Lithium battery residual life prediction method, device, equipment, medium and product

Abstract

The embodiment of the invention provides a lithium battery residual life prediction method and device, equipment, a medium and a product, and relates to the technical field of lithium batteries. According to the method, a preset two-stage aging model processes the current performance parameters and the environment temperature of the lithium battery to obtain the capacity attenuation accumulated value of the lithium battery under the current charging and discharging accumulated number of times. And according to the capacity attenuation value, obtaining a residual life prediction result including a first charging and discharging cumulative number corresponding to the end life EoL point. Wherein the two-stage aging model is a pseudo two-dimensional P2D model which is constructed on the basis of active lithium loss caused by a solid electrolyte interface film SEI and active lithium loss caused by lithium deposition and is used for determining a capacity fading accumulated value. Through the method, the prediction precision of the residual life of the lithium battery can be improved.

Description

Technical Field

[0001] This application relates to the field of lithium battery technology, and in particular to a method, apparatus, device, medium and product for predicting the remaining life of a lithium battery. Background Technology

[0002] With the rapid development of lithium batteries, using battery aging models to predict the remaining lifespan of lithium batteries has become an important research hotspot in the field of lithium batteries.

[0003] Existing battery aging models mainly determine the inflection points between the linear and nonlinear aging stages based on empirical values ​​or by analyzing the tangent points in the lithium battery aging curve, and then calculate the remaining lifespan of the lithium battery using the determined inflection points and preset mathematical formulas.

[0004] However, existing battery aging models do not take into account the aging mechanism of lithium batteries, resulting in low accuracy in predicting the remaining lifespan of lithium batteries. Summary of the Invention

[0005] This application provides a method, apparatus, device, medium, and product for predicting the remaining life of lithium batteries, in order to improve the accuracy of lithium battery remaining life prediction.

[0006] In a first aspect, embodiments of this application provide a method for predicting the remaining lifespan of a lithium battery, comprising:

[0007] Obtain the current performance parameters of the lithium battery;

[0008] Based on the performance parameters and the ambient temperature of the lithium battery, a preset two-stage aging model is used to process the performance parameters and the ambient temperature to obtain the cumulative capacity decay value of the lithium battery under the current cumulative number of charge and discharge cycles.

[0009] Based on the cumulative capacity decay value, the remaining lifetime prediction result is obtained, which includes the first cumulative number of charge and discharge cycles corresponding to the end lifetime EoL point.

[0010] The two-stage aging model is a pseudo-two-dimensional P2D model for determining the cumulative capacity decay value, based on the active lithium loss caused by the solid electrolyte interphase (SEI) membrane and the active lithium loss caused by lithium deposition.

[0011] In one possible design of the first aspect, the remaining lifetime prediction result further includes: the second cumulative number of charge-discharge cycles corresponding to the Knee point of the lithium battery;

[0012] The method further includes:

[0013] If the current cumulative charge-discharge count reaches the second cumulative charge-discharge count, a lithium battery usage strategy is output, which includes at least one usage method to slow down the aging of the lithium battery.

[0014] In one possible design of the first aspect, the method further includes:

[0015] The performance parameters and the ambient temperature of the lithium battery are input into the two-stage aging model to obtain the first cumulative capacity decay value and the second cumulative capacity decay value of the lithium battery under the current cumulative number of charge and discharge cycles. The first cumulative capacity decay value is used to represent the total capacity decay value of the lithium battery caused by the solid electrolyte interphase (SEI) film, and the second cumulative capacity decay value is used to represent the total capacity decay value of the lithium battery caused by lithium deposition.

[0016] Based on the first cumulative capacity decay value and the second cumulative capacity decay value, calculate the first capacity decay change value and the second capacity decay change value between adjacent charge and discharge cycles;

[0017] If the first capacity decay change value is less than the second capacity decay change value, then the current cumulative charge and discharge count is determined as the second cumulative charge and discharge count;

[0018] If the total cumulative capacity decay value between the first cumulative capacity decay value and the second cumulative capacity decay value is greater than or equal to the preset capacity decay threshold of the lithium battery, then the current cumulative charge and discharge count is determined as the first cumulative charge and discharge count.

[0019] In one possible design of the first aspect, the process of obtaining the two-stage aging model includes:

[0020] Obtain a preset pseudo-2D P2D model;

[0021] Determine the target coefficient configuration scheme for the target fitting function in the pseudo-two-dimensional P2D model;

[0022] Based on the target coefficient configuration scheme, configure the coefficients in the target fitting function to obtain the two-stage aging model.

[0023] In one possible design of the first aspect, determining the target coefficient configuration scheme of the target fitting function in the pseudo-two-dimensional P2D model includes:

[0024] Determine the target fitting function for the coefficients to be optimized;

[0025] The target coefficient configuration scheme is determined based on the modified fitting function and the target fitting function, wherein the modified fitting function is the sum of the first fitting function corresponding to the ambient temperature, the second fitting function, the third fitting function corresponding to the charging rate, the fourth fitting function, the fifth fitting function corresponding to the state of charge, the sixth fitting function, and the seventh fitting function corresponding to the ratio of negative electrode capacity to positive electrode capacity.

[0026] In one possible design of the first aspect, the process of obtaining the modified fitting function includes:

[0027] Obtain the historical aging dataset of the lithium battery, which includes historical ambient temperature aging dataset, historical charging rate aging dataset, historical state of charge aging dataset, and historical negative electrode capacity and positive electrode capacity ratio aging dataset.

[0028] Obtain the first, second, third, fourth, fifth, sixth, seventh, and eighth initial fitting functions for the coefficients to be determined;

[0029] Based on the historical environmental temperature aging dataset and the least squares fitting method, determine the coefficients to be determined in the first initial fitting function and the second initial fitting function, and obtain the first fitting function and the second fitting function.

[0030] Based on the historical charging rate aging dataset and the least squares fitting method, determine the coefficients to be determined in the third initial fitting function and the fourth initial fitting function, and obtain the third fitting function and the fourth fitting function;

[0031] Based on the historical state of charge aging dataset and the least squares fitting method, determine the coefficients to be determined in the fifth initial fitting function and the sixth initial fitting function, and obtain the fifth fitting function and the sixth initial fitting function;

[0032] Based on the historical negative electrode capacity and positive electrode capacity ratio aging dataset and the least squares fitting method, the coefficients to be determined in the seventh initial fitting function and the eighth initial fitting function are determined, and the seventh fitting function and the eighth fitting function are obtained.

[0033] In one possible design of the first aspect, the process of acquiring the historical aging dataset includes:

[0034] Initialize the charging rate, state of charge, and negative and positive capacity ratios in the pseudo-two-dimensional P2D model, and input different ambient temperatures into the pseudo-two-dimensional P2D model to obtain the historical ambient temperature aging dataset. The historical ambient temperature aging dataset includes the different ambient temperatures, the cumulative number of charge and discharge cycles of the lithium battery at the different ambient temperatures, the cumulative capacity decay caused by the SEI, the cumulative capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point.

[0035] The ambient temperature, state of charge, and ratio of negative electrode capacity to positive electrode capacity in the pseudo-two-dimensional P2D model are initialized, and different charging rates are input into the pseudo-two-dimensional P2D model to obtain the historical charging rate aging dataset. The historical charging rate aging dataset includes the different charging rates, the cumulative number of charge and discharge cycles of the lithium battery at the different charging rates, the cumulative value of capacity decay caused by SEI, the cumulative value of capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point.

[0036] Initialize the ambient temperature, charging rate, and negative electrode capacity to positive electrode capacity ratio in the pseudo-two-dimensional P2D model, and input different states of charge into the pseudo-two-dimensional P2D model to obtain the historical state of charge aging dataset. The historical state of charge aging dataset includes the different states of charge, the cumulative number of charge and discharge cycles of the lithium battery at the different charging rates, the cumulative capacity decay caused by the SEI, the cumulative capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point.

[0037] The ambient temperature, charging rate, and state of charge in the pseudo-two-dimensional P2D model are initialized, and different negative electrode capacities and positive electrode capacity ratios are input into the pseudo-two-dimensional P2D model to obtain the historical negative electrode capacity and positive electrode capacity ratio aging dataset. The historical negative electrode capacity and positive electrode capacity ratio aging dataset includes the different negative electrode capacities and positive electrode capacity ratios, the cumulative number of charge and discharge cycles of the lithium battery under the different negative electrode capacities and positive electrode capacity ratios, the cumulative value of capacity decay caused by the SEI, the cumulative value of capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point.

[0038] Secondly, this application provides a device for predicting the remaining life of a lithium battery, comprising:

[0039] The acquisition module is used to obtain the current performance parameters of the lithium battery;

[0040] The processing module is used to process the performance parameters according to the performance parameters and the ambient temperature of the lithium battery using a preset two-stage aging model to obtain the cumulative capacity decay value of the lithium battery under the current cumulative number of charge and discharge cycles.

[0041] The processing module is further configured to obtain a remaining lifetime prediction result based on the cumulative capacity decay value, wherein the remaining lifetime prediction result includes the first cumulative charge and discharge count corresponding to the end lifetime EoL point.

[0042] The two-stage aging model is a pseudo-two-dimensional P2D model for determining the cumulative capacity decay value, based on the active lithium loss caused by the solid electrolyte interphase (SEI) membrane and the active lithium loss caused by lithium deposition.

[0043] Thirdly, this application provides a computer device, including: a transceiver, a processor, and a memory communicatively connected to the processor; the memory stores computer-executed instructions.

[0044] The processor executes computer execution instructions stored in the memory to implement the method for predicting the remaining life of a lithium battery as described in any of the first aspects.

[0045] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method for predicting the remaining life of a lithium battery as described in any of the first aspects.

[0046] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the method for predicting the remaining lifespan of a lithium battery as described in any of the first aspects.

[0047] The methods, apparatus, devices, media, and products for predicting the remaining life of lithium batteries provided in this application relate to the field of lithium battery technology. In this scheme, the two-stage aging model is a pseudo-two-dimensional (P2D) model that determines the cumulative capacity decay value based on the active lithium loss caused by the solid electrolyte interface (SEI) and the active lithium loss caused by lithium deposition. From the perspective of the aging mechanism of lithium batteries, it comprehensively considers both the linear and nonlinear aging stages. Simultaneously, using the current performance parameters of the lithium battery and the ambient temperature as inputs to the two-stage aging model, and the cumulative capacity decay value of the lithium battery after the current number of charge-discharge cycles as the output, it can accurately reflect the aging process of the lithium battery under actual usage conditions. Furthermore, based on the cumulative capacity decay value, prediction results can be obtained for the End of Life (EoL) point (the remaining life corresponding to the first cumulative charge-discharge cycle) and the Knee point (the remaining life corresponding to the second cumulative charge-discharge cycle). This allows for more accurate prediction of the end of the lithium battery's lifespan, improving the accuracy and reliability of the remaining lifespan prediction. Consequently, it provides users and battery management systems with opportunities for early warning and optimized maintenance. Attached Figure Description

[0048] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0049] Figure 1 A schematic diagram illustrating a scenario for the method of predicting the remaining lifespan of lithium batteries provided in this application;

[0050] Figure 2 A flowchart illustrating the method for predicting the remaining lifespan of lithium batteries provided in this application. Figure 1 ;

[0051] Figure 3 A flowchart illustrating the method for predicting the remaining lifespan of lithium batteries provided in this application. Figure 2 ;

[0052] Figure 4 A flowchart illustrating the method for predicting the remaining lifespan of lithium batteries provided in this application. Figure 3 ;

[0053] Figure 5 A flowchart illustrating the method for predicting the remaining lifespan of lithium batteries provided in this application. Figure 4 ;

[0054] Figure 6 A flowchart illustrating the method for predicting the remaining lifespan of lithium batteries provided in this application. Figure 5 ;

[0055] Figure 7 A flowchart illustrating the method for predicting the remaining lifespan of lithium batteries provided in this application. Figure 6 ;

[0056] Figure 8 A flowchart illustrating the method for predicting the remaining lifespan of lithium batteries provided in this application. Figure 7 ;

[0057] Figure 9 A graph showing the change of capacity degradation curve of a lithium battery under two-stage aging with the number of cycles, provided in this application;

[0058] Figure 10 A schematic flowchart of a method for predicting the remaining life of a lithium battery provided in this application;

[0059] Figure 11 An experimental result comparison provided for this application Figure 1 ;

[0060] Figure 12 An experimental result comparison provided for this application Figure 2 ;

[0061] Figure 13 An experimental result comparison provided for this application Figure 3 ;

[0062] Figure 14 An experimental result comparison provided for this application Figure 4 ;

[0063] Figure 15 This application provides a schematic diagram of lithium battery operating parameters;

[0064] Figure 16 A schematic diagram of a fitting function provided for this application;

[0065] Figure 17 A schematic diagram of determining the target coefficients in a fitting function provided in this application Figure 1 ;

[0066] Figure 18 A schematic diagram of determining the target coefficients in a fitting function provided in this application Figure 2 ;

[0067] Figure 19 A schematic diagram of a lithium battery remaining life prediction device provided in this application;

[0068] Figure 20 A schematic diagram of the structure of the computer device provided in this application.

