Cell capacity prediction method, device, equipment, medium and program system
By predicting the capacity of lithium battery cells, using design parameters and charging thresholds, the problem of resource occupancy and strict temperature control is solved by the battery cell capacity division process, and efficient and low-cost battery cell capacity prediction is achieved.
Patent Information
- Application Number
- CN202411607908.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-12
- Publication Date
- 2025-03-04
AI Technical Summary
In the prior art, the battery cell capacity separation process occupies a large amount of field and equipment, extends the process time, and strictly controls the ambient temperature, otherwise the battery cell capacity test deviation will be large.
By obtaining the design parameters of the target battery cell, determining its theoretical capacity, and predicting the capacity of the battery cell based on the charging capacity when the charging volume reaches the preset charging threshold, canceling the capacity separation process, and reducing the battery cell cost.
It realizes that the battery cell capacity can be predicted without the capacity separation process, and the control capacity deviation is within 1.5%, meeting product demands and reducing battery cell manufacturing costs.
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Figure CN119556145A_ABST
Abstract
Description
Technical Field
[0001] Embodiments of the present invention relate to the technical field of lithium batteries, and in particular to a method, device, equipment, medium and program system for predicting the capacity of a battery cell. Background Art
[0002] At present, lithium battery safety accidents occur frequently. In-depth analysis of the reasons shows that the main safety hazards are spontaneous combustion, leakage, explosion and other safety hazards at the battery cell level. For battery packs or battery clusters or system integration products that need to be grouped, if the consistency of the cells in the group cannot meet the actual working conditions, it may cause the performance of a small number of single cells to decay rapidly, causing the battery pack to fail prematurely, and at the same time causing a series of safety problems.
[0003] At present, in order to ensure the consistency of battery cells, the battery industry usually uses capacity, internal resistance, voltage, K value, weight, etc. to make unified grouping. Most lithium battery companies still use the full capacity inspection method to perform the capacity division process to determine the capacity of each battery cell. However, the capacity division process not only takes up a lot of space and equipment, prolongs the process time, but also requires strict control of the ambient temperature of the capacity division, otherwise the battery cell capacity test will have a large deviation. Summary of the invention
[0004] The purpose of the present invention is to at least provide a method, device, equipment, medium and program system for predicting the capacity of a battery cell, which can at least solve the problem that the capacity division process in the prior art not only occupies a large amount of space and equipment, prolongs the process time, but also requires strict control of the ambient temperature of the capacity division, otherwise the battery cell capacity test deviation is large. After using the method, the capacity division process can be directly cancelled, the battery cell cost can be reduced as much as possible, and the deviation of the predicted capacity can be controlled within 1.5% to meet current product needs.
[0005] To solve the above technical problems, this application proposes five aspects.
[0006] In a first aspect, the present application provides a method for predicting the capacity of a battery cell, comprising: obtaining target design parameters of a target battery cell; determining a theoretical capacity of the target battery cell based on the target design parameters; determining a first formation charging capacity when the charge amount reaches a preset charging threshold based on the theoretical capacity; and predicting a predicted capacity of the target battery cell based on the first formation charging capacity.
[0007] In some embodiments, determining the first formation charging capacity when the charge amount reaches a preset charging threshold based on the theoretical capacity includes: obtaining the first efficiency of the target battery cell and the preset charging threshold; determining the first formation charging capacity based on the product of the quotient of the theoretical capacity and the first efficiency and the preset charging threshold.
[0008] In some embodiments, predicting the predicted capacity of the target battery cell based on the first formation charging capacity includes: obtaining a target association model corresponding to the preset charging threshold, the target association model being used to express the association between the first formation charging capacity and the actual divided capacity under the preset charging threshold; predicting the predicted capacity of the target battery cell based on the first formation charging capacity and the target association model.
[0009] In some embodiments, the obtaining of the target association model corresponding to the preset charging threshold comprises: designing a plurality of test cell groups with different design parameters, wherein each of the test cell groups comprises a plurality of reference cell groups, and each of the reference cell groups comprises a plurality of reference cells; charging the reference cells of each reference cell group in each of the test cell groups to different charging depths; performing a capacity division operation on each reference cell to obtain an actual capacity division discharge capacity of each reference cell; drawing fitting curves at different charging depths according to the charging depth, design parameters and actual capacity division discharge capacity of each reference cell; determining the degree of fit of each fitting curve; and determining the preset charging threshold and the target association model according to the degree of fit of each fitting curve.
