Battery life prediction parameter determination method, battery life prediction method and device

Abstract

This application relates to a method for determining battery life prediction parameters, a battery life prediction method, and an apparatus. The method for determining battery life prediction parameters includes: acquiring test result data of the battery under test under different charge-discharge cycles; and determining battery life prediction parameters based on the test result data. These parameters include thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters. This method analyzes the sources of battery life degradation from multiple dimensions, which is beneficial for designing batteries with longer lifespans. The battery life prediction method further includes: acquiring battery life prediction parameters of the battery under test; performing iterative prediction of battery life degradation based on these parameters to obtain predicted battery life degradation data. The battery life prediction parameters are obtained based on the aforementioned method for determining battery life prediction parameters. This method improves the accuracy of battery life prediction data.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a method, apparatus, computer equipment, storage medium and computer program product for determining battery life prediction parameters. Background Technology

[0002] With the booming development of the power battery industry, power batteries have become the main power source for modern electric vehicles due to their high energy and high power density. However, the aging problem of power batteries restricts the development and promotion of electric vehicles. Battery aging can affect battery performance and even internal structure, and may even cause safety issues. Therefore, assessing the aging degree of power batteries and predicting battery life has become increasingly important.

[0003] Currently, data-driven battery life prediction methods are commonly used. These methods extract relevant features from early-stage battery degradation data and then establish a mapping relationship between these features and battery life through algorithms, ultimately predicting the battery's lifespan. However, a drawback of this approach is that the prediction accuracy depends on the amount of training data, making it impossible to guarantee the accuracy of battery life predictions.

[0004] Therefore, there is a need to provide a solution that can improve the accuracy of battery life prediction. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer device, storage medium, and computer program product for determining battery life prediction parameters that supports accurate prediction of battery life, as well as a method, apparatus, computer device, storage medium, and computer program product for predicting battery life that can achieve accurate prediction of battery life, in order to address the above-mentioned technical problems.

[0006] Firstly, this application provides a method for determining battery life prediction parameters. The method includes:

[0007] Cyclic aging tests are performed on the battery under test to obtain test result data of the battery under test under different charge and discharge cycles;

[0008] Based on the test results data, battery life prediction parameters are determined, including thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters.

[0009] In this embodiment of the application, the battery under test is subjected to cyclic charge-discharge tests to obtain test result data under different charge-discharge cycles. Based on the test result data under different charge-discharge cycles, battery life prediction parameters, including thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters, are determined. This approach considers not only material property degradation factors and thermodynamic degradation factors, but also kinetic degradation factors, analyzing the potential sources of battery life degradation from multiple dimensions. It supports quantitative analysis of the impact of different prediction parameters on battery life degradation, which is more conducive to designing longer-lasting batteries.

[0010] In some embodiments, determining battery life prediction parameters based on test result data includes:

[0011] The test results data were processed using the established battery life degradation model to determine the battery life prediction parameters.

[0012] Among them, the battery life degradation model is constructed based on different battery life degradation mechanisms and historical test results data of the battery under test under charge-discharge cycles.

[0013] In the solution of this application embodiment, based on the battery life decay mechanism and combined with the historical test result data of the battery under test under different charge and discharge cycles, a battery life decay model is pre-constructed, and then the test result data is processed by the constructed battery life decay model, which can directly and efficiently determine the battery life prediction parameters and ensure the accuracy of the battery life prediction parameters.

[0014] In some embodiments, the material property prediction parameters include the remaining amount of positive and negative electrode active materials under different charge-discharge cycles;

[0015] The test results data are processed using the established battery life degradation model to determine the battery life prediction parameters, including the following steps:

[0016] The test results were processed based on the thermodynamic charge-discharge capacity calculation principle to obtain the optimal remaining amount of positive and negative electrode active materials.

[0017] Based on the optimal remaining amount of positive and negative electrode active materials, the preset total amount of positive and negative electrode active materials, and the preset cell throughput, the remaining amount of positive and negative electrode active materials under different charge and discharge cycles is determined.

[0018] In the embodiments of this application, the influence of cell throughput on the loss of positive and negative electrode materials is considered. Combined with the thermodynamic charge and discharge capacity calculation principle and parameter optimization algorithm, the remaining amount of positive and negative electrode active materials under different charge and discharge cycles is obtained objectively and accurately.

[0019] In some embodiments, determining the remaining amount of positive and negative electrode active materials under different charge-discharge cycles based on the optimal remaining amount of positive and negative electrode active materials, the preset total amount of positive and negative electrode active materials, and the preset cell throughput includes:

[0020] The loss of positive and negative electrode active materials is obtained based on the optimal remaining amount of positive and negative electrode active materials and the preset total amount of positive and negative electrode active materials.

[0021] The loss rate of positive and negative electrode materials is obtained based on the loss of positive and negative electrode active materials and the preset cell throughput.

[0022] Based on the loss rate of positive and negative electrode materials, the preset cell throughput, and the preset total amount of positive and negative electrode active materials, the remaining amount of positive and negative electrode active materials under different charge and discharge cycles is determined.

[0023] In the scheme of this application embodiment, the remaining amount of positive and negative electrode active materials is determined from two dimensions: cell throughput and positive and negative electrode material loss rate. This can accurately obtain the remaining amount of positive and negative electrode materials related to cell throughput under different cycle periods.

[0024] In some embodiments, the battery under test includes a lithium battery under test, and the test result data includes SEI film formation reaction parameters, lithium plating reaction parameters, and positive and negative electrode OCV (Open Circuit Voltage) curves. The thermodynamic prediction parameters include the maximum and minimum lithium intercalation amounts of the positive and negative electrodes under different charge and discharge cycles.

[0025] The test results data are processed using the established battery life degradation model to determine the battery life prediction parameters, including the following steps:

[0026] The remaining lithium amount under different charge-discharge cycles is determined based on the total amount of positive and negative electrode active materials, the loss of positive and negative electrode active materials, the SEI (Solid Electrolyte Interface) film formation reaction parameters, and the lithium plating reaction parameters.

[0027] Based on the positive and negative electrode OCV curves, remaining lithium content, preset upper and lower cutoff voltages, and preset initial values ​​of electrode lithium intercalation, the OCV curves are reconstructed using the Newton-Raphson iterative algorithm to obtain the maximum and minimum lithium intercalation of the positive and negative electrodes under different charge-discharge cycles.

[0028] In the scheme of this application embodiment, the remaining lithium amount under different charge-discharge cycles is first determined, and then the OCV is reconstructed by combining the positive and negative electrode OCV curves and preset parameters, so as to accurately obtain the maximum and minimum lithium intercalation amount of the positive and negative electrodes under different charge-discharge cycles.

[0029] In some embodiments, the SEI film-forming reaction parameters include porous SEI film-forming reaction parameters and fragmented SEI film-forming reaction parameters;

[0030] Based on the total amount of positive and negative electrode active materials, the loss of positive and negative electrode active materials, SEI film formation reaction parameters, and lithium plating reaction parameters, the remaining lithium amount under different charge-discharge cycles was determined as follows:

[0031] The first lithium loss caused by SEI film formation was obtained based on the reaction parameters of porous SEI and broken SEI.

[0032] Based on the lithium plating reaction parameters, the amount of the second lithium loss caused by the lithium plating reaction is obtained;

[0033] The total lithium loss is obtained based on the first lithium loss, the second lithium loss, and the loss of positive and negative electrode active materials;

[0034] The remaining lithium amount under different charge-discharge cycles is determined based on the total lithium loss and the total amount of positive and negative electrode active materials.

[0035] In the solution of this application embodiment, not only the lithium loss caused by the loss of positive and negative electrode materials is considered, but also the lithium loss caused by SEI film formation and the lithium loss caused by lithium plating reaction. This can more accurately determine the remaining lithium amount under different charge and discharge cycles, thereby ensuring the accuracy of the maximum and minimum lithium intercalation amount of the positive and negative electrodes.

[0036] In some embodiments, the battery under test includes a lithium battery under test, and the kinetic prediction parameters include the variation data of the solid-phase diffusion coefficients of the positive and negative electrodes and the variation data of the surface reaction rate constants of the positive and negative electrodes.

[0037] Cyclic aging tests were performed on the lithium battery under test, and the test results data of the lithium battery under test under different charge-discharge cycles were obtained, including:

[0038] Cyclic electrochemical impedance spectroscopy was performed on the lithium battery under test to obtain the initial solid-phase diffusion coefficients and initial positive and negative electrode reaction rate constants under different charge-discharge cycles.

[0039] The test results data are processed using the established battery life degradation model to determine the battery life prediction parameters, including the following steps:

[0040] For each charge-discharge cycle, the product of the initial positive and negative electrode solid-phase diffusion coefficients and the preset kinetic decay coefficients is obtained to obtain the target positive and negative electrode solid-phase diffusion coefficients for each charge-discharge cycle. The product of the initial positive and negative electrode reaction rate constants and the preset positive and negative electrode reaction rate constant coefficients is obtained to obtain the target positive and negative electrode reaction rate constants for each charge-discharge cycle.

[0041] Based on the target positive and negative electrode solid-phase diffusion coefficients and target positive and negative electrode reaction rate constants under each charge-discharge cycle, the variation data of the positive and negative electrode solid-phase diffusion coefficients and the variation data of the positive and negative electrode surface reaction rate constants are obtained.

[0042] In the embodiments of this application, parameters that affect battery life degradation due to increased impedance are considered, represented by the solid-phase diffusion coefficients of the positive and negative electrodes and the surface reaction rate constants. From a new perspective, kinetic prediction parameters that include the variation data of the solid-phase diffusion coefficients of the positive and negative electrodes and the variation data of the surface reaction rate constants of the positive and negative electrodes are determined.

[0043] In some embodiments, the test result data includes the increased thickness of the positive and negative electrode monolayers, the thickness of the SEI film, the initial preload of the cell clamp, and the elastic modulus of the main material. The kinetic prediction parameters include the change data of the first material parameters of the positive and negative electrode sheets and the separator caused by the consumption of electrolyte. The first material parameters include thickness, solid volume fraction, liquid volume fraction, and Brugmann coefficient.

[0044] The test results data are processed using the established battery life degradation model to determine the battery life prediction parameters, including the following steps:

[0045] Based on the SEI film thickness, the initial pre-tightening force of the cell clamp, the elastic modulus of the main material, and the preset expansion force balance equation, the first material parameters of the positive and negative electrode sheets and the separator under different charge and discharge cycles are obtained. The preset expansion force balance equation is constructed based on the elastic modulus of the clamp locking bolt, the positive and negative electrode sheets, and the separator.

