electrical performance analysis during this calibration; Advanced mode: configure when formulating multi-column and multi-row data calculations.

[0106] The chart analysis algorithm model library is configured to match different analysis chart types for electrical performance test items based on historical electrical test data and electrical test production lines, processes, and equipment, including add modules, add-special modules, edit modules, delete modules, and page display area modules, such as Figure 12 As shown, in the newly added module, set the configuration items such as project name, equipment manufacturer, electrical measurement step, step number, chart title, chart type, lowest and second lowest difference, chart order, column name, and configuration column information.

[0107] The electrical performance detection module is used to configure the data names to be extracted for different projects, equipment manufacturers, and electrical test steps, and select the corresponding chart type (scatter chart or line chart). The background automatically highlights the maximum value, minimum value, and second minimum value of each chart, which is convenient for intuitive data display and analysis, and realizes online big data analysis of electrical test files, and finally obtains electrical performance parameter analysis and related discrete analysis charts. Figure 13-15 As shown, Figure 13 Analyze the scatter diagram for the discharge static data. Figure 14 Analyze the line chart for DCR data. Figure 15 Scatter plot for charge and discharge dynamic voltage data analysis.

[0108] Data management module, used to record operation errors, warnings and other prompts;

[0109] The data analysis module is used to query the analysis results of historical electrical measurement data; query or export the project name, equipment manufacturer, electrical measurement date, electrical measurement process, judgment result, and battery pack code; filter the data analyzed by the system; and display all battery packs with bad data in yellow on the centralized interface, and display abnormal data of the detection items in red.

[0110] The data comparison module is used to compare the analysis results of historical electrical measurement data with offline manual statistical data, perform comparative analysis on battery packs of the same dimension, verify the accuracy of the data, and display summary information of system data.

[0111] The system management module includes functions such as part management, user management, and role management, and is used to authorize and group user accounts.

[0112] The present invention constructs a specific data structure and a matching efficient parsing algorithm in the intelligent algorithm library module. The standard algorithm library is configured to integrate multiple algorithm models, which can perform consistency analysis on different types of electrical measurement data. The electrical performance algorithm model library is configured to match the electrical performance detection items to the corresponding algorithms from the standard algorithm library based on historical electrical measurement data and electrical measurement production lines, processes, and equipment. It can process huge electrical measurement data files, solving the problems of huge data, time-consuming manual screening, high labor costs, and insufficient consistency and accuracy in traditional methods. By integrating more than a dozen efficient algorithms, a unified statistical algorithm is constructed for each small table data (the same group), which can process each small table in a loop and calculate the maximum and minimum values ​​in the vertical direction. The concept of horizontal difference calculation is introduced. By calculating the difference of all columns, the maximum and minimum values ​​of the horizontal difference are calculated, achieving more comprehensive and in-depth data analysis.

[0113] This system reduces the data analysis time for a single electrical measurement file from 12 minutes manually to 7.2 seconds automatically, significantly improving efficiency by up to 100 times. Furthermore, the system achieves a high analysis accuracy of 99.5%, ensuring the accuracy and reliability of detection and analysis.

[0114] Through system algorithm configuration and scheduled file scanning, automated analysis is performed, and the statistical maximum and minimum values ​​are compared with the preset thresholds. Data that exceeds the threshold range is automatically colored and highlighted, making abnormal data clear at a glance. Defective battery packs are automatically intercepted according to the preset process standard threshold, which is convenient for users to quickly identify and process. At the same time, the data is uploaded to the MES system immediately after the automated analysis is completed, and unqualified products are intercepted and controlled in the next process, effectively preventing defective products from entering the market and ensuring product quality and customer satisfaction. Through this series of technological innovations and optimizations, the present invention not only improves the efficiency and accuracy of battery pack electrical performance detection and analysis, but also makes important contributions to improving the intelligence and automation level of the battery pack manufacturing industry.

[0115] Continue reading Figure 1 The basic service layer of the intelligent detection system based on battery pack electrical performance big data consistency of the present invention adopts distributed cache, distributed message middleware, and WCF service. The distributed cache adopts Redis non-relational memory database storage. Redis mainly uses memory to store data. For cached frequently accessed data (such as statistical results, intermediate calculation results, etc.), Redis non-relational memory database storage is adopted, which makes the system have very fast reading and writing speed, improves data access speed, reduces database load, and improves overall system performance.

[0116] The distributed message middleware uses RabbitMQ distributed message middleware to realize the asynchronous execution of tasks such as real-time processing of data streams, concurrent processing, batch processing of data, and complex calculations.

[0117] WCF services are used to implement communication and data sharing between different services, such as data interaction between the front-end and back-end, service calls between different modules, and asynchronous communication between the front-end and back-end, thereby improving the response speed and interactive experience of the user interface.

