Battery life cycle estimation method and device, and storage medium

Abstract

The application discloses a battery life cycle estimation method and device and a storage medium. The method comprises the following steps: performing a charging and discharging test on a battery, and obtaining a charging parameter; calculating a model parameter of a battery attenuation rate model according to the charging parameter; fitting a battery temperature change curve in a charging and discharging process according to the charging parameter; continuously simulating a charging and discharging process of the battery according to the charging parameter, and calculating a capacity attenuation rate of the battery according to the model parameter and the battery temperature; and calculating a cycle number of the battery charging and discharging when the capacity attenuation rate reaches a preset attenuation rate. The technical scheme provided by the application can solve the technical problem of the prior art that a battery needs to be continuously tested in a charging and discharging cycle to evaluate the battery life cycle, which is low in efficiency and long in test period.

Description

Technical Field

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

[0002] With the increasing popularity of new energy electric vehicles, consumers are paying more and more attention to battery life. The lifespan of a power battery is affected by many factors, such as charging and discharging rates and charging / discharging temperatures. Generally, when the actual capacity of a battery reaches 80% of its rated capacity, its lifespan is considered to have ended. The number of charge-discharge cycles between the rated capacity and the end of the battery's lifespan is called the battery's cycle life; the higher the number of cycles, the higher the battery performance.

[0003] Currently, the cycle life of battery cells is generally assessed by performing charge-discharge cycle tests at corresponding rates until the battery reaches the end of its lifespan. This assessment method is not only time-consuming and inefficient, but also requires long-term human monitoring to prevent anomalies during testing, resulting in a significant workload. Therefore, it is crucial to find a way to quickly and efficiently estimate the number of cycles a battery cell can perform under different conditions.

[0004] Although existing lifespan models take into account various factors and can predict the health status of batteries, they cannot simulate test data under certain conditions, nor can they deduce the number of cycles to the end of the full lifespan under those conditions based on the lifespan model. Summary of the Invention

[0005] This invention provides a method, apparatus, and storage medium for estimating battery lifespan, aiming to effectively solve the technical problems of low efficiency and long testing cycles in the prior art, which require continuous charge-discharge cycle testing of batteries to evaluate battery lifespan.

[0006] According to one aspect of the present invention, a method for estimating battery lifespan is provided, the method comprising: Perform charge and discharge tests on the battery and obtain charging parameters. Calculate the model parameters of the battery degradation rate model based on the charging parameters. The battery temperature change curve during the charging and discharging process is fitted based on the charging parameters. The charging and discharging process of the battery is continuously simulated based on the charging parameters, and the capacity decay rate of the battery is calculated based on the model parameters and the battery temperature. When the capacity decay rate reaches the preset decay rate, the number of charge-discharge cycles of the battery is calculated.

[0007] Furthermore, the charging parameters include current rate, charging temperature, and sampling time, and the method further includes: Before performing a charge / discharge test on the battery, the charging current of the battery is determined.

[0008] Further, determining the charging current of the battery includes: If the charging current is a constant current, the charging current is calculated based on the rated current and current ratio of the battery; If the charging current is not a constant current, retrieve the fast charging MAP table from the storage space, and calculate the charging current based on the battery's rated current and the fast charging MAP table.

[0009] Furthermore, the charge-discharge test of the battery includes: When the current temperature of the battery is the charging temperature, the battery is placed in a resting state, and after the resting time reaches a first preset time, the battery is charged according to the charging current. The battery voltage is monitored in real time. When the battery voltage is greater than or equal to the upper limit voltage, the battery is put on hold and discharged after the holding time reaches a second preset time. The battery voltage is monitored in real time, and when the battery voltage is less than or equal to the lower limit voltage, the battery is put on hold.

[0010] Furthermore, the model parameters for calculating the battery degradation rate model based on the charging parameters include: For each sample taken during the charge-discharge test, the capacity throughput is calculated based on the charging time and charging current. The remaining charge of the battery at the current sampling time is calculated based on the capacity throughput, initial capacity, and capacity decay rate, and the remaining charge influence factor is calculated based on the remaining charge. The decay rate model is fitted based on the capacity throughput, battery temperature, residual charge influence factor and molar gas constant to obtain the model parameters, wherein the model parameters include the pre-exponential factor, activation energy and dimensionless number.

[0011] Furthermore, the calculation of capacity throughput based on charging time and charging current includes: The capacity throughput is calculated according to the following formula: , in, This indicates the capacity throughput. This indicates the charging current. This indicates the charging time.

