A lithium battery storage attenuation life prediction method based on negative lithium loss process

Abstract

The disclosure discloses a lithium battery storage attenuation life prediction method based on a negative lithium loss process, steps comprising: establishing a mechanism model of battery self-discharge based on a negative lithium loss process, and establishing a theoretical model of capacity attenuation changing with time; performing battery self-discharge tests under different SOC and different storage temperatures to obtain the capacity attenuation change law of the battery under different influencing factors; fitting the undetermined parameters in the theoretical model through test data and the change law to obtain the capacity attenuation model of the battery during storage under different initial SOC and different storage temperatures; establishing a lithium battery storage attenuation life prediction model based on the capacity attenuation model; obtaining initial SOC and storage temperature data of a lithium ion battery to be predicted, and substituting into the lithium battery storage attenuation life prediction model to obtain a predicted storage life. The present application can accurately predict the remaining life of a lithium battery in a storage state for a long time.

Description

Technical Field

[0001] This disclosure belongs to the field of lithium battery storage life prediction, specifically involving a method for predicting lithium battery storage degradation life based on the lithium loss process of the negative electrode. Background Technology

[0002] The clean energy sector has developed rapidly, and lithium batteries have been widely used in power and energy storage. However, the high energy density of lithium batteries has also brought about safety issues, requiring attention to the health status and lifespan of the batteries.

[0003] To meet users' demands for battery performance and safety, suitable models or solutions are needed to monitor the battery's health status, assess and predict its lifespan, and thus anticipate its current state and performance. Compared to the widely discussed battery cycle life, storage life, another crucial component of battery lifespan, receives less attention. However, in many real-world applications, lithium-ion batteries spend significantly more time in a near-open-circuit storage state than during charging and discharging. During storage, performance also degrades due to self-discharge, impacting lifespan. Therefore, storage life has a significant influence on the battery's overall lifespan.

[0004] Self-discharge of a battery refers to the spontaneous loss of capacity over time when the battery is not connected to any external circuit and is in a non-operating state. Studying the self-discharge phenomenon during battery storage using appropriate methods, obtaining relevant characteristics, and establishing a capacity change model not only provides comprehensive battery information for battery management systems, improving the safety of battery pack use and avoiding accelerated performance degradation and more serious safety accidents caused by individual battery inconsistencies, but also serves as an important supplement to conventional battery charge-discharge cycle life models, making battery life models more robust, improving battery life management, preventing battery abuse, and extending battery life. Therefore, establishing models to estimate battery storage life is of great significance for improving battery life models and ensuring safe battery applications. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this disclosure is to provide a method for predicting the storage degradation lifetime of lithium batteries based on the negative electrode lithium loss process. This method utilizes the negative electrode lithium loss and SEI film growth as the main causes of battery aging during storage self-discharge, and can accurately predict the remaining lifetime of lithium batteries that have been stored for a long time.

[0006] To achieve the above objectives, this disclosure provides the following technical solutions:

[0007] A method for predicting the storage degradation lifetime of lithium batteries based on the lithium loss process in the negative electrode includes the following steps:

[0008] S100: A mechanism model of battery self-discharge is established based on the lithium loss process of the negative electrode, and a theoretical model of capacity decay over time is established.

[0009] S200: Conduct battery self-discharge tests at different SOCs and storage temperatures to obtain test data and the battery capacity decay pattern under different influencing factors;

[0010] S300: Fit the undetermined parameters in the theoretical model using the experimental data and the changing patterns to obtain a capacity decay model for battery storage under different initial SOC and storage temperatures;

[0011] S400: Based on the capacity decay model, establish a lithium battery storage decay lifetime prediction model;

[0012] S500: Obtain the initial SOC and storage temperature data of the lithium-ion battery to be predicted, and substitute the initial SOC and storage temperature data into the lithium battery storage degradation lifetime prediction model to obtain the predicted storage lifetime.

