Battery storage capacity loss prediction method and device, equipment and storage medium

Abstract

The invention relates to the technical field of batteries, in particular to a battery storage capacity loss prediction method, device and equipment and a storage medium, and the method comprises the steps: obtaining the capacity attenuation data and impedance data of a tested battery at different test temperatures and different test cycle times; fitting the capacity attenuation data and the impedance data to obtain a capacity attenuation model of the tested battery; and predicting the storage capacity loss of the target battery by using the capacity fading model. Therefore, the problems that the prediction precision of the battery storage capacity loss is low and the prediction method is relatively complex 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 and storage medium for predicting battery storage capacity loss. Background Art

[0002] Lithium-ion batteries are widely used in portable electronic devices, electric vehicles, energy storage systems, etc. due to their high energy density, long cycle life, and low self-discharge rate. The cycle life of a battery is an important aspect of judging battery performance, so accurately predicting the cycle life of a battery, that is, the storage capacity of the battery, is of great significance for extending the battery life cycle and improving system safety.

[0003] In the related art, the storage capacity loss of the battery is predicted by a simple statistical method based on experience and historical data. This method relies on a large amount of historical data and cannot accurately reflect the performance changes of the battery under different conditions, and the prediction accuracy is low. Alternatively, a complex mathematical model is established based on the physical and chemical processes inside the battery to predict the storage capacity loss of the battery. However, the physical model in this method requires a large amount of experimental data and complex calculations, and the model parameters are difficult to determine, so the scope of application is limited. Summary of the invention

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

[0005] The first aspect of the present application provides a method for predicting battery storage capacity loss, comprising the following steps: obtaining capacity decay data and impedance data of a test battery at 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 the present application, the capacity decay data and the impedance data are fitted to obtain a capacity decay model of the test battery, including: establishing a capacity decay model; obtaining the fitting results of the capacity decay data and the impedance data; and correcting the model parameters of the capacity decay model using the fitting results until the capacity decay model meets preset conditions, and then stopping the correction of the model parameters.

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

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

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

[0010] Optionally, in one embodiment of the present application, capacity attenuation data and impedance data of the test battery at different test temperatures and different test cycle numbers are obtained, including: obtaining the remaining storage capacity of the test battery at different test temperatures and different test cycle numbers; obtaining the actual storage capacity of the test battery before the test, and calculating the capacity attenuation data based on the actual storage capacity and the remaining storage capacity.

[0011] Optionally, in one embodiment of the present 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 the present application, the test cycle process includes: obtaining a target charging current and a target discharging current of the test battery; charging and discharging according to the target charging current and the target discharging current until the storage capacity of the test battery reaches a preset calibrated capacity, and then stopping the test.

[0013] Optionally, in one embodiment of the present application, after fitting the capacity decay data and the impedance data to obtain the capacity decay model of the test battery, it also includes: obtaining the expansion force of the test battery at different test temperatures and different test cycles; correcting the model parameters of the capacity decay model based on the expansion force, wherein the changing trend of the expansion force is proportional to the changing trend of the capacity decay.

[0014] The second aspect of the present application provides a device for predicting battery storage capacity loss, including: an acquisition module for acquiring impedance data of capacity decay data of a test battery at different test temperatures and different test cycle numbers; a fitting module for fitting 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 the present application, the fitting module is further used to: establish a capacity decay model; obtain fitting results of capacity decay data and impedance data; and use the fitting results to correct model parameters of the capacity decay model until the capacity decay model meets preset conditions, then stop correcting the model parameters.

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

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

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

[0019] Optionally, in one embodiment of the present application, the acquisition module is further used to: obtain the remaining storage capacity of the test battery at different test temperatures and different test cycle numbers; obtain the actual storage capacity of the test battery before the test, and calculate the capacity attenuation data based on the actual storage capacity and the remaining storage capacity.

[0020] Optionally, in one embodiment of the present application, the acquisition module is further used to: preprocess the capacity attenuation data and the 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.

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

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

[0023] A third aspect of the present application provides an electronic device, 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 perform a method for predicting battery storage capacity loss as described in the above embodiment.

[0024] The fourth aspect of the present application provides a computer-readable storage medium having a computer program or instructions stored thereon, and the computer program or instructions are executed by a processor to perform a method for predicting battery storage capacity loss as described in the above embodiment.