[0012] Furthermore, the calculation of the remaining charge of the battery at the current sampling time based on the capacity throughput, initial capacity, and capacity decay rate includes: , in, This indicates the remaining charge of the battery at the current sampling time. This indicates the remaining charge of the battery at the previous sampling time. This indicates the capacity throughput. This indicates the initial capacity. This represents the capacity decay rate.

[0013] Further, the step of calculating the capacity decay rate of the battery based on the model parameters and the battery temperature includes: The capacity decay rate is calculated using the following formula: , in, This represents the capacity decay rate. Indicates the pre-exponential factor, This represents an exponential function with base e. This represents the activation energy. This represents the molar gas constant. This indicates the battery temperature. This indicates the capacity throughput. This represents the dimensionless number. This represents the factor influencing the multiplier. This represents the residual charge influence factor.

[0014] According to another aspect of the present invention, the present invention also provides a battery lifespan estimation device, the device comprising: The model parameter calculation module is used to perform charge and discharge tests on the battery, obtain charging parameters, and calculate the model parameters of the battery degradation rate model based on the charging parameters. A battery temperature fitting module is used to fit the battery temperature change curve during the charging and discharging process based on the charging parameters. The capacity decay rate calculation module is used to continuously simulate the charging and discharging process of the battery according to the charging parameters, and calculate the capacity decay rate of the battery according to the model parameters and the battery temperature. The cycle count calculation module is used to calculate the number of charge-discharge cycles of the battery when the capacity decay rate reaches a preset decay rate.

[0015] According to another aspect of the invention, the invention also provides a storage medium storing a plurality of instructions adapted to be loaded by a processor to execute any of the battery life estimation methods described above.

[0016] Through one or more embodiments of the above embodiments of the present invention, at least the following technical effects can be achieved: In the technical solution disclosed in this invention, charging parameters are obtained based on at least one set of existing test data, a degradation rate model is constructed, model parameters of the degradation rate model are obtained based on the test data, the battery temperature change during battery charging and discharging is fitted, and the charging and discharging process is simulated. This allows for the calculation of the number of charge-discharge cycles when the capacity degradation rate reaches a preset degradation rate throughout the entire lifespan of the power battery. This invention can simulate and reconstruct test data based on a set of measured data, calibrating the temperature and calculating the capacity throughput. This saves testing resources and can predict the battery cell performance under other test conditions when test data is limited, predicting the number of cycles required for the battery to degrade to its full lifespan under different conditions. Furthermore, this solution does not require manual monitoring of the battery cycle testing process, saving time and improving efficiency. Attached Figure Description

[0017] The technical solution and other beneficial effects of the present invention will become apparent from the following detailed description of specific embodiments of the invention, in conjunction with the accompanying drawings.

[0018] Figure 1 A flowchart illustrating the steps of a method for estimating battery lifespan according to an embodiment of the present invention; Figure 2 A comparison chart of measured temperature and simulated temperature provided for an embodiment of the present invention; Figure 3 A flowchart of a battery charging and discharging process is provided as an embodiment of the present invention; Figure 4 This is a schematic diagram of a battery life prediction device provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0021] Figure 1 The diagram shows a flowchart of the battery lifespan estimation method provided in an embodiment of the present invention. According to one aspect of the present invention, a battery lifespan estimation method is provided, such as... Figure 1 As shown, the method includes: Step 101: Perform a charge and discharge test on the battery and obtain the charging parameters. Calculate the model parameters of the battery degradation rate model based on the charging parameters. Step 102: Fit the battery temperature change curve during the charging and discharging process based on the charging parameters; Step 103: Continuously simulate the charging and discharging process of the battery according to the charging parameters, and calculate the capacity decay rate of the battery according to the model parameters and the battery temperature; Step 104: When the capacity decay rate reaches the preset decay rate, calculate the number of charge-discharge cycles of the battery.

[0022] In the technical solution disclosed in this invention, the number of battery cycles is estimated under different conditions throughout the entire life cycle of the power battery. The test data is calibrated by simulation using at least one set of existing test data, and the number of cycles when the battery degrades to 80% is obtained by life model simulation.

[0023] The following is a detailed description of steps 101 to 104 above.

[0024] In step 101, the battery is subjected to a charge and discharge test, and the charging parameters are obtained. The model parameters of the battery degradation rate model are calculated based on the charging parameters. For example, existing test cycle data can be directly obtained, and charging parameters can be derived based on the test conditions, such as a test temperature of 25°C, a rate of 0.5C / 1C, data sampling time, current and voltage changes during charging, or a long rest period between charging and discharging. Alternatively, charging parameters can be preset, and charge-discharge tests can be performed based on these parameters.