[0046] Based on the first material parameters of the positive and negative electrode plates and the separator under different charge and discharge cycles, the variation data of the first material parameters of the positive and negative electrode plates and the separator are obtained.

[0047] In the embodiments of this application, the changes in the solid volume fraction, liquid volume fraction, and Brugmann coefficient of the positive and negative electrodes / separator membranes are considered due to the cell expansion force caused by the formation of the SEI film, and the sources affecting the battery life degradation are determined from a kinetic perspective.

[0048] In some embodiments, the test result data includes total pore volume, initial active area, critical volume ratio of electrolyte, initial liquid phase volume fraction of positive and negative electrode plates and separator, and current remaining electrolyte volume under different charge and discharge cycles. The lithium battery prediction parameters include the change data of second material parameters of positive and negative electrode plates and separator caused by electrolyte consumption. The second material parameters include liquid phase volume fraction and effective active area.

[0049] The test results data are processed using the established battery life degradation model to determine the battery life prediction parameters, including the following steps:

[0050] Obtain the ratio of the current remaining electrolyte volume to the total pore volume under different charge-discharge cycles;

[0051] Based on the ratio and the initial liquid volume fraction of the positive and negative electrode plates and the separator, the liquid volume fraction of the positive and negative electrode plates and the separator under different charge and discharge cycles is obtained.

[0052] Based on the current remaining electrolyte volume, total pore volume, initial active area and critical electrolyte volume ratio in each charge-discharge cycle, the effective active areas of the positive and negative electrode plates and separators under different charge-discharge cycles are obtained.

[0053] Based on the liquid volume fraction and effective active area of ​​the positive and negative electrode plates under different charge and discharge cycles, the change data of liquid volume fraction and effective active area of ​​the positive and negative electrode plates are obtained.

[0054] Based on the liquid phase volume fraction and effective active area of ​​the separator under different charge-discharge cycles, the change data of the liquid phase volume fraction and the change data of the effective active area of ​​the separator are obtained.

[0055] In the embodiments of this application, the relationship between SEI film formation and electrolyte consumption is considered, thereby determining the relevant parameters that affect battery life degradation due to electrolyte consumption caused by SEI film formation. This approach is more comprehensive and can help design batteries with longer lifespans.

[0056] Secondly, this application provides a method for predicting battery life. The method includes:

[0057] Obtain the battery life prediction parameters for the battery under test;

[0058] Based on the battery life prediction parameters, the battery under test is iteratively predicted to reduce its lifespan, and the predicted lifespan data of the battery under test is obtained.

[0059] The battery life prediction parameters are obtained based on the above-mentioned battery life prediction parameter determination method.

[0060] In the scheme of this application embodiment, battery life prediction parameters, including thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters, are obtained for the battery under test. Based on the thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters, the battery life of the battery under test is iteratively predicted. The factors affecting battery life degradation are considered from multiple dimensions, which makes the obtained life degradation prediction data more accurate. Furthermore, it can support quantitative analysis of the impact of thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters on battery life degradation, which is more conducive to finding the bottlenecks affecting battery life degradation and designing batteries with longer lifespans.

[0061] In some embodiments, the battery under test is subjected to iterative prediction of life decay based on battery life prediction parameters to obtain life decay prediction data of the battery under test, including:

[0062] Based on the battery life prediction parameters, the battery under test is simulated to obtain the initial life decay prediction data and life decay parameters.

[0063] Update the battery life prediction parameters based on the life degradation parameters;

[0064] The process returns to the steps of simulating the life decay of the battery under test based on the battery life prediction parameters, obtaining the initial life decay prediction data and life decay parameters, until the preset iteration termination condition is reached, and the life decay prediction data of the battery under test is obtained.

[0065] In the technical solution of this application embodiment, the battery life prediction parameters are updated according to the life decay parameters, and then the life decay simulation of the battery under test is performed again according to the updated life decay parameters. Through iterative prediction, the accuracy of the life decay prediction data is ensured.

[0066] Thirdly, this application also provides a battery life prediction parameter determination device. The device includes:

[0067] The test result data acquisition module is used to acquire test result data of the battery under test under different charge and discharge cycles.

[0068] The battery life prediction parameter determination module is used to determine battery life prediction parameters based on the test result data. The battery life prediction parameters include thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters.

[0069] Fourthly, this application also provides a battery life prediction device. The device includes:

[0070] The battery life prediction parameter acquisition module is used to acquire the battery life prediction parameters of the lithium battery under test.

[0071] The battery life prediction module is used to perform iterative prediction of the life decay of the battery under test based on the battery life prediction parameters, so as to obtain the life decay prediction data of the battery under test.

[0072] The battery life prediction parameters are obtained based on the battery life prediction parameter determination method described above.

[0073] Fifthly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above-described battery life prediction parameter determination method and battery life prediction method.

[0074] Sixthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps in the above-described battery life prediction parameter determination method and battery life prediction method.

[0075] Seventhly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps in the above-described battery life prediction parameter determination method and battery life prediction method.

[0076] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0077] Figure 1 This is a diagram illustrating the application environment of a battery life prediction parameter determination method in one embodiment.

[0078] Figure 2 This is a flowchart illustrating a method for determining battery life prediction parameters in one embodiment;

[0079] Figure 3 This is a flowchart illustrating the method for determining battery life prediction parameters in another embodiment;

[0080] Figure 4 This is a flowchart illustrating the steps for obtaining the variation data of the solid-phase diffusion coefficients of the positive and negative electrodes and the variation data of the surface reaction rate constants of the positive and negative electrodes in one embodiment.

[0081] Figure 5 This is a detailed flowchart illustrating the method for determining battery life prediction parameters in another embodiment;

[0082] Figure 6 This is a diagram illustrating the application environment of a battery life prediction method in one embodiment.

[0083] Figure 7 This is a flowchart illustrating a battery life prediction method in one embodiment;

[0084] Figure 8 This is a flowchart illustrating the battery life prediction method in another embodiment;

[0085] Figure 9 This is a structural block diagram of a battery life prediction parameter determination device in one embodiment;

[0086] Figure 10This is a structural block diagram of a battery life prediction device in one embodiment;

[0087] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0088] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0089] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0090] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0091] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0092] Currently, the application of power batteries is becoming increasingly widespread in the market. Power batteries are not only used in energy storage systems such as hydropower, thermal power, wind power, and solar power plants, but also extensively used in electric vehicles such as electric bicycles, electric motorcycles, and electric cars, as well as in military equipment and aerospace. With the continuous expansion of power battery applications, market demand is also constantly increasing. While power batteries have become the main power source for modern electric vehicles due to their high energy and high power density advantages, the aging problem of power batteries has also constrained the development and promotion of electric vehicles. Battery aging can affect battery performance and even internal structure, potentially leading to safety issues. Therefore, predicting battery performance and designing power batteries with longer lifespans is becoming increasingly important.

[0093] In traditional technologies, battery storage life or cycle life testing requires a significant amount of time. Therefore, researchers in the battery field typically employ life prediction methods to assess battery reliability. Currently, data-driven battery life prediction is a commonly used approach. This method extracts relevant features from early-stage battery degradation data and then establishes a mapping relationship between features and lifespan using algorithms, ultimately predicting the battery's lifespan. However, this method also has some drawbacks: First, the prediction accuracy depends on the amount of training data; second, the prediction dimension is singular, considering only the loss of active lithium due to changes in a single variable, without comprehensively considering the coupled impact of positive and negative electrode material loss, electrolyte consumption, expansion forces, and other factors on lifespan. It fails to predict the battery's rate charge / discharge performance, power performance, and temperature performance at different life stages, and it cannot use the performance at the end-of-life (EOL) stage to reverse-engineer battery design.

[0094] The inventors have observed that during cycling or storage, the loss of positive and negative electrode materials is correlated with the cell throughput. As positive and negative electrode active materials insert or extract ions during charge-discharge cycles, factors such as the thickness of side reaction accumulation and graphite layer peeling in the cell system cause cell bulging, i.e., the positive and negative electrode sheets expand outwards. Electrode bulging has adverse effects on battery performance and lifespan. For example, pressure can reduce electrode porosity, affecting electrolyte wetting, altering ion transport paths, and leading to lithium plating problems. Electrodes subjected to prolonged high pressure may also break, posing a risk of internal short circuits. Furthermore, the electrolyte is continuously consumed during charge-discharge cycles, and after a certain period of cell use, localized electrolyte depletion may occur. Cell bulging further exacerbates this localized electrolyte shortage. Considering kinetic degradation factors, the decay of the solid-phase diffusion coefficient and surface reaction rate of the positive and negative electrodes is also a contributing factor to battery lifespan reduction.

[0095] To address the aforementioned issues, this application provides a method for determining battery life degradation by analyzing the sources of battery life degradation from multiple dimensions. This method involves conducting cyclic aging tests on the lithium battery under test to obtain test results data at different charge-discharge cycles. Then, based on the test results data and electrochemical data, battery life prediction parameters are determined. These parameters include thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters. This approach analyzes the factors that may cause battery life degradation from the perspectives of thermodynamic degradation, kinetic degradation, and material property degradation, which is more conducive to reverse optimization of battery design.

[0096] The battery life prediction parameter determination method provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, the controller 102 communicates with the testing device 106 of the battery under test 104 via a network. A data storage system can store the data that the controller 102 needs to process. The data storage system can be integrated into the controller 102 or placed in the cloud or on another network server. Specifically, the tester can perform a cyclic aging test on the battery under test 104. The testing device 106 records the test results data and sends the test results data for each charge-discharge cycle to the controller 102. Then, it sends a battery life prediction parameter determination command to the controller 102. The controller 102 responds to the command, obtains the test results data of the battery under test 104 under the discharge cycle, and then determines the battery life prediction parameters based on the test results data. These battery life prediction parameters include thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters. The controller 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc.

[0097] In some embodiments, such as Figure 2 As shown, a method for determining battery life prediction parameters is provided, which can be applied to... Figure 1 Taking controller 102 as an example, the following steps are included:

[0098] Step 100: Obtain test result data of the battery under test under different charge and discharge cycles.