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

[0026] The embodiment of the present application can obtain the capacity decay data and impedance data of the test battery at different test temperatures and different test cycle times, fit the capacity decay data and impedance data to build a capacity decay model at different temperatures, without taking up a lot of test resources and time, which can greatly save test costs and improve efficiency, and the battery capacity retention rate can be quickly and accurately predicted through impedance data, and the storage capacity loss of the battery can be determined based on the battery capacity retention rate. As a result, the technical problems of low prediction accuracy for battery storage capacity loss and relatively complex prediction methods in related technologies are solved.

[0027] 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

[0028] 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:

[0029] Figure 1 A flowchart of a method for predicting battery storage capacity loss according to an embodiment of the present application;

[0030] Figure 2 This is an example diagram of a device for predicting battery storage capacity loss according to an embodiment of the present application;

[0031] Figure 3 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] 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.

[0033] Before describing the solution of this application, the following current related technologies are introduced:

[0034] Related technology 1 provides a battery cycle life prediction method, device and storage medium. The battery cycle life prediction method includes: performing periodic cycle charge and discharge on the battery to be tested with a set number of cycle charge and discharge times; determining the internal resistance value and capacity retention rate of the battery to be tested after each cycle of charge and discharge; predicting the cycle life of the battery to be tested based on the internal resistance value and capacity retention rate, but this method requires a long time to evaluate the battery cycle life, but has high requirements on data quality and model complexity.

[0035] Related technology 2 provides a method for quickly testing the cycle life of lithium-ion batteries, which includes charging the lithium-ion battery at a low rate under constant current conditions after discharge and charging at a low rate to less than or equal to 80% of the design capacity of the lithium-ion battery; the method is to reduce the charging current when the battery is at a low SOC, so that the newly formed SEI in each charging process is more compact, and the reaction consumption of available lithium ions in the battery is accelerated; at the same time, by increasing the charging cutoff voltage of the charging step in the conventional high-temperature intermittent cycle process, the reaction consumption of lithium ions, the decay of electrode materials and the decomposition of electrolytes are accelerated. However, this method has high requirements for test equipment and test conditions, and there may be a risk of battery damage.

[0036] Related technology 3 provides a method for quickly testing the cycle life of a lithium-ion battery, the method includes performing constant current discharge on the lithium-ion battery at a constant current discharge current I to a discharge lower limit voltage U', wherein the constant current discharge current I is less than the known constant current discharge current I', and the discharge lower limit voltage U' is less than the known discharge lower limit voltage Ug'; the method is to set a staged multiple discharge process in the lithium-ion battery cycle discharge step, and to reduce the discharge cut-off voltage so that the SEI film inside the battery is more severely damaged during the discharge process, and accordingly, more available lithium ions are consumed in the subsequent charging process due to the repair of the SEI film, thereby accelerating the reaction consumption of available lithium ions inside the battery to achieve a rapid test of the cycle life of the lithium-ion battery. However, this method has high requirements on the test equipment and test conditions, and may cause significant damage to the battery.

[0037] The following describes the prediction method, device, equipment and storage medium of the battery storage capacity loss of the embodiment of the present application with reference to the accompanying drawings. In view of the problem that the related technology mentioned in the background technology center cannot accurately reflect the performance changes of the battery under different conditions, the prediction accuracy is low, and the prediction method is relatively complicated, the present application provides a prediction method for battery storage capacity loss, in which the capacity decay data and impedance data of the test battery at different test temperatures and different test cycle times can be obtained, and the capacity decay data and impedance data are fitted to construct a capacity decay model at different temperatures. There is no need to occupy a large amount of test resources and time, which can greatly save test costs and improve efficiency, and the battery capacity retention rate can be quickly and accurately predicted through impedance data, and the battery storage capacity loss is determined based on the battery capacity retention rate. As a result, the problems of low prediction accuracy and complex prediction methods for battery storage capacity loss in the related technology are solved.

[0038] Specifically, Figure 1 A schematic flow chart of a method for predicting battery storage capacity loss provided in an embodiment of the present application.

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

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

[0041] Among them, the test battery, test temperature, and number of test cycles can be set according to the specific situation, and there is no specific limitation on the number of times. This application is described as an example in which the test battery is a 3.33Ah soft-pack lithium iron phosphate battery and the test temperatures are 25°C, 0°C, and 45°C respectively; the impedance data can be obtained by performing an EIS (Electrochemical Impedance Spectroscopy) scan on the test battery to facilitate the subsequent construction of a capacity decay model.

[0042] It is understandable that the embodiments of the present application can obtain capacity decay data and impedance data tested at different test temperatures and different test cycle numbers, such as capacity decay data and impedance data of 100 cycle tests at 25°C.

