Methods, apparatus, equipment, and storage media for predicting battery storage capacity loss

Abstract

The application relates to the technical field of batteries, in particular to a battery storage capacity loss prediction method and device, equipment and a storage medium, wherein the method comprises the following steps: obtaining capacity attenuation data and impedance data of a test battery under different test temperatures and different test cycle numbers; fitting the capacity attenuation data and the impedance data to obtain a capacity attenuation model of the test battery; and predicting the storage capacity loss of a target battery by using the capacity attenuation model. Therefore, the problems of low prediction accuracy of the battery storage capacity loss and complex prediction methods in the related art are solved.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a method, apparatus, device, and storage medium for predicting battery storage capacity loss. Background Technology

[0002] Lithium-ion batteries are widely used in portable electronic devices, electric vehicles, and energy storage systems due to their high energy density, long cycle life, and low self-discharge rate. Cycle life is a crucial aspect of battery performance; therefore, accurately predicting battery cycle life, or storage capacity, is essential for extending battery lifespan and improving system safety.

[0003] Related technologies rely on experience and historical data to predict battery storage capacity loss using simple statistical methods. However, this approach depends on a large amount of historical data and cannot accurately reflect battery performance changes under different conditions, resulting in low prediction accuracy. Alternatively, complex mathematical models can be built based on the battery's internal physical and chemical processes to predict storage capacity loss. However, this method requires a large amount of experimental data and complex calculations for the physical model, and the model parameters are difficult to determine, limiting its applicability. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for predicting battery storage capacity loss, in order to solve the problems of low prediction accuracy and complex prediction methods in related technologies.

[0005] The first aspect of this application provides a method for predicting battery storage capacity loss, comprising the following steps: acquiring capacity decay data and impedance data of a test battery under different test temperatures and different test cycle numbers; fitting the capacity decay data and impedance data to obtain a capacity decay model of the test battery; and using the capacity decay model to predict the storage capacity loss of a target battery.

[0006] Optionally, in one embodiment of this application, fitting capacity decay data and impedance data to obtain a capacity decay model of the test battery includes: establishing a capacity decay model; obtaining fitting results of capacity decay data and impedance data; using the fitting results to correct the model parameters of the capacity decay model until the capacity decay model meets preset conditions, then stopping the correction of the model parameters.

[0007] Optionally, in one embodiment of this application, the capacity decay model is:

[0008] f(x) = ax 4 +bx 3 +cx 2 +dx+e;

[0009] Where f(x) is the battery capacity retention rate, x is the battery impedance, and a, b, c, d, and e are model parameters, which are constants.

[0010] Optionally, in one embodiment of this application, obtaining capacity decay data and impedance data of the test battery under different test temperatures and different test cycle numbers includes: obtaining the remaining storage capacity of the test battery under different test temperatures and different test cycle numbers; obtaining the actual storage capacity of the test battery before the test, and calculating the capacity decay data based on the actual storage capacity and the remaining storage capacity.

[0011] Optionally, in one embodiment of this application, obtaining the fitting results of capacity decay data and impedance data includes: preprocessing the capacity decay data and impedance data, wherein the preprocessing includes at least one of data cleaning, data standardization and data normalization; and fitting the preprocessed data using the least squares method to obtain the fitting results.

[0012] Optionally, in one embodiment of this application, the test cycle process includes: obtaining the target charging current and target discharging current of the test battery; charging and discharging according to the target charging current and target discharging current until the storage capacity of the test battery reaches the preset calibrated capacity, and then stopping the test.

[0013] Optionally, in one embodiment of this application, after obtaining the capacity decay model of the test battery by fitting the capacity decay data and impedance data, the method further includes: obtaining the expansion force of the test battery under different test temperatures and different test cycles; and correcting the model parameters of the capacity decay model based on the expansion force, wherein the trend of the expansion force is proportional to the trend of the capacity decay.

[0014] A second aspect of this application provides a device for predicting battery storage capacity loss, comprising: an acquisition module for acquiring impedance data of a test battery under different test temperatures and different test cycle numbers; a fitting module for fitting the capacity decay data and impedance data to obtain a capacity decay model of the test battery; and a prediction module for predicting the storage capacity loss of a target battery using the capacity decay model.