[0099] The test results include battery capacity data, cutoff voltage data, equilibrium potential data of the positive and negative electrodes, SOC-OCV curve, and temperature for each charge-discharge cycle. In practical applications, the battery under test can be charged and discharged at a preset charge-discharge rate, such as 0.04C, every fixed number of cycles, for example, 100 cycles. The test results for each charge-discharge cycle are then recorded. The battery under test can be a lithium-ion battery (hereinafter referred to as a lithium battery). It is understood that the number of cycles and the charge-discharge rate can also be other values, depending on the actual situation, and are not limited here.

[0100] Step 200: Based on the test result data, determine the battery life prediction parameters, which include thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters.

[0101] Battery life prediction parameters refer to parameters that can be used to predict battery life. Thermodynamic prediction parameters are temperature-related parameters used to predict battery life. Kinetic prediction parameters are related to battery reaction rate and diffusion coefficient. Material property prediction parameters are related to changes in active materials and used to predict battery life. After obtaining test results data, a multi-dimensional analysis of battery life degradation mechanisms can be conducted to identify the sources affecting battery life degradation and determine the predictive parameters. Specifically, this can involve processing the test results data to determine multiple battery life prediction parameters, considering factors such as changes in the crystal structure of electrode materials, decomposition, peeling, or corrosion leading to a reduction in active material, electrolyte decomposition causing decreased conductivity and increased impedance, gases generated by side reactions, insoluble substances, binder modification, and current collector corrosion leading to increased impedance, as well as other mechanisms that may cause capacity degradation. From a dimensional perspective, the determined battery life prediction parameters can be summarized as thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters. Thermodynamic prediction parameters can include the amount of intercalated / deintercalated active material. Kinetic prediction parameters may include the diffusion coefficients of the positive and negative electrode solid phases and the surface reaction rate. Material property prediction parameters may include the amount of active material loss, the amount of active material remaining, the change in the volume fraction of the liquid phase in the electrode, and the change in the effective active area.

[0102] In the scheme of this application embodiment, the test battery is subjected to cyclic charge-discharge tests to obtain test result data of the test battery under different charge-discharge cycles. Based on the test result data of the test battery under different charge-discharge cycles, battery life prediction parameters, including thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters, are determined. The above scheme not only considers material property degradation factors and thermodynamic degradation factors, but also kinetic degradation factors, analyzing the sources of lifespan degradation that may affect battery life from multiple dimensions. It can support quantitative analysis of the impact of different prediction parameters on battery lifespan degradation, which is more conducive to designing batteries with longer lifespans.

[0103] like Figure 3 As shown, in some embodiments, step 200 includes:

[0104] Step 210: The test result data is processed using the constructed battery life degradation model to determine the battery life prediction parameters. The battery life degradation model is constructed based on different battery life degradation mechanisms and historical test result data of the battery under test under charge-discharge cycles.

[0105] In specific implementation, a battery life degradation model can be constructed based on the aforementioned battery life degradation mechanism and combined with historical test results data of the battery under test under different charge-discharge cycles. In this embodiment, the battery life degradation model can be a model composed of multiple battery life degradation sub-models. Specifically, the battery life degradation sub-model can include a positive and negative electrode material loss degradation sub-model, a kinetic degradation sub-model, an expansion force sub-model, an electrolyte consumption degradation sub-model, an SEI film growth sub-model, and a lithium plating sub-model. It is understood that in other embodiments, the battery life degradation sub-model can also include other degradation sub-models, which can be constructed according to different battery life degradation mechanisms and are not limited here. In this embodiment, the test result data can be processed according to the positive and negative electrode material loss degradation sub-model, the lithium plating sub-model, and the SEI film growth sub-model to determine thermodynamic prediction parameters; the test result data can be processed according to the kinetic degradation sub-model and the expansion force sub-model to determine kinetic prediction parameters; and the test result data can be processed according to the electrolyte consumption model and the expansion force sub-model to determine material property prediction parameters.

[0106] In the solution of this application embodiment, based on the battery life decay mechanism and combined with the historical test result data of the battery under test under different charge and discharge cycles, a battery life decay model is pre-constructed, and then the test result data is processed by the constructed battery life decay model, which can directly and efficiently determine the battery life prediction parameters and ensure the accuracy of the battery life prediction parameters.

[0107] In some embodiments, the material property prediction parameters include the remaining amount of positive and negative electrode active materials under different charge-discharge cycles;

[0108] Step 210 includes the following steps:

[0109] Step 220: Process the test results data according to the thermodynamic charge-discharge capacity calculation principle to obtain the optimal remaining amount of positive and negative electrode active materials.

[0110] Step 222: Determine the remaining amount of positive and negative electrode active materials under different charge-discharge cycles based on the optimal remaining amount of positive and negative electrode active materials, the preset total amount of positive and negative electrode active materials, and the preset cell throughput.

[0111] Charge / discharge capacity refers to the total amount of charge a battery can accept or release under specified charge / discharge conditions. Positive and negative electrode material loss refers to the reduction in active material during lithium-ion battery cycling or storage due to particle surface phase changes, particle breakage, structural damage, or isolation caused by electrolyte consumption leading to localized material not being wetted. In this embodiment, the remaining amount of positive and negative electrode active material includes the remaining amount of positive electrode active material and the remaining amount of negative electrode active material, and the total amount of positive and negative electrode active material includes the total amount of positive electrode active material and the total amount of negative electrode active material.

[0112] This embodiment provides a detailed explanation of how to process test results data using a loss attenuation sub-model of positive and negative electrode materials to determine the remaining amount of positive and negative electrode active materials under different charge-discharge cycles. The thermodynamic charge-discharge capacity calculation principle refers to the principle of calculating the thermodynamic charge-discharge capacity of a battery cell. Specifically, the calculation principle can be:

[0113] Cap_cell=(SOCmax_Pos-SOCmin_Pos)*Pos_eff*Q_Pos

[0114] Cap_cell=(SOCmax_NegSOCmin_Neg)*Neg_eff*Q_Neg

[0115] Wherein, Neg_eff represents the remaining amount of negative electrode active material, Pos_eff represents the remaining amount of positive electrode active material, Cap_cell represents the low-rate charge / discharge capacity, SOCmax_Pos represents the maximum lithium intercalation amount of the positive electrode, SOCmin_Pos represents the minimum lithium intercalation amount of the positive electrode, Q_Pos represents the specific capacity of the positive electrode, Q_Neg represents the negative specific capacity, SOCmax_Neg represents the maximum lithium intercalation amount of the negative electrode, and SOCmin_Neg represents the minimum lithium intercalation amount of the negative electrode. Test results can include SOC-OCV curves, charge / discharge capacity data, specific capacities of the positive and negative electrodes, and equilibrium potential curves of the positive and negative electrode materials under a fixed number of cycles, such as 100 cycles of low-rate charge / discharge testing. Specifically, the initial values ​​of the minimum lithium intercalation amount of the positive electrode and the minimum lithium intercalation amount of the negative electrode can be given in advance. Combined with the equilibrium potential curves of the positive and negative electrode materials, and based on the above thermodynamic charge and discharge capacity calculation principle, the maximum lithium intercalation amount of the positive electrode, the minimum lithium intercalation amount of the positive electrode, the maximum lithium intercalation amount of the negative electrode, the maximum lithium intercalation amount of the negative electrode, the remaining amount of positive active material, and the remaining amount of negative active material can be obtained under the corresponding fixed number of cycles. Then, based on the maximum and minimum lithium intercalation amounts of the positive and negative electrodes, a predicted SOC-OCV curve is constructed. The predicted SOC-OCV curve is compared with the actual tested SOC-OCV curve. If they are inconsistent, a Newton-Raphson iterative algorithm is used to continuously adjust the maximum and minimum lithium intercalation amounts of the positive and negative electrodes, as well as the remaining amounts of the positive and negative active materials. If the error between the optimized SOC-OCV curve and the actual tested SOC-OCV curve meets the preset requirements (i.e., the two curves are basically consistent), then the optimal maximum and minimum lithium intercalation amounts of the positive and negative electrodes, as well as the remaining amounts of the positive and negative active materials, are obtained. After obtaining the optimal remaining amounts of the positive and negative active materials, these values, along with a preset total amount of positive and negative active materials and a preset cell throughput, can be input into the positive and negative electrode material loss attenuation sub-model to obtain the remaining amounts of positive and negative active materials under different charge-discharge cycles. It is understood that in other embodiments, other parameter optimization algorithms, such as simulated degradation algorithms, may also be used for optimization.

[0116] In the embodiments of this application, the influence of cell throughput on the loss of positive and negative electrode materials is considered. Combined with the thermodynamic charge and discharge capacity calculation principle and parameter optimization algorithm, the remaining amount of positive and negative electrode active materials under different charge and discharge cycles is obtained objectively and accurately.

[0117] In some embodiments, step 222 includes:

[0118] Step 224: Based on the optimal remaining amount of positive and negative electrode active materials and the preset total amount of positive and negative electrode active materials, obtain the loss amount of positive and negative electrode active materials.

[0119] Step 226: Based on the loss of positive and negative electrode active materials and the preset cell throughput, obtain the loss rate of positive and negative electrode materials.

[0120] Step 228: Determine the remaining amount of positive and negative electrode active materials under different charge-discharge cycles based on the loss rate of positive and negative electrode materials, the preset cell throughput, and the preset total amount of positive and negative electrode active materials.