[0069] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0070] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0071] First, let me explain the terms used in this application:

[0072] Solid Electrolyte Interface (SEI): refers to a solid interface film formed on the surface of the negative electrode of a lithium battery.

[0073] Pseudo-Two-Dimensional (P2D) models are mathematical models used to simulate the internal electrochemical processes of lithium-ion batteries. They consider factors such as electrochemical reactions, ion transport, and thermal effects within the battery and are typically used to study battery performance and aging behavior.

[0074] End of Life (EoL): refers to the point at which a battery reaches the end of its service life.

[0075] Lithium-ion batteries, as rechargeable batteries capable of multiple charge and discharge cycles, are increasingly being used in portable electronic devices, electric vehicles, and energy storage. With their widespread application, rapid and accurate assessment of battery aging status, precise prediction of remaining lifespan, and the regulation of battery usage conditions and strategies to improve battery performance have become common research hotspots in both academia and industry.

[0076] In existing technical solutions, empirical aging models are widely used in battery management systems across various application scenarios due to their high computational efficiency and ease of use. Clearly defining the aging mechanism of lithium batteries is fundamental to establishing aging models, and SEI film growth is currently recognized as the primary aging mechanism for lithium batteries. There are many common forms of empirical lithium battery aging models based on the SEI growth mechanism, including cycle number 1 / 2 power models, double exponential models, temperature-dependent Arrhenius kinetic models, exponential models considering depth of discharge, and hybrid empirical-data driven models. Most of these empirical or semi-empirical models are derived from historical data and have been experimentally verified, demonstrating good accuracy under the applied conditions.

[0077] The aging process of lithium batteries is generally divided into linear aging and nonlinear aging stages. However, the aforementioned empirical aging models only consider the linear aging stage, resulting in low accuracy in predicting the EoL (Energy Overload) point. Furthermore, existing empirical aging models typically determine the Knee point for the linear and nonlinear aging stages using empirical values ​​or aging curves. Specifically, they either use the 80% remaining capacity point as the Knee point based on experience, or they analyze aging curves obtained from experimental tests and use methods such as the tangent method or the derivative method to determine the Knee point. In summary, existing technical solutions do not consider the aging mechanism of lithium batteries, leading to low accuracy in predicting the remaining lifespan of lithium batteries.

[0078] To address the aforementioned technical problems, the inventors, during their research on methods for predicting the remaining lifespan of lithium batteries, discovered that existing empirical aging models do not consider the nonlinear aging stage of lithium batteries. Through analysis of the aging mechanism of lithium batteries, the inventors proposed a two-stage aging model. Specifically, this two-stage aging model is a P2D model that determines the cumulative capacity decay value based on active lithium loss caused by SEI and active lithium loss caused by lithium deposition, fully considering the nonlinear aging stage of lithium batteries. The inputs to this two-stage aging model are the current performance parameters of the lithium battery and the ambient temperature. The output is the cumulative capacity decay value of the lithium battery at the current number of charge-discharge cycles. Based on the obtained cumulative capacity decay value, the remaining lifespan prediction result, including the first charge-discharge cycle corresponding to the EoL point, can be obtained, thereby improving the prediction accuracy of the remaining lifespan of lithium batteries.

[0079] Figure 1 A schematic diagram illustrating a scenario for the method used to predict the remaining lifespan of lithium batteries provided in this application. Figure 1 As shown, the application scenario of the lithium battery remaining life prediction method provided in this application includes a lithium battery management system 100 and a lithium battery 101. The lithium battery management system 100 deploys a two-stage aging model 1001. Figure 1Only one lithium battery management system 100 and lithium battery 101 are shown, but it should be understood that there may be two or more lithium battery management systems 100 and lithium batteries 101.

[0080] In the actual application of lithium battery 101, the two-stage aging model 1001 deployed in the lithium battery management system 100 acquires the current performance parameters and ambient temperature of lithium battery 101 in real time, and outputs the cumulative capacity decay value of lithium battery under the current cumulative charge and discharge cycles. Based on the current cumulative capacity decay value of lithium battery 101, the lithium battery management system 100 obtains the remaining life prediction result including the first cumulative charge and discharge cycles corresponding to the EoL point.

[0081] It should be noted that the above scenario is only an example of an application scenario provided by the embodiments of this application. The embodiments of this application do not limit the actual form of the various devices included in the scenario. In the specific application of the solution, it can be set according to actual needs.

[0082] For ease of description, the following section uses a lithium battery management system as an example to introduce the implementation method of the lithium battery remaining life prediction method. It should be understood that using a lithium battery management system as the implementing entity is merely an illustrative example and should not be construed as a limitation of the method.

[0083] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0084] Figure 2 A flowchart illustrating the method for predicting the remaining lifespan of lithium batteries provided in this application. Figure 1 ,like Figure 2 As shown, the method includes:

[0085] S201: Obtain the current performance parameters of the lithium battery.

[0086] In this step, in order to accurately predict the remaining lifespan of the current lithium battery, it is necessary to obtain the current performance parameters of the lithium battery whose remaining lifespan is to be predicted.

[0087] The performance parameters include, but are not limited to, any one or any combination of the following: current cumulative charge / discharge cycles, charge rate, state of charge, negative electrode capacity, and positive electrode capacity ratio.

[0088] Specifically, the current cumulative charge / discharge cycle count refers to the number of charge and discharge cycles a lithium battery has completed since its first use. Each completed charge and subsequent discharge counts as one charge / discharge cycle. It's important to note that the performance and capacity of a lithium battery typically decrease gradually with increasing charge / discharge cycle count. Monitoring the cumulative cycle count can help determine the battery's health and remaining lifespan.

[0089] The charging rate refers to the amount of electrical energy a lithium battery receives per unit of time. The common unit is the charging rate, such as 1x, which means the lithium battery can be fully charged in one hour. The charging rate is determined by the ratio of the charging current to the lithium battery capacity. It's important to note that the charging rate affects the thermal management and efficiency of lithium batteries; a higher charging rate may lead to increased battery temperature, impacting safety and lifespan.

[0090] State of charge (SOC) refers to the ratio of the electrical energy currently stored in a lithium-ion battery to its maximum storage capacity, usually expressed as a percentage. For example, a SOC of 50% means the battery currently has half of its maximum capacity. It's important to note that SOC is a crucial indicator for assessing the current state of a lithium-ion battery, guiding charging and discharging decisions to ensure efficient battery use and extend its lifespan.

[0091] The ratio of negative electrode capacity to positive electrode capacity involves both the negative and positive electrode capacities. Negative electrode capacity refers to the number of lithium ions that the negative electrode (usually graphite) of a lithium battery can store, directly affecting the battery's discharge capability and energy density. Positive electrode capacity refers to the number of lithium ions that the positive electrode (usually lithium cobalt oxide, lithium iron phosphate, etc.) can store, affecting the overall performance and safety of the lithium battery. The ratio of negative electrode capacity to positive electrode capacity reflects the battery's equilibrium state and charge / discharge performance. Ideally, the capacities of the negative and positive electrodes should be matched to avoid excessive fatigue or damage to one electrode during charge / discharge.

[0092] In practical applications, the performance parameters of lithium batteries do not exist in isolation, but rather influence each other. By comprehensively considering the current cumulative number of charge-discharge cycles, charging rate, state of charge, and the capacity ratio of the negative and positive electrodes, the health status and remaining lifespan of the lithium battery can be assessed more comprehensively.

[0093] The methods for obtaining lithium battery performance parameters include, but are not limited to, any one or any combination of lithium battery management systems, data acquisition modules, etc., and this application does not impose specific limitations on them. In practical applications, the methods for obtaining lithium battery performance parameters are not limited to a single tool or method. A combination of lithium battery management systems, data acquisition modules, and other testing equipment can be selected as needed to obtain more comprehensive and accurate data.

[0094] S202: Based on the performance parameters and the ambient temperature of the lithium battery, a preset two-stage aging model is used to process the performance parameters and ambient temperature to obtain the cumulative capacity decay value of the lithium battery under the current cumulative number of charge and discharge cycles.

[0095] In this step, according to step S201, after obtaining the performance parameters of the lithium battery, it is necessary to simultaneously obtain the ambient temperature of the lithium battery.

[0096] The ambient temperature of a lithium battery refers to the temperature of the external environment in which it is located, and it typically affects the battery's performance and lifespan. Both excessively high and low ambient temperatures can negatively impact the charging and discharging efficiency, capacity, and safety of lithium batteries. For example, high temperatures can cause the lithium battery to overheat, affecting its performance or creating safety hazards, while low temperatures can reduce its discharge capacity.

[0097] The methods for obtaining the ambient temperature of the lithium battery include, but are not limited to, any one or any combination of temperature sensors, environmental monitoring modules, etc., and this application does not make any specific limitations in this regard.

[0098] After obtaining the performance parameters of the lithium battery and the ambient temperature of the lithium battery, a preset two-stage aging model will be used to process the obtained performance parameters and ambient temperature to obtain the cumulative capacity decay value of the lithium battery under the current cumulative number of charge and discharge cycles.

[0099] The preset two-stage aging model is a P2D model that determines the cumulative capacity decay value based on the active lithium loss caused by SEI and the active lithium loss caused by lithium deposition.

[0100] Specifically, the P2D model is a mathematical model commonly used in lithium battery research. It takes into account a variety of physicochemical processes inside the lithium battery. The characteristic of this model is that it can simultaneously describe the electrochemical behavior and transport phenomena of the lithium battery.

[0101] The two stages refer to the linear aging stage and the nonlinear aging stage of lithium batteries. Under different operating conditions, the capacity decay of lithium batteries will exhibit different characteristics. In the linear aging stage, i.e., the SEI growth stage, and the nonlinear aging stage, i.e., the lithium deposition stage, the mechanism and rate of capacity decay will be different.

[0102] Specifically, during the charging and discharging process of a lithium battery, lithium ions react with the electrolyte on the surface of the negative electrode to form an SEI layer. This SEI protects the electrode material, but it also consumes lithium ions, leading to the loss of active lithium. Specifically, the formation of the SEI occupies a portion of the lithium that could otherwise participate in the charging and discharging reaction; this portion of lithium is called inactive lithium. As charge-discharge cycles continue, the continuous formation and thickening of the SEI further leads to lithium loss, affecting the capacity of the lithium battery.

[0103] Lithium deposition refers to the rapid deposition of lithium ions on the negative electrode surface during charging, caused by excessively fast charging or low ambient temperature, resulting in lithium metal formation. This deposition consumes active lithium, further impacting battery performance. The reduced availability of lithium ions affects the overall battery capacity. Deposited lithium may not participate in electrochemical reactions, leading to a decrease in battery energy density.

[0104] Based on the two-stage active lithium loss mechanism of lithium batteries, the two-stage aging model can accurately calculate the cumulative capacity decay of lithium batteries. The cumulative capacity decay refers to the total decrease in capacity of the lithium battery over a cumulative number of charge-discharge cycles. Detailed calculation procedures can be found in step S401 below, and will not be repeated here.

[0105] S203: Based on the cumulative capacity decay value, the remaining lifetime prediction result is obtained, which includes the first cumulative charge and discharge count corresponding to the EoL point.

[0106] In this step, based on the cumulative capacity decay value of the lithium battery obtained in step S202 under the current cumulative charge-discharge cycles, the remaining life prediction result can be obtained. The remaining life prediction result includes the first cumulative charge-discharge cycle corresponding to the EoL point.

[0107] Specifically, the EoL point refers to a specific state reached by a lithium battery during use, typically indicating a point where the battery's performance significantly degrades or fails to meet usage requirements. In the case of lithium batteries, the EoL point refers to when the battery capacity drops to a certain percentage of its initial capacity.

[0108] The first cumulative charge-discharge cycle refers to the total number of charge-discharge cycles a lithium battery undergoes before reaching the EoL point, and is used to measure the lifespan of a lithium battery.