[0043] In an embodiment of the present application, capacity attenuation data and impedance data of a test battery at different test temperatures and different test cycle numbers are obtained, including: obtaining the remaining storage capacity of the test battery at different test temperatures and different test cycle numbers; obtaining the actual storage capacity of the test battery before the test, and calculating the capacity attenuation data based on the actual storage capacity and the remaining storage capacity.

[0044] It is understandable that the embodiments of the present application can obtain the remaining storage capacity of the test battery at different test temperatures and different test cycle numbers, and calculate the capacity decay data based on the actual storage capacity and the remaining storage capacity before the test.

[0045] For example, after the test battery is tested for multiple cycles at 0°C, the remaining storage capacity is 990 mAh, while the actual storage capacity is 1000 mAh, and the calculated capacity decay data is 99%.

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

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

[0048] It can be understood that the embodiment of the present application can cyclically 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 calibration capacity, and then stop the test. The test process is relatively simple, and the test process does not require a large amount of test resources and time, which can greatly save test costs and improve efficiency.

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

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

[0051] In an embodiment of the present application, the capacity decay data and the impedance data are fitted to obtain a capacity decay model of the test battery, including: establishing a capacity decay model; obtaining the fitting results of the capacity decay data and the impedance data; and correcting the model parameters of the capacity decay model using the fitting results until the capacity decay model meets the preset conditions, and then stopping the correction of the model parameters.

[0052] The preset condition may 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 can be understood that the embodiments of the present 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 a capacity decay model related to the battery and impedance at different temperatures.

[0054] In the embodiment of the present application, the capacity attenuation model is:

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

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

[0057] In an embodiment of the present 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 can be understood that the embodiments of the present application can preprocess the capacity decay data and impedance data, and use the least squares method to fit the preprocessed data to obtain a fitting result, so that the model parameters of the capacity decay model can be corrected using the fitting result, thereby ensuring the optimal estimation of the model parameters and improving the prediction accuracy of the subsequent model.

[0059] In an embodiment of the present application, after fitting the capacity decay data and the impedance data to obtain the capacity decay model of the test battery, it also includes: obtaining the expansion force of the test battery at different test temperatures and different test cycles; correcting the model parameters of the capacity decay model based on the expansion force, wherein the changing trend of the expansion force is proportional to the changing trend of the capacity decay.

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

[0061] Since the changing trend of the expansion force of the battery is proportional to the changing trend of the capacity decay, the embodiment of the present application can also obtain the expansion force of the test battery at 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 the charge and discharge process gradually increases. The change in the battery expansion force can be used to prove that the battery attenuation trend is gradually increasing, thereby correcting the model parameters of the capacity attenuation model.

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

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

[0065] The following is a description of the method for predicting battery storage capacity loss of the present application through a specific embodiment, which includes the following steps:

[0066] 1. Select 6 parallel sample batteries, 2 parallel samples at each temperature, and perform cycle tests at 25℃, 0℃ and 45℃ respectively. The test is terminated when the capacity decays to 80% of the rated capacity;

[0067] 2. Use an in-situ expansion instrument to monitor changes in battery expansion force during cycling;

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

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

[0070] 1. Take 6 3.33Ah soft-pack lithium iron phosphate batteries;

[0071] 2. Take 2 batteries and place them at 25℃, 0℃ and 45℃ respectively;

[0072] 3. Leave in respective environment for 2 hours;

[0073] 4. 1C constant current discharge to 2V;

[0074] 5. Let stand for 30 minutes;

[0075] 6. 1C constant current and constant voltage charge to 3.65V;

[0076] 7. Let stand for 30 minutes;

[0077] 8. 1C constant current discharge to 2V;

[0078] 9. Let stand for 30 minutes;

[0079] 10. Take the discharge capacity in step 8 as the calibration capacity C0;

[0080] 11. 1C0 constant current and constant voltage charge to 3.65V;

[0081] 12. Let stand for 30 minutes;

[0082] 13. 1C0 constant current discharge for 30min;

[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. 1C0 constant current discharge to 2V;

[0087] 18. Let stand for 30 minutes;

[0088] 19. 0.05C0 constant current and constant voltage charge to 3.65V;

[0089] 20. Let stand for 30 minutes;

[0090] 21. 0.05C0 constant current discharge to 2V;

[0091] 22. Let stand for 30 minutes;

[0092] 23. 1C0 constant current and constant voltage charge to 3.65V;

[0093] 24. Let stand for 30 minutes;

[0094] 25. 1C0 constant current discharge to 2V;

[0095] 26. Let stand for 30 minutes;

[0096] 27. Repeat steps 23 to 26 50 times;

[0097] 28. Repeat steps 6 to 27 16 times;

[0098] The test was stopped when the cycle reached 80% of the rated capacity, and the in-situ expansion instrument was used to monitor the change in battery expansion force during the cycle. The capacity decay curve and in-situ expansion curve at different temperatures were obtained by testing according to the above method. The battery was scanned by EIS at 0, 200, 400, 600, and 800 cycles to obtain impedance data.