[0121] The loss rates of positive and negative electrode materials include the loss rate of positive electrode material and the loss rate of negative electrode material. The loss attenuation sub-models for positive and negative electrode materials include a loss attenuation sub-model for positive electrode material and a loss attenuation sub-model for negative electrode material. The governing equation for the loss attenuation sub-model for positive electrode material can be Pos_eff(i) = Pos_eff_0 – k_AM_Pos*Q, and the governing equation for the loss attenuation sub-model for negative electrode material can be Neg_eff(i) = Neg_eff_0 – k_AM_Neg*Q. Here, Pos_eff(i) represents the remaining amount of positive electrode active material in the i-th cycle, Q is the preset cell throughput, Pos_eff_0 is the total amount of positive electrode active material in the 0-th cycle, k_AM_Pos is the loss rate of positive electrode material, Neg_eff(i) represents the remaining amount of negative electrode active material in the i-th cycle, and Neg_eff_0 is the total amount of negative electrode active material in the 0-th cycle. Following the above embodiments, after obtaining the optimal remaining amount of positive and negative electrode active materials, the optimal remaining amount of positive and negative electrode active materials can be subtracted from the preset total amount of positive and negative electrode active materials to obtain the loss amount of positive and negative electrode active materials. Then, based on the loss amount of positive and negative electrode active materials and the cell throughput, a relationship curve between the loss amount of positive and negative electrode active materials and the cell throughput is constructed, and the slope of the loss amount of positive and negative electrode active materials relative to the cell throughput is obtained to obtain the loss rate of positive and negative electrode materials. Then, from both the positive and negative electrode dimensions, the loss rate of positive electrode materials, the preset cell throughput, and the preset total amount of positive electrode active materials are substituted into the positive electrode material loss attenuation sub-model to obtain the remaining amount of positive electrode active materials. Similarly, the loss rate of negative electrode materials, the preset cell throughput, and the preset total amount of negative electrode active materials are substituted into the negative electrode material loss attenuation sub-model to obtain the remaining amount of negative electrode active materials. It is understood that in this embodiment, the expression for positive and negative electrode material loss related to cell throughput is only one implementation method. In other embodiments, it may also be other positive and negative electrode material loss control equations, including but not limited to those considering particle breakage, surface phase change, etc.

[0122] In the scheme of this application embodiment, the remaining amount of positive and negative electrode active materials is determined from two dimensions: cell throughput and positive and negative electrode material loss rate. This can accurately obtain the remaining amount of positive and negative electrode materials related to cell throughput under different cycle periods.

[0123] In some embodiments, the battery under test includes a lithium battery under test, and the test result data includes SEI film formation reaction parameters, lithium plating reaction parameters, and positive and negative electrode OCV curves. The thermodynamic prediction parameters include the maximum and minimum lithium intercalation amounts of the positive and negative electrodes under different charge and discharge cycles.

[0124] Step 210 includes the following steps:

[0125] Step 230: Determine the remaining lithium amount under different charge-discharge cycles based on the total amount of positive and negative electrode active materials, the loss of positive and negative electrode active materials, SEI film formation reaction parameters, and lithium plating reaction parameters.

[0126] Step 232: Based on the positive and negative electrode OCV curves, remaining lithium content, preset upper and lower cutoff voltages, and preset initial values ​​of electrode lithium intercalation, the OCV curves are reconstructed using the Newton-Raphson iterative algorithm to obtain the maximum and minimum lithium intercalation of the positive and negative electrodes under different charge-discharge cycles.

[0127] In this embodiment, the battery under test is a lithium battery. The SEI film refers to a layer of solid electrolyte on the surface of the negative electrode particles. SEI film formation reaction parameters refer to the parameters involved in the SEI film formation process (film formation reaction). Examples include SEI film formation current, SEI film formation reaction transfer coefficient, SEI film formation overpotential, solvent diffusion coefficient, and the SEI volume generated by 1 coulomb of SEI film formation current, etc. Lithium deposition reaction parameters refer only to the parameters involved in the process of lithium ions depositing on the negative electrode surface during the lithium battery charging stage, forming a layer of gray material. Examples include the local current density of lithium deposition, the exchange current density of lithium deposition, and the cathode transfer coefficient of lithium deposition, etc.

[0128] This embodiment provides a detailed explanation of how to calculate the maximum and minimum lithium intercalation amounts of the positive and negative electrodes based on a lithium plating model and test results. In practical applications, lithium losses due to SEI film formation, lithium losses due to lithium plating reactions, and lithium losses due to the loss of active materials in both positive and negative electrodes are considered in each charge-discharge cycle. Combined with the total amount of active materials in both electrodes, the remaining lithium amount under different charge-discharge cycles is obtained. Furthermore, combining the equation for determining the remaining lithium content: Li_left*F=SOCmax_Pos*Pos_eff*Q_Pos+SOCmin_Neg*Neg_eff*Q_Neg=SOCmin_Pos*Pos_eff*Q_Pos+SOCmax_Neg*Neg_eff*Q_Neg, given Li_left, Pos_eff, Neg_eff, Q_Pos, Q_Neg, and a given initial value of SOCmin_Pos, SOCmax_Neg can be calculated. Based on the OCV curves of the positive and negative electrodes, the equilibrium potentials of the positive and negative electrodes can be obtained. The difference between the positive electrode equilibrium potential and the negative electrode equilibrium potential is the upper and lower cutoff voltages of the cell. The difference between this and the preset upper and lower cutoff voltages is ΔV. Using a Newton-Raphson iterative algorithm, SOCmin_Pos is continuously adjusted so that ΔV is less than the preset error value. This yields SOCmin_Pos and SOCmax_Neg for different charge-discharge cycles. Similarly, SOCmax_Pos and SOCmin_Neg can be obtained, where Li_left is the remaining lithium amount.

[0129] In the scheme of this application embodiment, the remaining lithium amount under different charge-discharge cycles is first determined, and then the OCV is reconstructed by combining the positive and negative electrode OCV curves and preset parameters, so as to accurately obtain the maximum and minimum lithium intercalation amount of the positive and negative electrodes under different charge-discharge cycles.

[0130] In some embodiments, the SEI film-forming reaction parameters include porous SEI film-forming reaction parameters and fragmented SEI film-forming reaction parameters;

[0131] Step 230 includes: obtaining the first lithium loss caused by SEI film formation based on the porous SEI film formation reaction parameters and the broken SEI film formation reaction parameters; obtaining the second lithium loss caused by lithium plating reaction based on the lithium plating reaction parameters; obtaining the total lithium loss based on the first lithium loss, the second lithium loss, and the loss of positive and negative electrode active materials; and determining the remaining lithium amount under different charge-discharge cycles based on the total lithium loss and the total amount of positive and negative electrode active materials.

[0132] Following the above embodiments, the process of obtaining the first lithium loss caused by SEI film formation can be calculated using an SEI film growth sub-model that considers solvent diffusion as the dominant factor. Specifically, this SEI film growth sub-model simultaneously considers the different formation rates of both porous SEI and fragmented SEI, meaning the porous SEI film formation reaction parameters include both the porous SEI film formation rate and the fragmented SEI film formation rate. The SEI film growth governing equation is expressed as follows:

[0133] I_SEI=(1+H*Kcrd)*J / (exp(α*η SEI *F / (R*T))+Q SEI *f*J)

[0134] Kcrd=2*I_charge / I_1C,x<0.3, Kcrd=1*I_charge / I_1C,x>0.7; Kcrd=0,0.3<x<0.7, f=V / (c*Ds_EC*F*A 2 In (1+H*Kcrd), 1 represents the porous SEI film-forming part, and H*Kcrd represents the fragmented SEI film-forming part.

[0135] Where I_SEI is the SEI film-forming current; H is a temperature-dependent constant; Kcrd is the SEI fragmentation film-forming coefficient; J is a temperature-dependent constant; α is the SEI film-forming reaction transfer coefficient; η SEI For SEI film formation, F is the Faraday constant; R is the ideal gas constant; T is the temperature; Q is the temperature. SEI The cumulative SEI coulombic value is given; I_charge is the charging current; I_1C is the IC current; x is the anode lithium insertion amount; V is the SEI volume generated by 1 coulomb SEI film formation current; c is the solvent concentration; Ds_EC is the solvent diffusion coefficient, which is temperature-dependent; and A is the anode particle surface area. It is understood that the SEI film growth control equation expression in this embodiment is only one implementation method. In other embodiments, it may be an SEI film growth control equation expression considering other factors.

[0136] The aforementioned governing equations consider the effects of different temperatures on Ds_EC (solvent diffusion coefficient) and H through activation energy, thereby obtaining the SEI film-forming current at different storage times or cycle periods and temperatures, and thus the molar amount of lithium loss. Specifically, the test results data can be processed based on the aforementioned governing equations to obtain the SEI film-forming current I_SEI. Then, the product of I_SEI and the charge-discharge cycle duration T is obtained, and the ratio of this product to the Faraday constant F is determined as the amount of lithium loss caused by SEI film formation, i.e., the first lithium loss.

[0137] To determine the amount of secondary lithium loss caused by the lithium plating reaction, the test results can be processed using a lithium plating sub-model. Specifically,

[0138] The current density for lithium deposition is:

[0139] The equivalent thickness of the lithium layer is:

[0140] The total thickness of the lithium layer and SEI film thickening is:

[0141] The thickening of the lithium layer and SEI film increases the surface resistance of the negative electrode particles.

[0142] Increasing the film thickness will reduce the anodic porosity:

[0143] Where, j lpl j is the local current density for lithium deposition. tot For the total current density, i 0，lpl α is the exchange current density for lithium deposition. c，lpl φ is the lithium deposition cathode transfer coefficient. s For solid-state potential, φ e Let be the liquid phase potential, 'a' be the specific surface area, and 'c' be the liquid phase potential. SEI ρ SEI M SEI ω SEI κ SEI The values ​​are: SEI film concentration, density, molar mass, volume fraction of SEI film in total film thickness, ionic conductivity, and R. film δ film These are membrane resistance and membrane thickness, respectively.

[0144] In specific implementation, the test results data can be processed using the aforementioned lithium plating sub-model to obtain the lithium plating current density. Similarly, the product of the lithium plating current density and the charge-discharge cycle duration T is obtained, and the ratio of this product to the Faraday constant F is determined as the amount of lithium loss caused by the lithium plating reaction, i.e., the second lithium loss. It is understood that the lithium plating sub-model used in this embodiment is only for illustration; in other embodiments, the lithium plating governing equation can also be used.

[0145] Following the above process, after obtaining the first lithium loss, the second lithium loss, and the loss of positive and negative electrode active materials under different charge-discharge cycles, the first lithium loss, the second lithium loss, and the loss of positive and negative electrode active materials are added together to obtain the total lithium loss. Then, the total positive and negative electrode active materials are subtracted from the total lithium loss to obtain the remaining lithium under different charge-discharge cycles.

[0146] In the solution of this application embodiment, not only the lithium loss caused by the loss of positive and negative electrode materials is considered, but also the lithium loss caused by SEI film formation and the lithium loss caused by lithium plating reaction. This can more accurately determine the remaining lithium amount under different charge and discharge cycles, thereby ensuring the accuracy of the maximum and minimum lithium intercalation amount of the positive and negative electrodes.