[0025] Then, by analyzing the test data, including test conditions, operating conditions, temperature rise changes, and sampling time, the model parameters of the battery degradation rate model are calculated based on the charging parameters.

[0026] In step 102, the battery temperature change curve during the charging and discharging process is fitted according to the charging parameters; For example, during the testing of battery cells, the temperature of the battery will gradually decrease when it is left idle, and will gradually increase when the battery is charged and discharged. Therefore, a thermal model needs to be added to simulate the temperature rise process of the battery cells. Figure 2 A comparison chart of measured temperature and simulated temperature is provided for an embodiment of the present invention, such as... Figure 2As shown, at the start of the process, the battery finishes charging, and its temperature reaches a relatively high value due to charging, approximately 34°C in the diagram. Subsequently, the battery is left fully charged, and its temperature drops rapidly. After the preset resting time, when the battery temperature drops to approximately 28°C, the battery is discharged. The battery temperature gradually rises slightly during the discharge process, reaching approximately 29°C when the discharge ends. The battery is then left to rest again for a period of time, and its temperature drops rapidly once more, returning to approximately 28°C. The battery is then charged, and during charging, its temperature rises rapidly, reaching a relatively high temperature, such as approximately 34°C, when fully charged.

[0027] In this scheme, a thermal model is established to simulate temperature changes. During a charge-discharge cycle, the predicted test data shows that the temperature change is basically consistent with the overall trend of the actual temperature change, with a temperature difference of about 2℃, which is within the error range of cell testing.

[0028] In step 103, the charging and discharging process of the battery is continuously simulated according to the charging parameters, and the capacity decay rate of the battery is calculated according to the model parameters and the battery temperature. For example, the software simulates a real charge-discharge test process, setting the required charging current, initial temperature, and initial state of charge (SOC) based on charging parameters. The simulation performs charge-discharge cycle tests on the battery cell at the corresponding rate, acquiring charge-discharge data at each sampling time during the simulation. Combining this with model parameters, the battery's capacity degradation rate is calculated using a degradation rate model.

[0029] In step 104, when the capacity decay rate reaches the preset decay rate, the number of charge-discharge cycles of the battery is calculated.

[0030] For example, during the simulation of charge and discharge, the current battery capacity decay rate is continuously calculated until it reaches a preset decay rate. For instance, when the battery capacity is 80%, the capacity decay rate is 0.2. When the capacity decay rate is calculated to be 0.2 using the decay rate model, the number of simulated charge and discharge cycles from the start of the simulation to the current time is calculated to quickly estimate the number of battery cycles from full charge to 80% charge. Through the technical solution of this invention, the number of battery cycles over its entire lifespan under any operating conditions can be predicted.

[0031] Furthermore, the charging parameters include current rate, charging temperature, and sampling time, and the method further includes: Before performing a charge / discharge test on the battery, the charging current of the battery is determined.

[0032] For example, the battery charging parameters have a variety of data. In order to accurately establish the degradation rate model and accurately simulate the charging and discharging process, this solution requires at least the current rate, charging temperature and sampling time during charging.

[0033] Further, in step 101, determining the charging current of the battery includes: If the charging current is a constant current, the charging current is calculated based on the rated current and current ratio of the battery; If the charging current is not a constant current, retrieve the fast charging MAP table from the storage space, and calculate the charging current based on the battery's rated current and the fast charging MAP table.

[0034] For example, during charge / discharge simulation, the charging current, initial temperature, and initial state of charge (SOC) for the required operating conditions are set. During charging, the charging current is determined based on the charging parameters. The charging current may be constant or different currents may be used at different charging stages. If it is constant current charging, the charging current is calculated based on the rated current and current multiplier. For example, if the charging current is 2000mA and the current multiplier is 1C, the charging current is 2000mA; if the current multiplier is 0.5C, the charging current is 1000mA. After determining the charging current, only the current value needs to be input. If the test is a Step_charge test, the fast charging MAP table needs to be called. This MAP table is a multiplier matrix table for different temperatures and SOCs. The charging current is calculated based on the current values ​​and current multipliers on the fast charging MAP table.