[0010] In some embodiments, the fitting curves of different charging depths are drawn according to the charging depth, design parameters and actual fractional discharge capacity of each reference battery cell, including: determining the second formation charging capacity of each reference battery cell according to the charging depth and design parameters of each reference battery cell; and drawing the fitting curves of each reference battery cell at different charging depths according to the second formation charging capacity of each reference battery cell at different charging depths and the actual fractional discharge capacity.
[0011] In some embodiments, determining the preset charging threshold and the target association model according to the degree of fit of each fitting curve includes: obtaining a preset degree of fit threshold; determining the charging depth corresponding to the fitting curve whose degree of fit is greater than or equal to the degree of fit threshold as the preset charging threshold; and determining the fitting curve corresponding to the preset charging threshold as the target association model.
[0012] In the second aspect, the present application proposes a device for predicting the power of a battery cell, comprising: a first acquisition module, used to acquire target design parameters of a target battery cell; a first determination module, used to determine the theoretical capacity of the target battery cell according to the target design parameters; a second determination module, used to determine a first formation charging capacity when the charge amount reaches a preset charging threshold according to the theoretical capacity; and a first prediction module, used to predict the predicted capacity of the target battery cell according to the first formation charging capacity.
[0013] In a third aspect, the present application proposes a computer electronic production device, characterized in that it includes a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of any one of the methods described in the first aspect.
[0014] In a fourth aspect, the present application proposes a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of any one of the methods described in the first aspect are implemented.
[0015] In a fifth aspect, the present application proposes a computer program product, comprising a computer program, which implements the steps of any one of the methods described in the first aspect when executed by a processor.
[0016] The present application solves the problem in the prior art that the capacity division process not only occupies a large amount of space and equipment, prolongs the process time, but also requires strict control of the ambient temperature for the capacity division, otherwise the deviation of the battery cell capacity test will be large. After using the present method, the capacity division process can be directly cancelled, the battery cell cost can be reduced as much as possible, and at the same time, the deviation of the predicted capacity can be controlled within 1.5%, which can meet the current product demand. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] One or more embodiments are exemplarily described by the pictures in the corresponding drawings, and these exemplary descriptions do not constitute limitations on the embodiments.
[0018] Figure 1 A main flow chart of a method for predicting cell capacity provided in an embodiment of the present application;
[0019] Figure 2 This is a fitting curve diagram at different charging depths provided in the embodiment of the present application;
[0020] Figure 3 This is a main structural block diagram of a device for predicting cell capacity provided in an embodiment of the present application. DETAILED DESCRIPTION
[0021] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the present application, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can also be implemented. The division of the following embodiments is for the convenience of description, and the specific implementation of the present application should not be construed as any limitation, and the various embodiments can be combined and referenced with each other under the premise of no contradiction.
[0022] At present, lithium battery safety accidents occur frequently. In-depth analysis of the reasons shows that the main safety hazards are spontaneous combustion, leakage, explosion and other safety hazards at the battery cell level. For battery packs or battery clusters or system integration products that need to be grouped, if the consistency of the cells in the group cannot meet the actual working conditions, it may cause the performance of a small number of single cells to decay rapidly, causing the battery pack to fail prematurely, and at the same time causing a series of safety problems.
[0023] At present, in order to ensure the consistency of battery cells, the battery industry usually uses capacity, internal resistance, voltage, K value, weight, etc. to make unified grouping. Most lithium battery companies still use the full capacity inspection method to perform the capacity division process to determine the capacity of each battery cell. However, the capacity division process not only takes up a lot of space and equipment, prolongs the process time, but also requires strict control of the ambient temperature of the capacity division, otherwise the battery cell capacity test will have a large deviation.
[0024] In order to solve the above technical problems, the present invention proposes a method for predicting the capacity of a battery cell. The implementation details of the method for determining the bandwidth of this embodiment are specifically described below. The following content is only the implementation details provided for easy understanding and is not necessary for implementing this solution.