[0147] like Figure 4 As shown, in some embodiments, the battery under test includes a lithium battery under test, and the kinetic prediction parameters include the variation data of the solid-phase diffusion coefficients of the positive and negative electrodes and the variation data of the surface reaction rate constants of the positive and negative electrodes.

[0148] Step 100 includes: Step 120, performing cyclic electrochemical impedance spectroscopy on the lithium battery under test to obtain the initial positive and negative electrode solid-phase diffusion coefficients and initial positive and negative electrode reaction rate constants under different charge-discharge cycles.

[0149] Step 210 includes the following steps:

[0150] Step 240: For each charge-discharge cycle, obtain the product of the initial positive and negative electrode solid-phase diffusion coefficient and the preset kinetic decay coefficient to obtain the target positive and negative electrode solid-phase diffusion coefficient for each charge-discharge cycle; obtain the product of the initial positive and negative electrode reaction rate constant and the preset positive and negative electrode reaction rate constant coefficient to obtain the target positive and negative electrode reaction rate constant for each charge-discharge cycle.

[0151] Step 242: Based on the target positive and negative electrode solid-phase diffusion coefficients and target positive and negative electrode reaction rate constants under each charge-discharge cycle, obtain the change data of the positive and negative electrode solid-phase diffusion coefficients and the change data of the positive and negative electrode surface reaction rate constants.

[0152] In this embodiment, the kinetic decay parameters considered are the positive and negative electrode solid-phase diffusion coefficients and the surface reaction rate constant. Specifically, considering that the decay factor of the positive and negative electrode solid-phase diffusion coefficients and the surface reaction rate constant is related to time, the decay factor λ = 1 / (a*t+1) for the cathode solid-phase diffusion coefficient and the surface reaction rate constant is pre-constructed. Then, the lithium battery under test is subjected to cyclic electrochemical impedance spectroscopy to obtain the initial positive and negative electrode solid-phase diffusion coefficients Ds1 and the initial positive and negative electrode reaction rate constants k1 under different charge-discharge cycles. Then, they are normalized. Specifically, in the first cycle test, the positive and negative electrode solid-phase diffusion coefficients and the surface reaction rate constant are initialized to 1. For each subsequent charge-discharge cycle, the product of the initial positive and negative electrode solid-phase diffusion coefficients Ds1 and λ is obtained to obtain the target positive and negative electrode solid-phase diffusion coefficients Ds under each charge-discharge cycle, and the product of the initial positive and negative electrode reaction rate constants k1 and λ is obtained to obtain the target positive and negative electrode reaction rate constants k under each charge-discharge cycle. Finally, by analyzing Ds and k under each charge-discharge cycle, we can obtain the variation data of the solid-phase diffusion coefficients of the positive and negative electrodes and the variation data of the surface reaction rate constants of the positive and negative electrodes.

[0153] In the embodiments of this application, parameters that affect battery life degradation due to increased impedance are considered, represented by the solid-phase diffusion coefficients of the positive and negative electrodes and the surface reaction rate constants. From a new perspective, kinetic prediction parameters including the variation data of the solid-phase diffusion coefficients of the positive and negative electrodes and the variation data of the surface reaction rate constants of the positive and negative electrodes are determined.

[0154] In some embodiments, the test result data includes the increased thickness of the positive and negative electrode monolayers, the thickness of the SEI film, the initial preload of the cell clamp, and the elastic modulus of the main material. The kinetic prediction parameters include the change data of the first material parameters of the positive and negative electrode sheets and the separator caused by the consumption of electrolyte. The first material parameters include thickness, solid volume fraction, liquid volume fraction, and Brugmann coefficient.

[0155] Step 210 includes: Step 250, obtaining the first material parameters of the positive and negative electrode sheets and separator under different charge and discharge cycles based on the SEI film thickness, the initial pre-tightening force of the cell clamp, the elastic modulus of the main material and the preset expansion force balance equation. The preset expansion force balance equation is constructed based on the elastic modulus of the clamp locking bolt, the positive and negative electrode sheets and the separator.

[0156] Step 252: Based on the first material parameters of the positive and negative electrode plates and the separator under different charge and discharge cycles, obtain the change data of the first material parameters of the positive and negative electrode plates and the separator.

[0157] The elastic modulus is a measure of the resistance to elastic deformation of an object or material. The elastic modulus of an object is defined as the slope of its stress-strain curve in the elastic deformation region: the harder the material, the higher its elastic modulus. In practical applications, the inventors of this application realized that increasing the thickness of the SEI film increases the cell expansion force, leading to electrode expansion, which adversely affects battery performance and lifespan. Therefore, an expansion force sub-model was constructed by combining the elastic moduli of the clamping bolts, positive and negative electrodes, and the separator.

[0158] Specifically, in the expansion force sub-model, it is assumed that the increase in the thickness of the negative electrode sheet is consistent with the increase in the volume of a single negative electrode particle, that is:

[0159] Where ΔL refers to the increased thickness of the negative electrode monolayer, L_neg refers to the thickness of the negative electrode, and rp _neg 3 ΔL refers to the radius of the negative electrode particles, Th_film refers to the thickness of the SEI film, and ΔL multiplied by the total number of negative electrode layers in the cell represents the increase in thickness due to the cell's free expansion. During the actual cell expansion process, the clamping bolts are stretched, generating a reaction force equal to the force generated by compression after the cell's free expansion. Based on the equilibrium equation of the expansion force sub-model, the relationship between the magnitude of the expansion force and ΔL can be obtained. Based on the magnitude of ΔL calculated from the SEI film thickness under different cycle periods, the corresponding magnitude of the expansion force can be obtained. Then, based on the elastic modulus of the positive and negative electrode sheets and the separator, the thickness of the compressed positive and negative electrode sheets and the separator can be obtained, denoted as L_Pos / Neg / Sep. The thickness of the positive and negative electrode plates and the separator is compressed. Assuming the total solid volume remains constant (the volume occupied by each substance is the same), the solid volume fraction of the positive and negative electrode plates and the separator can be calculated, denoted as epss_Pos / Neg / Sep. Then, the liquid volume fraction (Epsl_Pos / Neg / Sep = 1 - epss_Pos / Neg / Sep) can be calculated. Simultaneously, the relationship between the liquid volume fraction and the Brugmann coefficient can be used to obtain the changes in the Brugmann coefficients of the positive and negative electrodes and the separator (Brug_Pos / Neg / Sep = a*(1-b*ln(Epsl_Pos / Neg / Sep))), where a and b are constants. It is understood that the expansion force model constructed in this embodiment is merely an example. In other embodiments, expansion force sub-models considering reversible expansion, gas-generating expansion, etc., can be used, depending on the actual situation.

[0160] In the embodiments of this application, the changes in the solid volume fraction, liquid volume fraction, and Brugmann coefficient of the positive and negative electrodes / separator membranes are considered due to the cell expansion force caused by the formation of the SEI film, and the sources affecting the battery life degradation are determined from a kinetic perspective.

[0161] In some embodiments, the test result data includes total pore volume, initial active area, critical volume ratio of electrolyte, initial liquid phase volume fraction of positive and negative electrode plates and separator, and current remaining electrolyte volume under different charge and discharge cycles. The lithium battery prediction parameters include the change data of second material parameters of positive and negative electrode plates and separator caused by electrolyte consumption. The second material parameters include liquid phase volume fraction and effective active area.

[0162] Step 210 includes the following steps:

[0163] Step 260: Obtain the ratio of the current remaining electrolyte volume to the total pore volume under different charge-discharge cycles.

[0164] Step 262: Based on the ratio and the initial liquid volume fraction of the positive and negative electrode plates and the separator, obtain the liquid volume fraction of the positive and negative electrode plates and the separator under different charge and discharge cycles.

[0165] Step 264: Based on the current remaining electrolyte volume, total pore volume, initial active area, and critical electrolyte volume ratio in each charge-discharge cycle, obtain the effective active areas of the positive and negative electrode plates and separator in different charge-discharge cycles.

[0166] Step 266: Based on the liquid volume fraction and effective active area of ​​the positive and negative electrode plates under different charge and discharge cycles, obtain the change data of liquid volume fraction and effective active area of ​​the positive and negative electrode plates.

[0167] Step 268: Based on the liquid phase volume fraction and effective active area of ​​the separator under different charge-discharge cycles, obtain the change data of the liquid phase volume fraction and the change data of the effective active area of ​​the separator.

[0168] In this embodiment, the correlation between electrolyte consumption and SEI film formation is considered. The changes in the second material parameters of the positive and negative electrode plates and the separator caused by electrolyte consumption can be calculated based on a pre-constructed electrolyte consumption model. Specifically, it can be assumed that the composition of EC solvent in the electrolyte is proportional to SEI film formation, such as one mole of EC solvent being consumed for every mole of SEI formed. Studies have found that as electrolyte is consumed, the pores in the electrode plates cannot be completely filled, thus affecting the liquid phase volume fraction of the positive and negative electrode plates and the separator. In this case, it can be considered that: Epsl'_Pos / Neg / Sep = EL_V / epsa * Epsl_Pos / Neg / Sep, where EL_V is the current remaining electrolyte volume, epsa is the total void volume (a constant), and Epsl_Pos is the liquid phase volume fraction when the pores in the electrode plates are completely filled with electrolyte. As the electrolyte is further consumed, the active particles cannot be completely wetted, resulting in a decrease in the active surface area. Based on the current remaining electrolyte volume, total pore volume, initial active area, and critical electrolyte volume ratio at each charge-discharge cycle, the effective active areas of the positive and negative electrodes and the separator at different charge-discharge cycles can be obtained. Specifically, when the active surface area decreases, we have: A_Pos=EL_V / (epsa*x)*a_pos, where A_Pos is the current effective active area, x is the critical electrolyte volume ratio, and a_pos is the initial active area. Then, by analyzing the liquid phase volume fraction and effective active area of ​​the positive and negative electrodes at different charge-discharge cycles, we obtain data on the changes in liquid phase volume fraction and effective active area of ​​the positive and negative electrodes. Similarly, based on the liquid phase volume fraction and effective active area of ​​the separator at different charge-discharge cycles, we obtain data on the changes in liquid phase volume fraction and effective active area of ​​the separator. It is understood that the electrolyte consumption model in this embodiment is only one implementation method. In other embodiments, it may be an electrolyte consumption model that takes into account other factors affecting electrolyte consumption.