[0013] Preferably, the SOC is defined as:

[0014] ,

[0015] In the formula: The current battery capacity in Ah; The current available capacity of the battery is expressed in Ah.

[0016] Preferably, in step S100, a mechanism model of battery self-discharge is established based on the lithium loss process of the negative electrode, and a theoretical model of capacity decay over time is established as follows:

[0017] ,

[0018] In the formula: Q represents the irreversible capacity loss during battery storage, and e represents the charge carried by a single electron, i.e., 1.6 × 10⁻⁶. -19 A·s, where N is Avogadro's constant, i.e., 6.02 × 10⁻⁶. 23 S is the total area of ​​the SEI film in the battery, Z x is the molar amount of product x generated during the storage process, k3 is the reaction rate constant, a and b are the reaction orders of electrons and products, respectively, c0 is the average electron concentration on the initial SEI film surface, and t is the corresponding reaction time.

[0019] Preferably, in step S200, the battery self-discharge test under different SOC and different storage temperatures includes the following procedures: capacity calibration procedure, self-discharge test procedure under different SOC, and self-discharge test procedure under different storage temperatures.

[0020] Preferably, in step S300, the fitting process for the undetermined parameters in the theoretical model is as follows:

[0021] S301: Simplifies the theoretical model of battery capacity decay over time, reducing the number of undetermined parameters in the capacity decay model under the influence of different SOCs and storage temperatures;

[0022] S302: Organize the self-discharge experimental data of the battery under different storage conditions, combine the theoretical model with the experimental data, and use the Levenberg-Marquardt algorithm optimization algorithm for fitting;

[0023] S303: Apply the fitting statistical parameters and correlation parameters, as well as the standard error, to verify the fitting effect on the undetermined parameters.

[0024] Preferably, in step S400, the lithium battery storage degradation lifetime prediction model is established using a storage lifetime distribution simulation based on the Monte Carlo method.

[0025] Preferably, the verification process of the parameter fitting results is performed by analyzing the linearity of the changes in the undetermined parameters under different influencing factors with changes in SOC and storage temperature.

[0026] This disclosure also proposes a lithium battery storage degradation lifetime prediction device based on the negative electrode lithium loss process, the device comprising:

[0027] The measurement unit is used for testing the SOC and temperature of lithium-ion batteries under long-term storage conditions.

[0028] The classification unit is used to classify and fit the capacity data obtained from measurements at different SOCs and temperatures.

[0029] Data units, categorized under different SOCs and storage temperatures, are used to fit the parameters to be determined in the theoretical model for data of different capacities.

[0030] The model unit, through fitting and validating the model parameters of the data unit, improves the capacity decay model of the battery under different initial SOC and different storage temperatures, so as to obtain a lithium battery storage decay life prediction model.

[0031] The detection unit is used to perform internal temperature and SOC tests on the lithium-ion battery under the storage state to be tested, and to input the obtained data into the lithium battery storage degradation lifetime prediction model to obtain the predicted storage lifetime.

[0032] Compared with the prior art, the beneficial effects of this disclosure are as follows:

[0033] The method described in this disclosure can quickly and accurately predict the storage life of lithium-ion batteries that have been stored for a long time, thus estimating the battery storage life, supplementing the battery life model, providing a reference for battery management strategies, and helping to improve battery safety.

[0034] The features and advantages of this invention can be specifically reflected in the following three aspects:

[0035] (1) Unlike the common industrial method of predicting based on experience when predicting the storage life of lithium-ion batteries, the present invention makes predictions by using a self-discharge mechanism model based on the loss process of negative electrode lithium, which can more accurately predict the state of the battery during storage by starting from the internal chemical reaction of the battery.

[0036] (2) In fitting the undetermined parameters of the theoretical model through experimental data, unlike the conventional overly idealized linear fitting method, the present invention uses the Levenberg-Marquardt optimization algorithm for fitting. This method not only combines the advantages of the Gauss-Newton algorithm and the gradient descent method, but also solves the problem that the inverse matrix of the two does not exist or the initial value is too far from the local minimum. It can provide a numerical solution for nonlinear minimization and has great advantages in fitting nonlinear curves.

