Battery cell capacity test method, device, equipment, storage medium and product

Abstract

The invention relates to the technical field of batteries, in particular to a cell capacity testing method, device and equipment, a storage medium and a product, and the method comprises the steps: obtaining to-be-tested cells of a current batch; classifying the to-be-tested battery cells to obtain first to third types of to-be-tested battery cells; capacity testing is conducted on the first type of to-be-tested battery cells and the second type of to-be-tested battery cells on the basis of a capacity testing model, capacity adjusting testing is conducted on the three types of to-be-tested battery cells, testing results corresponding to the to-be-tested battery cells of the current batch are generated, and the capacity testing model is obtained based on the testing results of the previous batch and testing data set optimization. Therefore, the problems of high cost and low test accuracy of capacity test in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method, device, equipment, storage medium and product for testing the capacity of a battery cell. Background Art

[0002] In the manufacturing process of lithium-ion batteries, capacity testing is a crucial step. It is not only used to calibrate the capacity of the battery, but also an important basis for eliminating defective products and ensuring the consistency of the battery. However, the current widely used production mode of actual measurement plus temperature fitting has some significant problems: 1) High testing cost: The current full-cell capacity test requires at least two complete discharge-charge cycles, which leads to serious power consumption and increases the time and labor costs of the overall test. 2) Poor data robustness: The one-time calibration process lacks the ability to cope with subsequent manufacturing system disturbances, which means that if the production conditions change, the calibrated data may no longer be accurate, which may cause batches of false defects or real defective products to be missed. Summary of the invention

[0003] The present application provides a method, device, equipment, storage medium and product for testing the capacity of a battery cell to solve the problems of high cost and low test accuracy in capacity testing in related technologies.

[0004] A first aspect of the present application provides a method for testing the capacity of a battery cell, comprising the following steps: obtaining a current batch of battery cells to be tested; classifying the battery cells to be tested into first to third categories of battery cells to be tested; performing a capacity test on the first category of battery cells to be tested and the second category of battery cells to be tested based on a capacity test model, performing a capacity adjustment test on the three categories of battery cells to be tested, and generating test results corresponding to the current batch of battery cells to be tested, wherein the capacity test model is optimized based on the test results of the previous batch and the test data set.

[0005] Optionally, after generating the test results corresponding to the current batch of battery cells to be tested, it also includes: merging the test results of the previous batch into the test data set; and optimizing the capacity test model based on the merged test data set and the test results corresponding to the current batch of battery cells to be tested.

[0006] Optionally, before obtaining the current batch of battery cells to be tested, it also includes: obtaining an initial test data set; establishing an initial test model based on the initial test data set, and obtaining any batch of battery cells, and testing any batch of battery cells based on the initial test model to obtain a test error; updating the initial test model according to the test error to obtain a capacity test model.

[0007] Optionally, an initial test model is established based on the initial capacity test data set, including: calculating the covariance of each test data and capacity in the test data set, and determining the target test data based on the covariance; constructing multiple exponential regression equations for each target test data and capacity, and generating an initial test model based on the multiple exponential regression equations.

[0008] Optionally, any batch of battery cells is tested based on the initial test model to obtain a test error, including: testing all battery cells in any batch to obtain the predicted capacity of each battery cell; selecting outlier battery cells among all battery cells and some battery cells other than outlier battery cells for testing to obtain the predicted capacity; and calculating the prediction error of the initial test model based on the predicted capacity and the predicted capacity.

[0009] Optionally, the test data set includes at least one of: a capacity data set, a liquid injection data set, an assembly data set, a formation data set, a second injection data set, and a DC internal resistance measurement data set.

[0010] A second aspect of the present application provides a device for testing the capacity of a battery cell, including: a first acquisition module, used to acquire a current batch of battery cells to be tested; a classification module, used to classify the battery cells to be tested into first to third categories of battery cells to be tested; a testing module, used to perform a capacity test on the first category of battery cells to be tested and the second category of battery cells to be tested based on a capacity test model, perform a capacity adjustment test on the three categories of battery cells to be tested, and generate test results corresponding to the current batch of battery cells to be tested, wherein the capacity test model is optimized based on the test results of a previous batch and the test data set.

[0011] Optionally, the battery cell capacity testing device also includes: an optimization module, which is used to merge the test results of the previous batch into the test data set after generating the test results corresponding to the current batch of battery cells to be tested; and optimize the capacity test model based on the merged test data set and the test results corresponding to the current batch of battery cells to be tested.