[0109] Based on the remaining life prediction results, users can assess the actual usage of the lithium battery and decide whether it needs to be replaced.

[0110] In one possible implementation, the remaining capacity of the lithium battery can be obtained based on its current cumulative capacity decay and rated capacity. With the battery's performance parameters and ambient temperature remaining constant, the remaining capacity at the EoL point is fixed. By comparing the current remaining capacity with the remaining capacity at the EoL point, the number of charge-discharge cycles required for the battery to reach the EoL point can be determined. Combining the current cumulative charge-discharge cycle count with the required number of cycles, the first cumulative charge-discharge cycle count can be calculated.

[0111] The lithium battery remaining life prediction method provided in this application's embodiments processes the current performance parameters of the lithium battery and the ambient temperature based on a preset two-stage aging model to obtain the cumulative capacity decay value of the lithium battery under the current cumulative charge-discharge cycles, thereby obtaining the remaining life prediction result including the first cumulative charge-discharge cycle corresponding to the EoL point. Compared with the empirical aging model in the prior art that only considers the linear aging stage, this method is linked to the dominant aging mechanism of the lithium battery, considering both the linear and nonlinear aging stages, reflecting the changes in the aging rate and mechanism of the two stages. This not only improves the accuracy of remaining life prediction but also optimizes lithium battery management, reduces maintenance and replacement costs, and enhances usage safety.

[0112] In one possible implementation, the remaining lifetime prediction result also includes: the second cumulative number of charge-discharge cycles corresponding to the Knee point of the lithium battery. Figure 3 A flowchart illustrating the method for predicting the remaining lifespan of lithium batteries provided in this application. Figure 2 ,like Figure 3 As shown, the method includes:

[0113] S301: Based on the cumulative capacity decay value, the remaining life prediction result is obtained. The remaining life prediction result includes the second cumulative charge and discharge count corresponding to the Knee point of the lithium battery.

[0114] In this step, based on the cumulative capacity decay value of the lithium battery obtained in step S202 under the current cumulative charge and discharge count, in addition to obtaining the predicted remaining lifespan of the first cumulative charge and discharge count corresponding to the EoL point, it also includes the second cumulative charge and discharge count corresponding to the Knee point of the lithium battery.

[0115] It's worth noting that the capacity of lithium batteries gradually decreases with each charge-discharge cycle during use. This degradation process can be divided into linear aging and non-linear aging stages. In the linear aging stage, the capacity degradation is relatively slow and stable, typically exhibiting a relatively uniform downward trend. The degradation rate is low in this stage, and users usually don't perceive a significant decrease in battery performance. In the non-linear aging stage, after a certain number of charge-discharge cycles, the rate of capacity degradation changes significantly, entering an accelerated degradation stage. In this stage, the performance of the lithium battery declines much faster, leading to a noticeable reduction in battery life for users.

[0116] The Knee point refers to the inflection point between the linear and nonlinear aging stages of a lithium battery, typically representing the point where the rate of capacity degradation changes significantly. The Knee point marks the transition from a slow to an accelerated rate of capacity decay and is a crucial parameter for assessing the remaining lifespan of a lithium battery.

[0117] The second charge-discharge cumulative count refers to the total number of charge-discharge cycles that a lithium battery undergoes before reaching the Knee point.

[0118] In one possible implementation, based on the current cumulative capacity decay of the lithium battery, the capacity decay caused by SEI and the capacity decay caused by lithium deposition can be obtained. With the lithium battery's performance parameters and ambient temperature remaining constant, the number of charge-discharge cycles required for the lithium battery to reach the Knee point can be determined based on the capacity decay caused by SEI and the capacity decay caused by lithium deposition. Combining the current cumulative charge-discharge cycle count and the required number of charge-discharge cycles, the second cumulative charge-discharge cycle count of the lithium battery can be calculated.

[0119] S302: If the current cumulative charge and discharge count reaches the second cumulative charge and discharge count, output the lithium battery usage strategy, which includes at least one usage method to slow down lithium battery aging.

[0120] In this step, according to step S301, after obtaining the remaining life prediction result of the second charge-discharge cumulative number of times corresponding to the Knee point of the lithium battery, if the current charge-discharge cumulative number of times reaches the second charge-discharge cumulative number of times, a lithium battery usage strategy is output to delay the aging of the lithium battery.

[0121] Specifically, if the cumulative number of charge and discharge cycles of a lithium battery reaches the second cumulative number of charge and discharge cycles, it indicates that the lithium battery has entered the accelerated aging stage. This application not only reminds users that the lithium battery has entered the accelerated aging stage, but also provides users with lithium battery usage strategies aimed at slowing down the aging process of the lithium battery.

[0122] The lithium battery usage strategy includes at least one method to slow down lithium battery aging, including but not limited to any one or any combination of lowering the charging limit, delaying charging time, moderate discharging, avoiding extreme temperatures, and reasonable application. This application does not make any specific limitations on this.

[0123] Specifically, lowering the charging limit refers to reducing the maximum charging capacity of the lithium battery from 100% to a preset threshold. This can reduce the stress on the lithium battery during charging and extend its cycle life.

[0124] Extending charging time refers to using a slow charging method and avoiding fast charging as much as possible. When rapid charging is not required, using a low-power charger can reduce heat generation and slow down aging.

[0125] Moderate discharge refers to avoiding discharging lithium batteries to extremely low levels. For example, keeping lithium batteries within the 30%-80% charge range can effectively extend battery life.

[0126] Avoiding extreme temperatures means ensuring that lithium batteries operate within a suitable temperature range. Temperatures that are too high or too low will accelerate the aging of lithium batteries; for example, controlling the operating temperature of lithium batteries between 20°C and 25°C.

[0127] Using applications rationally means minimizing the simultaneous operation of multiple high-load applications when performing high-power-consuming applications, in order to reduce the instantaneous load on the lithium battery and avoid overheating.

[0128] By adopting the above-mentioned usage strategies, the aging of lithium batteries can be effectively slowed down, their lifespan extended, and their safety and performance stability ensured during use. The implementation of these strategies needs to be adjusted according to specific application scenarios and user needs to achieve optimal results.

[0129] The lithium battery remaining life prediction method provided in this application determines the remaining life result, including the second charge-discharge cumulative number of cycles corresponding to the lithium battery's Knee point, based on the cumulative capacity decay value obtained from a two-stage aging model. It then outputs a battery usage strategy when the current charge-discharge cumulative number of cycles reaches the second cumulative number of cycles. This method can provide early warnings before lithium battery performance degrades, helping users adopt appropriate usage strategies, thereby effectively delaying the battery aging process and improving the overall lifespan and safety of the lithium battery. Furthermore, by accurately predicting the remaining life, users can optimize charging and discharging habits, reduce unnecessary lithium battery wear, lower maintenance costs, and ultimately achieve efficient resource utilization and sustainable development.

[0130] Figure 4 A flowchart illustrating the method for predicting the remaining lifespan of lithium batteries provided in this application. Figure 3 ,like Figure 4 As shown, in this embodiment... Figure 1 Examples and Figure 2 Based on the embodiments, the process of obtaining the first and second cumulative charge-discharge counts is described in detail. The method includes:

[0131] S401: Input the performance parameters and the ambient temperature of the lithium battery into the two-stage aging model to obtain the first cumulative capacity decay value and the second cumulative capacity decay value of the lithium battery under the current cumulative number of charge and discharge cycles. The first cumulative capacity decay value is used to represent the total capacity decay value of the lithium battery caused by SEI, and the second cumulative capacity decay value is used to represent the total capacity decay value of the lithium battery caused by lithium deposition.

[0132] In this step, the inputs to the two-stage aging model are the current performance parameters of the lithium battery and the ambient temperature of the lithium battery, and the outputs are the first and second cumulative capacity decay values ​​of the lithium battery under the current cumulative charge and discharge cycles.

[0133] The first cumulative capacity decay value represents the total capacity decay of the lithium battery caused by SEI. Specifically, SEI is a thin film formed on the electrode surface during the charging process of a lithium battery. As time accumulates, the thickness of the SEI increases, which prevents lithium ions from effectively penetrating, thereby reducing the usable capacity of the lithium battery.

[0134] In one possible implementation, the formula for calculating the first cumulative capacity decay value is:

[0135]

[0136] in, This represents the loss of active lithium in the lithium battery due to SEI at time t. It is worth noting that, considering that the loss of active lithium due to SEI is one of the main factors contributing to the aging process of lithium batteries, this application uses... The value is used as the first cumulative capacity decay value. t represents the current time. This represents the SEI-related side reaction current density at time t.

[0137] The second cumulative capacity decay value represents the total capacity decay of a lithium battery caused by lithium deposition. Lithium deposition occurs during charging, particularly when the charging speed is too fast or the ambient temperature is too low. Lithium ions deposit on the negative electrode to form lithium metal, instead of being properly intercalated into the electrode material, leading to a reduction in the usable capacity of the lithium battery.

[0138] In one possible implementation, the formula for calculating the second capacity attenuation cumulative value is as follows:

[0139]

[0140] in, This represents the loss of active lithium in the lithium battery due to lithium deposition at time t. It is worth noting that, considering that the loss of active lithium due to lithium deposition is one of the main factors contributing to the aging process of lithium batteries, this application uses... The value is used as the second cumulative capacity decay value. t represents the current time. This represents the current density of the side reactions associated with lithium deposition at time t.

[0141] S402: Based on the first cumulative capacity decay value and the second cumulative capacity decay value, calculate the first capacity decay change value and the second capacity decay change value between adjacent charge and discharge cycles.

[0142] In this step, after obtaining the first and second cumulative capacity decay values ​​of the lithium battery under the current cumulative number of charge and discharge cycles according to step S401, the first and second capacity decay change values ​​between adjacent charge and discharge cycles are calculated.

[0143] In one possible implementation, the formula for calculating the first capacity decay change value is as follows:

[0144] ΔQ SEI,M =Q SEI,M -Q SEI,M-1

[0145] Where, ΔQ SEI,M Q represents the first capacity decay change value between the first cumulative capacity decay value corresponding to the cumulative number of charge-discharge cycles of the lithium battery being M and the first cumulative capacity decay value corresponding to the cumulative number of charge-discharge cycles of the lithium battery being (M-1), where M represents the current cumulative number of charge-discharge cycles of the lithium battery, and Q represents the first capacity decay change value. SEI,M Q represents the first cumulative capacity decay value when the cumulative number of charge-discharge cycles of a lithium battery is M. SEI,M-1 This represents the first cumulative capacity decay value when the cumulative number of charge-discharge cycles of the lithium battery is (M-1).

[0146] In one possible implementation, the formula for calculating the second capacity decay change value is as follows:

[0147] ΔQ Li,M =Q Li,M -Q Li,M-1

[0148] Where, ΔQ Li,M Q represents the change in second capacity decay between the cumulative second capacity decay value corresponding to the number of charge-discharge cycles of the lithium battery being M and the cumulative second capacity decay value corresponding to the number of charge-discharge cycles of the lithium battery being (M-1), where M represents the current cumulative number of charge-discharge cycles of the lithium battery, and Q... Li,M Q represents the second cumulative capacity decay value when the cumulative number of charge-discharge cycles of the lithium battery is M. Li,M-1 This represents the second cumulative capacity decay value when the cumulative number of charge-discharge cycles of the lithium battery is (M-1).

[0149] S403: If the first capacity decay change value is less than the second capacity decay change value, then the current cumulative charge and discharge count is determined as the second cumulative charge and discharge count.

[0150] In this step, based on the first capacity decay change value and the second capacity decay change value obtained in step S402, the second charge-discharge cumulative number of times is determined based on the relationship between the first capacity decay change value and the second capacity decay change value.

[0151] If the first capacity decay change value is less than the second capacity decay change value, then the current cumulative charge and discharge count is determined as the second cumulative charge and discharge count.

[0152] In one possible implementation, the formula for determining the second cumulative charge-discharge count is as follows:

[0153]

[0154] Where, ΔQ SEI,M ΔQ represents the change in first capacity decay between the first cumulative capacity decay value corresponding to the cumulative number of charge-discharge cycles of the lithium battery being M and the first cumulative capacity decay value corresponding to the cumulative number of charge-discharge cycles of the lithium battery being (M-1). Li,M This represents the change in second capacity decay between the cumulative second capacity decay value corresponding to the number of charge-discharge cycles of the lithium battery (M) and the cumulative second capacity decay value corresponding to the number of charge-discharge cycles of the lithium battery (M-1), where M represents the current cumulative number of charge-discharge cycles of the lithium battery, and N represents the change in second capacity decay value. Knee This indicates the second cumulative number of charge-discharge cycles.