[0025] Embodiment 1:
[0026] The present application provides a method for predicting the capacity of a battery cell, and the method is applied to electronic production equipment, and the electronic production equipment can be a server, a mobile terminal, a computer, a cloud platform, etc. The functions implemented by the production equipment data processing provided in the embodiment of the present application can be implemented by calling a program code by a processor of the electronic production equipment, wherein the program code can be stored in a computer storage medium. Figure 1 As shown, the method for predicting the cell capacity includes:
[0027] Step S1: Obtain target design parameters of a target battery cell.
[0028] The main design parameters of the battery cell include the number of positive electrode layers, the number of negative electrode layers and the number of separator layers. The capacity of the battery cells with different design parameters is different. Therefore, if you want to predict the capacity of the target battery cell, you must first determine the design parameters of the target battery cell, that is, the target design parameters.
[0029] Step S2: Determine the theoretical capacity of the target battery cell according to the target design parameters.
[0030] The capacity of a battery cell will be affected by the area of each layer of positive and negative electrodes in the battery cell, and generally the area of each layer of positive and negative electrodes is the same for a battery cell, so the theoretical capacity of the entire target battery cell can be determined by determining the theoretical capacity of each layer of positive and negative electrodes. However, in the production process of the battery cell, it cannot be completely guaranteed that the area of each layer of positive electrodes and accessories reaches the set area accurately. Therefore, it is necessary to perform a capacity division process on each battery cell in the prior art to determine its actual capacity division capacity.
[0031] Step S3: determining a first formation charging capacity when the charging amount reaches a preset charging threshold according to the theoretical capacity.
[0032] In some embodiments, step S3 “determine, according to the theoretical capacity, a first formation charging capacity when the charging amount reaches a preset charging threshold” includes:
[0033] Step S31: obtaining the initial efficiency of the target battery cell and the preset charging threshold.
[0034] Step S32: determining the first formation charging capacity according to the product of the quotient of the theoretical capacity and the first efficiency and the preset charging threshold.
[0035] In a general capacity division process, the battery cell will be subjected to a charge and discharge test, and the actual capacity division capacitance of the battery cell will be determined by the charge and discharge test. However, when the lithium battery is subjected to the capacity division process, there will be a first efficiency, and the first efficiency of the battery cell is affected by the battery material. For a battery cell with a determined material, its first efficiency is also determined. The method for realizing the capacity prediction of the battery cell in the present application is mainly determined based on the fitting curve of the formation charging capacity and the actual capacity division capacity measured in advance, but this fitting curve needs to charge and discharge the battery cell during production, so the battery cell will have a first efficiency problem, and in order to better predict the predicted capacity of the target battery cell, we need to determine the corresponding formation charging capacity if the target battery cell is subjected to the capacity division process according to the target charging depth and the preset charging threshold of the target battery cell and the first efficiency of the battery cell, i.e., the first formation charging capacity of the present application.
[0036] Therefore, in the present application, the first formation charging capacity = theoretical capacity / first efficiency*preset charging threshold.
[0037] Step S4: predicting the predicted capacity of the target battery cell according to the first formation charging capacity.
[0038] After the first formation charging capacity of the target battery cell is obtained in step S3, the corresponding sub-capacity, ie, the predicted capacity of the present application, can be determined according to the fitting curve corresponding to the preset charging threshold of the target battery cell.
[0039] The overall solution of the present application is to determine the theoretical capacity based on the design parameters of the target battery cell, and then calculate the formation charging capacity of the target battery cell if it undergoes a capacity division process based on the theoretical capacity, the material of the battery cell, and the target charging depth. After the formation charging capacity is obtained, the corresponding predicted capacity can be determined based on the fitting curve corresponding to the preset charging threshold formed during the test and the formation charging capacity.
[0040] In some embodiments, step S4 of “predicting the predicted capacity of the target battery cell according to the first formation charging capacity” includes:
[0041] Step S41: acquiring a target association model corresponding to the preset charging threshold, wherein the target association model is used to express the association relationship between the first formation charging capacity and the actual divided capacity under the preset charging threshold.
[0042] In order to ensure the accuracy of the prediction results, how to select the appropriate charging depth to ensure the accuracy of the results is the focus of this application. Therefore, in order to ensure the accuracy of the prediction results, this application needs to obtain the appropriate charging depth as the preset charging threshold through experiments.
