A lithium battery life prediction method and device, a terminal device, and a medium

Abstract

The application provides a lithium battery life prediction method and device, terminal equipment and medium, the method comprises the following steps: based on the deterioration mechanism of the SEI film of the lithium battery in the charging and discharging process, an initial cycle life prediction model is constructed; based on the degradation mechanism of the SEI film of the lithium battery in the storage process, an initial storage life prediction model is constructed; for any one of the initial cycle life model and the initial storage life model, the experimental battery data used for model verification of the one is obtained, and the one is fitted and trained by using the experimental battery data, to obtain the trained cycle life model and storage life model that converges; the calibration battery capacity, temperature data, charging and discharging rate, battery storage state of charge and working mode of the lithium battery to be measured are obtained, and the cycle life model and the storage life model are combined to predict the life of the lithium battery to be measured under the working mode. The application can improve the accuracy of lithium battery life prediction.

Description

Technical Field

[0001] This invention belongs to the field of lithium-ion battery technology, specifically relating to a method, device, terminal equipment, and medium for predicting the lifespan of lithium batteries. Background Technology

[0002] As lithium batteries age, their performance degrades, potentially causing equipment to overheat or malfunction. Predicting the remaining lifespan of lithium batteries helps equipment owners replace them in a timely manner, avoiding potential safety risks. By predicting the remaining lifespan of lithium batteries, equipment owners can more effectively plan battery replacements and inventory management, avoiding efficiency losses caused by replacing batteries too early or too late, while ensuring the consistency of the entire battery pack and optimizing overall performance. Furthermore, it allows for a more accurate estimation of the recycling and reuse value of lithium batteries, thereby promoting environmental protection and sustainable development.

[0003] Currently, the remaining lifespan of lithium batteries is mainly predicted through the following methods: Empirical formula method: Based on a large amount of data and experimental results, empirical formulas are summarized to predict the remaining lifespan of batteries. The advantage of this method is its relatively low computational cost, but the difficulty lies in considering the diverse factors and the difficulty in accurately fitting the parameters and formula construction. Model method: Using machine learning or deep learning models, large amounts of battery data are trained and learned to achieve accurate prediction of the remaining lifespan of batteries. This method requires a large amount of data and powerful computing capabilities. Accelerated aging test: Charge and discharge tests are conducted on batteries under accelerated conditions, and the remaining lifespan is evaluated using the number of cycles. The accuracy of this method is limited by the difference between the accelerated environment and the actual usage scenario.

[0004] Furthermore, the use of lithium batteries includes many storage stages (when the battery is in storage, if the battery is not charged or discharged, the SEI film changes relatively slowly) and cycling stages (during the charging and discharging process, the SEI film undergoes continuous damage and repair). The aging rate of the SEI film is different in different stages. Traditional lithium battery life prediction methods do not consider the impact of the different aging rates of the SEI film in different stages on the prediction of lithium battery life under different operating modes, which reduces the accuracy of lithium battery life prediction. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method, device, terminal equipment and medium for predicting the life of lithium batteries, so as to improve the accuracy of lithium battery life prediction.

[0006] In a first aspect, the present invention provides a method for predicting the lifespan of a lithium battery, the method comprising the following steps:

[0007] Based on the degradation mechanism of the SEI film in lithium batteries during charging and discharging, an initial cycle life prediction model is constructed. The initial cycle life prediction model is used to describe the correlation between battery capacity loss and temperature and charge / discharge rate.

[0008] Based on the degradation mechanism of the SEI film in lithium batteries during storage, an initial storage lifetime prediction model is constructed. The initial storage lifetime prediction model is used to describe the correlation between battery capacity loss and battery storage state of charge.

[0009] For either the initial cycle lifetime model or the initial storage lifetime model, experimental battery data is obtained to validate the model for that model. The experimental battery data is then used to fit and train the model to obtain the cycle lifetime model and storage lifetime model that have been trained to convergence.

[0010] The system acquires the calibrated battery capacity, temperature data, charge / discharge rate, battery storage state of charge, and operating mode of the lithium battery under test. Combining the cycle life model and storage life model, it predicts the lifespan of the lithium battery under test in the operating mode.