[0155] S404: If the total cumulative capacity decay value between the first and second cumulative capacity decay values ​​is greater than or equal to the preset capacity decay threshold of the lithium battery, then the current cumulative charge and discharge count is determined as the first cumulative charge and discharge count.

[0156] In this step, based on the first capacity decay change value and the second capacity decay change value obtained in step S402, the first charge-discharge cumulative number of times is determined based on the relationship between the first capacity decay change value and the second capacity decay change value.

[0157] If the total cumulative capacity decay value between the first and second cumulative capacity decay values ​​is greater than or equal to the preset capacity decay threshold of the lithium battery, then the current cumulative charge and discharge count is determined as the first cumulative charge and discharge count.

[0158] For example, suppose there is a lithium battery with a first capacity decay cumulative value of 200 mAh and a second capacity decay cumulative value of 300 mAh when the cumulative number of charge-discharge cycles is A. The total cumulative capacity decay between the first and second capacity decay cumulative values ​​is 500 mAh. The preset capacity decay threshold is 400 mAh. At this time, 500 mAh is greater than 400 mAh, so the current cumulative number of charge-discharge cycles A is determined as the first cumulative number of charge-discharge cycles.

[0159] It is worth noting that the preset capacity decay threshold can be determined by the rated capacity of the lithium battery and a predefined percentage. For example, if the rated capacity of the lithium battery is 600 mAh and the predefined percentage is 40%, then the preset capacity decay threshold is the product of the rated capacity of the lithium battery (600 mAh) and the predefined percentage (40%), which is 240 mAh.

[0160] The lithium battery remaining life prediction method provided in this application determines the second cumulative charge-discharge count based on the relationship between a first capacity decay change value and a second capacity decay change value, and determines the first cumulative charge-discharge count based on the relationship between a preset capacity decay threshold and the cumulative capacity decay value. This method effectively improves prediction accuracy, enabling users to more accurately understand the lithium battery's degradation characteristics and remaining lifespan, thereby optimizing lithium battery usage strategies, avoiding overcharging and discharging, and extending the actual lifespan of the lithium battery. Simultaneously, this method helps users rationally plan lithium battery replacement cycles, reducing maintenance and replacement costs.

[0161] Figure 5 A flowchart illustrating the method for predicting the remaining lifespan of lithium batteries provided in this application. Figure 4 ,like Figure 5 As shown, in this embodiment... Figure 1 Based on the examples, the process of obtaining the two-stage aging model is described in detail. The method includes:

[0162] S501: Obtain the preset P2D model.

[0163] In this step, when applying the two-stage aging model to process the performance parameters of the lithium battery and the ambient temperature to obtain the cumulative capacity decay value of the lithium battery under the current cumulative charge and discharge cycles, it is necessary to obtain the two-stage aging model in advance.

[0164] Specifically, the two-stage aging model is based on the pre-defined P2D model. The two-stage aging model establishes the mathematical framework of the P2D model by comprehensively considering key electrochemical processes in lithium batteries, such as solid-phase diffusion, liquid-phase diffusion, electrochemical reactions, and charge transfer, as well as solid-liquid phase material, energy, and charge conservation equations and kinetic equations.

[0165] The P2D model describes the behavior of lithium batteries through a series of fitting functions. These equations typically cover factors such as electrochemical reactions at the electrodes, ion diffusion, and temperature changes. Notably, determining the unknown coefficients in the relevant fitting functions of the P2D model is a key problem addressed in this application.

[0166] S502: Determine the target coefficient configuration scheme for the target fitting function in the P2D model.

[0167] Once the preset P2D model is obtained, the target coefficient configuration scheme of the target fitting function in the P2D model will be determined.

[0168] The target fitting function refers to the determined fitting function used in the P2D model to describe the behavior of the lithium battery. Its type includes, but is not limited to, any one or any combination of power functions with constant terms, power functions without constant terms, exponential functions, and linear functions. This application does not impose specific limitations on this. The target coefficient configuration scheme refers to the constant term in the target fitting function whose value is to be determined. When determining the target coefficient configuration scheme for the target fitting function in the P2D model, the methods that can be applied include, but are not limited to, any one or any combination of optimization algorithms (such as genetic algorithms, least squares methods, etc.), experimental verification, and empirical values. This application does not impose specific limitations on this.

[0169] It is worth noting that the specific method for determining the target coefficient configuration scheme of the target fitting function in the P2D model can be found in steps S601 to S602, and will not be repeated here.

[0170] S503: Configure the coefficients in the target fitting function according to the target coefficient configuration scheme to obtain a two-stage aging model.

[0171] In this step, based on the target coefficient configuration scheme of the target fitting function in the P2D model determined in step S502, each coefficient in the target fitting function is configured one by one to construct a complete two-stage aging model.

[0172] This step involves precisely adjusting the parameters of each fitting function to accurately reflect the electrochemical behavior and performance changes of lithium batteries at different aging stages. Through this precise parameter configuration, the model can more effectively simulate the capacity decay, internal resistance changes, and other key characteristics of batteries in real-world use.

[0173] Through this refined configuration, the resulting two-stage aging model can provide a solid foundation for lithium battery life prediction, performance evaluation, and management strategies.

[0174] The lithium battery remaining life prediction method provided in this application uses a preset P2D model as its basic framework. By determining the target coefficient configuration scheme of the target fitting function, a two-stage aging model is constructed. This method can accurately capture the electrochemical characteristics and performance changes of lithium batteries at different aging stages, thereby improving the accuracy of remaining life prediction. By using the mathematical framework of the P2D model, combined with an optimized fitting function and accurate coefficient configuration, the two-stage aging model can better simulate the actual use of lithium batteries and support more reliable life prediction.

[0175] Figure 6 A flowchart illustrating the method for predicting the remaining lifespan of lithium batteries provided in this application. Figure 5 ,like Figure 6 As shown, in this embodiment... Figure 5Based on the examples, a detailed explanation is provided on the scheme for determining the configuration of the target coefficients of the target fitting function in the P2D model. The method includes:

[0176] S601: Determine the target fitting function for the coefficients to be optimized.

[0177] In this step, after obtaining the preset P2D model according to step S501, the target fitting function of the coefficients to be optimized in the preset P2D model is determined so as to achieve higher accuracy and reliability in the two-stage aging model.

[0178] Specifically, the target fitting function is first identified from the P2D model. All fitting functions involved in the P2D model describe the behavioral characteristics of lithium batteries under different operating conditions, including electrochemical reactions, ion diffusion, and temperature changes. Through analysis, the target fitting function for the coefficients to be optimized is identified.

[0179] Next, in each target fitting function, the coefficients that need to be optimized are identified. These coefficients are key parameters affecting the output of the target fitting function, and their accuracy directly affects the overall performance of the subsequent two-stage aging model. Specifically, these coefficients may include constant terms, exponential terms, or other forms of parameters, depending on the mathematical form of the target fitting function; this application does not impose specific limitations on this.

[0180] S602: Determine the target coefficient configuration scheme based on the modified fitting function and the target fitting function. The modified fitting function is the sum of the first fitting function corresponding to the ambient temperature, the second fitting function, the third fitting function corresponding to the charging rate, the fourth fitting function, the fifth fitting function corresponding to the state of charge, the sixth fitting function, and the seventh and eighth fitting functions corresponding to the ratio of negative electrode capacity to positive electrode capacity.

[0181] In this step, based on the target fitting function of the coefficients to be optimized determined in step S601, and combined with the modified fitting function, the target coefficient configuration scheme is determined to improve the accuracy of the final two-stage aging model.

[0182] The correction fitting function is an auxiliary function used to supplement and adjust the target fitting function, helping the two-stage aging model to better adapt to actual usage conditions.

[0183] Specifically, the modified fitting function in this application includes eight key parts, which respectively capture the effects of temperature, charging rate, state of charge, and electrode capacity ratio on lithium battery performance. Among them, the modified fitting functions are the first fitting function corresponding to ambient temperature, the second fitting function, the third fitting function and the fourth fitting function corresponding to charging rate, the fifth fitting function and the sixth fitting function corresponding to state of charge, and the seventh fitting function and the eighth fitting function corresponding to the ratio of negative electrode capacity to positive electrode capacity.

[0184] The first fitting function corresponding to ambient temperature is mainly used to capture the impact of ambient temperature changes on the capacity decay of lithium batteries caused by SEI. The second fitting function corresponding to ambient temperature is mainly used to capture the impact of ambient temperature changes on the capacity decay of lithium batteries caused by lithium deposition. Ambient temperature changes affect the electrochemical reaction rate and ionic conductivity of lithium batteries; therefore, the first and second fitting functions are used to adjust the target fitting function to adapt to different ambient temperature conditions.

[0185] The third fitting function corresponding to the charging rate is mainly used to describe the impact of different charging rates on the capacity decay of lithium batteries caused by SEI. The fourth fitting function corresponding to the charging rate is mainly used to describe the impact of different charging rates on the capacity decay of lithium batteries caused by lithium deposition. Changes in the charging rate affect the voltage and internal resistance of the lithium battery. The third and fourth fitting functions help the target fitting function to accurately predict under different discharge conditions.

[0186] The fifth fitting function corresponding to the state of charge (SOC) is mainly used to simulate the impact of different SOCs on the capacity decay of lithium batteries caused by SEI. The sixth fitting function corresponding to the SOC is mainly used to simulate the impact of different SOCs on the capacity decay of lithium batteries caused by lithium deposition. Changes in SOC affect the remaining capacity and performance of lithium batteries. The fifth and sixth fitting functions are used to adjust the target fitting function to adapt to different SOC conditions.

[0187] The seventh fitting function, corresponding to the ratio of negative electrode capacity to positive electrode capacity, is mainly used to describe the impact of the electrode capacity ratio on the capacity decay of lithium batteries caused by SEI (Sedimentation in Intercalation). The eighth fitting function, also corresponding to the ratio of negative electrode capacity to positive electrode capacity, is mainly used to describe the impact of the electrode capacity ratio on the capacity decay of lithium batteries caused by lithium deposition. Changes in the electrode capacity ratio affect the overall performance of the lithium battery; the seventh and eighth fitting functions help optimize the target fitting function under different electrode capacity ratio conditions.

[0188] When determining the target coefficient configuration scheme, these modified fitting functions are combined with the target fitting function to ensure that the target fitting function can more comprehensively consider various influencing factors, thereby improving the accuracy of the two-stage aging model. By optimizing the target coefficients, the two-stage aging model can accurately predict the remaining life and performance changes of lithium batteries under different ambient temperatures, charging rates, states of charge, and negative electrode capacity to positive electrode capacity ratios.

[0189] Optionally, in one possible implementation, when determining the target coefficient configuration scheme, the difference between the target fitting function and the modified fitting function is taken to determine the target coefficients in the target fitting function.

[0190] It is worth noting that the process of obtaining the modified fitting function can refer to steps S701 to S706 below, and will not be repeated here.

[0191] The lithium battery remaining life prediction method provided in this application determines a target coefficient configuration scheme based on a modified fitting function and a target fitting function. Specifically, the modified fitting function helps the target fitting function to more accurately capture and reflect the complex behavior of the lithium battery under the influence of different ambient temperatures, charging rates, states of charge, and the ratio of negative electrode capacity to positive electrode capacity. Simultaneously, by optimizing the target coefficients of the target fitting function, the two-stage aging model can better adapt to dynamic changes in the actual usage environment, improving the accuracy and reliability of the prediction.

[0192] Figure 7 A flowchart illustrating the method for predicting the remaining lifespan of lithium batteries provided in this application. Figure 6 ,like Figure 7 As shown, in this embodiment... Figure 6 Based on the examples, the process of obtaining the corrected fitting function is described in detail. The method includes:

[0193] S701: Obtain historical aging datasets for lithium batteries. These datasets include historical ambient temperature aging datasets, historical charging rate aging datasets, historical state of charge aging datasets, and historical negative electrode capacity and positive electrode capacity ratio aging datasets.

[0194] In this step, the modified fitting function needs to be obtained in advance before determining the target coefficients in the target fitting function based on the modified fitting function.

[0195] Specifically, the determination of the corrected fitting function is mainly based on the historical aging dataset of lithium batteries. The historical aging dataset includes historical ambient temperature aging dataset, historical charge rate aging dataset, historical state of charge aging dataset, and historical negative electrode capacity and positive electrode capacity ratio aging dataset.