[0099] The cycle capacity decay data and impedance data were fitted to obtain the cycle capacity decay model at different temperatures. The capacity decay model at 25°C was: f(x) = -0.0005x 3 +0.0109x 2 -0.0877x+1.2082 function expression; 0℃ capacity attenuation model is: f(x)=0.000004x 3 -0.0048x 2 +0.0315x+0.9536; 45℃ capacity attenuation model is: f(x)=-0.00009x 4 +0.0026x 3 -0.0274x 2 +0.1085x+0.8608, where x represents impedance and f(x) is the corresponding battery capacity retention rate.

[0100] Based on the above embodiments, it can be obtained that the prediction method of the battery storage capacity loss of the embodiment of the present application can monitor the battery status in real time and quickly give the prediction result, without occupying a large amount of test resources and time, which can greatly save the test cost and improve the efficiency, can be promoted and applied on a large scale, and has low requirements for test equipment and test conditions. Not only can the capacity decay curve be obtained through battery charging and discharging to obtain the decay trend, but also combined with the in-situ expansion instrument, the changes in other physical and chemical properties of the battery can be obtained, which can greatly shorten the number of battery cycles and obtain the battery decay situation, that is, the battery capacity loss situation, with less damage to the battery and low model complexity, but the capacity retention rate predicted by the model can keep the error within 1%.

[0101] The storage capacity is evaluated using the above-mentioned attenuation simulation model, and the error results are shown in the following table. 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., test cycle numbers) at 0°C, 25°C, and 45°C. Table 2 shows the data of the expansion force during charging and discharging of the battery at different cycle numbers.

[0102] Table 1

[0103]

[0104] Table 2

[0105] Number of cycles Expansion force at the beginning of charging (N) Maximum expansion force during charging (N) Charging change expansion force N 0 270 568 298 200 254 674 420 400 247 795 548 600 232 867 635 800 219 912 693 Number of cycles Discharge initial expansion force N Minimum discharge expansion force N 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 method for predicting battery storage capacity loss proposed in the embodiment of the present application, the capacity decay data and impedance data of the test battery at different test temperatures and different test cycle numbers can be obtained, and the capacity decay data and impedance data can be fitted to construct a capacity decay model at different temperatures. This does not require a large amount of test resources and time, can greatly save test costs and improve efficiency, and the battery capacity retention rate can be quickly and accurately predicted through impedance data, and the battery storage capacity loss can be determined based on the battery capacity retention rate.

[0107] Next, a device for predicting battery storage capacity loss according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0108] Figure 2 It is a block diagram of a device for predicting battery storage capacity loss according to an embodiment of the present 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] Among them, the first acquisition module 100 is used to obtain the capacity decay data and impedance data of the test battery at different test temperatures and different test cycle numbers; 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; the prediction module 300 is used to use the capacity decay model to predict the storage capacity loss of the target battery.

[0111] In the embodiment of the present application, the fitting module 200 is further used to: establish a capacity decay model; obtain the fitting results of the capacity decay data and the 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, and then stop correcting the model parameters.

[0112] In the embodiment of the present application, the capacity attenuation model is:

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

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

[0115] In an embodiment of the present application, the acquisition module 100 is further used to: obtain the remaining storage capacity of the test battery at different test temperatures and different test cycle numbers; obtain the actual storage capacity of the test battery before the test, and calculate the capacity attenuation data based on the actual storage capacity and the remaining storage capacity.

[0116] In an embodiment of the present application, the acquisition module 100 is further used to: preprocess the capacity attenuation data and the 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 an embodiment of the present application, the test cycle process includes: obtaining a target charging current and a target discharging current of the test battery; charging and discharging according to the target charging current and the target discharging current until the storage capacity of the test battery reaches a preset calibrated capacity, and then stopping the test.

[0118] In the embodiment of the present application, the device 10 of the embodiment of the present application further includes: a correction module.

[0119] Among them, the correction module is used to obtain the expansion force of the test battery at different test temperatures and different test cycles after fitting the capacity decay data and the impedance data to obtain the capacity decay model of the test battery; and correct the model parameters of the capacity decay model based on the expansion force, wherein the change trend of the expansion force is proportional to the change trend of the capacity decay.