[0035] Further, in step 101, the charge-discharge test of the battery includes: When the current temperature of the battery is the charging temperature, the battery is placed in a resting state, and after the resting time reaches a first preset time, the battery is charged according to the charging current. The battery voltage is monitored in real time. When the battery voltage is greater than or equal to the upper limit voltage, the battery is put on hold and discharged after the holding time reaches a second preset time. The battery voltage is monitored in real time, and when the battery voltage is less than or equal to the lower limit voltage, the battery is put on hold.

[0036] For example, the battery charging and discharging test process and the simulation process are consistent. Specifically, when actually testing the battery's charging and discharging, at least one complete charging and discharging process is performed, or several consecutive charging and discharging tests can be performed, depending on the application requirements. During the simulation process, the battery's charging and discharging process needs to be continuously simulated.

[0037] Figure 3A flowchart of a battery charging and discharging process is provided as an embodiment of the present invention. Figure 3 The simulation demonstrates a complete charge-discharge process. After the battery is fully discharged, it is first left to rest for a first preset time, such as 3600 seconds. Then, the battery is continuously charged, with the charging current and current rate depending on the rate. As charging continues, the charge inside the battery gradually accumulates, and the cell voltage gradually increases. When the battery voltage reaches the upper limit voltage, charging is complete. The battery is then left to rest again for a second preset time, such as 900 seconds. Next, the battery is discharged. As discharging continues, the charge inside the battery gradually decreases, and the cell voltage gradually decreases. When the battery voltage reaches the lower limit voltage, discharging is complete. The battery charge-discharge process needs to be simulated repeatedly.

[0038] Further, in step 101, the model parameters for calculating the battery degradation rate model based on the charging parameters include: For each sample taken during the charge-discharge test, the capacity throughput is calculated based on the charging time and charging current. The remaining charge of the battery at the current sampling time is calculated based on the capacity throughput, initial capacity, and capacity decay rate, and the remaining charge influence factor is calculated based on the remaining charge. The decay rate model is fitted based on the capacity throughput, battery temperature, residual charge influence factor and molar gas constant to obtain the model parameters, wherein the model parameters include the pre-exponential factor, activation energy and dimensionless number.

[0039] Further, in step 101, calculating the capacity throughput based on charging time and charging current includes: The capacity throughput is calculated according to the following formula: , in, This indicates the capacity throughput. This indicates the charging current. This indicates the charging time.

[0040] Further, in step 101, calculating the remaining charge of the battery at the current sampling time based on the capacity throughput, initial capacity, and capacity decay rate includes: , in, This indicates the remaining charge of the battery at the current sampling time. This indicates the remaining charge of the battery at the previous sampling time. This indicates the capacity throughput. This represents the initial capacity. This represents the capacity decay rate.

[0041] For example, by analyzing test data, test conditions, operating conditions, temperature rise changes, sampling time, and other data, the current capacity throughput and remaining charge are calculated using the test data and the two formulas mentioned above. As the number of cycles increases, the charge and discharge capacity continuously decreases, and the remaining charge is continuously corrected according to the capacity decay.

[0042] Then, based on the capacity throughput, battery temperature, remaining charge, and molar gas constant obtained from the test data, the model parameters of the decay rate model can be fitted, and the pre-exponential factor, activation energy, and dimensionless number can be calculated.

[0043] Further, in step 103, calculating the capacity decay rate of the battery based on the model parameters and the battery temperature includes: The capacity decay rate is calculated using the following formula: , in, This represents the capacity decay rate. Indicates the pre-exponential factor, This represents an exponential function with base e. This represents the activation energy. This represents the molar gas constant. This indicates the battery temperature. This indicates the capacity throughput. This represents the dimensionless number. This represents the factor influencing the multiplier. This represents the residual charge influence factor.

[0044] For example, by simulating charge and discharge, the current and temperature values ​​of a cycle can be obtained, and then the remaining charge and capacity decay rate can be calculated. The above formula is calculated once at each sampling time. During this process, the capacity decay rate gradually increases until a preset decay rate is reached. For instance, when the preset decay rate is 0.2 and the state of charge (SOH) is 80%, the simulation of the charge and discharge cycle is stopped, and the number of battery cycles from the start to the end of the simulation is calculated. This method can quickly estimate the number of battery cycles over its entire lifespan under any operating conditions.