[0037] (3) Regarding the accuracy of battery storage life estimation based on capacity decay model, unlike the battery storage life with large deviations obtained directly through algebraic equations, the present invention adopts Monte Carlo statistical simulation method after perfecting the parameters of the self-discharge mechanism model. This method has great advantages in solving problems involving stochastic processes. It can not only be used to simulate the change process of battery capacity over time under different storage aging influencing factors, but also to estimate the uncertainty of parameters in the established capacity model and improve the accuracy of battery storage life prediction. Attached Figure Description

[0038] Figure 1 This is a flowchart of a lithium battery storage degradation lifetime prediction method based on the negative electrode lithium loss process provided in one embodiment of this disclosure;

[0039] Figure 2 This is a flowchart of a lithium-ion battery capacity calibration experiment provided in another embodiment of this disclosure;

[0040] Figure 3 This is a flowchart of a self-discharge experiment of a lithium-ion battery under different states of charge (SOC) according to another embodiment of this disclosure;

[0041] Figure 4 This is a flowchart of a self-discharge experiment of a lithium-ion battery at different storage temperatures, provided in another embodiment of this disclosure;

[0042] Figures 5(a) to 5(b) are graphs showing the change in battery capacity decay over storage time under different influencing factors for a lithium-ion battery provided in another embodiment of this disclosure.

[0043] Figure 6 This is a flowchart of fitting undetermined parameters for a theoretical model of self-discharge of a lithium-ion battery provided in another embodiment of this disclosure;

[0044] Figure 7 This is a graph showing the relationship between the k value and the battery SOC in a theoretical model provided in another embodiment of this disclosure;

[0045] Figure 8 This is a graph showing the relationship between the value of k and the battery storage temperature in a theoretical model provided in another embodiment of this disclosure;

[0046] Figures 9(a) to 9(d) are battery storage lifetime estimation diagrams under different initial SOC conditions provided in another embodiment of this disclosure; wherein, Figure 9(a) is 20% SOC, Figure 9(b) is 50% SOC, Figure 9(c) is 60% SOC, and Figure 9(d) is 80% SOC;

[0047] Figures 10(a) to 10(d) are battery storage life estimation diagrams under different storage temperature conditions provided in another embodiment of the present disclosure; wherein, Figure 10(a) is 20°C, Figure 10(b) is 25°C, Figure 10(c) is 30°C, and Figure 10(d) is 35°C. Detailed Implementation

[0048] The following will refer to the appendix. Figures 1 to 1 0(d) A detailed description of specific embodiments of this disclosure. While specific embodiments of this disclosure are shown in the accompanying drawings, it should be understood that this disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art.

[0049] It should be noted that certain terms are used in the specification and claims to refer to specific components. Those skilled in the art will understand that different terms may be used to refer to the same component. This specification and claims do not distinguish components based on differences in terminology, but rather on differences in function. The terms "comprising" or "including" used throughout the specification and claims are open-ended and should be interpreted as "comprising but not limited to." The following descriptions of preferred embodiments of this disclosure are for the purpose of implementing the general principles of the specification and are not intended to limit the scope of this disclosure. The scope of protection of this disclosure is determined by the appended claims.

[0050] To facilitate understanding of the embodiments of this disclosure, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. The accompanying drawings do not constitute a limitation on the embodiments of this disclosure.

[0051] In one embodiment, such as Figure 1 As shown, a method for predicting the storage degradation lifetime of a lithium battery based on the lithium loss process in the negative electrode includes the following steps:

[0052] S100: A mechanism model of battery self-discharge is established based on the lithium loss process of the negative electrode, and a theoretical model of capacity decay over time is established.