[0015] Optionally, in one embodiment of this application, the fitting module is further configured to: establish a capacity decay model; obtain fitting results of capacity decay data and impedance data; and use the fitting results to correct the model parameters of the capacity decay model until the capacity decay model meets preset conditions, at which point the correction of the model parameters is stopped.

[0016] Optionally, in one embodiment of this application, the capacity decay model is:

[0017] f(x) = ax 4 +bx3 +cx 2 +dx+e;

[0018] Where f(x) is the battery capacity retention rate, x is the battery impedance, and a, b, c, d, and e are model parameters, which are constants.

[0019] Optionally, in one embodiment of this application, the acquisition module is further configured to: acquire the remaining storage capacity of the test battery under different test temperatures and different test cycles; acquire the actual storage capacity of the test battery before the test; and calculate capacity decay data based on the actual storage capacity and the remaining storage capacity.

[0020] Optionally, in one embodiment of this application, the acquisition module is further configured to: preprocess the capacity attenuation data and impedance data, wherein the preprocessing includes at least one of data cleaning, data standardization and data normalization; and fit the preprocessed data using the least squares method to obtain a fitting result.

[0021] Optionally, in one embodiment of this application, the test cycle process includes: obtaining the target charging current and target discharging current of the test battery; charging and discharging according to the target charging current and target discharging current until the storage capacity of the test battery reaches the preset calibrated capacity, and then stopping the test.

[0022] Optionally, in one embodiment of this application, it further includes: a correction module, used to obtain the expansion force of the test battery under different test temperatures and different test cycles after obtaining the capacity decay model of the test battery by fitting the capacity decay data and impedance data; and to correct the model parameters of the capacity decay model based on the expansion force, wherein the trend of the expansion force is proportional to the trend of the capacity decay.

[0023] A third aspect of this 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 perform a battery storage capacity loss prediction method as described above.

[0024] A fourth aspect of this application provides a computer-readable storage medium having a computer program or instructions stored thereon, which are executed by a processor to perform the battery storage capacity loss prediction method as described above.

[0025] Therefore, this application has at least the following beneficial effects:

[0026] This application's embodiments can acquire capacity decay and impedance data of the test battery during different test temperatures and test cycles. By fitting the capacity decay and impedance data, a capacity decay model at different temperatures can be constructed. This eliminates the need for significant testing resources and time, greatly saving testing costs and improving efficiency. Furthermore, the battery capacity retention rate can be quickly and accurately predicted using the impedance data, and the battery's storage capacity loss can be determined based on the capacity retention rate. Therefore, this solves the technical problems of low prediction accuracy and complex prediction methods for battery storage capacity loss in related technologies.

[0027] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0028] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0029] Figure 1 This is a flowchart of a method for predicting battery storage capacity loss according to an embodiment of this application;

[0030] Figure 2 This is an example diagram of a battery storage capacity loss prediction device provided according to an embodiment of this application;

[0031] Figure 3 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0032] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0033] Before describing the solution of this application, the following relevant technologies will be introduced:

[0034] Related technology 1 provides a method, apparatus, and storage medium for predicting battery cycle life. The battery cycle life prediction method includes: performing periodic charge-discharge cycles on the battery under test at a set number of charge-discharge cycles; determining the internal resistance and capacity retention rate of the battery under test after each charge-discharge cycle; and predicting the cycle life of the battery under test based on the internal resistance and capacity retention rate. However, this method requires a long time to evaluate the battery cycle life, but it has high requirements for data quality and model complexity.

[0035] Related technology 2 provides a method for rapidly testing the cycle life of lithium-ion batteries. This method includes constant-current charging of a lithium-ion battery under low-rate conditions after it has been discharged and allowed to rest, charging it at a low rate to less than or equal to 80% of the battery's design capacity. The method reduces the charging current when the battery is at low SOC, making the newly formed SEI (Sediment Inlet) more compact during each charge, thus accelerating the reaction and consumption of available lithium ions inside the battery. Simultaneously, it accelerates lithium-ion reaction consumption, electrode material degradation, and electrolyte decomposition by increasing the charging cutoff voltage during conventional high-temperature intermittent cycling. However, this method has high requirements for testing equipment and conditions and may pose a risk of battery damage.