[0120] It should be noted that the above explanations of the embodiment of the method for predicting battery storage capacity loss are also applicable to the device for predicting battery storage capacity loss of this embodiment, and will not be repeated here.

[0121] According to the device for predicting battery storage capacity loss proposed in the embodiment of the present application, the capacity decay data and impedance data of the test battery at different test temperatures and different test cycle numbers can be obtained, and the capacity decay data and impedance data can be fitted to construct a capacity decay model at different temperatures. This does not require a large amount of test resources and time, can greatly save test costs and improve efficiency, and the battery capacity retention rate can be quickly and accurately predicted through impedance data, and the storage capacity loss of the battery 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 the present application. The electronic device may include:

[0123] A memory 301 , a processor 302 , and a computer program stored in the memory 301 and executable on the processor 302 .

[0124] When the processor 302 executes the program, the method for predicting battery storage capacity loss provided in the above embodiment is implemented.

[0125] Furthermore, the electronic device further comprises:

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

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

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

[0129] If the memory 301, the processor 302 and the communication interface 303 are implemented independently, the communication interface 303, the memory 301 and the processor 302 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 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.

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

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

[0132] An embodiment of the present application also provides a computer-readable storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed by a processor, the above-mentioned method for predicting battery storage capacity loss is implemented.

[0133] 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 do not necessarily refer 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.

[0134] 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.

[0135] 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 N 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.

[0136] 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 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 or a combination of multiple of the following technologies known in the art: 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 (PGA), a field programmable gate array (FPGA), etc.

[0137] 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.

Claims

1. A method for predicting battery storage capacity loss, characterized in that: The following steps are involved: Obtain capacity attenuation data and impedance data of the test battery at different test temperatures and different test cycle numbers; Fitting the capacity decay data and the impedance data to obtain a capacity decay model of the test battery; The capacity decay model is used to predict the storage capacity loss of the target battery.

2. The method for predicting battery storage capacity loss according to claim 1, characterized in that: The fitting of the capacity decay data and the impedance data to obtain a capacity decay model of the test battery includes: Establishing the capacity decay model; Obtaining a fitting result of the capacity decay data and the impedance data; The model parameters of the capacity decay model are corrected using the fitting result until the capacity decay model meets a preset condition, and then the correction of the model parameters is stopped.

3. The method for predicting battery storage capacity loss according to claim 2, characterized in that: The capacity decay model is: f(x)=ax 4 +bx 3 +cx 2 +dx+e; Among them, f(x) is the capacity retention rate of the battery, x is the impedance of the battery, and a, b, c, d, and e are model parameters respectively.

4. The method for predicting battery storage capacity loss according to claim 1, characterized in that: The obtaining of capacity attenuation data and impedance data of the test battery at different test temperatures and different test cycle numbers includes: Obtaining the remaining storage capacity of the test battery at different test temperatures and different test cycle numbers; The actual storage capacity of the test battery before the test is obtained, and the capacity decay data is calculated according to the actual storage capacity and the remaining storage capacity.

5. The method for predicting battery storage capacity loss according to claim 2, characterized in that: The obtaining the fitting result of the capacity decay data and the impedance data includes: Preprocessing the capacity decay data and the impedance data, wherein the preprocessing includes at least one of data cleaning, data standardization and data normalization; The fitting result is obtained by fitting the preprocessed data using the least square method.

6. The method for predicting battery storage capacity loss according to claim 1, characterized in that: The test cycle process includes: Obtaining a target charging current and a target discharging current of the test battery; Charging and discharging are performed according to the target charging current and the target discharging current until the storage capacity of the test battery reaches a preset calibrated capacity, and then the test is stopped.

7. The method for predicting battery storage capacity loss according to claim 2, characterized in that: After fitting the capacity decay data and the impedance data to obtain the capacity decay model of the test battery, the method further includes: Obtaining the expansion force of the test battery at the different test temperatures and during the different test cycles; Model parameters of the capacity decay model are corrected based on the expansion force, wherein a change trend of the expansion force is proportional to a change trend of the capacity decay.

8. A device for predicting battery storage capacity loss, characterized in that: include: An acquisition module is used to acquire capacity attenuation data and impedance data of the test battery at different test temperatures and different test cycle numbers; A fitting module, used for fitting the capacity decay data and the impedance data to obtain a capacity decay model of the test battery; A prediction module is used to predict the storage capacity loss of the target battery using the capacity decay model.

9. 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 predicting battery storage capacity loss according to any one of claims 1 to 7.

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

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