[0053] S200: Conduct battery self-discharge tests at different SOCs and storage temperatures to obtain test data and the battery capacity decay pattern under different influencing factors;

[0054] S300: Fit the undetermined parameters in the theoretical model using the experimental data and the changing patterns to obtain a capacity decay model for battery storage under different initial SOC and storage temperatures;

[0055] S400: Based on the capacity decay model, establish a lithium battery storage decay lifetime prediction model;

[0056] S500: Obtain the initial SOC and storage temperature data of the lithium-ion battery to be predicted, and substitute the initial SOC and storage temperature data into the lithium battery storage degradation lifetime prediction model to obtain the storage lifetime.

[0057] The above embodiments constitute the complete technical solution of this disclosure. The method described in this embodiment can quickly and accurately predict the storage life of lithium-ion batteries that have been stored for a long time, realize the estimation of battery storage life, supplement the battery life model, provide a reference for battery management strategies, and help improve battery safety.

[0058] In another embodiment, a mechanism model of battery self-discharge is established based on the lithium loss process of the negative electrode, and a theoretical model of capacity decay over time is established.

[0059] In this embodiment, the theoretical model establishment process is as follows:

[0060] To establish the relationship between battery capacity decay and time during self-discharge, this embodiment describes the battery capacity decay process as the growth of the SEI film, and the initial SEI film and the newly formed SEI film have different effects on Li. + and e - The conductivity remains consistent. Therefore, e - The diffusion process is characterized by a linear diffusion model, i.e., the surface e of the SEI film. - concentration and e -The diffusion distance is proportional to the film thickness, as shown in equation (1).

[0061] (1)

[0062] In the formula: c it —Li in the SEI film region at time t (point i) + or e - Concentration per unit area; c i0 —Li in the region at point i on the SEI film surface at the initial time + or e - concentration; d i —The distance, or thickness, between the newly formed SEI film surface and the initial SEI film within the i-th region; k1—The effect of the newly formed SEI film on Li + or e - The conductivity coefficient. At the same time, since the thickness of the SEI film is not uniform at the beginning, the capacity loss rate of the battery is a combination of the electron loss rates of different regions on the surface of the SEI film, as shown in equation (2).

[0063] (2)

[0064] Where: Q—irreversible capacity loss during battery storage; r—overall reaction rate during aging; c it —Li in the SEI film region at time t (point i) + or e - The concentration per unit area; k2—reaction rate constant.

[0065] Secondly, the reaction rate equation for the overall battery aging reaction is as follows:

[0066] (3)

[0067] In the formula: k3—reaction rate constant; a—reaction order of electrons; b—reaction order of products; —The average density of the newly formed SEI film, i.e., the molar amount per unit area; The average electron concentration on the SEI film surface at time t can also be expressed as:

[0068] (4)

[0069] In the formula: S i —Unit electron concentration is The area of ​​the SEI film / graphite anode at point i is therefore... This indicates the total area of ​​the battery's SEI film.

[0070] According to the relationship between the amount of electron transfer and the change in battery capacity, the capacity loss during battery storage can be expressed by the consumption of electrons, as shown in Equation (5).

[0071] (5)

[0072] In the formula: Q — the irreversible capacity loss during battery storage, n — the number of moles of electrons consumed during this process, e — the charge carried by a single electron, 1.6×10 -19 A·s, N — Avogadro's constant, 6.02 ×10 23 。

[0073] Considering that when the battery starts self-discharging, the side reaction just begins, so the thickness of the newly formed SEI film is much thinner than the existing SEI film, that is, d t <<d0, it can be approximately considered that d i in Equation (1) is equal to 0, so we get

[0074] (6)

[0075] Solve the simultaneous equations (1) to (6), and combine the initial boundary condition that the capacity loss Q = 0 at the initial time t = 0, and finally get

[0076] (7)

[0077] In the formula: Q is the irreversible capacity loss during battery storage, e is the charge carried by a single electron, that is, 1.6×10 -19 A·s, N is Avogadro's constant, that is, 6.02×10 23 , S is the total area of the battery SEI film, Z x is the number of moles of product x generated during storage, k3 is the reaction rate constant, a and b are the reaction orders of electrons and products respectively, c0 is the average electron concentration on the surface of the initial SEI film, and t is the corresponding reaction time.