[0045] In the technical solution disclosed in this invention, charging parameters are obtained based on at least one set of existing test data, a degradation rate model is constructed, model parameters of the degradation rate model are obtained based on the test data, the battery temperature change during battery charging and discharging is fitted, and the charging and discharging process is simulated. This allows for the calculation of the number of charge-discharge cycles when the capacity degradation rate reaches a preset degradation rate throughout the entire lifespan of the power battery. This invention can simulate and reconstruct test data based on a set of measured data, calibrating the temperature and calculating the capacity throughput. This saves testing resources and can predict the battery cell performance under other test conditions when test data is limited, predicting the number of cycles required for the battery to degrade to its full lifespan under different conditions. Furthermore, this solution does not require manual monitoring of the battery cycle testing process, saving time and improving efficiency.

[0046] Based on the same inventive concept as the battery life estimation method in the embodiments of the present invention, the embodiments of the present invention provide a battery life estimation device, please refer to... Figure 4 The device includes: The model parameter calculation module 201 is used to perform charge and discharge tests on the battery, obtain charging parameters, and calculate the model parameters of the battery degradation rate model based on the charging parameters. The battery temperature fitting module 202 is used to fit the battery temperature change curve during the charging and discharging process based on the charging parameters. The capacity decay rate calculation module 203 is used to continuously simulate the charging and discharging process of the battery according to the charging parameters, and calculate the capacity decay rate of the battery according to the model parameters and the battery temperature. The cycle count calculation module 204 is used to calculate the number of charge and discharge cycles of the battery when the capacity decay rate reaches a preset decay rate.

[0047] Furthermore, the charging parameters include current rate, charging temperature, and sampling time, and the device is also used for: Before performing a charge / discharge test on the battery, the charging current of the battery is determined.

[0048] Furthermore, the model parameter calculation module 201 is also used for: If the charging current is a constant current, the charging current is calculated based on the rated current and current ratio of the battery; If the charging current is not a constant current, retrieve the fast charging MAP table from the storage space, and calculate the charging current based on the battery's rated current and the fast charging MAP table.

[0049] Furthermore, the model parameter calculation module 201 is also used for: The charging current of the battery is determined. When the current temperature of the battery is the charging temperature, the battery is placed in a resting position. After the resting time reaches a first preset time, the battery is charged according to the charging current. The battery voltage is monitored in real time. When the battery voltage is greater than or equal to the upper limit voltage, the battery is put on hold and discharged after the holding time reaches a second preset time. The battery voltage is monitored in real time, and when the battery voltage is less than or equal to the lower limit voltage, the battery is put on hold.

[0050] Furthermore, the model parameter calculation module 201 is also used for: For each sample taken during the charge-discharge test, the capacity throughput is calculated based on the charging time and charging current. The remaining charge of the battery at the current sampling time is calculated based on the capacity throughput, initial capacity, and capacity decay rate, and the remaining charge influence factor is calculated based on the remaining charge. The decay rate model is fitted based on the capacity throughput, battery temperature, residual charge influence factor and molar gas constant to obtain the model parameters, wherein the model parameters include the pre-exponential factor, activation energy and dimensionless number.

[0051] Furthermore, the model parameter calculation module 201 is also used for: The capacity throughput is calculated according to the following formula: , in, This indicates the capacity throughput. This indicates the charging current. This indicates the charging time.

[0052] Furthermore, the model parameter calculation module 201 is also used for: , in, This indicates the remaining charge of the battery at the current sampling time. This indicates the remaining charge of the battery at the previous sampling time. This indicates the capacity throughput. This represents the initial capacity. This represents the capacity decay rate.

[0053] Furthermore, the capacity decay rate calculation module 203 is also used for: The capacity decay rate is calculated using the following formula: , in, This represents the capacity decay rate. Indicates the pre-exponential factor, This represents an exponential function with base e. This represents the activation energy. This represents the molar gas constant. This indicates the battery temperature. This indicates the capacity throughput. This represents the dimensionless number. This represents the factor influencing the multiplier. This represents the residual charge influence factor.

[0054] Other aspects and implementation details of the battery life prediction device are the same as or similar to the battery life prediction method described above, and will not be repeated here.

[0055] According to another aspect of the invention, the invention also provides a storage medium storing a plurality of instructions adapted to be loaded by a processor to execute any of the battery life estimation methods described above.

[0056] In summary, although the present invention has been disclosed above with reference to preferred embodiments, the above preferred embodiments are not intended to limit the present invention. Those skilled in the art can make various modifications and refinements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the scope defined in the claims.