[0036] Related technology 3 provides a method for rapidly testing the cycle life of lithium-ion batteries. The method includes constant-current discharge of the lithium-ion battery to a lower discharge limit voltage Ulower' under a constant-current discharge current I, where the constant-current discharge current I is less than a known constant-current discharge current I', and the lower discharge limit voltage Ulower' is less than a known lower discharge limit voltage Ug'. The method achieves rapid testing of the lithium-ion battery cycle life by setting multiple discharge stages in the lithium-ion battery cycle discharge process. By lowering the discharge cutoff voltage, the SEI film inside the battery is more severely damaged during discharge. Consequently, more usable lithium ions are consumed during subsequent charging due to SEI film repair, accelerating the reaction and consumption of usable lithium ions inside the battery. However, this method has high requirements for testing equipment and conditions and may cause significant damage to the battery.

[0037] The following description, with reference to the accompanying drawings, outlines a method, apparatus, device, and storage medium for predicting battery storage capacity loss according to embodiments of this application. Addressing the issues mentioned in the background section regarding the inability to accurately reflect battery performance changes under different conditions, low prediction accuracy, and complex prediction methods, this application provides a method for predicting battery storage capacity loss. This method acquires capacity decay data and impedance data of the test battery at different test temperatures and different test cycles. It then fits the capacity decay data and impedance data to construct a capacity decay model at different temperatures. This eliminates the need for significant testing resources and time, greatly saving testing costs and improving efficiency. Furthermore, the battery capacity retention rate can be quickly and accurately predicted using the impedance data, and the battery storage capacity loss can be determined based on the battery capacity retention rate. Therefore, this method solves the problems of low prediction accuracy and complex prediction methods for battery storage capacity loss in related technologies.

[0038] Specifically, Figure 1 This is a flowchart illustrating a method for predicting battery storage capacity loss provided in an embodiment of this application.

[0039] like Figure 1As shown, the method for predicting battery storage capacity loss includes the following steps:

[0040] In step S101, capacity decay data and impedance data of the test battery under different test temperatures and different test cycles are obtained.

[0041] The test battery, test temperature, and number of test cycles can be set according to specific circumstances, and there is no specific limit to the number of cycles. This application uses a 3.33Ah soft-pack lithium iron phosphate battery as an example and test temperatures of 25℃, 0℃, and 45℃ as test temperatures. Impedance data can be obtained by performing EIS (Electrochemical Impedance Spectroscopy) scans on the test battery to facilitate the subsequent construction of a capacity decay model.

[0042] It is understood that the embodiments of this application can obtain capacity decay data and impedance data under different test temperatures and different test cycles, such as capacity decay data and impedance data after 100 cycles at 25°C.

[0043] In this embodiment of the application, obtaining capacity decay data and impedance data of the test battery under different test temperatures and different test cycle numbers includes: obtaining the remaining storage capacity of the test battery under different test temperatures and different test cycle numbers; obtaining the actual storage capacity of the test battery before the test, and calculating the capacity decay data based on the actual storage capacity and the remaining storage capacity.

[0044] It is understood that the embodiments of this application can obtain the remaining storage capacity of the test battery under different test temperatures and different test cycles, and calculate the capacity decay data based on the actual storage capacity before the test and the remaining storage capacity.

[0045] For example, if a battery is tested at 0°C and its remaining storage capacity is 990mAh, while its actual storage capacity is 1000mAh, then the calculated capacity decay data is 99%.

[0046] In this embodiment of the application, the test cycle process includes: obtaining the target charging current and target discharging current of the test battery; charging and discharging according to the target charging current and target discharging current until the storage capacity of the test battery reaches the preset calibrated capacity, and then stopping the test.

[0047] The target charging current and target discharging current can be pre-calibrated, and the preset calibration capacity can be set according to specific circumstances, such as 80% of the calibration capacity of the test battery.

[0048] It is understood that the embodiments of this application can cycle charge and discharge the test battery according to the target charging current and the target discharging current until the storage capacity of the test battery reaches the preset calibrated capacity, and then stop the test. The test process is relatively simple, and the test does not require a lot of test resources and time, which can greatly save test costs and improve efficiency.