[0078] It can be seen that the capacity attenuation of the battery during storage is mainly affected by the storage time, the surface area of the battery negative electrode / SEI film, the initial concentration of electrons at the SEI film interface, the concentration of reaction products, and the reaction orders a, b and the reaction rate constant.

[0079] In another embodiment, battery self-discharge tests are carried out under different SOCs and different storage temperatures to obtain the variation law of battery capacity attenuation under different influencing factors, which generally includes three processes: the battery capacity calibration experiment process, the self-discharge experiment process under different states of charge (SOC), and the self-discharge experiment process under different storage temperatures.

[0080] In this embodiment, the steps of the three processes are explained as follows:

[0081] The battery capacity calibration experiment flowchart is as follows: Figure 2 As shown. First, place the battery in a constant temperature chamber (25℃) for 1 hour until it stabilizes. Then, charge it with a constant current of 0.5C until the upper limit cutoff voltage of 4.2V. After that, switch to constant voltage charging at 4.2V until the charging current is less than 0.05C. At this point, the battery is considered to be fully charged. After the battery stabilizes for 1 hour, discharge it with a constant current of 0.5C until the lower limit cutoff voltage of 2.5V. Calculate the integral of the discharge current over time to obtain the current capacity of the battery.

[0082] Experimental flowcharts for self-discharge under different states of charge (SOC) are as follows: Figure 3 As shown in the diagram. First, different states of charge (SOCs) were selected: 100%, 80%, 60%, 40%, and 20%. Then, batteries with good consistency were selected, and capacity calibration experiments were performed on each, calculating the required discharge capacity based on the target SOC. The batteries were first charged at a constant current of 0.5C to the upper limit cutoff voltage of 4.2V, then switched to a constant voltage of 4.2V until the charging current was less than 0.05C. After stabilizing for 1 hour, they were discharged at a current of 0.5C to the required SOC. Finally, self-discharge experiments were conducted on the batteries with the set state of charge, maintaining the same experimental temperature for all batteries.

[0083] The flowchart of the self-discharge experiment at different storage temperatures is as follows: Figure 4 As shown in the diagram, batteries that have undergone consistency screening and pre-experimentation were first selected for capacity calibration experiments. Then, they were charged at a constant current of 0.5C and a constant voltage of 4.2V until fully charged. After being left to stand for 1 hour until the battery polarization process disappeared and the state stabilized, they were discharged at a current of 0.5C to the same state of charge (SOC) to prevent differences in battery SOC from affecting the experiment. Finally, the temperature of the constant temperature chamber was adjusted to the required experimental temperature, and the battery self-discharge experiments described above were conducted. Considering the temperature range within which the battery can operate normally, this embodiment selected 25℃, 35℃, 45℃, and 55℃ for the experiments. Based on the aging data stored at equal temperature intervals, an appropriate model was constructed to analyze the data and obtain the degree of influence of storage temperature on the battery self-discharge process.

[0084] Self-discharge experiments were conducted on the battery under different storage conditions, and the relationship between battery capacity decay and self-discharge time is shown in Figures 5(a) to 5(b). The battery capacity changes over time consistently under different initial SOC and storage temperatures. When the storage temperature is higher or the state of charge is higher, the capacity loss rate is faster, and the performance degradation is greater under the same storage time.

[0085] In another embodiment, the undetermined parameters in the theoretical model are fitted by experimental data and variation patterns to improve the capacity decay model of batteries stored at different initial SOCs and storage temperatures.