[0043] Therefore, in some embodiments, step S41 of "obtaining a target association model corresponding to the preset charging threshold" includes:
[0044] Step S411: designing a plurality of test cell groups with different design parameters, wherein each of the test cell groups includes a plurality of reference cell groups, and each of the reference cell groups includes a plurality of reference cells.
[0045] Step S412: charging the reference cells of each reference cell group in each test cell group to different depths of charge.
[0046] Step S413: performing a capacity division operation on each reference battery cell to obtain an actual capacity division discharge capacity of each reference battery cell.
[0047] Step S414: drawing fitting curves at different charging depths according to the charging depths, design parameters and the actual divided-capacity discharge capacities of the reference cells.
[0048] In some embodiments, step S414 of “drawing fitting curves at different charging depths according to the charging depths, design parameters and the actual divided capacity discharge capacities of each reference battery cell” includes:
[0049] Step S4141: determining the second formation charging capacity of each reference battery cell according to the charging depth and design parameters of each reference battery cell.
[0050] Step S4142: drawing fitting curves of each reference cell at different charging depths according to the second formation charging capacity of each reference cell at different charging depths and the actual fractional discharge capacity.
[0051] Step S415: Determine the fitting degree of each fitting curve.
[0052] Step S416: determining the preset charging threshold and the target association model according to the fitting degrees of each fitting curve.
[0053] In some embodiments, step S416 “determining the preset charging threshold and the target association model according to the fitting degree of each fitting curve” includes:
[0054] Step S4161: Obtain a preset fitting threshold.
[0055] Step S4162: Determine the charging depth corresponding to the fitting curve whose fitting degree is greater than or equal to the fitting degree threshold as the preset charging threshold.
[0056] Step S4163: determining the fitting curve corresponding to the preset charging threshold as the target association model.
[0057] Step S42: predicting the predicted capacity of the target battery cell according to the first formation charging capacity and a target association model.
[0058] Through steps S41 to S4163, we obtain a suitable charging depth as a preset charging threshold and a suitable fitting curve, i.e., the target association model in this application. Finally, the prediction result of the target battery cell capacity, i.e., the predicted capacity, can be realized according to the design parameters of the target battery cell.
[0059] The present application solves the problem in the prior art that the capacity division process not only occupies a large amount of space and equipment, prolongs the process time, but also requires strict control of the ambient temperature for the capacity division, otherwise the deviation of the battery cell capacity test will be large. After using the present method, the capacity division process can be directly cancelled, the battery cell cost can be reduced as much as possible, and at the same time, the deviation of the predicted capacity can be controlled within 1.5%, which can meet the current product demand.
[0060] Embodiment 2:
[0061] In order to better explain the technical solution of the present application, the present application will be described below in combination with detailed data. First, the process in the test phase is as follows:
[0062] Taking the lithium iron phosphate system stacking structure as an example, the present invention adopts the following technical solutions:
[0063] Step 1: Lamination process, stack the following materials into 1pcs bare cell respectively (Note: the finished cells are named Cell 1, Cell 2, Cell 3, Cell 4, Cell 5, where the capacity of each layer of positive and negative electrodes is set to 2Ah, so the theoretical capacity of each type of cell is 24Ah, 32Ah, 40Ah, 48Ah, 56Ah respectively)
[0064] 1) The number of positive electrode, negative electrode and separator layers are 12, 13 and 26 respectively
[0065] 2) The number of positive electrode, negative electrode and separator layers are 16, 17 and 34 respectively
[0066] 3) The number of positive electrode, negative electrode and separator layers are 20, 21 and 42 respectively
[0067] 4) The number of positive electrode, negative electrode and separator layers are 24, 25 and 50 respectively
[0068] 5) The number of positive electrode, negative electrode and separator layers are 28, 29 and 58 respectively
[0069] Step 2: The above bare cells are produced normally according to the welding, packaging and liquid injection processes
[0070] Step 3: Cell 1, Cell 2, Cell 3, Cell 4, Cell 5 are grouped into 20 cells each. Each group is charged to 30% SOC, 50% SOC, 70% SOC, and 90% SOC, 5 cells each. The formation charge capacity is marked as X. The process of different formation charge depths is as follows:
[0071] 1) Let stand for 2 minutes;
[0072] 2) 0.2C constant current charging for 30 minutes;
[0073] 3) 0.5C constant current charging to 3.37V, 3.39V, 3.41V, 3.45V (corresponding to SOC 30% SOC, 50% SOC, 70% SOC, 90% SOC respectively)
[0074] 4) End
[0075] Note: "C" refers to the rate, such as cell 1 0.2C constant current charging means that cell 1 is charged with a constant current of 4.8A; "SOC" refers to State of Charge, charging depth; "min" refers to Minute, and pcs is the quantifier for a single cell.