[0011] Optionally, the expression for the initial cycle lifetime prediction model is as follows:

[0012]

[0013] Among them, Q loss1 This represents the capacity loss of a lithium battery during charging and discharging, where A represents the pre-exponential factor, and E represents the capacity loss during charging and discharging. a The activation energy is represented by T, and the temperature is represented by C. rate The value represents the cycle ratio, Ah represents the cumulative ampere-hours (Ah), which is the product of the current and the charge / discharge time, and R represents the ideal gas constant. A represents the additional temperature rise caused by the rate stress, 'a' represents the rate variation factor under the rate effect, 'z' represents the cycle base decay rate, 'm' represents the temperature amplitude adjustment factor, and 'n' represents the rate amplitude adjustment factor. a ,m,n,a,z are the parameters to be solved in the initial cycle life prediction model.

[0014] Optionally, the expression for the initial storage lifetime prediction model is as follows:

[0015]

[0016] Among them, Q loss2 This represents the capacity loss of a lithium battery during storage. A0 represents the calendar decay base magnitude factor, Bs represents the SOC independent influence factor, and SOC represents the battery storage state of charge, which is the ratio between the currently stored electrical energy of the lithium battery and its maximum battery capacity. C sThe SOC and temperature combined influence factor is represented by k, Z, and t, where k is the Boltzmann constant, Z is the empirical constant, t is the calendar storage time in days, and A0, Bs, E are the factors. a0 C s These are the parameters to be solved for the initial storage lifetime prediction model.

[0017] Optionally, the operating mode includes at least one stage; the stage is either a charge / discharge stage or a storage stage.

[0018] Optionally, the calibrated battery capacity, temperature data, charge / discharge rate, battery storage state of charge, and operating mode of the lithium battery under test are acquired. Combined with cycle life and storage life models, the lifespan of the lithium battery under test in the operating mode is predicted, including:

[0019] For each stage, the battery capacity loss of the previous stage is used as the initial battery capacity loss of the current stage. If the current stage is a charge / discharge stage, the temperature data and charge / discharge rate are input into the cycle life model to obtain the current battery capacity loss of the lithium battery under test in the current stage. The total battery capacity loss of the lithium battery under test in the current stage is calculated based on the initial battery capacity loss and the current battery capacity loss. The predicted life of the lithium battery under test in the current stage is calculated using the total battery capacity loss and the calibrated battery capacity of the lithium battery under test. If the current stage is a storage stage, the battery storage state of charge is input into the storage life model to obtain the current battery capacity loss of the lithium battery under test in the current stage. The total battery capacity loss of the lithium battery under test in the current stage is calculated based on the initial battery capacity loss and the current battery capacity loss. The predicted life of the lithium battery under test in the current stage is calculated using the total battery capacity loss and the calibrated battery capacity of the lithium battery under test.

[0020] The predicted lifetime of the last stage is defined as the predicted lifetime of the lithium battery under test in its operating mode.

[0021] Optionally, the predicted lifespan of the lithium battery under test at the current stage is calculated using the total battery capacity loss and the calibrated battery capacity of the lithium battery under test, including:

[0022] Calculate the capacity loss rate of the lithium battery under test based on the calibrated battery capacity and the total battery capacity loss;

[0023] Through calculation formula

[0024]

[0025] The predicted lifespan is obtained.

[0026] In a second aspect, the present invention provides a lithium battery life prediction device, comprising:

[0027] The cycle life prediction module constructs an initial cycle life prediction model based on the degradation mechanism of the SEI film in lithium batteries during charging and discharging. The initial cycle life prediction model is used to describe the correlation between battery capacity loss and temperature and charge / discharge rate.

[0028] The storage lifetime prediction module constructs an initial storage lifetime prediction model based on the degradation mechanism of the lithium battery SEI film during storage. The initial storage lifetime prediction model is used to describe the correlation between battery capacity loss and battery storage state of charge.

[0029] The model training module is used to acquire experimental battery data for model validation of either the initial cycle lifetime model or the initial storage lifetime model, and to use the experimental battery data to fit and train the model to obtain the cycle lifetime model and storage lifetime model that have been trained to convergence.

[0030] The lifespan prediction module is used to acquire the calibrated battery capacity, temperature data, charge / discharge rate, battery storage state of charge, and operating mode of the lithium battery under test. Combining the cycle life model and storage life model, it predicts the lifespan of the lithium battery under test in the operating mode.

[0031] Thirdly, the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method.