[0086] The flowchart of fitting the undetermined parameters of the theoretical model for self-discharge of lithium-ion batteries is as follows: Figure 6 As shown below, the simplified theoretical model of battery capacity degradation over time is as follows:

[0087] In equation (7), k3 is determined by the temperature, while c0 is closely related to the battery's SOC and storage temperature. The remaining parameters can be considered constants, so the capacity loss can be simplified using equation (8):

[0088] (8)

[0089] In the formula, p is the exponential term of time t, reflecting that the time trend is determined by the reaction order, and k is a combination of unknown parameters of the remaining terms of the equation, which are related to self-discharge influencing factors such as temperature and SOC.

[0090] When studying the effect of SOC on battery capacity decay during storage, the experiment kept the temperature and other influencing factors constant. Therefore, c0 in equation (7) is a parameter that is only related to SOC and is not affected by other factors. The larger the SOC of the battery, the higher the polarization potential of the negative electrode, the more lithium intercalation, the sufficient electron and lithium ion content at the negative electrode / SEI film interface, the more electrons available for reaction, and the larger c0. Therefore, the capacity loss for the same storage time is also greater. Therefore, it can be approximately assumed that c0 and the battery's state of charge are linearly related. The experimental data is then used to fit and verify the rationality of the assumption. The simplified relationship between the corresponding capacity loss Q and the battery SOC is shown in equation (9), where A and B are constants.

[0091] (9)

[0092] When studying the effect of temperature on the capacity change during battery storage, k3 and c0 in equation (7) are parameters that are only related to temperature and are not affected by SOC or other influencing factors. The remaining parameters can also be regarded as constants. At this time, the capacity loss Q can be expressed as equation (10), where A and B are constants and T represents temperature.

[0093] (10)

[0094] Secondly, when determining the unknown parameters, this example uses the Levenberg-Marquardt algorithm for fitting. This method not only combines the advantages of the Gauss-Newton algorithm and gradient descent, but also solves the problems of the absence of inverse matrices or the initial values ​​being too far from local minima. It can provide a numerical solution for nonlinear minimization and has a significant advantage in fitting nonlinear curves. In this embodiment, by using k to represent the combination of unknown parameters related to factors such as temperature and SOC, the relationship between battery capacity decay and self-discharge time is fitted to determine the p-value as 0.5, and reliability verification is performed. Based on this, the values ​​of parameters A and B are then fitted.

[0095] This embodiment fits the k values ​​corresponding to different SOC conditions calculated after the experiment to obtain the relationship between the k value and the battery SOC, as follows: Figure 7 As shown. Among the fitted statistical parameters, R0... 2 The coefficient of performance (COP) is 0.9998, and the RMSE is 0.00081, indicating a high degree of fit. The fitted values ​​of parameters A and B in the model are 2.228 and 1.3, respectively. Therefore, when the temperature remains constant at 55℃, the model for the change in self-discharge capacity loss of the battery under different initial SOC conditions with storage time can be expressed as follows:

[0096] (11)

[0097] By comparing the experimentally measured data of battery capacity decay used to build the model with the model's predicted values ​​and recording the prediction errors, Table 1 lists some data under SOC, where the units of the measured and predicted capacity values ​​are both mAh.

[0098] Table 1. Comparison of model predictions and measured values ​​of battery capacity degradation under some initial SOC conditions.

[0099]

[0100] The errors between the model's predictions and the experimental measurements are small, all within 3%, which is within the allowable range of experimental error. This indicates that the model has achieved good prediction of the capacity decay trend during battery storage under different states of charge.

[0101] This embodiment fits the k values ​​obtained after the experiment for different storage temperatures to obtain the relationship between the k value and the battery's operating temperature, as follows: Figure 8 As shown. Among the fitted statistical parameters, R0... 2The value of the equation is 0.9999, and the RMSE is 0.00259, indicating a high degree of fit. The values ​​of parameters A and B in the fitted model are 3.501 and -24.28, respectively. Therefore, when the SOC remains constant at 80%, the model for the change in self-discharge capacity loss of the battery with storage time under different storage temperatures can be expressed as follows:

[0102] (12)

[0103] By comparing the experimentally measured data of battery capacity decay used to build the model with the model's predicted values ​​and recording the prediction errors, Table 2 lists some data at storage temperatures, where the units of the measured and predicted capacity values ​​are both in mAh.