[0049] In step S102, the capacity decay data and impedance data are fitted to obtain the capacity decay model of the test battery.

[0050] It is understood that the embodiments of this application can fit the capacity decay data and impedance data to obtain the capacity decay model of the test battery, and the specific steps are as follows.

[0051] In this embodiment of the application, the capacity decay model of the test battery is obtained by fitting capacity decay data and impedance data, including: establishing a capacity decay model; obtaining the fitting results of capacity decay data and impedance data; using the fitting results to correct the model parameters of the capacity decay model until the capacity decay model meets the preset conditions, and then stopping the correction of the model parameters.

[0052] The preset condition can be that the error between the capacity retention rate calculated using the capacity decay model and the actual capacity retention rate is 1%.

[0053] It is understood that the embodiments of this application can establish a decay model, the fitting results of capacity decay data and impedance data, and use the fitting results to correct the model parameters of the capacity decay model until the capacity decay model meets the preset conditions, then stop correcting the model parameters, to ensure the accuracy and robustness of the model, and obtain the capacity decay model of battery and impedance related at different temperatures.

[0054] In this embodiment of the application, the capacity decay model is as follows:

[0055] f(x) = ax 4 +bx 3 +cx 2 +dx+e;

[0056] Where f(x) is the battery capacity retention rate, x is the battery impedance, and a, b, c, d, and e are model parameters, which are constants.

[0057] In this embodiment of the application, obtaining the fitting results of capacity decay data and impedance data includes: preprocessing the capacity decay data and impedance data, wherein the preprocessing includes at least one of data cleaning, data standardization and data normalization; and fitting the preprocessed data using the least squares method to obtain the fitting results.

[0058] It is understood that the embodiments of this application can preprocess the capacity decay data and impedance data, and use the least squares method to fit the preprocessed data to obtain the fitting result, so as to use the fitting result to correct the model parameters of the capacity decay model, ensure the optimal estimation of the model parameters, and improve the prediction accuracy of the subsequent model.

[0059] In this embodiment of the application, after obtaining the capacity decay model of the test battery by fitting the capacity decay data and impedance data, the method further includes: obtaining the expansion force of the test battery under different test temperatures and different test cycles; and correcting the model parameters of the capacity decay model based on the expansion force, wherein the trend of the expansion force is proportional to the trend of the capacity decay.

[0060] Among them, the expansion force can be monitored and obtained using an in-situ dilatometer.

[0061] Since the trend of battery expansion force change is proportional to the trend of capacity decay change, the embodiments of this application can also obtain the expansion force of the test battery under different test temperatures and different test cycles, and correct the model parameters of the capacity decay model based on the expansion force, thereby improving the accuracy of the model.

[0062] In other words, as the number of cycles increases, the change in expansion force during charging and discharging gradually increases. The magnitude of the change in battery expansion force can be used to corroborate the gradually increasing battery degradation trend, thereby correcting the model parameters of the capacity degradation model.

[0063] In step S103, the storage capacity loss of the target battery is predicted using a capacity decay model.

[0064] It is understood that the embodiments of this application can use the capacity decay model to predict the storage capacity loss of the target battery, thereby achieving the prediction of the battery storage capacity.

[0065] The method for predicting battery storage capacity loss according to this application is described below through a specific embodiment, including the following steps:

[0066] 1. Select 6 parallel sample batteries, 2 parallel samples for each temperature, and conduct cyclic tests at 25℃, 0℃ and 45℃ respectively. The test ends when the capacity decays to 80% of the rated capacity.

[0067] 2. Monitor changes in battery expansion force during cycling using an in-situ dilatometer;

[0068] 3. Perform an EIS scan on the battery every 200 revolutions.

[0069] The specific test loop steps are as follows:

[0070] 1. Take 6 x 3.33Ah pouch lithium iron phosphate batteries;

[0071] 2. Take two batteries from each of the three conditions and place them at 25℃, 0℃, and 45℃ respectively;

[0072] 3. Let stand for 2 hours in their respective environments;

[0073] 4. Discharge at a constant current of 1C to 2V;

[0074] 5. Let stand for 30 minutes;

[0075] 6. Charge to 3.65V using a constant current and constant voltage method (1C).