[0196] In one possible implementation, the process of obtaining historical aging datasets of lithium batteries includes: obtaining the rated parameters of the lithium batteries whose remaining lifespan is to be predicted, establishing a P2D model, setting different operating conditions, and simulating under different operating conditions.

[0197] The rated parameters of the lithium battery whose remaining lifespan is to be predicted include, but are not limited to, the positive and negative electrode materials, charge / discharge voltage range, rated voltage, height, radius, surface area, and charging cutoff current. This application does not impose specific limitations on these parameters. It is worth noting that the rated parameters of the lithium battery provide the basic data for the subsequent establishment of the P2D model, ensuring the accuracy of the simulation.

[0198] Based on the obtained rated parameters, a P2D model considering multiple aging mechanisms is established to simulate the electrochemical behavior and aging process of lithium batteries under different operating conditions.

[0199] The settings for different operating conditions include different ambient temperatures, different charging rates, different states of charge, and different ratios of negative and positive electrode capacity.

[0200] The simulation process under different operating conditions mainly involves inputting different operating conditions into the P2D model to obtain historical ambient temperature aging datasets, historical charging rate aging datasets, historical state of charge aging datasets, and historical negative electrode capacity and positive electrode capacity ratio aging datasets under different conditions.

[0201] S702: Obtain the first, second, third, fourth, fifth, sixth, seventh, and eighth initial fitting functions for the coefficients to be determined.

[0202] In this step, after obtaining the historical aging dataset of the lithium battery in step S701, the first initial fitting function, the second initial fitting function, the third initial fitting function, the fourth initial fitting function, the fifth initial fitting function, the sixth initial fitting function, the seventh initial fitting function, and the eighth initial fitting function for the coefficients to be determined will be obtained.

[0203] The first initial fitting function describes the effect of ambient temperature on lithium loss caused by SEI in lithium batteries. The second initial fitting function describes the effect of ambient temperature on lithium loss caused by lithium deposition in lithium batteries. The first and second initial fitting functions are determined by analyzing the trend of lithium battery capacity decay under different ambient temperature conditions.

[0204] The third initial fitting function is used to capture the effect of charging rate on lithium loss caused by SEI in lithium batteries. The fourth initial fitting function is used to capture the effect of charging rate on lithium loss caused by lithium deposition in lithium batteries. The third and fourth initial fitting functions are determined by analyzing the changing trends of lithium battery capacity decay under different charging rate conditions.

[0205] The fifth initial fitting function is used to simulate the effect of state of charge (SOC) on lithium loss caused by SEI in lithium-ion batteries. The sixth initial fitting function is used to simulate the effect of SOC on lithium loss caused by lithium deposition in lithium-ion batteries. The fifth and sixth initial fitting functions are determined by analyzing the changing trends of lithium-ion battery capacity decay under different SOC conditions.

[0206] The seventh initial fitting function is used to simulate the effect of the ratio of negative electrode capacity to positive electrode capacity on lithium loss caused by SEI in lithium batteries. The eighth initial fitting function is used to simulate the effect of the ratio of negative electrode capacity to positive electrode capacity on lithium loss caused by lithium deposition in lithium batteries. The seventh and eighth initial fitting functions are determined by analyzing the changing trends of lithium battery capacity decay under different negative electrode capacity and positive electrode capacity ratios.

[0207] In one possible implementation, in order to ensure a small overall error and a small number of coefficients to be determined for the fitting function, the first, second, third, fourth, fifth, sixth, seventh, and eighth initial fitting functions in this application are all set to be power functions with fixed constant terms.

[0208] Specifically, the formula for calculating the first initial fitting function is as follows:

[0209]

[0210] Among them, Q SEI (M,T) represents the loss of active lithium in a lithium battery due to SEI as the cumulative number of charge-discharge cycles M increases at different ambient temperatures T. T1 and b T1 All of these represent coefficients to be determined;

[0211] The formula for calculating the second initial fitting function is as follows:

[0212]

[0213] Among them, Q Li (M,T) represents the loss of active lithium in a lithium battery due to lithium deposition as the cumulative number of charge-discharge cycles M increases at different ambient temperatures T. T2 and b T2 All of these represent coefficients to be determined;

[0214] The formula for calculating the third initial fitting function is as follows:

[0215]

[0216] Among them, Q SEI (M,C) represents the loss of active lithium in a lithium battery due to SEI as the cumulative number of charge-discharge cycles M increases under different charge rates C. C2 and b C2 All of these represent coefficients to be determined;

[0217] The formula for calculating the fourth initial fitting function is as follows:

[0218]

[0219] Among them, Q Li (M,C) represents the loss of active lithium in a lithium battery due to lithium deposition as the cumulative number of charge-discharge cycles M increases at different charge rates C. C2 and b C2 All of these represent coefficients to be determined;

[0220] The formula for calculating the fifth initial fitting function is as follows:

[0221]

[0222] in, This indicates the loss of active lithium due to SEI in a lithium battery under different states of charge (SOC) as the cumulative number of charge-discharge cycles (M) increases. soc1 and b soc1 All represent coefficients to be determined. This represents the average of the maximum and minimum SOC, and DOD represents the depth of discharge, which is the difference between the maximum and minimum SOC.

[0223] The formula for calculating the sixth initial fitting function is as follows:

[0224]

[0225] in, This indicates the loss of active lithium due to lithium deposition in a lithium battery as the cumulative number of charge-discharge cycles (M) increases under different states of charge (SOC). soc2 and b soc2 All represent coefficients to be determined. This represents the average of the maximum and minimum SOC, and DOD represents the depth of discharge, which is the difference between the maximum and minimum SOC.

[0226] The formula for calculating the seventh initial fitting function is as follows:

[0227]

[0228] Among them, Q SEI (M,N / P) represents the loss of active lithium due to SEI in a lithium battery as the cumulative number of charge-discharge cycles M increases, under different negative electrode capacity and positive electrode capacity ratios. (N / P) and b (N / P) All of these represent coefficients to be determined;

[0229] The formula for calculating the eighth initial fitting function is as follows:

[0230]

[0231] Among them, QLi (M,N / P) represents the loss of active lithium due to lithium deposition in a lithium battery as the cumulative number of charge-discharge cycles (M) increases, under different negative electrode capacity and positive electrode capacity ratios. (N / P) and ∈ (N / P) All of these represent coefficients to be determined;

[0232] S703: Based on the historical ambient temperature aging dataset and the least squares fitting method, determine the coefficients to be determined in the first initial fitting function and the second initial fitting function, and obtain the first fitting function and the second fitting function.

[0233] In this step, in order to determine the coefficients to be determined in the first and second initial fitting functions, the analysis is performed based on the historical environmental temperature aging dataset obtained in step S701 and the least squares fitting method.

[0234] Optionally, in one possible implementation, the historical ambient temperature aging dataset is first preprocessed to ensure the integrity and accuracy of the data. Then, the least squares fitting method is applied to calculate the coefficients to be determined in the first and second initial fitting functions by minimizing the sum of squared errors between the predicted values ​​and the actual data.

[0235] In one possible implementation, based on historical environmental temperature aging datasets and the least squares fitting method, the first fitting function is calculated as follows:

[0236]

[0237] Among them, Q SEI (M,T) represents the loss of active lithium in a lithium battery due to SEI as the cumulative number of charge-discharge cycles M increases under different ambient temperatures T. T represents different ambient temperatures, and M represents the cumulative number of charge-discharge cycles.

[0238] The formula for calculating the second fitting function is as follows:

[0239]

[0240] Among them, Q Li (M,T) represents the loss of active lithium in a lithium battery due to lithium deposition as the cumulative number of charge-discharge cycles M increases under different ambient temperatures T. T represents different ambient temperatures, and M represents the cumulative number of charge-discharge cycles.

[0241] S704: Based on the historical charging rate aging dataset and the least squares fitting method, determine the coefficients to be determined in the third and fourth initial fitting functions, and obtain the third and fourth fitting functions.

[0242] In this step, in order to determine the coefficients to be determined in the third and fourth initial fitting functions, the analysis is performed based on the historical charging rate aging dataset obtained in step S701 and the least squares fitting method.

[0243] Optionally, in one possible implementation, the historical charging rate aging dataset is first preprocessed to ensure the integrity and accuracy of the data. Then, the least squares fitting method is applied to calculate the coefficients to be determined in the third and fourth initial fitting functions by minimizing the sum of squared errors between the predicted values ​​and the actual data.

[0244] In one possible implementation, based on historical charging rate aging datasets and the least squares fitting method, the third fitting function is calculated as follows:

[0245]

[0246] Among them, Q SEI (M,C) represents the loss of active lithium in a lithium battery due to SEI as the cumulative number of charge-discharge cycles M increases under different charging rates C. C represents different charging rates, and M represents the cumulative number of charge-discharge cycles.

[0247] The formula for calculating the fourth fitting function is as follows:

[0248]

[0249] Among them, Q Li (M,C) represents the loss of active lithium in a lithium battery due to lithium deposition as the cumulative number of charge-discharge cycles M increases under different charging rates C. C represents different charging rates, and M represents the cumulative number of charge-discharge cycles.

[0250] S705: Based on the historical state of charge aging dataset and the least squares fitting method, determine the coefficients to be determined in the fifth and sixth initial fitting functions, and obtain the fifth and sixth initial fitting functions.

[0251] In this step, in order to determine the coefficients to be determined in the fifth and sixth initial fitting functions, the analysis is performed based on the historical state of charge aging dataset obtained in step S701 and the least squares fitting method.

[0252] Optionally, in one possible implementation, the historical state of charge aging dataset is first preprocessed to ensure the integrity and accuracy of the data. Then, the least squares fitting method is applied to calculate the coefficients to be determined in the fifth and sixth initial fitting functions by minimizing the sum of squared errors between the predicted values ​​and the actual data.

[0253] In one possible implementation, based on the historical state of charge aging dataset and the least squares fitting method, the fifth fitting function is calculated as follows:

[0254]

[0255] in, This indicates that, under different states of charge (SOC), as the cumulative number of charge-discharge cycles (M) increases, the loss of active lithium due to SEI in the lithium battery... This represents the average of the maximum and minimum SOC; DOD represents the depth of discharge, which is the difference between the maximum and minimum SOC; and M represents the cumulative number of charge-discharge cycles.

[0256] The formula for calculating the sixth fitting function is as follows:

[0257]

[0258] in, This indicates the loss of active lithium due to lithium deposition in lithium batteries as the cumulative number of charge-discharge cycles M increases under different states of charge (SOC). This represents the average of the maximum and minimum SOC; DOD represents the depth of discharge, which is the difference between the maximum and minimum SOC; and M represents the cumulative number of charge-discharge cycles.

[0259] S706: Based on the historical negative electrode capacity and positive electrode capacity ratio aging dataset and the least squares fitting method, determine the coefficients to be determined in the seventh and eighth initial fitting functions, and obtain the seventh and eighth fitting functions.

[0260] In this step, in order to determine the coefficients to be determined in the seventh and eighth initial fitting functions, the analysis is performed based on the historical negative electrode capacity and positive electrode capacity ratio aging dataset obtained in step S701 and the least squares fitting method.

[0261] Optionally, in one possible implementation, the historical negative electrode capacity and positive electrode capacity ratio aging dataset is first preprocessed to ensure data integrity and accuracy. Then, the least squares fitting method is applied to calculate the coefficients to be determined in the seventh and eighth initial fitting functions by minimizing the sum of squared errors between the predicted values ​​and the actual data.

[0262] In one possible implementation, based on the historical state of charge aging dataset and the least squares fitting method, the seventh fitting function is calculated as follows:

[0263]

[0264] Among them, Q SEI(M,N / P) represents the loss of active lithium in a lithium battery due to SEI as the cumulative number of charge-discharge cycles M increases under different negative electrode capacity and positive electrode capacity ratios. N / P represents different negative electrode capacity and positive electrode capacity ratios, and M represents the cumulative number of charge-discharge cycles.

[0265] The formula for calculating the eighth fitting function is as follows:

[0266]

[0267] Among them, Q Li (M,N / P) represents the loss of active lithium in a lithium battery due to lithium deposition as the cumulative number of charge-discharge cycles M increases, under different negative electrode capacity and positive electrode capacity ratios. N / P represents different negative electrode capacity and positive electrode capacity ratios, and M represents the cumulative number of charge-discharge cycles.

[0268] The lithium battery remaining life prediction method provided in this application uses historical aging datasets under different operating conditions and a least-squares fitting method to determine a corrected fitting function. This method can realistically reflect the performance changes of lithium batteries under actual use conditions, capture the specific impact of different influencing factors on aging, and improve the accuracy of the two-stage aging model. Furthermore, by utilizing rich historical aging datasets, this method not only improves prediction accuracy but also provides strong data support for battery management and optimization.