[0012] Optionally, the battery cell capacity testing device also includes: a second acquisition module, used to obtain an initial test data set before obtaining the current batch of battery cells to be tested; an establishment module, used to establish an initial test model based on the initial test data set, and obtain any batch of battery cells, and test any batch of battery cells based on the initial test model to obtain a test error; an update module, used to update the initial test model according to the test error to obtain a capacity test model for the next batch.

[0013] Optionally, the establishment module is further used to calculate the covariance of each test data and capacity in the test data set, and determine the target test data based on the covariance; construct multiple exponential regression equations for each target test data and capacity, and generate an initial test model based on the multiple exponential regression equations.

[0014] Optionally, the establishment module is further used to test all battery cells in any batch to obtain the predicted capacity of each battery cell; select outlier batteries among all battery cells and some battery cells other than outlier batteries for testing to obtain the predicted capacity; and calculate the prediction error of the initial test model based on the predicted capacity and the predicted capacity.

[0015] Optionally, the test data set includes at least one of: a capacity data set, a liquid injection data set, an assembly data set, a formation data set, a second injection data set, and a DC internal resistance measurement data set.

[0016] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the electronic device as described in the above embodiment.

[0017] A fourth aspect of the present application provides a computer-readable storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed, it is used to implement a method for testing the capacity of a battery cell as in the above embodiment.

[0018] The fifth aspect of the present application provides a computer program product, including: a computer program or instructions, which, when executed, implements the method for testing the capacity of a battery cell as in the above embodiment.

[0019] Therefore, this application has at least the following beneficial effects:

[0020] The embodiment of the present application classifies the cells to be tested, uses some cells for actual capacity testing, and uses the pure capacity fitting method for other cells, thereby reducing the overall testing cost, and by continuously optimizing and updating the capacity testing model, it is possible to maintain a high degree of reliability and robustness while maintaining a high prediction accuracy. Thus, the problems of high cost and low test accuracy of capacity testing in related technologies are solved.

[0021] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0023] Figure 1 A flow chart of a method for testing the capacity of a battery cell provided according to an embodiment of the present application;

[0024] Figure 2 This is an example diagram of a test of a battery cell capacity provided according to an embodiment of the present application;

[0025] Figure 3 This is an example diagram of a battery cell capacity testing device provided according to an embodiment of the present application;

[0026] Figure 4 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0028] The following describes the testing method, device, equipment, storage medium and product of the battery cell capacity of the embodiment of the present application with reference to the accompanying drawings. In response to the problems mentioned in the above background technology, the present application provides a method for testing the battery cell capacity. In this method, by classifying the battery cells to be tested, some battery cells are used for actual capacity testing, while other battery cells use a pure fitting capacity method, which significantly reduces the number of full discharge-charge cycles and reduces the overall testing cost. By continuously optimizing and updating the capacity test model, it is possible to maintain a high degree of reliability and robustness while maintaining a high prediction accuracy. As a result, the problems of high cost and low test accuracy in capacity testing in related technologies are solved.

[0029] Specifically, Figure 1 A schematic flow chart of a method for testing the capacity of a battery cell provided in an embodiment of the present application.

[0030] like Figure 1 As shown, the method for testing the battery cell capacity includes the following steps:

[0031] In step S101, cells to be tested in the current batch are obtained.

[0032] In the embodiment of the present application, a certain number of cells (eg, 1,000 to 50,000) may be selected as the current batch, and these cells will be used as the basis for subsequent classification and testing.

[0033] In step S102, the cells to be tested are classified into first to third categories of cells to be tested.

[0034] Specifically, the embodiments of the present application can randomly select a portion of battery cells (such as 5% to 30%) as the first category of battery cells, select battery cells that are expected to be on the edge of performance or may deviate from the normal range as the second category of battery cells, which usually account for 5% to 10% of the total number of battery cells, and the remaining battery cells as the third category of battery cells.

[0035] In step S103, capacity tests are performed on the first type of battery cells to be tested and the second type of battery cells to be tested based on the capacity test model, and capacity adjustment tests are performed on the third type of battery cells to be tested, to generate test results corresponding to the current batch of battery cells to be tested, wherein the capacity test model is optimized based on the test results of the previous batch and the test data set.

[0036] The embodiment of the present application can use conventional capacity testing methods to perform comprehensive charge and discharge cycle tests on the first type of battery cells to be tested to obtain their actual capacity data. The second type of battery cells also use capacity testing to detect those predicted to be marginal or potential outliers, thereby identifying possible abnormalities. For the remaining third type of battery cells, the embodiment of the present application does not perform a complete capacity test, but instead performs capacity predictions based on a previously established and continuously updated capacity test model. This approach can significantly reduce the time and resource consumption required for actual testing while maintaining sufficient accuracy. Therefore, the embodiment of the present application can efficiently generate test results corresponding to all battery cells to be tested in the current batch by implementing different testing strategies for the above three types of battery cells, which helps to reduce overall testing costs.