[0076] Step 4: The above groups of cells with different charging depths are further divided into different capacities. The actual divided discharge capacity is marked as Y. The division process is as follows:
[0077] 1) Let stand for 2 minutes;
[0078] 2) 0.5C charging to 3.65V, 0.05C cut-off;
[0079] 3) Let stand for 2 minutes;
[0080] 4) 0.5C discharge to 2.5V
[0081] 5) End
[0082] According to the formation charge capacity X and the actual capacity distribution discharge capacity Y, linear fitting is performed for different charge SOCs. Figure 2 It can be seen from the four figures that: the higher the formation charge SOC, the higher the fitting degree, and the better the correlation between the formation charge capacity X and the actual divided capacity discharge capacity Y; when the charging depth is above 70%, the formation charge capacity X and the actual divided capacity discharge capacity Y have a strong correlation, and the fitting degree of the fitting curve meets the preset fitting degree, so 70% of the charging depth is selected as the preset charging threshold in this application, and the fitting curve corresponding to 70% charging depth is the target association model corresponding to the preset charging threshold in this application.
[0083] In the prediction stage of the target battery cell, the positive electrode, negative electrode, and separator layers are set to 20, 21, and 42 layers respectively to form 1 pcs of bare battery cell, whose theoretical capacity is 40Ah, and the charging depth of 70% is used as the preset charging threshold. At this time, the first formation depth obtained is 31.81Ah, and the predicted capacity corresponding to the target battery cell according to the fitting curve corresponding to the 70% charging depth is 40.673Ah.
[0084] In order to ensure the accuracy of the results, this application also further verifies the predicted capacity, and the verification process is as follows:
[0085] Step 1: Lamination process, stack the positive electrode, negative electrode, and separator layers of 20, 21, and 42 layers respectively into 1 pcs of bare battery cell, with a theoretical capacity of 40Ah.
[0086] Step 2: The above bare cells are produced normally according to the welding, packaging and liquid injection processes.
[0087] Step 3: There are 500 cells in total, and the charge depth is 70%. The charging process is as follows:
[0088] 1) Let stand for 2 minutes;
[0089] 2) 0.2C constant current charging for 30 minutes;
[0090] 3) 0.5C constant current charging to 3.41V (corresponding to a charging depth of 70% SOC)
[0091] 4) End
[0092] Step 4: The cells are further divided into different capacities. The process is as follows:
[0093] 1) Let stand for 2 minutes;
[0094] 2) 0.5C charging to 3.65V, 0.05C cut-off;
[0095] 3) Let stand for 2 minutes;
[0096] 4) 0.5C discharge to 2.5V, end, and obtain the actual capacity Y of each battery cell.
[0097] The deviation is calculated by combining the predicted capacity C0 of each battery cell and the actual capacity Y. Finally, the deviation between the predicted capacitance and the actual divided capacitance at a charging depth of 70% is within 1.5%, which meets the industry requirements and standards. The cell capacity can be determined without the divided capacitance process, thereby reducing the cell manufacturing cost and improving the product cost advantage. This solves the problem in the prior art that the divided capacitance process not only occupies a large amount of space and equipment, prolongs the process time, but also requires strict control of the ambient temperature for divided capacitance, otherwise the cell capacity test will have a large deviation.
[0098] Embodiment 3:
[0099] Based on the foregoing embodiments, the embodiments of the present application provide a device for predicting the capacity of a battery cell. The modules included in the device and the units included in each module can be implemented by a processor in a computer device; of course, they can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU, Central Processing Unit), a microprocessor (MPU, Microprocessor Unit), a digital signal processor (DSP, Digital Signal Processing) or a field programmable gate array (FPGA, Field Programmable Gate Array), etc.
[0100] like Figure 3 As shown, a device for predicting the power of a battery cell by capacity distribution includes: a first acquisition module 1, a first determination module 2, a second determination module 3 and a first prediction module 4.