[0269] Figure 8 A flowchart illustrating the method for predicting the remaining lifespan of lithium batteries provided in this application. Figure 7 ,like Figure 8 As shown, in this embodiment... Figure 7 Based on the examples, the process of obtaining historical aging datasets is described in detail. The method includes:

[0270] S801: Initialize the charging rate, state of charge, and negative and positive capacity ratios in the P2D model, and input different ambient temperatures into the P2D model to obtain the historical ambient temperature aging dataset. The historical ambient temperature aging dataset includes different ambient temperatures, the cumulative number of charge and discharge cycles of the lithium battery at different ambient temperatures, the cumulative capacity decay caused by SEI, the cumulative capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point.

[0271] In this step, this application obtains a historical aging dataset by simulating the cyclic aging of lithium batteries under different operating conditions.

[0272] Among them, different operating conditions include, but are not limited to, any one or any combination of different ambient temperatures, different charging rates, different states of charge, and different ratios of negative and positive capacity.

[0273] It is worth noting that this application focuses on different ambient temperatures, different charging rates, different states of charge, and different ratios of negative electrode capacity to positive electrode capacity. This step mainly considers different ambient temperatures.

[0274] Specifically, before inputting different preset ambient temperatures into the P2D model, the P2D model needs to be initialized. This includes setting the charging rate, state of charge, and the ratio of negative electrode capacity to positive electrode capacity. By fixing these parameters, their impact on lithium battery aging can be studied under different ambient temperature conditions. It is worth noting that the initial settings for the charging rate, state of charge, and ratio of negative electrode capacity to positive electrode capacity should be determined according to the actual situation, and this application does not impose specific limitations on them.

[0275] After initialization, different preset ambient temperatures are input into the preset P2D model to obtain a historical ambient temperature aging dataset including different preset ambient temperatures, the cumulative number of charge and discharge cycles of the lithium battery at different ambient temperatures, the cumulative value of capacity decay caused by SEI, the cumulative value of capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point.

[0276] The preset different ambient temperatures refer to multiple temperature points set during the simulation process. These temperature points should cover the possible temperature range of lithium batteries in actual applications, from low to high temperatures, in order to comprehensively evaluate the impact of different ambient temperatures on battery aging. The cumulative number of charge-discharge cycles of the lithium battery at different ambient temperatures is obtained by simulating the cyclic charge-discharge process of the lithium battery under various temperature conditions in the P2D model. This data reflects the usage intensity of the lithium battery under different ambient temperatures. The cumulative capacity decay value caused by SEI can be obtained by the calculation formula of the first cumulative capacity decay value in step S401. The cumulative capacity decay value caused by lithium deposition is calculated by simulating the impact of the lithium deposition process on the battery capacity. The specific calculation formula can be found in the calculation formula of the second cumulative capacity decay value in step S401. The cumulative number of charge-discharge cycles corresponding to the Knee point refers to the point where the lithium battery changes from the linear aging stage to the nonlinear aging stage. This data can be obtained by the calculation formula determined by the second cumulative number of charge-discharge cycles in step S403.

[0277] Through these steps, the resulting historical environmental temperature aging dataset provides an important foundation for in-depth analysis of lithium battery aging behavior and prediction of remaining lifespan.

[0278] S802: Initialize the ambient temperature, state of charge, and negative and positive electrode capacity ratios in the P2D model, and input different charging rates into the P2D model to obtain the historical charging rate aging dataset. The historical charging rate aging dataset includes different charging rates, the cumulative number of charge and discharge cycles of the lithium battery at different charging rates, the cumulative value of capacity decay caused by SEI, the cumulative value of capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point.

[0279] This step mainly considers the process of obtaining historical charging rate aging datasets under different charging rate conditions.

[0280] Specifically, before inputting different preset charging rates into the P2D model, the P2D model needs to be initialized. This includes setting the ambient temperature, state of charge, and the ratio of negative electrode capacity to positive electrode capacity. By fixing these parameters, the effects of different charging rates on lithium battery aging can be studied. It is worth noting that the initial settings for ambient temperature, state of charge, and the ratio of negative electrode capacity to positive electrode capacity should be determined according to the actual situation, and this application does not impose specific limitations on them.

[0281] After initialization, inputting different charging rates into the P2D model yields a historical charging rate aging dataset, including different charging rates, the cumulative number of charge-discharge cycles of the lithium battery at different charging rates, the cumulative capacity decay caused by SEI, the cumulative capacity decay caused by lithium deposition, and the cumulative number of charge-discharge cycles corresponding to the Knee point.

[0282] Different charging rates refer to various discharge rates set during the simulation process. These rates should reflect various possible situations of lithium batteries in actual use, in order to comprehensively evaluate the impact of charging rate on battery aging. The cumulative number of charge-discharge cycles at different charging rates is obtained by simulating the cyclic charge-discharge process of the battery under various discharge rate conditions in the P2D model. This data reflects the usage intensity of the battery under different discharge conditions. The cumulative capacity decay caused by SEI can be obtained by the calculation formula for the first cumulative capacity decay value in step S401. The cumulative capacity decay caused by lithium deposition is calculated by simulating the impact of the lithium deposition process on the battery capacity. The specific calculation formula can be found in the calculation formula for the second cumulative capacity decay value in step S401. The cumulative number of charge-discharge cycles corresponding to the Knee point refers to the point where the lithium battery changes from the linear aging stage to the nonlinear aging stage. This data can be obtained by the calculation formula determined by the second cumulative number of charge-discharge cycles in step S403.

[0283] Through these steps, the resulting historical charge rate aging dataset provides an important foundation for analyzing the aging behavior of lithium batteries under different discharge conditions and predicting their remaining lifespan.

[0284] S803: Initialize the ambient temperature, charging rate, and negative and positive electrode capacity ratios in the P2D model, and input different states of charge into the P2D model to obtain the historical state of charge aging dataset. The historical state of charge aging dataset includes different states of charge, the cumulative number of charge and discharge cycles of the lithium battery at different charging rates, the cumulative capacity decay caused by SEI, the cumulative capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point.

[0285] This step mainly considers the process of obtaining historical state of charge aging datasets under different state of charge conditions.

[0286] Specifically, before inputting different preset states of charge into the P2D model, the P2D model needs to be initialized. This includes setting the ambient temperature, charging rate, and the ratio of negative electrode capacity to positive electrode capacity. By fixing these parameters, the effects of different states of charge on lithium battery aging can be studied. It is worth noting that the initial settings for ambient temperature, charging rate, and the ratio of negative electrode capacity to positive electrode capacity should be determined according to the actual situation, and this application does not impose specific limitations on them.

[0287] After initialization, different states of charge are input into the P2D model to obtain a historical state of charge aging dataset, including different states of charge, cumulative charge and discharge times of the lithium battery at different charging rates, cumulative capacity decay caused by SEI, cumulative capacity decay caused by lithium deposition, and cumulative charge and discharge times corresponding to the Knee point.

[0288] Different states of charge (SOCs) refer to various battery charge levels set during the simulation process. These SOCs should reflect various possible situations of lithium batteries in actual use to comprehensively assess the impact of SOCs on battery aging. The cumulative number of charge-discharge cycles for lithium batteries under different SOCs is obtained by simulating the cyclic charge-discharge process of the battery under various charge levels in a P2D model. This data reflects the usage intensity of the battery under different charge conditions. The cumulative capacity decay caused by SEI can be obtained using the calculation formula for the first cumulative capacity decay value in step S401. The cumulative capacity decay caused by lithium deposition is calculated by simulating the impact of the lithium deposition process on battery capacity; the specific calculation formula can be found in the calculation formula for the second cumulative capacity decay value in step S401. The cumulative charge-discharge cycles corresponding to the Knee point refer to the point where the lithium battery changes from the linear aging stage to the nonlinear aging stage. This data can be obtained using the calculation formula determined by the second cumulative charge-discharge cycle in step S403.

[0289] Through these steps, the resulting historical state-of-charge aging dataset provides an important foundation for analyzing the aging behavior of lithium batteries under different charging conditions and predicting their remaining lifespan.

[0290] S804: Initialize the ambient temperature, charging rate, and state of charge in the P2D model, and input different negative electrode capacities and positive electrode capacity ratios into the P2D model to obtain the historical negative electrode capacity and positive electrode capacity ratio aging dataset. The historical negative electrode capacity and positive electrode capacity ratio aging dataset includes different negative electrode capacities and positive electrode capacity ratios, the cumulative number of charge and discharge cycles of the lithium battery under different negative electrode capacities and positive electrode capacity ratios, the cumulative value of capacity decay caused by SEI, the cumulative value of capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point.

[0291] This step mainly considers the process of obtaining historical aging datasets of negative electrode capacity and positive electrode capacity ratio under different negative electrode capacity and positive electrode capacity ratio conditions.

[0292] Specifically, before inputting different preset negative electrode capacities and positive electrode capacity ratios into the P2D model, the P2D model needs to be initialized. This includes setting the ambient temperature, charging rate, and state of charge. By fixing these parameters, the effects of different negative electrode capacities and positive electrode capacity ratios on lithium battery aging can be studied. It is worth noting that the initial settings for ambient temperature, charging rate, and state of charge should be determined according to actual conditions, and this application does not impose specific limitations on them.

[0293] After initialization, inputting different negative electrode capacities and positive electrode capacity ratios into the P2D model yields a historical negative electrode capacity and positive electrode capacity ratio aging dataset, including negative electrode capacity and positive electrode capacity ratio, cumulative charge and discharge cycles of the lithium battery under different negative electrode capacities and positive electrode capacity ratios, cumulative capacity decay caused by SEI, cumulative capacity decay caused by lithium deposition, and cumulative charge and discharge cycles corresponding to the Knee point.

[0294] Here, different negative electrode capacity and positive electrode capacity ratios refer to various electrode capacity ratios set during the simulation process. These ratios should reflect various possible configurations of lithium batteries in actual design and use, in order to comprehensively evaluate the impact of electrode capacity ratios on battery aging. The cumulative charge-discharge cycles of the lithium battery under different negative electrode capacity and positive electrode capacity ratios are obtained by simulating the cyclic charge-discharge process of the battery under various electrode capacity ratio conditions in a P2D model. This data reflects the usage intensity of the battery under different electrode configurations. The cumulative capacity decay value caused by SEI can be obtained using the calculation formula for the first cumulative capacity decay value in step S401. The cumulative capacity decay value caused by lithium deposition is calculated by simulating the impact of the lithium deposition process on battery capacity; the specific calculation formula can be found in the calculation formula for the second cumulative capacity decay value in step S401. The cumulative charge-discharge cycles corresponding to the Knee point refer to the point where the lithium battery changes from the linear aging stage to the nonlinear aging stage; this data can be obtained using the calculation formula determined by the second cumulative charge-discharge cycle in step S403.

[0295] Through these steps, the resulting historical negative electrode capacity and positive electrode capacity ratio aging dataset provides an important foundation for analyzing the aging behavior and predicting the remaining life of lithium batteries under different electrode configurations.

[0296] The method for predicting the remaining lifespan of lithium batteries provided in this application mainly describes the process of acquiring historical aging datasets. Specifically, different operating conditions are input into the P2D model to obtain historical aging datasets under different operating conditions. This method can systematically simulate the aging behavior of lithium batteries under various practical usage conditions, ensuring that the two-stage aging model can comprehensively reflect the performance changes of lithium batteries. Simultaneously, the historical aging dataset generated using the P2D model can reduce reliance on actual experiments, lowering experimental costs and time. Furthermore, this rich historical aging data provides strong data support for determining the target coefficients in the two-stage aging model.

[0297] Figure 9 This application provides a graph showing the capacity degradation curve of a lithium battery under two-stage aging as a function of cycle number. Figure 9 As shown, the capacity retention of a lithium battery gradually decreases with increasing cycle number (cumulative charge-discharge cycles). The period before the Knee point represents the linear aging stage of the lithium battery, while the period after the Knee point represents the accelerated aging stage (non-linear aging stage). It is worth noting that the capacity retention rate is the ratio of the difference between the rated capacity and the degraded capacity to the rated capacity.

[0298] Figure 10 This is a schematic flowchart illustrating a method for predicting the remaining lifespan of a lithium battery, as provided in this application. Figure 10 As shown, the process of predicting the remaining lifespan of a lithium battery may include:

[0299] S1001: Establish a P2D model of the lithium battery and simulate the aging data of SEI and lithium deposition under different operating conditions, and collect experimental test data of the lithium battery under the corresponding conditions.