[0037] In the actual execution process, the capacity test model is continuously optimized based on the test results of the previous batch and the accumulated data set. This means that after each batch of tests is completed, the system will collect the newly generated data and integrate it into the existing data set, and adjust the model parameters, such as weights, through the feedback mechanism to generate a more accurate new version of the model (for example, updating from α0 to α1). This process ensures that the model can adapt to changes in the production environment and maintain high prediction accuracy and robustness.

[0038] In one embodiment of the present application, after generating the test results corresponding to the current batch of battery cells to be tested, it also includes: merging the test results of the previous batch into the test data set; and optimizing the capacity test model based on the merged test data set and the test results corresponding to the current batch of battery cells to be tested.

[0039] It is understandable that the embodiment of the present application can add the actual test results and related feature data of all battery cells (including the first category, the second category and the third category) of the previous batch to the existing test data set. This step helps to increase the diversity and richness of the data set, thereby improving the quality of model training, and regenerates a new capacity test model based on the merged data set, which will be used in the next batch. Through such a cyclic iterative process, the system can continuously learn and self-correct from each test, which not only improves the prediction accuracy, but also enhances the adaptability of the entire test system to external changes. This adaptability is an advantage that traditional static models do not have, enabling the new prediction system to maintain a high degree of reliability and robustness while ensuring high efficiency.

[0040] In one embodiment of the present application, before obtaining the current batch of battery cells to be tested, it also includes: obtaining an initial test data set; establishing an initial test model based on the initial test data set, and obtaining any batch of battery cells, and testing any batch of battery cells based on the initial test model to obtain a test error; updating the initial test model based on the test error to obtain a capacity test model.

[0041] The test data set includes at least one of a capacity data set, a liquid injection data set, an assembly data set, a formation data set, a second injection data set and a DC internal resistance measurement data set.

[0042] The embodiment of the present application can collect an initial test data set: collect data such as the complete current I 0～n ~Voltage V 0～n ~Time T O Data / Formation Current I 1～n ~Voltage V 1～n ~ time data T1 / electrode surface density data M1, etc., and establish a model α0 corresponding to the starting data. By selecting a new batch of battery cells, the capacity of the battery cells in the batch is predicted using the established initial test model, and the results predicted by the model are compared with the actual results of the batch of battery cells after actual capacity testing. The prediction error is calculated, and based on the error feedback, the parameter settings of the model are adjusted or different algorithms are tried to improve the prediction effect, which helps to continuously improve the accuracy of the capacity test model.

[0043] Furthermore, an initial test model is established based on the initial capacity test data set, including: calculating the covariance of each test data and capacity in the test data set, and determining the target test data based on the covariance; constructing multiple exponential regression equations for each target test data and capacity, and generating an initial test model based on the multiple exponential regression equations.

[0044] It is understandable that the embodiment of the present application can perform correlation analysis on all data corresponding to each battery cell and the capacity, and calculate the formation voltage V 11 The covariance X1 of C with the test capacity is obtained, and so on, the covariance X2, X3...Xn of each data with the test capacity C are obtained.

[0045] Furthermore, in the embodiment of the present application, the covariance values ​​can be sorted from large to small by data screening. After the sorting, the largest preset number of values ​​(such as 10 to 15) are selected, and the data tuples S corresponding to the selected values ​​are named S1, S2, S3...S10~15. The exponential regression between S1, S2, S3...S10~15 and C is calculated using methods such as R (least squares method as loss function), and the exponential regression equations fx are f1, f2, f3...f10~15 respectively. The exponential regression corresponding to S1, S2, S3...S10~15 assumes that the initial weight ratio of each data is the same, and the weight is set to wx0. After normalization, the initial test model is obtained:

[0046]

[0047] Furthermore, any batch of battery cells is tested based on the initial test model to obtain a test error, including: testing all battery cells in any batch to obtain a predicted capacity of each battery cell; selecting outlier battery cells among all battery cells and some battery cells other than outlier battery cells for testing to obtain predicted capacity; and calculating the prediction error of the initial test model based on the predicted capacity and the predicted capacity.