[0118] Example 2

[0119] like Figure 16 As shown, this embodiment provides an intelligent detection method based on battery pack electrical performance big data consistency, including the following steps:

[0120] Step 1: After the battery pack test is completed, the electrical test file is automatically generated, and the data access module scans the electrical test file at set intervals to obtain the electrical test file data;

[0121] On-site personnel scan the code to enter information such as the battery pack code. After the test is completed, a CSV electrical measurement file is automatically generated and saved to the specified path. The data access module then performs automated file scanning every 30 minutes to obtain electrical measurement file data.

[0122] Step 1 specifically includes the following process:

[0123] 1.1. Introduce the OpenXML library and use C#'s file I / O functions to open and read the electrical test file. To read the contents of the electrical test file, use the SpreadsheetDocument class in the OpenXML library to read the CSV file content. Use the SpreadsheetDocument.Open method to open the file and obtain components such as WorkbookPart and WorksheetPart.

[0124] 1.2. Traverse the Sheets elements in WorkbookPart, obtain the names and IDs of all worksheets, select the worksheet to be parsed based on user selection or default settings, find the corresponding WorksheetPart by the worksheet ID, parse the SheetData element, which contains all row and column information in the worksheet, traverse the Row elements in SheetData and the Cell elements in each Row, determine the position of each cell (row number and column number), and construct the two-dimensional table structure of the CSV file.

[0125] 1.3. According to user needs, set rules or conditions for extracting cells. These rules are based on the cell value, format, position or other attributes. When traversing the two-dimensional table structure of the CSV file, perform logical judgment on each cell, determine which cells meet the extraction requirements according to the set rules, and mark them. Based on the marked cell position, extract the content of the target cell from the two-dimensional table structure, perform necessary processing (such as removing spaces, converting data types, etc.), and save it to a custom data structure in a predetermined format.

[0126] Step 2: After configuring the electrical test items, test item ranges, test steps, and display chart types in the standard configuration module, the model library in the intelligent algorithm library module is called to perform real-time analysis on the electrical test file data;

[0127] Step 2 specifically includes the following processes:

[0128] 2.1. The user enters a unique work step number on the interface to identify the currently processed CSV file or data set. Visual tools (such as scroll bars and row number input boxes) are provided. The user selects the table row area to be processed and locates the table row area. A date selector and time input box are provided. The user enters or selects a date range to filter rows that meet the time conditions. A drop-down list is provided to list all column names in the CSV file. The user can select one or more column names and determine the column area. Specific text filtering, value range filtering, and format filtering are also supported.

[0129] 2.2. Select an algorithm from the standard algorithm library in the electrical performance algorithm model library, combine the selected row area, column area, and algorithm, and build a custom model data structure that meets your needs. The system provides visualization tools to help users complete the construction.

[0130] 2.3. The backend system receives the configuration information submitted by the user. Based on the configuration information, the backend system obtains the algorithm in the standard algorithm library and automatically maps and matches it. It extracts the table content based on the selected row and column areas and passes it to the algorithm for calculation. The system provides visualization functions and displays the calculation results to the user in the form of charts, reports, etc.

[0131] Step 3: The electrical performance detection module highlights the analysis results and sends them to emails, intercepting defective batteries whose electrical performance indicators exceed the process threshold.

[0132] 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 they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. 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 various embodiments of the present invention.

Claims

1. Based on the big data consistency intelligent detection system of battery pack performance, the characteristics are: include: A data access module is used to extract historical electrical measurement data and configuration parameters. The historical electrical measurement data includes dynamic temperature difference, charge and discharge dynamic pressure difference, charge and discharge static pressure difference, charge and discharge capacity, charge and discharge energy, charge and discharge DCR, current accuracy, SOC, ICC, discharge temperature rise, process module temperature difference, and pre-measurement temperature. Standard configuration module, used to configure and manage electrical test items, test item ranges, test steps, and display chart types; An intelligent algorithm library module is used to adapt to the analysis of electrical performance test data for multiple types of products, equipment, and electrical test files. The intelligent algorithm library module includes a standard algorithm library, an electrical performance algorithm model library, and a chart analysis algorithm model library. The standard algorithm library is configured to integrate multiple algorithm models. The electrical performance algorithm model library is configured to match corresponding algorithms from the standard algorithm library for electrical performance test items based on historical electrical test data and electrical test production lines, processes, and equipment. The chart analysis algorithm model library is configured to match different analysis chart types for electrical performance test items based on historical electrical test data and electrical test production lines, processes, and equipment. The electrical performance testing module is used to configure the data names to be extracted for different projects, equipment manufacturers, and electrical testing steps, and select the corresponding chart types. The background automatically highlights the maximum, minimum, and next-to-minimum values ​​of each chart. The data analysis module is used to query the analysis results of historical electrical measurement data.

2. The intelligent detection system based on battery pack electrical performance big data consistency according to claim 1 is characterized by: The standard algorithm library includes slice-type difference algorithm, slice-type accumulation algorithm, data extraction, interval maximum value divide-and-conquer calculation, interval minimum value divide-and-conquer calculation, interval extreme value summation calculation, interval extreme value difference calculation, horizontal derivative difference calculation, vertical derivative difference calculation, slice-type difference-quotient operation, row threshold positioning difference calculation, inter-module voltage trend algorithm, and linear regression.