Claims

1. A method for predicting battery lifespan, characterized in that, The method includes: The battery is subjected to charge and discharge tests, and the charging parameters are obtained. The model parameters of the battery degradation rate model are calculated based on the charging parameters, including the current rate, charging temperature and sampling time. The model parameters for calculating the battery degradation rate model based on the charging parameters include: For each sample taken during the charge-discharge test, the capacity throughput is calculated based on the charging time and charging current. The remaining charge of the battery at the current sampling time is calculated based on the capacity throughput, initial capacity, and capacity decay rate, and the remaining charge influence factor is calculated based on the remaining charge. The decay rate model is fitted based on capacity throughput, battery temperature, residual charge influence factor and molar gas constant to obtain the model parameters, wherein the model parameters include pre-exponential factor, activation energy and dimensionless number; Based on the charging parameters, the battery temperature change curve during the charging and discharging process is fitted by a thermal model; the charging and discharging process of the battery is continuously simulated based on the charging parameters, and the charging and discharging data at each sampling time during the simulation are obtained. The capacity decay rate of the battery is calculated by combining the model parameters and the battery temperature. When the capacity decay rate reaches the preset decay rate, the number of charge-discharge cycles of the battery is calculated.

2. The method as described in claim 1, characterized in that, The method further includes: Before performing a charge / discharge test on the battery, the charging current of the battery is determined.

3. The method as described in claim 2, characterized in that, Determining the charging current of the battery includes: If the charging current is a constant current, the charging current is calculated based on the rated current and current ratio of the battery; If the charging current is not a constant current, retrieve the fast charging MAP table from the storage space, and calculate the charging current based on the battery's rated current and the fast charging MAP table.

4. The method as described in claim 2, characterized in that, The battery charge / discharge test includes: When the current temperature of the battery is the charging temperature, the battery is placed in a resting state, and after the resting time reaches a first preset time, the battery is charged according to the charging current. The battery voltage is monitored in real time. When the battery voltage is greater than or equal to the upper limit voltage, the battery is put on hold and discharged after the holding time reaches a second preset time. The battery voltage is monitored in real time, and when the battery voltage is less than or equal to the lower limit voltage, the battery is put on hold.

5. The method as described in claim 2, characterized in that, The calculation of capacity throughput based on charging time and charging current includes: The capacity throughput is calculated according to the following formula: ， in, This indicates the capacity throughput. This indicates the charging current. This indicates the charging time.

6. The method as described in claim 5, characterized in that, The calculation of the remaining charge of the battery at the current sampling time based on the capacity throughput, initial capacity, and capacity decay rate includes: ， in, This indicates the remaining charge of the battery at the current sampling time. This indicates the remaining charge of the battery at the previous sampling time. This indicates the capacity throughput. This represents the initial capacity. This represents the capacity decay rate.

7. The method as described in claim 6, characterized in that, The calculation of the battery capacity decay rate based on the model parameters and the battery temperature includes: The capacity decay rate is calculated using the following formula: ， in, This represents the capacity decay rate. Indicates the pre-exponential factor, This represents an exponential function with base e. This represents the activation energy. This represents the molar gas constant. This indicates the battery temperature. This indicates the capacity throughput. This represents the dimensionless number. This represents the factor influencing the multiplier. This represents the residual charge influence factor.

8. A device for predicting battery lifespan, characterized in that, The device includes: The model parameter calculation module is used to perform charge and discharge tests on the battery and obtain charging parameters. Based on the charging parameters, it calculates the model parameters of the battery degradation rate model. The charging parameters include current rate, charging temperature and sampling time. The model parameters for calculating the battery degradation rate based on the charging parameters include: calculating the capacity throughput for each sample in the charge-discharge test based on the charging time and charging current; calculating the remaining charge of the battery at the current sampling time based on the capacity throughput, initial capacity, and capacity degradation rate, and calculating the remaining charge influence factor based on the remaining charge; fitting the degradation rate model based on the capacity throughput, battery temperature, remaining charge influence factor, and molar gas constant to obtain the model parameters, wherein the model parameters include the pre-exponential factor, activation energy, and dimensionless number; A battery temperature fitting module is used to fit the battery temperature change curve during the charging and discharging process based on the charging parameters. The capacity decay rate calculation module is used to continuously simulate the charging and discharging process of the battery according to the charging parameters, obtain the charging and discharging data at each sampling time during the simulation, and calculate the capacity decay rate of the battery by combining the model parameters and the battery temperature. The cycle count calculation module is used to calculate the number of charge-discharge cycles of the battery when the capacity decay rate reaches a preset decay rate.

9. A storage medium, characterized in that, The storage medium stores a plurality of instructions adapted for loading by a processor to perform the method as described in any one of claims 1 to 7.

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