[0048] Specifically, the embodiment of the present application can import a batch of battery cells (a batch of battery cells 1000-50000ea) for prediction, first randomly select a batch of battery cells R1, and the other remaining battery cells are divided into test battery cells A1 and outlier battery cells B1. Using α0 as the judgment basis, the whole batch of battery cells is imported for prediction to obtain the capacity prediction value T, and the outlier battery cell B and the first battery cell R are subjected to a complete capacity test Cr1, and the data tuples corresponding to the selected values ​​are calculated to calculate the test error ex of fx corresponding to each tuple:

[0049]

[0050] The weight parameters of the model are further adjusted according to the test error to generate a new model α1. Through continuous data input and model iteration, the capacity prediction accuracy and robustness can be gradually improved. The specific formula is as follows:

[0051] fx=1 / 2ln((1-ex) / ex)

[0052]

[0053] In summary, the embodiment of the present application is based on fully automated self-convergence as a system strategy for long-term prediction, focuses on real-time fluctuations in actual status, and also takes historical data into consideration as a stage strategy. It can effectively distinguish between individual data fluctuations and changes in the overall system, greatly reducing the maintenance cost of the system and improving data stability.

[0054] Combine the following Figure 2 The method for testing the battery capacity of the embodiment of the present application is described in detail, including the following steps:

[0055] 1) Perform capacity testing as normal according to the conventional protocol and collect initial data;

[0056] 2) Establish the model α0 corresponding to the initial data;

[0057] 3) Classify the subsequent battery cells of a certain order of magnitude (1000-50000ea) as a batch;

[0058] A. Randomly select cells (accounting for 5% to 30% of the total)

[0059] B. Modeling and predicting overall marginal outlier data (accounting for 5% to 10% of the total)

[0060] C. Remaining batteries

[0061] 4) Test three types of batteries, conduct conventional capacity test on A / B type batteries, and conduct capacity adjustment test on C type batteries;

[0062] 5) Collect all process data A0 of category A;

[0063] 6) Add A0 data as new data to the starting data set as feedforward verification of the starting data set;

[0064] 7) After the feedforward verification is completed, the weights are adjusted through feedback to generate a new model α1.

[0065] 8) Repeat steps 3 to 7, using cyclic data modes such as feedforward and feedback to make real-time changes to the data and adjust historical weights to ensure the robustness of the data system.

[0066] According to the battery capacity test method proposed in the embodiment of the present application, by classifying the battery cells to be tested, using some of the battery cells for actual capacity testing, and using the pure fitting capacity method for other battery cells, the number of full discharge-charge cycles is significantly reduced, the overall test cost is reduced, and by continuously optimizing and updating the capacity test model, it is possible to maintain a high degree of reliability and robustness while maintaining a high prediction accuracy. Thus, the problems of high cost and low test accuracy in capacity testing in related technologies are solved.

[0067] Next, a device for testing the capacity of a battery cell according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0068] Figure 3 It is a block diagram of a device for testing the cell capacity of an embodiment of the present application.

[0069] like Figure 3As shown, the battery cell capacity testing device 10 includes: a first acquisition module 100 , a classification module 200 and a testing module 300 .

[0070] Among them, the first acquisition module 100 is used to obtain the current batch of battery cells to be tested; the classification module 200 is used to classify the battery cells to be tested into first to third categories of battery cells to be tested; the testing module 300 is used to perform capacity tests on the first and second categories of battery cells to be tested based on the capacity test model, and perform capacity adjustment tests on the three categories of battery cells to be tested, to generate test results corresponding to the current batch of battery cells to be tested, wherein the capacity test model is optimized based on the test results of the previous batch and the test data set.

[0071] In one embodiment of the present application, the battery cell capacity testing device 10 also includes: an optimization module for merging the test results of the previous batch into the test data set after generating the test results corresponding to the current batch of battery cells to be tested; and optimizing the capacity test model based on the merged test data set and the test results corresponding to the current batch of battery cells to be tested.

[0072] In one embodiment of the present application, the battery cell capacity testing device 10 also includes: a second acquisition module, used to obtain an initial test data set before obtaining the current batch of battery cells to be tested; an establishment module, used to establish an initial test model based on the initial test data set, and obtain any batch of battery cells, and test any batch of battery cells based on the initial test model to obtain a test error; an update module, used to update the initial test model according to the test error to obtain a capacity test model for the next batch.

[0073] In one embodiment of the present application, the establishment module is further used to calculate the covariance of each test data and capacity in the test data set, and determine the target test data based on the covariance; construct multiple exponential regression equations for each target test data and capacity, and generate an initial test model based on the multiple exponential regression equations.