[0032] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0033] The beneficial effects of this invention are:

[0034] The lithium battery life prediction method provided by this invention constructs an initial cycle life prediction model based on the degradation mechanism of the SEI film during charging and discharging, and an initial storage life prediction model based on the degradation mechanism of the SEI film during storage. It considers the different aging rates of the SEI film during charging / discharging and storage, enabling more accurate prediction and calculation of battery capacity loss during charging / discharging and storage, thus improving the accuracy of lithium battery life prediction. Furthermore, the method incorporates the operating mode of the lithium battery under test, coupling the battery capacity loss during charging / discharging and storage under different operating modes, further enhancing the accuracy of lithium battery life prediction. Attached Figure Description

[0035] Figure 1 This is a flowchart of a lithium battery life prediction method in one embodiment of this application;

[0036] Figure 2 This is a schematic diagram illustrating the fitting effect of the cycle life prediction model in one embodiment of this application;

[0037] Figure 3 This is a schematic diagram illustrating the fitting effect of the storage lifetime model in one embodiment of this application;

[0038] Figure 4 This is a schematic diagram of the lithium battery operating mode in one embodiment of this application;

[0039] Figure 5 This is a schematic diagram of the structure of a lithium battery life prediction device in one embodiment of this application;

[0040] Figure 6 This is a schematic diagram of the structure of a terminal device in one embodiment of this application. Detailed Implementation

[0041] To address the issue of low accuracy in traditional lithium battery life prediction methods due to the lack of consideration for the varying SEI film aging rates under different operating modes, this invention provides a lithium battery life prediction method, apparatus, terminal equipment, and medium. This method constructs an initial cycle life prediction model based on the SEI film degradation mechanism during charging and discharging, and an initial storage life prediction model based on the SEI film degradation mechanism during storage. By considering the different SEI film aging rates during charging / discharging and storage, it can more accurately predict and calculate battery capacity loss during these processes, thus improving the accuracy of lithium battery life prediction. Furthermore, the life prediction of the lithium battery under test incorporates its operating mode, coupling the battery capacity loss during charging / discharging and storage under different operating modes, further enhancing the accuracy of lithium battery life prediction.

[0042] The lithium battery life prediction method provided by this invention will be described below.

[0043] like Figure 1 As shown, the lithium battery life prediction method includes steps 11 to 14.

[0044] Step 11: Based on the degradation mechanism of the SEI film of lithium battery during charging and discharging, construct an initial cycle life prediction model.

[0045] It should be noted that the above initial cycle life prediction model is used to describe the relationship between battery capacity loss and temperature and charge / discharge rate.

[0046] It should be understood that the SEI film (solid electrolyte interface film) is a very thin and stable film layer formed, especially on the surface of the negative electrode, during the charge and discharge process of a lithium-ion battery. It is formed by the reaction between the electrolyte and the electrode materials and is typically composed of organic and inorganic compounds. During charge-discharge cycles, the SEI film on the negative electrode surface continuously forms, breaks down, and regenerates (repairs). Specifically, during lithium battery charging, the negative electrode potential is high, and the SEI film may rupture or disintegrate, leading to contact between the electrolyte and the electrode materials, which can trigger side reactions such as lithium metal deposition. During lithium battery discharging, the negative electrode potential decreases, and the SEI film partially repairs itself, generating a new protective layer to maintain the stability of the negative electrode material. This process is dynamic; the quality and thickness of the SEI film change with the number of charge-discharge cycles. After multiple charge-discharge cycles, the thickness of the SEI film gradually increases. This is because during each charge-discharge process, some parts of the film may rupture or be affected by chemical reactions, requiring continuous regeneration or repair. While this thickened film layer protects the negative electrode, it also increases internal resistance, affecting the capacity and cycle performance of the lithium battery.

[0047] Based on the degradation mechanism of the SEI film in lithium batteries during the charging and discharging process described above, the expression of the initial cycle life prediction model constructed in this invention is as follows:

[0048]

[0049] Among them, Q loss1 This represents the capacity loss of a lithium battery during charging and discharging, where A represents the pre-exponential factor, and E represents the capacity loss during charging and discharging. a The activation energy is represented by T, and the temperature is represented by C. rate The value represents the cycle ratio, Ah represents the cumulative ampere-hours (Ah), which is the product of the current and the charge / discharge time, and R represents the ideal gas constant. A represents the additional temperature rise caused by the rate stress, 'a' represents the rate variation factor under the rate effect, 'z' represents the cycle base decay rate, 'm' represents the temperature amplitude adjustment factor, and 'n' represents the rate amplitude adjustment factor. a ,m,n,a,z are the parameters to be solved in the initial cycle life prediction model.