[0300] In this step, the P2D model of the lithium battery is constructed based on the rated parameters of the lithium battery. Different operating conditions include different ambient temperatures, different charging rates, different states of charge, and different ratios of negative electrode capacity to positive electrode capacity. Specific implementation details for this step can be found in steps S801 to S804, and will not be repeated here.

[0301] S1002: Based on the defined two-stage division method, the Knee point is determined as the cycle number at which SEI causes the greatest loss of active lithium and lithium deposition causes the greatest loss of active lithium.

[0302] In this step, the two-stage division method defined, and the determination of the Knee point, can refer to the calculation formula for determining the second charge-discharge cumulative number of cycles in step S403, which will not be repeated here. It is worth noting that the cycle number here refers to the cumulative number of charge-discharge cycles of the lithium battery.

[0303] S1003: Based on two aging stages divided by Knee points, aging models are constructed separately to predict the capacity degradation and remaining lifespan of lithium batteries.

[0304] In this step, based on the two aging stages defined by the Knee point, linear and nonlinear aging models are constructed to predict the capacity degradation and remaining lifespan of lithium batteries.

[0305] In one possible implementation, the calculation formula for the linear aging model is as follows:

[0306]

[0307] Where M represents the cumulative number of charge-discharge cycles of the lithium battery, T represents the ambient temperature, and C represents the charging rate. This represents the average of the maximum and minimum SOC; DOD represents the depth of discharge, which is the difference between the maximum and minimum SOC; N / P represents the ratio of negative electrode capacity to positive electrode capacity; a SEI The calculation formula is as follows:

[0308]

[0309] b SEI The calculation formula is as follows:

[0310]

[0311] The calculation formula for the nonlinear aging stage is as follows:

[0312]

[0313] Where M represents the cumulative number of charge-discharge cycles of the lithium battery, T represents the ambient temperature, and C represents the charging rate. This represents the average of the maximum and minimum SOC. DOD represents the depth of discharge, which is the difference between the maximum and minimum SOC. N / P represents the ratio of negative electrode capacity to positive electrode capacity. When T < 25℃, a1 = 2.44 × 10⁻⁶. -10 b2 = -116.38, a Li The calculation formula is as follows:

[0314]

[0315] b Li The calculation formula is as follows:

[0316]

[0317] Where T represents the ambient temperature and C represents the charging rate. This represents the average of the maximum and minimum SOC; DOD represents the depth of discharge, which is the difference between the maximum and minimum SOC; and N / P represents the ratio of negative electrode capacity to positive electrode capacity.

[0318] When T≥25℃, a1=2.44×10 -10 b2 = -109.74, a Li The calculation formula is as follows:

[0319]

[0320] Where T represents the ambient temperature and C represents the charging rate. This represents the average of the maximum and minimum SOC; DOD represents the depth of discharge, which is the difference between the maximum and minimum SOC; and N / P represents the ratio of negative electrode capacity to positive electrode capacity.

[0321] b Li The calculation formula is as follows:

[0322]

[0323] Where T represents the ambient temperature and C represents the charging rate. This represents the average of the maximum and minimum SOC; DOD represents the depth of discharge, which is the difference between the maximum and minimum SOC; and N / P represents the ratio of negative electrode capacity to positive electrode capacity.

[0324] The formula for calculating the overall aging of lithium batteries is as follows:

[0325] Q total =Q SEI +Q Li ;

[0326] Among them, Q total Q represents the total capacity degradation of lithium batteries. SEI Q indicates the capacity degradation of lithium batteries caused by SEI. Li This indicates the capacity degradation of lithium batteries caused by lithium deposition.

[0327] Compared to traditional empirical aging models that only consider the linear stage, the two-stage aging model proposed in this application has higher accuracy in the accelerated aging stage (nonlinear aging stage), covering the period from new battery to end-of-life. It can be used not only in power batteries but also for the secondary use of lithium batteries, and is particularly applicable to energy storage systems used throughout their entire life cycle. Furthermore, compared to traditional methods such as dividing Knee points based on preset empirical values ​​of remaining capacity and the tangent method, the quantitatively defined method for determining the number of cycles at the Knee point in this application is linked to the dominant aging mechanism of lithium batteries, reflecting the changes in the two-stage aging rate and mechanism.

[0328] Figure 11 An experimental result comparison provided for this application Figure 1 .like Figure 11 As shown, the experimental results are compared with... Figure 1 This is used to represent the variation of aging caused by SEI and lithium deposition with the number of cycles at different charge rates. Figure 11 It can be concluded that as the charging rate increases, the rate of capacity decay caused by SEI increases, while the rate of reaching the extreme value decreases. Aging caused by lithium deposition occurs earlier and quickly brings lithium batteries to the end of their lifespan.

[0329] Figure 12 An experimental result comparison provided for this application Figure 2 .like Figure 12 As shown, the experimental results are compared with... Figure 2 This is used to represent the variation of aging caused by SEI and lithium deposition with cycle number under different ambient temperatures. Figure 12 It can be concluded that as the ambient temperature increases, the capacity decay caused by SEI accelerates and eventually reaches a higher extreme value, while aging caused by lithium deposition occurs later and reaches a relatively lower extreme value at the end of life.

[0330] Figure 13 An experimental result comparison provided for this application Figure 3 .like Figure 13 As shown, the experimental results are compared with... Figure 3 This is used to compare experiments under different empirical models at an ambient temperature of 25°C and a state of charge of 0-100%. Empirical Model 1 Q emp1 This is a semi-empirical model for aging prediction that takes temperature into account, and its specific expression is as follows:

[0331]

[0332] Among them, Q emp1 This indicates that the aging of lithium batteries is calculated using empirical model one, where B and z are the fitting coefficient and exponent, respectively, and E... aThe value represents the activation energy of the lithium battery, R represents the gas constant, and Ah represents the throughput of the lithium battery.

[0333] Empirical Model 2Q emp2 This is a semi-empirical model for aging prediction that considers temperature and 80% remaining capacity as the inflection point. The specific expression is as follows:

[0334] Q emp2 =e (AT+H) n J +e (DT+E) n (FT+G)

[0335] Among them, Q emp2 This represents the lithium battery aging calculated by empirical model 2, where A, D, E, F, G, H, and J are all fitting coefficients or exponents.

[0336] from Figure 13 From this, we can conclude that the aging Q caused by SEI SEI With Q emp1 The experimental data largely overlaps with the data before the Knee point, representing the first stage of aging. The second stage of accelerated aging, after the Knee point, is more pronounced compared to the Q... emp1 The two-stage aging model Q proposed in this application total This is closer to actual battery aging data, where lithium deposition is the main cause of battery aging. Q emp2 The capacity decay curves in the two stages show similar trends to the experimental data, but the difference between them and the experimental data is greater than that of the two-stage aging model Q proposed in this application. total The simulation results.

[0337] Figure 14 An experimental result comparison provided for this application Figure 4 .like Figure 14 As shown, the experimental results are compared with... Figure 4 This is used to represent experimental comparisons under different empirical models at an ambient temperature of 25℃ and a state of charge of 10%-90%. From Figure 14 From this, it can be concluded that the two-stage aging model Q proposed in this application... total Compared to Q emp2 The predictive performance is better, indicating that the two-stage aging model Q proposed in this application has better predictive performance. total It can also adapt to aging prediction in different states of charge ranges.

[0338] Figure 15 This is a schematic diagram illustrating the operating parameters of a lithium battery provided in this application. Figure 15As shown, the operating parameters of a lithium battery include charging rate, ambient temperature, state of charge (SOC) range, negative electrode capacity, and positive electrode capacity ratio. Specifically, the charging rate is 1C, 1.5C, 2C, and 2.5C; the ambient temperature is 0℃, 5℃, 15℃, 25℃, 35℃, and 45℃; the SOC range is 0–20%, 20%–40%, 40%–60%, 60%–80%, 80%–100%, 10%–90%, 25%–75%, and 45%–55%; and the negative electrode capacity to positive electrode capacity ratio is 1.028, 1.06, 1.092, 1.124, and 1.155.

[0339] Figure 16 This is a schematic diagram of a fitting function provided in this application. Figure 16 As shown, the fitting functions include the forms y = ax + b, y = alan(bx) + c, and y = ax b +c, y = ax b -0.6, y = ae bx +c. Where y = ax + b has 2 parameters, R 2 The parameter value is 0.9955, the parameter value of y = alan(bx) + c is 3, and R is... 2 The value is 0.9051, y = ax b +c has 3 parameters, R 2 The value is 0.9995, y = ax b The parameter value for -0.6 is 2, R 2 The value is 0.9994, y = ae bx +c has 3 parameters, R 2 It is 0.9999.

[0340] Figure 17 A schematic diagram of determining the target coefficients in a fitting function provided in this application Figure 1 .like Figure 17 As shown, the target coefficients and corresponding R values ​​in the fitting function determined by simulation are presented for different charging rates. 2 Specifically, at a charging rate of 1C, target coefficient 1 is 0.1142, target coefficient 2 is 0.7963, and R... 2 =0.9994. At a charging rate of 1.5C, target coefficient 1 is 0.1407, target coefficient 2 is 0.7670, R 2 = 0.9985. At a charging rate of 2C, target coefficient 1 is 0.2709, target coefficient 2 is 0.6503, R 2 =0.9941. At a charging rate of 2.5C, target coefficient 1 is 0.4824, target coefficient 2 is 0.5428, R 2 =0.9878.

[0341] Figure 18 A schematic diagram of determining the target coefficients in a fitting function provided in this application Figure 2 .like Figure 18 As shown, the target coefficients and corresponding R values ​​in the fitting function determined by simulation are displayed under different ambient temperatures. 2 Specifically, at an ambient temperature of 0℃, the target coefficient 1 is 0.0520, the target coefficient 2 is 0.8230, and R... 2 = 0.9890. At an ambient temperature of 5℃, target coefficient 1 is 0.0683, target coefficient 2 is 0.8150, and R... 2 =0.9984. At an ambient temperature of 15°C...

[0342] At ℃, the target coefficient 1 is 0.0880, the target coefficient 2 is 0.8082, and R 2 = 0.9996. At an ambient temperature of 25℃, target coefficient 1 is 0.1142, target coefficient 2 is 0.7963, R... 2 = 0.9994. At an ambient temperature of 35℃, target coefficient 1 is 0.1856, target coefficient 2 is 0.7530, and R... 2 =

[0343] 0.9983. At an ambient temperature of 45℃, target coefficient 1 is 0.3282, and target coefficient 2 is 0.6992.

[0344] R 2 =0.9969.

[0345] Figure 19 A schematic diagram of a lithium battery remaining life prediction device provided in this application. Figure 19 As shown, the lithium battery remaining life prediction device 1900 includes:

[0346] Module 1901 is used to acquire the current performance parameters of the lithium battery;

[0347] Processing module 1902 is used to process the performance parameters and ambient temperature of the lithium battery using a preset two-stage aging model to obtain the cumulative capacity decay value of the lithium battery under the current cumulative number of charge and discharge cycles.

[0348] The processing module 1902 is also used to obtain the remaining lifetime prediction result based on the cumulative capacity decay value, the remaining lifetime prediction result including the first cumulative charge and discharge count corresponding to the EoL point;

[0349] Among them, the two-stage aging model is a P2D model that determines the cumulative value of capacity decay based on the active lithium loss caused by SEI and the active lithium loss caused by lithium deposition.

[0350] Optionally, the remaining life prediction results may also include: the second cumulative number of charge-discharge cycles corresponding to the Knee point of the lithium battery;

[0351] Optionally, the processing module 1902 is further configured to: if the current cumulative charge-discharge count reaches the second cumulative charge-discharge count, output a lithium battery usage strategy, wherein the battery usage strategy includes at least one usage method to slow down lithium battery aging.

[0352] Optionally, the processing module 1902 is also used to: input the performance parameters and the ambient temperature of the lithium battery into the two-stage aging model to obtain the first cumulative capacity decay value and the second cumulative capacity decay value of the lithium battery under the current cumulative number of charge and discharge cycles. The first cumulative capacity decay value is used to represent the total capacity decay value of the lithium battery caused by SEI, and the second cumulative capacity decay value is used to represent the total capacity decay value of the lithium battery caused by lithium deposition.

[0353] Based on the first cumulative capacity decay value and the second cumulative capacity decay value, calculate the first capacity decay change value and the second capacity decay change value between adjacent charge and discharge cycles.