[0076] 7. Let stand for 30 minutes;

[0077] 8. Discharge at a constant current of 1C to 2V;

[0078] 9. Let stand for 30 minutes;

[0079] 10. The discharge capacity in step 8 is used as the calibration capacity C0;

[0080] 11. Charge 1C0 to 3.65V using constant current and constant voltage.

[0081] 12. Let stand for 30 minutes;

[0082] 13. ICO constant current discharge for 30 minutes;

[0083] 14. Let stand for 30 minutes;

[0084] 15. 1.5C0 constant current discharge for 10s;

[0085] 16. Let stand for 30 minutes;

[0086] 17. Discharge 1C0 at a constant current to 2V;

[0087] 18. Let stand for 30 minutes;

[0088] 19. Charge to 3.65V using a constant current and constant voltage method with 0.05C0.

[0089] 20. Let stand for 30 minutes;

[0090] 21. Discharge at a constant current of 0.05C0 to 2V;

[0091] 22. Let stand for 30 minutes;

[0092] 23. Charge 1C0 to 3.65V using constant current and constant voltage.

[0093] 24. Let stand for 30 minutes;

[0094] 25. Discharge 1C0 at a constant current to 2V;

[0095] 26. Let stand for 30 minutes;

[0096] 27. Repeat steps 23-26 for 50 cycles;

[0097] 28. Repeat steps 6-27 for 16 cycles;

[0098] The test was stopped when the battery reached 80% of its rated capacity, and the change in battery expansion force during the cycle was monitored using an in-situ dilatometer. Capacity decay curves and in-situ expansion curves at different temperatures were obtained using the above method. EIS scans were performed on the battery at 0, 200, 400, 600, and 800 cycles to obtain impedance data.

[0099] By fitting the cycling capacity decay data and impedance data, cycling capacity decay models at different temperatures were obtained. The capacity decay model at 25℃ is: f(x)=-0.0005x 3 +0.0109x 2 The function expression for -0.0877x + 1.2082; the capacity decay model at 0℃ is: f(x) = 0.000004x 3 -0.0048x 2 The function expression is +0.0315x + 0.9536; the capacity decay model at 45℃ is: f(x) = -0.00009x 4 +0.0026x 3 -0.0274x 2 The function expression is +0.1085x+0.8608, where x represents impedance and f(x) is the corresponding battery capacity retention rate.

[0100] Based on the above embodiments, the battery storage capacity loss prediction method of this application can monitor the battery status in real time and quickly provide prediction results without occupying a large amount of testing resources and time. It can greatly save testing costs and improve efficiency, and can be promoted and applied on a large scale. Moreover, it does not have high requirements for testing equipment and testing conditions. It not only obtains the capacity decay curve and decay trend through battery charging and discharging, but also obtains the changes in other physical and chemical properties of the battery by combining an in-situ expansion instrument. It can greatly shorten the battery cycle number while obtaining the battery decay situation, that is, the battery capacity loss situation. It has less damage to the battery and lower model complexity, but the capacity retention rate predicted by the model can keep the error within 1%.

[0101] The storage capacity was evaluated using the above-mentioned attenuation simulation model, and the error results are shown in the following tables. Table 1 shows the impedance, actual capacity retention rate, model-predicted capacity retention rate, and the error between the actual capacity retention rate and the model-predicted capacity retention rate of the battery at different cycle numbers (i.e., the number of test cycles) at 0℃, 25℃, and 45℃. Table 2 shows the data of the expansion force of the battery during charging and the expansion force during discharging at different cycle numbers.

[0102] Table 1

[0103]

[0104] Table 2

[0105] Number of cycles Initial expansion force N during charging Maximum expansion force N during charging Expansion force N due to charging change 0 270 568 298 200 254 674 420 400 247 795 548 600 232 867 635 800 219 912 693 Number of cycles Discharge initiation expansion force N Minimum expansion force N during discharge Discharge change expansion force N 0 564 278 286 200 668 275 393 400 783 280 503 600 859 263 596 800 896 261 635

[0106] According to the battery storage capacity loss prediction method proposed in the embodiments of this application, capacity decay data and impedance data of the test battery during different test temperatures and different test cycles can be obtained. The capacity decay data and impedance data are fitted to construct a capacity decay model at different temperatures. This method does not require a large amount of test resources and time, which can greatly save test costs and improve efficiency. Furthermore, the battery capacity retention rate can be quickly and accurately predicted through the impedance data, and the battery storage capacity loss can be determined based on the battery capacity retention rate.