[0050] Step 12: Based on the degradation mechanism of the lithium battery SEI film during storage, construct an initial storage lifetime prediction model.

[0051] It should be noted that the above initial storage lifetime prediction model is used to describe the relationship between battery capacity loss and battery storage state of charge.

[0052] It should be understood that when lithium batteries are in storage, the SEI film changes relatively slowly because they are not being charged or discharged. However, it is still affected by the battery's state of charge (SOC) during storage. If the lithium battery is stored at a high SOC (e.g., fully charged), the negative electrode potential is high, and the SEI film may undergo strong chemical reactions, leading to film damage or instability, thus accelerating battery aging. At a low SOC, the negative electrode potential is low, and the damage and changes to the SEI film are relatively small, but lithium dendrite deposition may occur, affecting the safety and capacity of the lithium battery. Lithium batteries may experience self-discharge during storage, leading to a voltage drop, which in turn alters the electrochemical environment of the negative electrode, affecting the stability of the SEI film.

[0053] Based on the degradation mechanism of the lithium battery SEI film during storage, the expression of the initial storage lifetime prediction model constructed in this invention is as follows:

[0054]

[0055] Among them, Q loss2 This represents the capacity loss of a lithium battery during storage. A0 represents the calendar decay base magnitude factor, Bs represents the SOC independent influence factor, and SOC represents the battery storage state of charge, which is the ratio between the currently stored electrical energy of the lithium battery and its maximum battery capacity. C s The SOC and temperature combined influence factor is represented by k, Z, and t, where k is the Boltzmann constant, Z is the empirical constant, t is the calendar storage time in days, and A0, Bs, E are the factors. a0 C s These are the parameters to be solved for the initial storage lifetime prediction model.

[0056] Step 13: For either the initial cycle lifetime model or the initial storage lifetime model, obtain experimental battery data for model validation of that model, and use the experimental battery data to fit and train that model to obtain the cycle lifetime model and storage lifetime model that have been trained to convergence.

[0057] It should be noted that the purpose of step 13 is to fit and solve the parameters to be solved in the initial cycle lifetime prediction model and the initial storage lifetime prediction model.

[0058] In one embodiment of the present invention, for the initial cycle life model, the experimental battery data includes: aging cycles at different temperatures and different rates, and capacity measurements every 50 cycles to obtain the change in capacity decay rate with the number of cycles;

[0059] For the initial storage lifetime model, the experimental battery data included: storage aging under different temperatures and SOC conditions, with capacity measurements taken every 30 days to obtain the change in capacity decay rate over storage time.

[0060] The process of fitting and solving is explained below.

[0061] For example, the parameters to be solved are initialized, and the initialized parameter values ​​are used as individuals in the population. Through a genetic algorithm, the parameter values ​​corresponding to the best individuals in the population are used as the final parameter values ​​to be solved, thus obtaining the storage lifetime prediction model and the cycle lifetime model. The fitness of the population is negatively correlated with the difference between the model prediction results and the actual results.

[0062] In another embodiment of the present invention, the parameters to be solved in the initial cycle lifetime prediction model and the initial storage lifetime prediction model can be backpropagated according to the loss function until the loss value of the model is less than a preset loss threshold, thereby obtaining the storage lifetime prediction model and the cycle lifetime model. The loss function can be the cross-entropy loss function.

[0063] To further verify the accuracy of the cycle lifetime prediction model and storage lifetime model provided by this invention, in one embodiment of this invention, fitting verification was performed based on laboratory-tested sample degradation data at 45℃+1P. The fitting results are as follows: Figure 2 , Figure 3 As shown. Among them Figure 2 This is a schematic diagram illustrating the fitting effect of the cycle life prediction model. Figure 2 It can be seen that the cycle life prediction model provided by the present invention has good fitting ability, with a fitting degree of 0.9304. Figure 3 This diagram illustrates the fitting effect of the storage lifetime model. The horizontal axis represents storage time, and the vertical axis represents the percentage of capacity degradation. The black curve is the degradation curve based on actual measurement points, while the colored curve is the model fitting curve. Figure 3 As can be seen, the fitting effect of the storage lifetime model provided by the present invention is in high agreement with the actual situation, further verifying the accuracy of the storage lifetime model provided by the present invention.