[0169] In the embodiments of this application, the relationship between SEI film formation and electrolyte consumption is considered, thereby determining the relevant parameters that affect battery life degradation due to electrolyte consumption caused by SEI film formation. This approach is more comprehensive and can help design batteries with longer lifespans.

[0170] like Figure 5 As shown, in order to provide a more detailed explanation of the battery life prediction parameter determination method provided in this application, the following is an explanation with reference to a specific embodiment. In this embodiment, the battery under test is a lithium battery, and the method includes the following steps:

[0171] Step 1: Perform a cycle aging test on the lithium battery under test to obtain test results data under different charge-discharge cycles. The test results data include battery capacity data, cutoff voltage data, equilibrium potential data of positive and negative electrodes, SOC-OCV curve, and temperature for each charge-discharge cycle.

[0172] Step 2: The test results data are processed using the established positive and negative electrode material loss attenuation sub-model. The test results data are processed according to the thermodynamic charge and discharge capacity calculation principle to obtain the optimal remaining amount of positive and negative electrode active materials. Based on the optimal remaining amount of positive and negative electrode active materials and the preset total amount of positive and negative electrode active materials, the loss amount of positive and negative electrode active materials is obtained. Based on the loss amount of positive and negative electrode active materials and the preset cell throughput, the loss rate of positive and negative electrode materials is obtained. Based on the loss rate of positive and negative electrode materials, the preset cell throughput, and the preset total amount of positive and negative electrode active materials, the remaining amount of positive and negative electrode active materials under different charge and discharge cycles is determined.

[0173] Step 3: The test results data are processed using the constructed SEI film growth sub-model to obtain the first lithium loss caused by SEI film formation. The test results data are then processed using the constructed lithium plating sub-model to obtain the second lithium loss caused by lithium plating reaction. Based on the first lithium loss, the second lithium loss, and the total amount of positive and negative electrode active materials, the remaining lithium amount under different charge-discharge cycles is determined. Based on the positive and negative electrode OCV curves, the remaining lithium amount, the preset upper and lower cutoff voltages, and the preset initial value of electrode lithium intercalation, the OCV curves are reconstructed using the Newton-Raphson iteration algorithm to obtain the maximum and minimum lithium intercalation amounts of the positive and negative electrodes under different charge-discharge cycles.

[0174] Step 4: Perform cyclic electrochemical impedance spectroscopy on the lithium battery under test to obtain the initial positive and negative electrode solid-phase diffusion coefficients and initial positive and negative electrode reaction rate constants under different charge-discharge cycles. Based on the constructed kinetic decay sub-model, for each charge-discharge cycle, obtain the product of the initial positive and negative electrode solid-phase diffusion coefficients and the preset kinetic decay coefficients to obtain the target positive and negative electrode solid-phase diffusion coefficients for each charge-discharge cycle. Obtain the product of the initial positive and negative electrode reaction rate constants and the preset positive and negative electrode reaction rate constant coefficients to obtain the target positive and negative electrode reaction rate constants for each charge-discharge cycle. Then, based on the target positive and negative electrode solid-phase diffusion coefficients and target positive and negative electrode reaction rate constants for each charge-discharge cycle, obtain the change data of the positive and negative electrode solid-phase diffusion coefficients and the change data of the positive and negative electrode surface reaction rate constants.

[0175] Step 5: Based on the SEI film thickness, the initial pre-tightening force of the cell clamp, the elastic modulus of the main material, and the preset expansion force balance equation, obtain the first material parameters of the positive and negative electrode sheets and the separator under different charge and discharge cycles. Based on the first material parameters of the positive and negative electrode sheets and the separator under different charge and discharge cycles, obtain the change data of the first material parameters of the positive and negative electrode sheets and the separator. The first material parameters include thickness, solid phase volume fraction, liquid phase volume fraction, and Brugmann coefficient.

[0176] Step 6: Based on the electrolyte consumption sub-model, obtain the ratio of the current remaining electrolyte volume to the total pore volume under different charge-discharge cycles. Based on this ratio, and the initial liquid phase volume fraction of the positive and negative electrode plates and the separator, obtain the liquid phase volume fraction of the positive and negative electrode plates and the separator under different charge-discharge cycles. Based on the liquid phase volume fraction and effective active area of ​​the positive and negative electrode plates and the separator under different charge-discharge cycles, obtain the change data of the liquid phase volume fraction and the change data of the effective active area of ​​the positive and negative electrode plates and the separator.

[0177] The battery life prediction and determination method provided in this application embodiment can be applied to, for example... Figure 6 In the application environment shown, terminal 108 communicates with server 110 via a network. A data storage system can store the data that server 110 needs to process. The data storage system can be integrated onto server 110 or placed on a cloud or other network server. Specifically, a tester can send a battery life prediction command carrying battery life prediction parameters to server 110 via terminal 108. Server 110 responds to the command, obtains the battery life prediction parameters of the battery under test, and performs iterative prediction of battery life degradation based on these parameters to obtain the battery life degradation prediction data. The battery life prediction parameters are obtained based on the aforementioned method for determining battery life prediction parameters. Server 110 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc.

[0178] In some embodiments, such as Figure 7 As shown, a method for determining battery life prediction parameters is provided, which can be applied to... Figure 6 Taking server 110 as an example, the explanation includes the following steps:

[0179] Step 300: Obtain the battery life prediction parameters of the battery under test, wherein the battery life prediction parameters are obtained based on the above-described battery life prediction parameter determination method.

[0180] As can be seen from the embodiments of the battery life prediction parameter determination method described above, the battery life prediction parameters include kinetic prediction parameters, thermodynamic prediction parameters, and material property prediction parameters. Specifically, the thermodynamic prediction parameters include the maximum and minimum lithium intercalation amounts of the positive and negative electrodes under different charge-discharge cycle periods. The material property prediction parameters include the remaining amount of positive and negative electrode active material, the thickness of the positive and negative electrode sheets and separator, the changes in solid phase volume fraction, liquid phase volume fraction and Brügmann coefficient, the changes in liquid phase volume fraction of the positive and negative electrode sheets and separator, and the changes in effective active area under different charge-discharge cycle periods. The kinetic prediction parameters include the changes in the solid-phase diffusion coefficient of the positive and negative electrodes and the changes in the surface reaction rate constants of the positive and negative electrodes.

[0181] Step 400: Based on the battery life prediction parameters, perform iterative prediction of battery life degradation to obtain the battery life degradation prediction data.

[0182] Lifetime degradation prediction data includes capacity degradation curves. After obtaining the aforementioned battery prediction parameters, iterative prediction of lifetime degradation can be performed on the battery under test based on these parameters to obtain the predicted lifetime degradation data. Specifically, iterative prediction of lifetime degradation can be performed using a battery simulation model. The iterative prediction can be input into the battery simulation model, with a preset lower capacity limit as the termination condition for the iteration, to obtain the corresponding predicted lifetime degradation data.

[0183] In the scheme of this application embodiment, battery life prediction parameters, including thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters, are obtained for the battery under test. Based on the thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters, the battery life of the battery under test is iteratively predicted. The factors affecting battery life degradation are considered from multiple dimensions, which makes the obtained life degradation prediction data more accurate. Furthermore, it can support quantitative analysis of the impact of thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters on battery life degradation, which is more conducive to finding the bottlenecks affecting battery life degradation and designing batteries with longer lifespans.

[0184] like Figure 8 As shown, in some embodiments, step 400 includes:

[0185] Step 420: Simulate the life decay of the battery under test based on the battery life prediction parameters to obtain the initial life decay prediction data and life decay parameters.

[0186] Step 440: Update the battery life prediction parameters based on the life decay parameters, and return to step 420.

[0187] Step 460: If the preset iteration termination condition is met, stop the iteration and obtain the life degradation prediction data of the battery under test.

[0188] In this embodiment, the lifespan degradation prediction data includes capacity degradation prediction data and power degradation prediction data. Simulating the lifespan degradation of the battery under test based on the battery lifespan prediction parameters can be achieved by inputting the battery lifespan prediction parameters into a preset electrochemical model to simulate capacity and power degradation. The preset electrochemical model can be a P2D electrochemical / thermal model. As described in the above embodiment, the battery lifespan prediction parameters are determined based on the battery lifespan degradation model. In this embodiment, updating the battery lifespan prediction parameters based on the lifespan degradation parameters can be achieved by feeding the lifespan degradation parameters back to the battery lifespan degradation model, causing the model to update the battery lifespan prediction parameters, and then continuing to input the updated battery lifespan prediction parameters into the P2D electrochemical / thermal model for iterative prediction. The battery lifespan degradation model can be a model composed of positive and negative electrode material loss degradation sub-models, kinetic degradation sub-models, expansion force sub-models, electrolyte consumption degradation sub-models, SEI film growth sub-models, and lithium plating sub-models, or it can be a model including positive and negative electrode material loss degradation sub-models, kinetic degradation sub-models, expansion force sub-models, electrolyte consumption degradation sub-models, SEI film growth sub-models, lithium plating sub-models, and other degradation sub-models.

[0189] In practice, after obtaining the battery lifetime prediction parameters, these parameters can be input into a P2D electrochemical / thermal model to simulate capacity and power degradation. The P2D electrochemical / thermal model will calculate lifetime degradation parameters such as anode potential, temperature, liquid phase potential, solid phase potential, and capacity based on the battery lifetime prediction parameters. These lifetime degradation parameters are then fed back to the lifetime degradation model. Specifically, the anode potential can be fed back to the SEI film growth sub-model, and the temperature value can be fed back to the positive and negative electrode material loss degradation sub-model and the kinetic degradation sub-model to update the positive and negative electrode material loss rate, SEI film growth solvent diffusion coefficient / H value, positive and negative electrode solid phase diffusion coefficient, and so on. The decay of the surface reaction rate constant k feeds back the liquid phase potential and solid phase potential to the lithium plating sub-model, so that the battery life decay sub-model updates the battery life prediction parameters output. The updated battery life prediction parameters are then input into the preset electrochemical model to simulate capacity and power decay, thereby updating the capacity prediction data, initial power decay prediction data and life decay parameters output by the P2D electrochemical / thermal model. The process is iterated in the above manner until the capacity value output by the P2D electrochemical / thermal model meets the preset iteration stop condition, such as when the capacity value reaches the preset capacity threshold, such as 70%, the iteration calculation stops, and the capacity decay curve and power decay curve of the battery under test are obtained.