3. The intelligent detection system based on battery pack electrical performance big data consistency according to claim 2 is characterized by: The calculation process of the slice difference algorithm is: take the value of step A at the Tth second as And the value of T+n (n>0) second is , calculate the difference respectively ,extract The value is displayed and made into a chart; the calculation process of the slice accumulation algorithm is: take the value of step A at the Tth second as , take the value of the Tth second of the A+n (n≥0) step as , do cumulative calculation value ,extract The values ​​are displayed and graphed.

4. The intelligent detection system based on battery pack electrical performance big data consistency according to claim 2 is characterized by: The process of dividing and conquering the interval maximum value is as follows: Take step A Corresponding within the second interval Within column interval Maximum The process of calculating the minimum value of the interval by dividing and conquering is: take step A Corresponding within the second interval Within column interval Minimum ,in T Represents time, Y Represents the number of columns.

5. The intelligent detection system based on battery pack electrical performance big data consistency according to claim 2 is characterized by: The process of calculating the sum of the interval extreme values ​​is as follows: Corresponding within the second interval In the column interval [ , ,.., Maximum value and minimum value ,Do + Sum calculation; the process of calculating the interval extreme value difference is: take step A Corresponding within the second interval In the column interval [ , ,.., Maximum value and minimum value ,Do - Difference calculation, T Represents time, Y Represents the number of columns.

6. The intelligent detection system based on battery pack electrical performance big data consistency according to claim 2 is characterized by: The process of calculating the lateral derivative difference is as follows: T Second Column values, calculate derived values , derived value , T Represents time, subscript a 、 b 、 c ... indicates the line number, subscript i 、 j 、 k ... represents the column number; the process of calculating the longitudinal derivative difference is: take the Column values, calculate the maximum value of the difference in derivative values , .

7. The intelligent detection system based on battery pack electrical performance big data consistency according to claim 2 is characterized by: Also includes: Data management module, used to record operation errors and warning prompts; The data comparison module is used to compare the analysis results of historical electrical measurement data with offline manual statistical data, perform comparative analysis on battery packs of the same dimension, verify the accuracy of the data, and display summary information of system data; The system management module includes part management, user management, and role management, and is used to authorize and group user accounts.

8. A method for intelligently detecting the consistency of battery pack electrical performance big data, using the intelligent detection system for intelligently detecting the consistency of battery pack electrical performance big data according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1: After the battery pack test is completed, the electrical test file is automatically generated, and the data access module scans the electrical test file at set intervals to obtain the electrical test file data; Step 2: After configuring the electrical test items, test item ranges, test steps, and display chart types in the standard configuration module, the model library in the intelligent algorithm library module is called to perform real-time analysis on the electrical test file data; Step 3: The electrical performance detection module highlights the analysis results and sends them to emails, intercepting defective batteries whose electrical performance indicators exceed the process threshold.

9. The intelligent detection method based on battery pack electrical performance big data consistency according to claim 8 is characterized by: The step one comprises: 1.

1. Introduce the OpenXML library and use the C# file I / O function to open and read the electrical test file. When reading the contents of the electrical test file, use the SpreadsheetDocument class in the OpenXML library to read the CSV file content. Use the SpreadsheetDocument.Open method to open the file and obtain the WorkbookPart and WorksheetPart components. 1.

2. Traverse the Sheets element in WorkbookPart, obtain the names and IDs of all worksheets, select the worksheet to be parsed based on the user's selection or default settings, find the corresponding WorksheetPart by the worksheet ID, parse the SheetData element therein, which contains all the row and column information in the worksheet, traverse the Row element in SheetData and the Cell element in each Row, determine the row and column number of each cell, and construct the two-dimensional table structure of the CSV file; 1.

3. According to user needs, set the rules or conditions for extracting cells. When traversing the two-dimensional table structure of the CSV file, perform logical judgment on each cell, determine the cells that meet the extraction requirements according to the set rules, and mark them. Based on the position of the marked cells, extract the content of the target cells from the two-dimensional table structure, remove spaces and convert data types, and save them to the custom data structure according to the predetermined format.

10. The intelligent detection method based on battery pack electrical performance big data consistency according to claim 8, characterized in that: The second step includes: 2.

1. Enter a unique process number to identify the CSV file or data set currently being processed. The user selects the table row area to be processed and locates the table row area. A date selector and time input box are provided. The user enters or selects a date range to filter rows that meet the time conditions. A drop-down list lists all column names in the CSV file. The user can select one or more column names to determine the column area. 2.

2. Select an algorithm from the standard algorithm library in the electrical performance algorithm model library, combine the selected row area, column area, and algorithm, and construct a custom model data structure that meets your needs; 2.

3. Based on the configuration information, the backend system obtains the algorithms in the standard algorithm library and automatically maps and matches them. It extracts the table content based on the selected row and column areas and passes them to the algorithm for calculation. The calculation results are presented to the user in the form of charts and reports.

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