[0064] Step 14: Obtain the calibrated battery capacity, temperature data, charge / discharge rate, battery storage state of charge, and operating mode of the lithium battery under test. Combine the cycle life model and storage life model to predict the lifespan of the lithium battery under test in the operating mode.

[0065] It should be understood that the operating mode of a lithium battery includes at least one stage. This stage can be a charge / discharge stage or a storage stage, specifically as follows: Figure 4 As shown. Considering the impact of different SEI film aging rates at different stages on lithium battery life prediction under different operating modes, this invention can accurately predict the life of lithium batteries based on their actual operating modes.

[0066] Specifically, this includes steps 14.1 to 14.2.

[0067] Step 14.1: For each stage, the battery capacity loss of the previous stage is used as the initial battery capacity loss of the current stage. If the current stage is a charge / discharge stage, the temperature data and charge / discharge rate are input into the cycle life model to obtain the current battery capacity loss of the lithium battery under test in the current stage. The total battery capacity loss of the lithium battery under test in the current stage is calculated based on the initial battery capacity loss and the current battery capacity loss. The predicted life of the lithium battery under test in the current stage is calculated using the total battery capacity loss and the calibrated battery capacity of the lithium battery under test. If the current stage is a storage stage, the battery storage state of charge is input into the storage life model to obtain the current battery capacity loss of the lithium battery under test in the current stage. The total battery capacity loss of the lithium battery under test in the current stage is calculated based on the initial battery capacity loss and the current battery capacity loss. The predicted life of the lithium battery under test in the current stage is calculated using the total battery capacity loss and the calibrated battery capacity of the lithium battery under test.

[0068] The process involves calculating the predicted lifespan of the lithium battery under test at the current stage using the total battery capacity loss and the calibrated battery capacity of the lithium battery under test, including steps I to II.

[0069] Step 1: Calculate the capacity loss rate of the lithium battery under test based on the calibrated battery capacity and the total battery capacity loss.

[0070]

[0071] Step II, through calculation formula

[0072]

[0073] The predicted lifespan is obtained.

[0074] Step 14.2: Determine the predicted lifetime of the last stage as the predicted lifetime of the lithium battery under test in the operating mode.

[0075] It is worth mentioning that this invention combines the working modes of the lithium battery under test, and can couple the battery capacity loss during the charging and discharging process and storage process under different working modes, thereby improving the accuracy of lithium battery life prediction.

[0076] The lithium battery life prediction device provided by the present invention will be described below.

[0077] like Figure 5 As shown, the lithium battery life prediction device 500 includes:

[0078] The cycle life prediction module 501 constructs an initial cycle life prediction model based on the degradation mechanism of the SEI film of lithium battery during charging and discharging. The initial cycle life prediction model is used to describe the correlation between battery capacity loss and temperature and charge / discharge rate.

[0079] The storage lifetime prediction module 502 constructs an initial storage lifetime prediction model based on the degradation mechanism of the lithium battery SEI film during storage. The initial storage lifetime prediction model is used to describe the correlation between battery capacity loss and battery storage state of charge.

[0080] The model training module 503 is used to acquire experimental battery data for model validation of either the initial cycle lifetime model or the initial storage lifetime model, and to use the experimental battery data to fit and train the model to obtain the cycle lifetime model and storage lifetime model trained to convergence.

[0081] The lifespan prediction module 504 is used to acquire the calibrated battery capacity, temperature data, charge and discharge rate, battery storage state of charge, and operating mode of the lithium battery under test. Combining the cycle life model and storage life model, it predicts the lifespan of the lithium battery under test in the operating mode.

[0082] It should be noted that the information interaction and execution process between the above-mentioned devices / units are different from the method of this application.

[0083] The embodiments are based on the same concept, and their specific functions and technical effects can be found in the method embodiment section, which will not be repeated here. Those skilled in the art will understand that, for ease of description and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0084] like Figure 6 As shown, embodiments of the present invention provide a terminal device, such as... Figure 6 As shown, the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 6The diagram shows only one processor, a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 executes the computer program D102 to implement the steps in any of the above method embodiments.