[0074] In one embodiment of the present application, a module is established to further test all battery cells in any batch to obtain the predicted capacity of each battery cell; select outlier batteries among all battery cells and some battery cells other than outlier batteries for testing to obtain the predicted capacity; and calculate the prediction error of the initial test model based on the predicted capacity and the predicted capacity.

[0075] In one embodiment of the present application, the test data set includes at least one of a capacity data set, a liquid injection data set, an assembly data set, a formation data set, a two-injection data set, and a DC internal resistance measurement data set.

[0076] It should be noted that the above explanation of the embodiment of the method for testing the capacity of a battery cell is also applicable to the device for testing the capacity of a battery cell of this embodiment, and will not be repeated here.

[0077] According to the battery cell capacity test device proposed in the embodiment of the present application, by classifying the battery cells to be tested, using some of the battery cells for actual capacity testing, and using the pure fitting capacity method for other battery cells, the number of full discharge-charge cycles is significantly reduced, the overall test cost is reduced, and by continuously optimizing and updating the capacity test model, it is possible to maintain a high degree of reliability and robustness while maintaining a high prediction accuracy. Thus, the problems of high cost and low test accuracy in capacity testing in related technologies are solved.

[0078] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0079] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0080] When the processor 402 executes the program, the method for testing the battery cell capacity provided in the above embodiment is implemented.

[0081] Furthermore, the electronic device further comprises:

[0082] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0083] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0084] The memory 401 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0085] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0086] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0087] The processor 402 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0088] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned method for testing the capacity of a battery cell when the program is executed by a processor.

[0089] The embodiment of the present application also provides a computer program product, including: a computer program or instructions, which, when executed, implements the above-mentioned method for testing the capacity of a battery cell.

[0090] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0091] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0092] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0093] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0094] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0095] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for testing the capacity of a battery cell, characterized in that: The following steps are involved: Get the cells to be tested in the current batch; Classifying the battery cells to be tested to obtain first to third categories of battery cells to be tested; Based on the capacity test model, capacity tests are performed on the first type of battery cells to be tested and the second type of battery cells to be tested, and capacity adjustment tests are performed on the three types of battery cells to be tested, to generate test results corresponding to the current batch of battery cells to be tested, wherein the capacity test model is optimized based on the test results of the previous batch and the test data set.

2. The method for testing the battery capacity according to claim 1, characterized in that: After generating the test results corresponding to the current batch of cells to be tested, the method further includes: Merging the test results of the previous batch into the test data set; The capacity test model is optimized based on the merged test data set and the test results corresponding to the current batch of cells to be tested.

3. The method for testing the battery capacity according to claim 1, characterized in that: Before obtaining the current batch of cells to be tested, it also includes: Get the initial test dataset; Establishing an initial test model according to the initial test data set, acquiring any batch of battery cells, and testing the any batch of battery cells based on the initial test model to obtain a test error; The initial test model is updated according to the test error to obtain a capacity test model.

4. The method for testing the battery capacity according to claim 3, characterized in that: Establishing an initial test model according to the initial capacity test data set includes: Calculating the covariance between each test data and the capacity in the test data set, and determining the target test data according to the covariance; A plurality of exponential regression equations are constructed for each target test data and the capacity, and the initial test model is generated according to the plurality of exponential regression equations.

5. The method for testing the battery capacity according to claim 4, characterized in that: Based on the initial test model, any batch of cells are tested to obtain the test error, including: Testing all the battery cells in the arbitrary batch to obtain the predicted capacity of each battery cell; Selecting outlier cells from all the cells and some cells other than the outlier cells for testing to obtain predicted capacities; A prediction error of the initial test model is calculated based on the predicted capacity and the predicted capacity.

6. The method for testing the battery capacity according to any one of claims 1 to 5, characterized in that: The capacity test data set includes at least one of a capacity data set, a liquid injection data set, an assembly data set, a formation data set, a second injection data set and a direct current internal resistance measurement data set.

7. A battery cell capacity testing device, characterized in that: include: An acquisition module is used to acquire the cells to be tested in the current batch; A classification module, used for classifying the battery cells to be tested into first to third categories of battery cells to be tested; A testing module is used to perform capacity tests on the first type of battery cells to be tested and the second type of battery cells to be tested based on a capacity test model, perform capacity adjustment tests on the three types of battery cells to be tested, and generate test results corresponding to the current batch of battery cells to be tested, wherein the capacity test model is optimized based on the test results of the previous batch and the test data set.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for testing the capacity of a battery cell as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed, the method for testing the battery cell capacity according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising: A computer program or instruction, characterized in that when the computer program or instruction is executed, it implements the method for testing the battery cell capacity according to any one of claims 1 to 6.

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