[0104] Table 2 Comparison of Model Predictions and Experimental Test Values ​​for Battery Capacity Degradation

[0105]

[0106] The model's prediction of battery capacity decay during self-discharge at different storage temperatures had a maximum error of 4.16% compared to the experimentally measured values, with all deviations within 5%, which meets the requirements of the experimental allowable error range. This indicates that the model has achieved good prediction of battery capacity decay changes at different storage temperatures.

[0107] In another embodiment, a lithium battery storage degradation lifetime prediction model is established based on the battery capacity degradation model.

[0108] To estimate battery storage life under various common conditions and compare the impact of different states of charge (SOCs) on lifespan, this embodiment selects batteries with SOCs of 20%, 50%, 60%, and 80% for storage life estimation (with a 20% capacity loss as the end point). For a specific initial SOC of the battery, this embodiment directly considers the range of values ​​for model parameter fluctuations. It is assumed that parameters k and p both follow a normal distribution, and their mean and variance are determined by the range of model values ​​and confidence intervals corresponding to the battery at a specific SOC. Similarly, parameter Q follows a normal distribution, and its mean and variance are determined by the capacity loss value at the end of the battery's lifespan and the percentage error of the capacity model prediction. The simulation was set to 10,000 times, and the storage life distributions of the four batteries with different initial SOCs after simulation are shown in Figures 9(a) to 9(d).

[0109] Using the Monte Carlo method combined with a battery capacity variation model under different storage temperatures, this embodiment estimates the lifespan (capacity loss of 20%) of batteries with high state of charge stored at different temperatures, taking 20℃, 25℃, 30℃, and 35℃ as examples. In the simulation, the individual differences of lithium batteries and the influence of measurement errors are ultimately reflected in the fluctuations of parameters such as Q, k, and p, whose value ranges all follow a normal distribution. The mean and variance of the normal distribution are determined based on their values ​​in the model and the confidence interval under specific storage conditions. The battery lifespan distributions under the four storage temperatures obtained after 10,000 simulations are shown in Figures 10(a) to 10(d).

[0110] In another embodiment, this disclosure also proposes a lithium battery storage degradation lifetime prediction device based on the negative electrode lithium loss process, the device comprising:

[0111] The measurement unit is used for testing the SOC and temperature of lithium-ion batteries under long-term storage conditions.

[0112] The classification unit is used to classify and fit the capacity data obtained from measurements at different SOCs and temperatures.

[0113] Data units, categorized under different SOCs and storage temperatures, are used to fit the parameters to be determined in the theoretical model for data of different capacities.

[0114] The model unit, through fitting and validating the model parameters of the data unit, improves the capacity decay model of the battery under different initial SOC and different storage temperatures, so as to obtain a lithium battery storage decay life prediction model.

[0115] The detection unit is used to perform internal temperature and SOC tests on the lithium-ion battery under the storage state to be tested, and to input the obtained data into the lithium battery storage degradation lifetime prediction model to obtain the predicted storage lifetime.

[0116] The foregoing general description of the invention and its specific embodiments should not be construed as a limitation on the technical solution of the invention. Those skilled in the art, based on the disclosure of this application, can add, reduce, or combine the disclosed technical features in the foregoing general description and / or specific embodiments (including examples) without departing from the constituent elements of the invention, to form other technical solutions within the scope of protection of this application.