[0085] Specifically, when the processor D100 executes the computer program D102, it constructs an initial cycle life prediction model based on the degradation mechanism of the lithium battery SEI film during charging and discharging; and constructs an initial storage life prediction model based on the degradation mechanism of the lithium battery SEI film during storage. For either the initial cycle life model or the initial storage life model, it acquires experimental battery data for model verification and uses the experimental battery data to fit and train the model to obtain a cycle life model and a storage life model that have been trained to convergence. It acquires the calibrated battery capacity, temperature data, charging and discharging rate, battery storage state of charge, and operating mode of the lithium battery under test, and combines the cycle life model and the storage life model to predict the life of the lithium battery under test in the operating mode. The lithium battery life prediction method provided by this invention constructs an initial cycle life prediction model based on the degradation mechanism of the SEI film during charging and discharging, and an initial storage life prediction model based on the degradation mechanism of the SEI film during storage. It considers the different aging rates of the SEI film during charging / discharging and storage, enabling more accurate prediction and calculation of battery capacity loss during charging / discharging and storage, thus improving the accuracy of lithium battery life prediction. Furthermore, the method incorporates the operating mode of the lithium battery under test, coupling the battery capacity loss during charging / discharging and storage under different operating modes, further enhancing the accuracy of lithium battery life prediction.

[0086] The processor D100 can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0087] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may be an external storage device of the terminal device D10, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal device D10. Furthermore, the memory D101 may include both internal and external storage units of the terminal device D10. The memory D101 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory D101 can also be used to temporarily store data that has been output or will be output.

[0088] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0089] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0090] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of protection of this application is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this application as described above, which are not provided in detail for the sake of brevity.

[0091] One or more embodiments in this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments in this application should be included within the protection scope of this application.

Claims

1. A method for predicting the lifespan of a lithium battery, characterized in that, include: Based on the degradation mechanism of the SEI film in lithium batteries during charging and discharging, an initial cycle life prediction model is constructed. The initial cycle life prediction model is used to describe the relationship between battery capacity loss and temperature and charge / discharge rate; Based on the degradation mechanism of the SEI film in lithium batteries during storage, an initial storage lifetime prediction model is constructed; the initial storage lifetime prediction model is used to describe the correlation between battery capacity loss and battery storage state of charge. For the initial cycle lifetime prediction model and the initial storage lifetime prediction model, experimental battery data for model validation is obtained, and the initial cycle lifetime prediction model and the initial storage lifetime prediction model are fitted and trained using the experimental battery data to obtain the cycle lifetime prediction model and storage lifetime prediction model that have been trained to convergence; the expression of the initial cycle lifetime prediction model is as follows: in, This indicates the capacity loss of a lithium battery during the charging and discharging process. Indicates pre-exponential factor, Indicates activation energy. Indicates temperature. Indicates the cycle ratio. This indicates the cumulative ampere-hours, which is the product of the current and the charging / discharging time. Represents the ideal gas constant. This indicates the additional temperature rise caused by the multiplier stress. This represents the rate change factor under the influence of magnification. Indicates the cyclic basic decay rate. Indicates the temperature amplitude adjustment factor. This represents the adjustment factor for the magnification. The parameters to be solved in the initial cycle lifetime prediction model; The process involves acquiring the calibrated battery capacity, temperature data, charge / discharge rate, battery storage state of charge, and operating mode of the lithium battery under test. Combining this with the cycle life prediction model and the storage life prediction model, the lifespan of the lithium battery under test in the operating mode is predicted. This includes: for each stage, using the battery capacity loss of the previous stage as the initial battery capacity loss for the current stage; if the current stage is a charge / discharge stage, inputting the temperature data and the charge / discharge rate into the cycle life prediction model to obtain the current battery capacity loss of the lithium battery under test in the current stage, and then... The initial battery capacity loss and the current battery capacity loss are used to calculate the total battery capacity loss of the lithium battery under test in the current stage. The predicted lifespan of the lithium battery under test in the current stage is then calculated using the total battery capacity loss and the calibrated battery capacity of the lithium battery under test. If the current stage is a storage stage, the battery storage state of charge is input into the storage lifespan prediction model to obtain the current battery capacity loss of the lithium battery under test in the current stage. The total battery capacity loss of the lithium battery under test in the current stage is calculated based on the initial battery capacity loss and the current battery capacity loss. The predicted lifespan of the lithium battery under test in the current stage is then calculated using the total battery capacity loss and the calibrated battery capacity of the lithium battery under test. The predicted lifespan of the last stage is determined as the predicted lifespan of the lithium battery under test in the operating mode.