[0190] In the technical solution of this application embodiment, the battery life prediction parameters are updated according to the life decay parameters, and then the life decay simulation of the battery under test is performed again according to the updated life decay parameters. Through iterative prediction, the accuracy of the life decay prediction data is ensured.

[0191] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0192] Based on the same inventive concept, this application also provides a battery life prediction parameter determination device and a battery life prediction device for implementing the battery life prediction parameter determination method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the battery life prediction parameter determination device provided below can be found in the limitations of the battery life prediction parameter determination method described above, and will not be repeated here.

[0193] In some embodiments, such as Figure 9 As shown, a battery life prediction parameter determination device is provided, comprising: a test result data acquisition module and a battery life prediction parameter determination module, wherein:

[0194] The test result data acquisition module 910 is used to acquire test result data of the battery under test under different charge and discharge cycles.

[0195] The battery life prediction parameter determination module 920 is used to determine battery life prediction parameters based on test result data. The battery life prediction parameters include thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters.

[0196] In this embodiment of the application, the battery under test is subjected to cyclic charge-discharge tests to obtain test result data under different charge-discharge cycles. Based on the test result data under different charge-discharge cycles, battery life prediction parameters, including thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters, are determined. This approach considers not only material property degradation factors and thermodynamic degradation factors, but also kinetic degradation factors, analyzing the potential sources of battery life degradation from multiple dimensions. It supports quantitative analysis of the impact of different prediction parameters on battery life degradation, which is more conducive to designing longer-lasting batteries.

[0197] In some embodiments, the battery life prediction parameter determination module 920 is further configured to process the test result data using a pre-constructed battery life decay model to determine the battery life prediction parameters, wherein the battery life decay model is constructed based on different battery life decay mechanisms and historical test result data of the battery under test under cyclic charge and discharge cycles.

[0198] In some embodiments, the material characteristic prediction parameters include the remaining amount of positive and negative electrode active materials under different charge-discharge cycles; the battery life prediction parameter determination module 920 is also used to process the test result data according to the thermodynamic charge-discharge capacity calculation principle to obtain the optimal remaining amount of positive and negative electrode active materials, and determine the remaining amount of positive and negative electrode active materials under different charge-discharge cycles based on the optimal remaining amount of positive and negative electrode active materials, the preset total amount of positive and negative electrode active materials and the preset cell throughput.

[0199] In some embodiments, the battery life prediction parameter determination module 920 is further configured to obtain the loss amount of positive and negative active materials based on the optimal remaining amount of positive and negative active materials and the preset total amount of positive and negative active materials, obtain the loss rate of positive and negative active materials based on the loss amount of positive and negative active materials and the preset cell throughput, and determine the remaining amount of positive and negative active materials under different charge-discharge cycles based on the loss rate of positive and negative active materials, the preset cell throughput and the preset total amount of positive and negative active materials.

[0200] In some embodiments, the battery under test includes a lithium battery under test, and the test result data includes SEI film formation reaction parameters, lithium plating reaction parameters, and positive and negative electrode OCV curves. The thermodynamic prediction parameters include the maximum and minimum lithium intercalation amounts of the positive and negative electrodes under different charge and discharge cycles.

[0201] The battery life prediction parameter determination module 920 is also used to determine the remaining lithium amount under different charge-discharge cycles based on the total amount of positive and negative electrode active materials, the loss of positive and negative electrode active materials, SEI film formation reaction parameters, and lithium plating reaction parameters. Based on the positive and negative electrode OCV curves, the remaining lithium amount, the preset upper and lower limit cutoff voltages, and the preset initial value of electrode lithium intercalation, the OCV curves are reconstructed using the Newton iteration algorithm to obtain the maximum and minimum lithium intercalation amounts of the positive and negative electrodes under different charge-discharge cycles.

[0202] In some embodiments, the SEI film-forming reaction parameters include porous SEI film-forming reaction parameters and fragmented SEI film-forming reaction parameters;

[0203] The battery life prediction parameter determination module 920 is also used to obtain the first lithium loss caused by SEI film formation based on the porous SEI film formation reaction parameters and the broken SEI film formation reaction parameters, obtain the second lithium loss caused by lithium plating reaction based on the lithium plating reaction parameters, obtain the total lithium loss based on the first lithium loss, the second lithium loss and the loss of positive and negative electrode active materials, and determine the remaining lithium under different charge and discharge cycles based on the total lithium loss and the total amount of positive and negative electrode active materials.

[0204] In some embodiments, the battery under test includes a lithium battery under test, and the kinetic prediction parameters include the variation data of the solid-phase diffusion coefficients of the positive and negative electrodes and the variation data of the surface reaction rate constants of the positive and negative electrodes.

[0205] The test result data acquisition module 910 is also used to perform cyclic electrochemical impedance testing on the lithium battery under test, and to obtain the initial positive and negative electrode solid phase diffusion coefficients and initial positive and negative electrode reaction rate constants under different charge and discharge cycles.

[0206] The battery life prediction parameter determination module 920 is also used to obtain, for each charge-discharge cycle, the product of the initial positive and negative electrode solid phase diffusion coefficient and the preset kinetic decay coefficient multiple, to obtain the target positive and negative electrode solid phase diffusion coefficient under each charge-discharge cycle, obtain the product of the initial positive and negative electrode reaction rate constant and the preset positive and negative electrode reaction rate constant coefficient, to obtain the target positive and negative electrode reaction rate constant under each charge-discharge cycle, and based on the target positive and negative electrode solid phase diffusion coefficient and the target positive and negative electrode reaction rate constant under each charge-discharge cycle, obtain the change data of the positive and negative electrode solid phase diffusion coefficient and the change data of the positive and negative electrode surface reaction rate constant.

[0207] In some embodiments, the test result data includes the increased thickness of the positive and negative electrode monolayers, the thickness of the SEI film, the initial preload of the cell clamp, and the elastic modulus of the main material. The kinetic prediction parameters include the change data of the first material parameters of the positive and negative electrode sheets and the separator caused by the consumption of electrolyte. The first material parameters include thickness, solid volume fraction, liquid volume fraction, and Brugmann coefficient.

[0208] The battery life prediction parameter determination module 920 is also used to obtain the first material parameters of the positive and negative electrode sheets and separator under different charge and discharge cycles based on the SEI film thickness, the initial pre-tightening force of the cell clamp, the elastic modulus of the main material, and the preset expansion force balance equation. The preset expansion force balance equation is constructed based on the elastic modulus of the clamp locking bolt, the positive and negative electrode sheets, and the separator. Based on the first material parameters of the positive and negative electrode sheets and separator under different charge and discharge cycles, the module obtains the change data of the first material parameters of the positive and negative electrode sheets and separator.

[0209] In some embodiments, the test result data includes total pore volume, initial active area, critical volume ratio of electrolyte, initial liquid phase volume fraction of positive and negative electrode plates and separator, and current remaining electrolyte volume under different charge and discharge cycles. The lithium battery prediction parameters include the change data of second material parameters of positive and negative electrode plates and separator caused by electrolyte consumption. The second material parameters include liquid phase volume fraction and effective active area.

[0210] The battery life prediction parameter determination module 920 is also used to obtain the ratio of the current remaining electrolyte volume to the total pore volume under different charge-discharge cycles. Based on the ratio and the initial liquid phase volume fraction of the positive and negative electrode plates and the separator, the liquid phase volume fraction of the positive and negative electrode plates and the separator under different charge-discharge cycles is obtained. Based on the current remaining electrolyte volume, total pore volume, initial active area and electrolyte critical volume ratio under each charge-discharge cycle, the effective active area of ​​the positive and negative electrode plates and the separator under different charge-discharge cycles is obtained. Based on the liquid phase volume fraction and effective active area of ​​the positive and negative electrode plates under different charge-discharge cycles, the change data of the liquid phase volume fraction of the positive and negative electrode plates and the change data of the effective active area are obtained. Based on the liquid phase volume fraction and effective active area of ​​the separator under different charge-discharge cycles, the change data of the liquid phase volume fraction and the change data of the effective active area of ​​the separator are obtained.

[0211] In some embodiments, such as Figure 10 As shown, a battery life prediction device is provided, including: a battery life prediction parameter acquisition module 960 and a battery life prediction module 980, wherein:

[0212] The battery life prediction parameter acquisition module 960 is used to acquire the battery life prediction parameters of the lithium battery under test.

[0213] The battery life prediction module 980 is used to perform iterative prediction of the life decay of the battery under test based on the battery life prediction parameters, and obtain the life decay prediction data of the battery under test.

[0214] The battery life prediction parameters are obtained based on the above-mentioned battery life prediction parameter determination method.

[0215] In the scheme of this application embodiment, battery life prediction parameters, including thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters, are obtained for the battery under test. Based on the thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters, the battery life of the battery under test is iteratively predicted. The factors affecting battery life degradation are considered from multiple dimensions, which makes the obtained life degradation prediction data more accurate. Furthermore, it can support quantitative analysis of the impact of thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters on battery life degradation, which is more conducive to finding the bottlenecks affecting battery life degradation and designing batteries with longer lifespans.

[0216] In some embodiments, the battery life prediction module 980 is further configured to simulate the life decay of the battery under test according to the battery life prediction parameters, obtain initial life decay prediction data and life decay parameters, update the battery life prediction parameters according to the life decay parameters, and control the battery life prediction parameter acquisition module 960 to perform the operation of simulating the life decay of the battery under test according to the battery life prediction parameters, obtaining initial life decay prediction data and life decay parameters, until a preset iteration end condition is reached, and obtain the life decay prediction data of the battery under test.

[0217] The aforementioned battery life prediction parameter determination device and each module in the battery life prediction process can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0218] In some embodiments, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 11As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores test result data, capacity degradation prediction data, and power degradation prediction data for the battery under test. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for determining battery life prediction parameters and a battery life prediction method.

[0219] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0220] In some embodiments, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described battery life prediction parameter determination method and battery life prediction method.

[0221] In some embodiments, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described battery life prediction parameter determination method and battery life prediction method.

[0222] In some embodiments, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described battery life prediction parameter determination method and battery life prediction method.