[0354] If the first capacity decay change value is less than the second capacity decay change value, then the current cumulative charge and discharge count is determined as the second cumulative charge and discharge count.

[0355] If the total cumulative capacity decay value between the first and second cumulative capacity decay values ​​is greater than or equal to the preset capacity decay threshold of the lithium battery, then the current cumulative charge and discharge count is determined as the first cumulative charge and discharge count.

[0356] Optionally, the acquisition module 1901 is also used to: acquire a preset P2D model.

[0357] Optionally, the processing module 1902 is also used to: determine the target coefficient configuration scheme of the target fitting function in the P2D model;

[0358] Based on the target coefficient configuration scheme, configure the coefficients in the target fitting function to obtain a two-stage aging model.

[0359] Optionally, the processing module 1902 is also used to: determine the target fitting function for the coefficients to be optimized;

[0360] Based on the modified fitting function and the target fitting function, the target coefficient configuration scheme is determined. The modified fitting function is the sum of the first fitting function corresponding to the ambient temperature, the second fitting function, the third fitting function corresponding to the charging rate, the fourth fitting function, the fifth fitting function corresponding to the state of charge, the sixth fitting function, and the seventh fitting function corresponding to the ratio of negative electrode capacity to positive electrode capacity.

[0361] Optionally, the acquisition module 1901 is also used to: acquire the historical aging dataset of the lithium battery, which includes the historical ambient temperature aging dataset, the historical charging rate aging dataset, the historical state of charge aging dataset, and the historical negative electrode capacity and positive electrode capacity ratio aging dataset.

[0362] Obtain the first, second, third, fourth, fifth, sixth, seventh, and eighth initial fitting functions for the coefficients to be determined;

[0363] Based on the historical environmental temperature aging dataset and the least squares fitting method, the coefficients to be determined in the first and second initial fitting functions are determined, and the first and second fitting functions are obtained.

[0364] Based on the historical charging rate aging dataset and the least squares fitting method, the coefficients to be determined in the third and fourth initial fitting functions are determined, and the third and fourth fitting functions are obtained.

[0365] Based on the historical state of charge aging dataset and the least squares fitting method, the coefficients to be determined in the fifth and sixth initial fitting functions are determined, and the fifth and sixth initial fitting functions are obtained.

[0366] Based on the historical negative electrode capacity and positive electrode capacity ratio aging dataset and the least squares fitting method, the coefficients to be determined in the seventh and eighth initial fitting functions are determined, and the seventh and eighth fitting functions are obtained.

[0367] Optionally, the processing module 1902 is also used to: initialize the charging rate, state of charge, and negative and positive capacity ratios in the P2D model, and input different ambient temperatures into the P2D model to obtain a historical ambient temperature aging dataset. The historical ambient temperature aging dataset includes different ambient temperatures, the cumulative number of charge and discharge cycles of the lithium battery at different ambient temperatures, the cumulative capacity decay caused by SEI, the cumulative capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point.

[0368] Initialize the ambient temperature, state of charge, and negative and positive electrode capacity ratios in the P2D model, and input different charging rates into the P2D model to obtain a historical charging rate aging dataset. The historical charging rate aging dataset includes different charging rates, the cumulative number of charge and discharge cycles of the lithium battery at different charging rates, the cumulative value of capacity decay caused by SEI, the cumulative value of capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point.

[0369] Initialize the ambient temperature, charging rate, and negative and positive electrode capacity ratios in the P2D model, and input different states of charge into the P2D model to obtain a historical state of charge aging dataset. The historical state of charge aging dataset includes different states of charge, the cumulative number of charge and discharge cycles of the lithium battery at different charging rates, the cumulative capacity decay caused by SEI, the cumulative capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point.

[0370] The ambient temperature, charging rate, and state of charge in the P2D model are initialized, and different negative electrode capacities and positive electrode capacity ratios are input into the P2D model to obtain the historical negative electrode capacity and positive electrode capacity ratio aging dataset. The historical negative electrode capacity and positive electrode capacity ratio aging dataset includes different negative electrode capacities and positive electrode capacity ratios, the cumulative number of charge and discharge cycles of the lithium battery under different negative electrode capacities and positive electrode capacity ratios, the cumulative value of capacity decay caused by SEI, the cumulative value of capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point.

[0371] The lithium battery remaining life prediction device provided in this embodiment can execute the lithium battery remaining life prediction method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0372] Figure 20 A schematic diagram of the structure of the computer device provided in this application. Figure 20 As shown, the computer device 2000 may specifically include a transceiver 2001, a processor 2002, and a memory 2003. The transceiver 2001 is used to realize data transmission between the lithium battery and the battery management system, and the memory 2003 stores computer execution instructions. The processor 2002 executes the computer execution instructions stored in the memory 2003 to implement the lithium battery remaining life prediction method in the above embodiment.

[0373] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0374] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0375] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for predicting the remaining lifespan of a lithium battery.

[0376] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the aforementioned method for predicting the remaining lifespan of a lithium battery.

[0377] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0378] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0379] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0380] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0381] In addition, the functional units in the various embodiments of the present invention 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.

[0382] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0383] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0384] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for predicting the remaining lifespan of a lithium battery, characterized in that, include: Obtain the current performance parameters of the lithium battery; Based on the performance parameters and the ambient temperature of the lithium battery, a preset two-stage aging model is used to process the performance parameters and the ambient temperature to obtain the cumulative capacity decay value of the lithium battery under the current cumulative number of charge and discharge cycles. Based on the cumulative capacity decay value, the remaining lifetime prediction result is obtained, which includes the first cumulative number of charge and discharge cycles corresponding to the end lifetime EoL point. The two-stage aging model is a pseudo-two-dimensional P2D model for determining the cumulative capacity decay value, based on the active lithium loss caused by the solid electrolyte interphase (SEI) membrane and the active lithium loss caused by lithium deposition.

2. The method according to claim 1, characterized in that, The remaining life prediction result also includes: the second cumulative number of charge and discharge cycles corresponding to the Knee point of the lithium battery; The method further includes: If the current cumulative charge-discharge count reaches the second cumulative charge-discharge count, a lithium battery usage strategy is output, which includes at least one usage method to slow down the aging of the lithium battery.

3. The method according to claim 2, characterized in that, The method further includes: The performance parameters and the ambient temperature of the lithium battery are input into the two-stage aging model to obtain the first cumulative capacity decay value and the second cumulative capacity decay value of the lithium battery under the current cumulative number of charge and discharge cycles. The first cumulative capacity decay value is used to represent the total capacity decay value of the lithium battery caused by the solid electrolyte interphase (SEI) film, and the second cumulative capacity decay value is used to represent the total capacity decay value of the lithium battery caused by lithium deposition. Based on the first cumulative capacity decay value and the second cumulative capacity decay value, calculate the first capacity decay change value and the second capacity decay change value between adjacent charge and discharge cycles; If the first capacity decay change value is less than the second capacity decay change value, then the current cumulative charge and discharge count is determined as the second cumulative charge and discharge count; If the total cumulative capacity decay value between the first cumulative capacity decay value and the second cumulative capacity decay value is greater than or equal to the preset capacity decay threshold of the lithium battery, then the current cumulative charge and discharge count is determined as the first cumulative charge and discharge count.

4. The method according to claim 1, characterized in that, The process of obtaining the two-stage aging model includes: Obtain a preset pseudo-2D P2D model; Determine the target coefficient configuration scheme for the target fitting function in the pseudo-two-dimensional P2D model; Based on the target coefficient configuration scheme, configure the coefficients in the target fitting function to obtain the two-stage aging model.

5. The method according to claim 4, characterized in that, The step of determining the target coefficient configuration scheme for the target fitting function in the pseudo-two-dimensional P2D model includes: Determine the target fitting function for the coefficients to be optimized; The target coefficient configuration scheme is determined based on the modified fitting function and the target fitting function, wherein the modified fitting function is the sum of the first fitting function corresponding to the ambient temperature, the second fitting function, the third fitting function corresponding to the charging rate, the fourth fitting function, the fifth fitting function corresponding to the state of charge, the sixth fitting function, and the seventh fitting function corresponding to the ratio of negative electrode capacity to positive electrode capacity.

6. The method according to claim 5, characterized in that, The process of obtaining the modified fitting function includes: Obtain the historical aging dataset of the lithium battery, which includes historical ambient temperature aging dataset, historical charging rate aging dataset, historical state of charge aging dataset, and historical negative electrode capacity and positive electrode capacity ratio aging dataset. Obtain the first, second, third, fourth, fifth, sixth, seventh, and eighth initial fitting functions for the coefficients to be determined; Based on the historical environmental temperature aging dataset and the least squares fitting method, determine the coefficients to be determined in the first initial fitting function and the second initial fitting function, and obtain the first fitting function and the second fitting function. Based on the historical charging rate aging dataset and the least squares fitting method, determine the coefficients to be determined in the third initial fitting function and the fourth initial fitting function, and obtain the third fitting function and the fourth fitting function; Based on the historical state of charge aging dataset and the least squares fitting method, determine the coefficients to be determined in the fifth initial fitting function and the sixth initial fitting function, and obtain the fifth fitting function and the sixth initial fitting function; Based on the historical negative electrode capacity and positive electrode capacity ratio aging dataset and the least squares fitting method, the coefficients to be determined in the seventh initial fitting function and the eighth initial fitting function are determined, and the seventh fitting function and the eighth fitting function are obtained.

7. The method according to claim 6, characterized in that, The process of obtaining the historical aging dataset includes: Initialize the charging rate, state of charge, and negative and positive capacity ratios in the pseudo-two-dimensional P2D model, and input different ambient temperatures into the pseudo-two-dimensional P2D model to obtain the historical ambient temperature aging dataset. The historical ambient temperature aging dataset includes the different ambient temperatures, the cumulative number of charge and discharge cycles of the lithium battery at the different ambient temperatures, the cumulative capacity decay caused by the SEI, the cumulative capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point. The ambient temperature, state of charge, and ratio of negative electrode capacity to positive electrode capacity in the pseudo-two-dimensional P2D model are initialized, and different charging rates are input into the pseudo-two-dimensional P2D model to obtain the historical charging rate aging dataset. The historical charging rate aging dataset includes the different charging rates, the cumulative number of charge and discharge cycles of the lithium battery at the different charging rates, the cumulative value of capacity decay caused by SEI, the cumulative value of capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point. Initialize the ambient temperature, charging rate, and negative electrode capacity to positive electrode capacity ratio in the pseudo-two-dimensional P2D model, and input different states of charge into the pseudo-two-dimensional P2D model to obtain the historical state of charge aging dataset. The historical state of charge aging dataset includes the different states of charge, the cumulative number of charge and discharge cycles of the lithium battery at the different charging rates, the cumulative capacity decay caused by the SEI, the cumulative capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point. The ambient temperature, charging rate, and state of charge in the pseudo-two-dimensional P2D model are initialized, and different negative electrode capacities and positive electrode capacity ratios are input into the pseudo-two-dimensional P2D model to obtain the historical negative electrode capacity and positive electrode capacity ratio aging dataset. The historical negative electrode capacity and positive electrode capacity ratio aging dataset includes the different negative electrode capacities and positive electrode capacity ratios, the cumulative number of charge and discharge cycles of the lithium battery under the different negative electrode capacities and positive electrode capacity ratios, the cumulative value of capacity decay caused by the SEI, the cumulative value of capacity decay caused by lithium deposition, and the cumulative number of charge and discharge cycles corresponding to the Knee point.

8. A device for predicting the remaining life of a lithium battery, characterized in that, include: The acquisition module is used to obtain the current performance parameters of the lithium battery; The processing module is used to process the performance parameters according to the performance parameters and the ambient temperature of the lithium battery using a preset two-stage aging model to obtain the cumulative capacity decay value of the lithium battery under the current cumulative number of charge and discharge cycles. The processing module is further configured to obtain a remaining lifetime prediction result based on the cumulative capacity decay value, wherein the remaining lifetime prediction result includes the first cumulative charge and discharge count corresponding to the end lifetime EoL point. The two-stage aging model is a pseudo-two-dimensional P2D model for determining the cumulative capacity decay value, based on the active lithium loss caused by the solid electrolyte interphase (SEI) membrane and the active lithium loss caused by lithium deposition.

9. A computer device, characterized in that, include: A transceiver, a processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method for predicting the remaining life of a lithium battery as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method for predicting the remaining life of a lithium battery as described in any one of claims 1 to 7.

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