2. The lithium battery life prediction method according to claim 1, characterized in that, The expression for the initial storage lifetime prediction model is as follows: in, This indicates the capacity loss of lithium batteries during storage. Indicates the calendar decay base magnitude factor. Indicates the independent impact factor of SOC. This indicates the battery's stored state of charge (SOC), which is the ratio between the electrical energy currently stored in the lithium battery and its maximum battery capacity. This represents the combined influence factor of SOC and temperature. This represents Boltzmann's constant. Represents an empirical constant. This indicates that the calendar stores time in days. These are the parameters to be solved in the initial storage lifetime prediction model.

3. The lithium battery life prediction method according to claim 2, characterized in that, The operating mode includes at least one stage; the stage is either a charging / discharging stage or a storage stage.

4. The lithium battery life prediction method according to claim 3, characterized in that, The step of calculating the predicted lifespan of the lithium battery under test at the current stage using the total battery capacity loss and the calibrated battery capacity of the lithium battery under test includes: Calculate the capacity loss rate of the lithium battery under test based on the calibrated battery capacity and the total battery capacity loss; Through calculation formula The predicted lifetime is obtained.

5. A lithium battery life prediction device, characterized in that, include: The cycle life prediction module constructs an initial cycle life prediction model based on the degradation mechanism of the SEI film in lithium batteries during charging and discharging. The initial cycle life prediction model is used to describe the relationship between battery capacity loss and temperature and charge / discharge rate; The storage lifetime prediction module constructs an initial storage lifetime prediction model based on the degradation mechanism of the lithium battery SEI film during storage; the initial storage lifetime prediction model is used to describe the correlation between battery capacity loss and battery storage state of charge. The model training module is used to acquire experimental battery data for model validation of the initial cycle lifetime prediction model and the initial storage lifetime prediction model, and to perform fitting training on the initial cycle lifetime prediction model and the initial storage lifetime prediction model using the experimental battery data, respectively, to obtain a cycle lifetime prediction model and a storage lifetime prediction model trained to convergence; the expression of the initial cycle lifetime prediction model is as follows: in, This indicates the capacity loss of a lithium battery during the charging and discharging process. Indicates pre-exponential factor, Indicates activation energy. Indicates temperature. Indicates the cycle ratio. This indicates the cumulative ampere-hours, which is the product of the current and the charging / discharging time. Represents the ideal gas constant. This indicates the additional temperature rise caused by the multiplier stress. This represents the rate change factor under the influence of magnification. Indicates the cyclic basic decay rate. Indicates the temperature amplitude adjustment factor. This represents the adjustment factor for the magnification. The parameters to be solved in the initial cycle lifetime prediction model; The lifespan prediction module is used to acquire the calibrated battery capacity, temperature data, charge / discharge rate, battery storage state of charge, and operating mode of the lithium battery under test. Combining this with the cycle life prediction model and the storage life prediction model, it predicts the lifespan of the lithium battery under test in the operating mode. The acquisition of the calibrated battery capacity, temperature data, charge / discharge rate, battery storage state of charge, and operating mode of the lithium battery under test, and the combination of the cycle life prediction model and the storage life prediction model to predict the lifespan of the lithium battery under test in the operating mode, includes: sequentially, for each stage, using the battery capacity loss of the previous stage as the initial battery capacity loss of the current stage; if the current stage is a charge / discharge stage, then inputting the temperature data and the charge / discharge rate into the cycle life prediction model to obtain the current battery capacity loss of the lithium battery under test in the current stage. The model calculates the total battery capacity loss of the lithium battery under test in the current stage based on the initial battery capacity loss and the current battery capacity loss. Then, it calculates the predicted lifespan of the lithium battery under test in the current stage using the total battery capacity loss and the calibrated battery capacity of the lithium battery under test. If the current stage is a storage stage, the battery storage state of charge is input into the storage lifespan prediction model to obtain the current battery capacity loss of the lithium battery under test in the current stage. The model then calculates the total battery capacity loss of the lithium battery under test in the current stage based on the initial battery capacity loss and the current battery capacity loss. Finally, it calculates the predicted lifespan of the lithium battery under test in the current stage using the total battery capacity loss and the calibrated battery capacity of the lithium battery under test. The predicted lifespan of the last stage is determined as the predicted lifespan of the lithium battery under test in the operating mode.

6. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 4.

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