[0101] The first acquisition module 1 is used to acquire the target design parameters of the target battery cell. The first determination module 2 is used to determine the theoretical capacity of the target battery cell according to the target design parameters. The second determination module 3 is used to determine the first formation charging capacity when the charge reaches a preset charging threshold according to the theoretical capacity. The first prediction module 4 is used to predict the predicted capacity of the target battery cell according to the first formation charging capacity.
[0102] Each module in the above-mentioned device for predicting the capacity of a battery cell can be fully or partially implemented by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the device in the form of hardware, or can be stored in the memory in the processing device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.
[0103] Embodiment 4:
[0104] In a third aspect, the present application provides a computer electronic production device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any one of the methods described in the first aspect.
[0105] Among them, the memory and the processor are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor.
[0106] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.
[0107] Embodiment 5:
[0108] In a fourth aspect, the present application proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect.
[0109] Embodiment 6:
[0110] In a fifth aspect, the present application proposes a computer program product, comprising a computer program / instructions, characterized in that when the computer program is executed by a processor, the steps of any one of the methods described in the first aspect are implemented.
[0111] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as: ROM), random access memory (Random Access Memory, referred to as: RAM), disk or optical disk and other media that can store program codes.
[0112] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present application, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present application.
Claims
1. A method for predicting battery cell capacity, characterized in that: include: Obtain target design parameters of the target battery cell; Determining the theoretical capacity of the target battery cell according to the target design parameters; Determine a first formation charging capacity when the charging amount reaches a preset charging threshold value according to the theoretical capacity; The predicted capacity of the target battery cell is predicted according to the first formation charging capacity.
2. The method according to claim 1, characterized in that The determining, according to the theoretical capacity, a first formation charging capacity when the charging amount reaches a preset charging threshold value comprises: Obtaining the first efficiency of the target battery cell and the preset charging threshold; The first formation charging capacity is determined according to a product of a quotient of the theoretical capacity and the first efficiency and the preset charging threshold.
3. The method according to claim 1, characterized in that The predicting the predicted capacity of the target battery cell according to the first formation charging capacity includes: Acquire a target association model corresponding to the preset charging threshold, wherein the target association model is used to express the association relationship between the first formation charging capacity and the actual divided capacity under the preset charging threshold; The predicted capacity of the target battery cell is predicted according to the first formation charging capacity and a target association model.
4. The method according to claim 3, characterized in that The acquiring of the target association model corresponding to the preset charging threshold comprises: Design multiple groups of test cell groups with different design parameters, wherein each group of the test cell groups includes multiple reference cell groups, and each reference cell group includes multiple reference cells; Charge the reference cells of each reference cell group in each test cell group to different depths of charge; Performing a capacity division operation on each reference battery cell to obtain an actual capacity division discharge capacity of each reference battery cell; According to the charging depth, design parameters and the actual divided capacity discharge capacity of each reference battery cell, a fitting curve at different charging depths is drawn; Determine the goodness of fit of each fitting curve; The preset charging threshold and the target association model are determined according to the fitting degree of each fitting curve.
5. The method according to claim 4, characterized in that The method of drawing fitting curves of different charging depths according to the charging depths, design parameters and actual capacity-divided discharge capacities of the reference cells comprises: Determining the second formation charging capacity of each reference battery cell according to the charging depth and design parameters of each reference battery cell; According to the second formation charging capacity of each reference battery cell at different charging depths and the actual fractional discharge capacity, a fitting curve of each reference battery cell at different charging depths is drawn.
6. The method according to claim 4, characterized in that The step of determining the preset charging threshold and the target association model according to the fitting degree of each fitting curve includes: Get the preset fitness threshold; Determine the charging depth corresponding to the fitting curve whose fitting degree is greater than or equal to the fitting degree threshold as a preset charging threshold; The fitting curve corresponding to the preset charging threshold is determined as the target association model.
7. A device for predicting the amount of electricity in a battery cell, characterized in that: include: A first acquisition module is used to acquire target design parameters of a target battery cell; A first determination module, used to determine the theoretical capacity of the target battery cell according to the target design parameters; A second determination module is used to determine a first formation charging capacity when the charging amount reaches a preset charging threshold according to the theoretical capacity; The first prediction module is used to predict the predicted capacity of the target battery cell according to the first formation charging capacity.
8. A computer electronic production equipment, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.
Citation Information
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