[0107] Next, with reference to the accompanying drawings, a battery storage capacity loss prediction device according to an embodiment of this application is described.

[0108] Figure 2 This is a block diagram of a battery storage capacity loss prediction device according to an embodiment of this application.

[0109] like Figure 2 As shown, the battery storage capacity loss prediction device 10 includes: an acquisition module 100, a fitting module 200, and a prediction module 300.

[0110] The first acquisition module 100 is used to acquire capacity decay data and impedance data of the test battery under different test temperatures and different test cycles; the fitting module 200 is used to fit the capacity decay data and impedance data to obtain the capacity decay model of the test battery; and the prediction module 300 is used to predict the storage capacity loss of the target battery using the capacity decay model.

[0111] In this embodiment, the fitting module 200 is further configured to: establish a capacity decay model; obtain fitting results of capacity decay data and impedance data; and use the fitting results to correct the model parameters of the capacity decay model until the capacity decay model meets preset conditions, at which point the correction of the model parameters is stopped.

[0112] In this embodiment of the application, the capacity decay model is as follows:

[0113] f(x) = ax 4 +bx 3 +cx 2 +dx+e;

[0114] Where f(x) is the battery capacity retention rate, x is the battery impedance, and a, b, c, d, and e are model parameters, which are constants.

[0115] In this embodiment of the application, the acquisition module 100 is further configured to: acquire the remaining storage capacity of the test battery under different test temperatures and different test cycles; acquire the actual storage capacity of the test battery before the test, and calculate the capacity decay data based on the actual storage capacity and the remaining storage capacity.

[0116] In this embodiment of the application, the acquisition module 100 is further configured to: preprocess the capacity attenuation data and impedance data, wherein the preprocessing includes at least one of data cleaning, data standardization and data normalization; and use the least squares method to fit the preprocessed data to obtain a fitting result.

[0117] In this embodiment of the application, the test cycle process includes: obtaining the target charging current and target discharging current of the test battery; charging and discharging according to the target charging current and target discharging current until the storage capacity of the test battery reaches the preset calibrated capacity, and then stopping the test.

[0118] In this embodiment of the application, the apparatus 10 further includes a calibration module.

[0119] The calibration module is used to obtain the expansion force of the test battery under different test temperatures and different test cycles after fitting the capacity decay data and impedance data to obtain the capacity decay model of the test battery; and to calibrate the model parameters of the capacity decay model based on the expansion force, wherein the trend of expansion force is proportional to the trend of capacity decay.

[0120] It should be noted that the explanation of the aforementioned method embodiment for predicting battery storage capacity loss also applies to the battery storage capacity loss prediction device of this embodiment, and will not be repeated here.

[0121] According to the battery storage capacity loss prediction device proposed in the embodiments of this application, the capacity decay data and impedance data of the test battery during different test temperatures and different test cycles can be obtained. The capacity decay data and impedance data are fitted to construct a capacity decay model at different temperatures. It does not require a lot of test resources and time, which can greatly save test costs and improve efficiency. Furthermore, the battery capacity retention rate can be quickly and accurately predicted through the impedance data, and the battery storage capacity loss can be determined based on the battery capacity retention rate.

[0122] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0123] The memory 301, the processor 302, and the computer program stored on the memory 301 and capable of running on the processor 302.

[0124] When the processor 302 executes the program, it implements the battery storage capacity loss prediction method provided in the above embodiments.

[0125] Furthermore, electronic devices also include:

[0126] Communication interface 303 is used for communication between memory 301 and processor 302.

[0127] The memory 301 is used to store computer programs that can run on the processor 302.

[0128] The memory 301 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0129] If the memory 301, processor 302, and communication interface 303 are implemented independently, then the communication interface 303, memory 301, and processor 302 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0130] Optionally, in a specific implementation, if the memory 301, processor 302, and communication interface 303 are integrated on a single chip, then the memory 301, processor 302, and communication interface 303 can communicate with each other through an internal interface.