[0223] It should be noted that the data involved in this application (including but not limited to data used for analysis, data stored, data displayed, etc.) are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0224] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0225] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0226] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for determining battery life prediction parameters, characterized in that, The method includes: Acquire test result data of the battery under test under different charge-discharge cycles; Based on the test results data, battery life prediction parameters are determined, including thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters. The test results data include SEI film formation reaction parameters, lithium plating reaction parameters, positive and negative electrode OCV curves, increased thickness of positive and negative electrode monolayers, SEI film thickness, initial pre-tightening force of the cell clamp, and elastic modulus of the main material. The thermodynamic prediction parameters include the maximum and minimum lithium intercalation amounts of the positive and negative electrodes under different charge-discharge cycles. The kinetic prediction parameters include the changes in the first material parameters of the positive and negative electrode sheets and separator caused by electrolyte consumption. Based on the test results data, the battery life prediction parameters are determined by: processing the test results data using a pre-constructed battery life degradation model to determine the battery life prediction parameters. The battery life degradation model is constructed based on different battery life degradation mechanisms and historical test results data of the battery under test under charge-discharge cycles.

2. The method for determining battery life prediction parameters according to claim 1, characterized in that, The predicted material properties parameters include the remaining amount of positive and negative electrode active materials under different charge-discharge cycle periods. The process of using the established battery life degradation model to process the test results data and determine the battery life prediction parameters includes the following steps: The test results data were processed according to the thermodynamic charge-discharge capacity calculation principle to obtain the optimal remaining amount of positive and negative electrode active materials; Based on the optimal remaining amount of positive and negative electrode active materials, the preset total amount of positive and negative electrode active materials, and the preset cell throughput, the remaining amount of positive and negative electrode active materials under different charge and discharge cycles is determined.

3. The method for determining battery life prediction parameters according to claim 2, characterized in that, The step of determining the remaining amount of positive and negative electrode active materials under different charge-discharge cycles based on the optimal remaining amount of positive and negative electrode active materials, the preset total amount of positive and negative electrode active materials, and the preset cell throughput includes: The loss of positive and negative electrode active materials is obtained based on the remaining amount of the optimal positive and negative electrode active materials and the preset total amount of positive and negative electrode active materials. The loss rate of the positive and negative electrode materials is obtained based on the loss of the positive and negative electrode active materials and the preset cell throughput. The remaining amount of positive and negative electrode active materials under different charge-discharge cycles is determined based on the loss rate of the positive and negative electrode materials, the preset cell throughput, and the preset total amount of positive and negative electrode active materials.

4. The method for determining battery life prediction parameters according to claim 3, characterized in that, The process of using the established battery life degradation model to process the test results data and determine the battery life prediction parameters further includes the following steps: Based on the total amount of positive and negative electrode active materials, the loss of positive and negative electrode active materials, the SEI film formation reaction parameters, and the lithium plating reaction parameters, the remaining lithium amount under different charge-discharge cycles is determined. Based on the positive and negative electrode OCV curves, the remaining lithium amount, the preset upper and lower cutoff voltages, and the preset initial value of electrode lithium intercalation, the OCV curves are reconstructed using the Newton-Raphson iterative algorithm to obtain the maximum and minimum lithium intercalation amounts of the positive and negative electrodes under different charge-discharge cycles.

5. The method for determining battery life prediction parameters according to claim 4, characterized in that, The SEI film-forming reaction parameters include porous SEI film-forming reaction parameters and fragmented SEI film-forming reaction parameters; The determination of the remaining lithium amount under different charge-discharge cycles based on the total amount of positive and negative electrode active materials, the loss of positive and negative electrode active materials, the SEI film formation reaction parameters, and the lithium plating reaction parameters includes: Based on the porous SEI film formation reaction parameters and the broken SEI film formation reaction parameters, the first lithium loss caused by SEI film formation is obtained; Based on the lithium plating reaction parameters, the second lithium loss caused by the lithium plating reaction is obtained; The total lithium loss is obtained based on the first lithium loss, the second lithium loss, and the loss of the positive and negative electrode active materials; The remaining lithium amount under different charge-discharge cycles is determined based on the total lithium loss and the total amount of positive and negative electrode active materials.

6. The method for determining battery life prediction parameters according to claim 1, characterized in that, The battery under test includes a lithium battery under test, and the kinetic prediction parameters also include the variation data of the solid-phase diffusion coefficient of the positive and negative electrodes and the variation data of the surface reaction rate constants of the positive and negative electrodes. The test results data of the lithium battery under test under different charge-discharge cycles include: Cyclic electrochemical impedance spectroscopy was performed on the lithium battery under test to obtain the initial positive and negative electrode solid-phase diffusion coefficients and initial positive and negative electrode reaction rate constants under different charge-discharge cycles. The process of using the established battery life degradation model to process the test results data and determine the battery life prediction parameters includes the following steps: For each charge-discharge cycle, the product of the initial positive and negative electrode solid-phase diffusion coefficient and the preset kinetic decay coefficient is obtained to obtain the target positive and negative electrode solid-phase diffusion coefficient for each charge-discharge cycle. The product of the initial positive and negative electrode reaction rate constant and the preset positive and negative electrode reaction rate constant coefficient is obtained to obtain the target positive and negative electrode reaction rate constant for each charge-discharge cycle. Based on the target positive and negative electrode solid-phase diffusion coefficients and target positive and negative electrode reaction rate constants under each charge-discharge cycle, the variation data of the positive and negative electrode solid-phase diffusion coefficients and the variation data of the positive and negative electrode surface reaction rate constants are obtained.

7. The method for determining battery life prediction parameters according to claim 1, characterized in that, The process of using the established battery life degradation model to process the test results data and determine the battery life prediction parameters includes the following steps: Based on the SEI film thickness, the initial preload of the cell clamp, the elastic modulus of the main material, and the preset expansion force balance equation, the first material parameters of the positive and negative electrode sheets and the separator under different charge and discharge cycles are obtained. The preset expansion force balance equation is constructed based on the elastic modulus of the clamp locking bolt, the positive and negative electrode sheets, and the separator. The first material parameters include thickness, solid phase volume fraction, liquid phase volume fraction, and Brugmann coefficient. Based on the first material parameters of the positive and negative electrode sheets and the separator under different charge and discharge cycles, the variation data of the first material parameters of the positive and negative electrode sheets and the separator are obtained.

8. The method for determining battery life prediction parameters according to claim 1, characterized in that, The test results data also include total pore volume, initial active area, critical volume ratio of electrolyte, initial liquid phase volume fraction of positive and negative electrode plates and separator, and current remaining electrolyte volume under different charge and discharge cycles. The battery life prediction parameters also include the change data of the second material parameters of positive and negative electrode plates and separator caused by electrolyte consumption. The second material parameters include liquid phase volume fraction and effective active area. The process of using the established battery life degradation model to process the test results data and determine the battery life prediction parameters includes the following steps: Obtain the ratio of the current remaining electrolyte volume to the total pore volume under different charge-discharge cycles; Based on the ratio and the initial liquid volume fraction of the positive and negative electrode plates and the separator, the liquid volume fraction of the positive and negative electrode plates and the separator under different charge and discharge cycles is obtained. Based on the current remaining electrolyte volume, the total pore volume, the initial active area, and the critical electrolyte volume ratio in each charge-discharge cycle, the effective active areas of the positive and negative electrode plates and the separator are obtained under different charge-discharge cycles. Based on the liquid volume fraction and effective active area of ​​the positive and negative electrode plates under different charge and discharge cycles, the change data of liquid volume fraction and effective active area of ​​the positive and negative electrode plates are obtained. Based on the liquid phase volume fraction and effective active area of ​​the separator under different charge-discharge cycles, the change data of the liquid phase volume fraction and the change data of the effective active area of ​​the separator are obtained.

9. A method for predicting battery life, characterized in that, The method includes: Obtain the battery life prediction parameters for the battery under test; Based on the battery life prediction parameters, the battery under test is subjected to iterative prediction of life decay to obtain the life decay prediction data of the battery under test. The battery life prediction parameters are obtained based on the battery life prediction parameter determination method as described in any one of claims 1-8.

10. The battery life prediction method according to claim 9, characterized in that, The step of performing iterative prediction of battery life degradation based on the battery life prediction parameters to obtain the battery life degradation prediction data includes: Based on the battery life prediction parameters, the battery under test is simulated for life decay to obtain initial life decay prediction data and life decay parameters. The battery life prediction parameters are updated based on the lifespan degradation parameters. Returning to the step of simulating the life decay of the battery under test based on the battery life prediction parameters to obtain initial life decay prediction data and life decay parameters, until the preset iteration termination condition is reached, the life decay prediction data of the battery under test is obtained.

11. A device for determining battery life prediction parameters, characterized in that, The device includes: The test result data acquisition module is used to acquire the test result data of the battery under test under different charge and discharge cycles. The test result data includes SEI film formation reaction parameters, lithium plating reaction parameters, positive and negative electrode OCV curves, increased thickness of positive and negative electrode single-layer plates, SEI film thickness, initial pre-tightening force of cell clamp, and elastic modulus of main material. A battery life prediction parameter determination module is used to determine battery life prediction parameters based on the test result data. The battery life prediction parameters include thermodynamic prediction parameters, kinetic prediction parameters, and material property prediction parameters. The thermodynamic prediction parameters include the maximum and minimum lithium intercalation amounts of the positive and negative electrodes under different charge-discharge cycle periods. The kinetic prediction parameters include the change data of the first material parameters of the positive and negative electrode plates and separator caused by electrolyte consumption. The battery life prediction parameter determination module is also used to process the test result data using the constructed battery life degradation model to determine the battery life prediction parameters. The battery life degradation model is constructed based on different battery life degradation mechanisms and historical test result data of the battery under test under cyclic charge and discharge cycles.

12. A battery life prediction device, characterized in that, The device includes: The battery life prediction parameter acquisition module is used to acquire the battery life prediction parameters of the lithium battery under test. The battery life prediction module is used to perform iterative prediction of the life decay of the battery under test based on the battery life prediction parameters, so as to obtain the life decay prediction data of the battery under test. The battery life prediction parameters are obtained based on the battery life prediction parameter determination method as described in any one of claims 1-8.

13. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the battery life prediction parameter determination method according to any one of claims 1-8, or the steps of the battery life prediction method according to any one of claims 9-10.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the battery life prediction parameter determination method according to any one of claims 1-8, or the steps of the battery life prediction method according to any one of claims 9-10.

15. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the battery life prediction parameter determination method according to any one of claims 1-8, or the steps of the battery life prediction method according to any one of claims 9-10.

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