Claims

1. A method for predicting the storage degradation lifetime of lithium batteries based on the lithium loss process of the negative electrode, characterized in that, The method includes the following steps: S100: A mechanism model of battery self-discharge is established based on the lithium loss process of the negative electrode, and a theoretical model of capacity decay over time is established. S200: Conduct battery self-discharge tests at different SOCs and storage temperatures to obtain test data and the battery capacity decay pattern under different influencing factors; S300: Fit the undetermined parameters in the theoretical model using the experimental data and the changing patterns to obtain a capacity decay model for battery storage under different initial SOC and storage temperatures; S400: Based on the capacity decay model, establish a lithium battery storage decay lifetime prediction model; S5 00: Obtain the initial SOC and storage temperature data of the lithium-ion battery to be predicted, and substitute the initial SOC and storage temperature data into the lithium battery storage degradation lifetime prediction model to obtain the predicted storage lifetime. In step S100, the theoretical model for the capacity decay over time is as follows: ， In the formula: Q represents the irreversible capacity loss during battery storage, and e represents the charge carried by a single electron, i.e., 1.6 × 10⁻⁶. -19 A·s, where N is Avogadro's constant, i.e., 6.02 × 10⁻⁶. 23 S is the total area of ​​the SEI film in the battery, Z x is the molar amount of product x generated during the storage process, k3 is the reaction rate constant, a and b are the reaction orders of electrons and products, respectively, c0 is the average electron concentration on the initial SEI film surface, and t is the corresponding reaction time.

2. The method according to claim 1, characterized in that, The SOC is defined as follows: ， In the formula: The current battery capacity in Ah; The current available capacity of the battery is expressed in Ah.

3. The method according to claim 1, characterized in that, In step S200, the battery self-discharge test under different SOC and storage temperatures includes the following procedures: capacity calibration procedure, self-discharge test procedure under different SOC and self-discharge test procedure under different storage temperatures.

4. The method according to claim 1, characterized in that, In step S300, the fitting process for the undetermined parameters in the theoretical model is as follows: S301: Simplifies the theoretical model of battery capacity decay over time, reducing the number of undetermined parameters in the capacity decay model under the influence of different SOCs and storage temperatures; S302: Compile the self-discharge test data of the battery under different storage conditions, combine the theoretical model with the test data, and use the Levenberg-Marquardt optimization algorithm for fitting; S303: Apply the fitting statistical parameters and correlation parameters, as well as the standard error, to verify the fitting effect of the undetermined parameters.

5. The method according to claim 1, characterized in that, In step S400, the lithium battery storage degradation lifetime prediction model is established using a storage lifetime distribution simulation based on the Monte Carlo method.

6. The method according to claim 4, characterized in that, The verification of the fitting effect of the undetermined parameters is achieved by analyzing the linearity of the changes in the undetermined parameters with SOC and storage temperature under different influencing factors to verify the final parameter fitting result.

7. A lithium battery storage degradation lifetime prediction device based on the negative electrode lithium loss process, characterized in that, The device includes: The measurement unit is used to conduct battery self-discharge tests at different SOCs and storage temperatures to obtain test data. A classification unit is used to classify and fit the experimental data. The data unit, categorized under different SOCs and different storage temperatures, is used to fit the undetermined parameters in the theoretical model to the experimental data; The model unit, through fitting and validating the model parameters of the data unit, improves the capacity decay model of the battery under different initial SOC and different storage temperatures, so as to obtain a lithium battery storage decay life prediction model. The detection unit is used to perform internal temperature and SOC tests on the lithium-ion battery under the storage state to be tested, and to input the obtained data into the lithium battery storage degradation lifetime prediction model to obtain the predicted storage lifetime. The theoretical model for capacity decay over time is as follows: ， In the formula: Q represents the irreversible capacity loss during battery storage, and e represents the charge carried by a single electron, i.e., 1.6 × 10⁻⁶. -19 A·s, where N is Avogadro's constant, i.e., 6.02 × 10⁻⁶. 23 S is the total area of ​​the SEI film in the battery, Z x is the molar amount of product x generated during the storage process, k3 is the reaction rate constant, a and b are the reaction orders of electrons and products, respectively, c0 is the average electron concentration on the initial SEI film surface, and t is the corresponding reaction time.

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