[0131] Processor 302 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0132] This application also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed by a processor, implements the above-described method for predicting battery storage capacity loss.

[0133] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0134] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0135] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0136] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0137] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

Claims

1. A method of predicting loss of battery storage capacity, characterized by, The method comprises the following steps: obtaining capacity attenuation data and impedance data of a test battery under different test temperatures and different test cycle times; The obtaining of the capacity attenuation data and the impedance data of the test battery under different test temperatures and different test cycle times comprises: obtaining residual storage capacities of the test battery under different test temperatures and different test cycle times; obtaining actual storage capacities of the test battery before testing, and calculating the capacity attenuation data according to the actual storage capacities and the residual storage capacities; fitting the capacity attenuation data and the impedance data to obtain a capacity attenuation model of the test battery; the fitting of the capacity attenuation data and the impedance data to obtain the capacity attenuation model of the test battery comprises: establishing the capacity attenuation model; obtaining a fitting result of the capacity attenuation data and the impedance data; correcting model parameters of the capacity attenuation model by using the fitting result until the capacity attenuation model meets a preset condition, and then stopping the correction of the model parameters, wherein the capacity attenuation model is: ； wherein, is a capacity retention rate of the battery, is an impedance of the battery, , , , , are model parameters, respectively; using the capacity attenuation model to predict storage capacity loss of a target battery.

2. The method of claim 1, wherein the battery storage capacity loss is predicted based on the state of charge of the battery, the state of health of the battery, and the state of function of the battery. The obtaining of the fitting result of the capacity attenuation data and the impedance data comprises: preprocessing the capacity attenuation data and the impedance data, wherein the preprocessing comprises at least one of data cleaning, data standardization and data normalization; fitting the preprocessed data by using a least square method to obtain the fitting result.

3. The method of claim 1, wherein the battery storage capacity loss is predicted based on the battery storage capacity loss model. The process of the test cycle comprises: obtaining target charging currents and target discharging currents of the test battery; charging and discharging according to the target charging currents and the target discharging currents until storage capacities of the test battery reach preset calibration capacities, and then stopping the test.

4. The method of claim 1, wherein the battery storage capacity loss is predicted based on the battery storage capacity loss model. After the fitting of the capacity attenuation data and the impedance data to obtain the capacity attenuation model of the test battery, the method further comprises: obtaining expansion forces of the test battery during different test temperatures and different test cycle times; correcting model parameters of the capacity attenuation model based on the expansion forces, wherein a change trend of the expansion forces is directly proportional to a change trend of the capacity attenuation.

5. A device for predicting loss of storage capacity of a battery, characterized in that comprise: an obtaining module, configured to obtain capacity attenuation data and impedance data of a test battery under different test temperatures and different test cycle times; The obtaining of the capacity attenuation data and the impedance data of the test battery under different test temperatures and different test cycle times comprises: obtaining residual storage capacities of the test battery under different test temperatures and different test cycle times; obtaining actual storage capacities of the test battery before testing, and calculating the capacity attenuation data according to the actual storage capacities and the residual storage capacities; a fitting module configured to fit the capacity fade data and the impedance data to obtain a capacity fade model of the test battery; the fitting the capacity fade data and the impedance data to obtain the capacity fade model of the test battery comprises: establishing the capacity fade model; obtaining a fitting result of the capacity fade data and the impedance data; and correcting model parameters of the capacity fade model by using the fitting result until the capacity fade model satisfies a preset condition, and then stopping the correction of the model parameters, wherein the capacity fade model is: ； wherein, is a capacity retention rate of the battery, is an impedance of the battery, , , , , are model parameters, respectively; a prediction module configured to predict a storage capacity loss of a target battery by using the capacity fade model.

6. An electronic device, comprising: comprising: 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 predicting a storage capacity loss of a battery according to any one of claims 1-4.

7. A computer readable storage medium having stored thereon a computer program or instructions, characterized in that, The computer program or instructions are executed by the processor to implement the method for predicting a storage capacity loss of a battery according to any one of claims 1-4.

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