Method, system and electronic device for predicting battery cycle life data

Abstract

The application provides a battery cycle life data prediction method, system and electronic equipment. The prediction method comprises obtaining cycle life data of a battery to be predicted under a target cycle parameter corresponding to the battery to be predicted through a battery life attenuation model corresponding to the target cycle parameter based on the target cycle parameter. The battery life attenuation model corresponding to the target cycle parameter is determined based on cycle life data under a measured cycle parameter. The target cycle parameter and the measured cycle parameter both comprise multiple parameters, and only one parameter has a different value. The application can effectively reduce experimental cost and time input and improve the practicability of the prediction method.

Description

Technical Field

[0001] This application relates to the field of lithium-ion battery technology, specifically to methods, systems, and electronic devices for predicting battery cycle life data. Background Technology

[0002] Lithium-ion batteries are widely used in electric vehicles, energy storage systems, and consumer electronics due to their high energy density, long cycle life, and lack of memory effect. Accurately predicting the cycle life of lithium-ion batteries is crucial for optimizing battery management systems, extending battery life, improving system reliability, and reducing operating costs.

[0003] However, existing methods for predicting battery cycle life data suffer from high experimental costs and time investment, as well as low practicality. Summary of the Invention

[0004] This application provides a method, system, and electronic device for predicting battery cycle life data, which reduces experimental costs and time investment and improves the practicality of the prediction method.

[0005] In a first aspect, this application provides a method for predicting battery cycle life data, including: Based on the target cycle parameters corresponding to the battery to be predicted, the cycle life data of the battery to be predicted under the target cycle parameters is obtained through the battery life degradation model corresponding to the target cycle parameters. The battery life degradation model corresponding to the target cycle parameter is determined based on the cycle life data under the measured cycle parameter; both the target cycle parameter and the measured cycle parameter include multiple parameters, and only one parameter has a different value.

[0006] Optionally, the battery life degradation model represents the mapping relationship between capacity loss rate and number of cycles, or the mapping relationship between capacity retention rate and number of cycles; wherein, the capacity loss rate is equal to the product of the pre-exponential factor and the number of cycles; the pre-exponential factor is a power function with the natural base as the base and the pre-exponential factor as the exponent; the number of cycles is a power function with the number of cycles as the base and the degradation coefficient as the exponent; and the sum of the capacity loss rate and the capacity retention rate is 1.

[0007] Optionally, the steps for obtaining the battery life degradation model corresponding to the target cycling parameters are as follows: Obtain cycle life data of a measured battery under at least two measured cycle parameters; wherein the measured battery and the battery to be predicted are of the same model; and the two measured cycle parameters differ in only one parameter value; Based on the measured cycle life data of the battery under at least two measured cycle parameters, obtain the pre-exponential factor and decay coefficient corresponding to at least two measured cycle parameters; Based on the pre-exponential factor and decay coefficient corresponding to the at least two measured cycling parameters, the battery life decay model corresponding to the target cycling parameter is determined.

[0008] Optionally, obtaining the pre-exponential factor and decay coefficient corresponding to at least two measured cycle parameters based on the measured battery's cycle life data under at least two measured cycle parameters includes: The following operations were performed on the cycle life data of the measured battery under each measured cycle parameter: Based on the cycle life data under the measured cycle parameters, the pre-exponential factor and the decay coefficient in the battery life decay model are estimated to obtain the pre-exponential factor and the decay coefficient corresponding to the measured cycle parameters.

[0009] Optionally, determining the battery life degradation model corresponding to the target cycling parameter based on the pre-exponential factor and degradation coefficient corresponding to the at least two measured cycling parameters includes: Obtain the first and second measured loop parameters that have the smallest difference from the target loop parameters; Based on the first measured cycle parameter, the second measured cycle parameter, the pre-exponential factor and decay coefficient corresponding to the first measured cycle parameter, and the pre-exponential factor and decay coefficient corresponding to the second measured cycle parameter, the pre-exponential factor and decay coefficient corresponding to the target cycle parameter are determined. Substitute the pre-exponential factor and decay coefficient corresponding to the target cycling parameters into the battery life decay model to obtain the battery life decay model corresponding to the target cycling parameters.

[0010] Optionally, the step of obtaining the cycle life data of the battery under the target cycle parameters based on the target cycle parameters of the battery to be predicted, through the battery life degradation model corresponding to the target cycle parameters, includes: The target cycle parameters and target capacity loss rate / target capacity retention rate of the battery to be predicted are input into the battery life degradation model corresponding to the target cycle parameters to obtain the predicted cycle number of the battery to be predicted under the target cycle parameters and the target capacity loss rate / target capacity retention rate.

[0011] Optionally, the step of obtaining the cycle life data of the battery under the target cycle parameters based on the target cycle parameters of the battery to be predicted, through the battery life degradation model corresponding to the target cycle parameters, includes: The target cycle parameters and target number of cycles corresponding to the battery to be predicted are input into the battery life degradation model corresponding to the target cycle parameters to obtain the predicted capacity loss rate / capacity retention rate of the battery to be predicted under the target cycle parameters and the target number of cycles.

[0012] Optionally, the plurality of parameters includes charge / discharge condition parameters and battery design parameters.

[0013] Secondly, this application provides a system for predicting battery cycle life data, comprising: The cycle life data prediction module is used to obtain the cycle life data of the battery under the target cycle parameters based on the target cycle parameters of the battery to be predicted, through the battery life degradation model corresponding to the target cycle parameters. The battery life degradation model corresponding to the target cycle parameter is determined based on the cycle life data under the measured cycle parameter; both the target cycle parameter and the measured cycle parameter include multiple parameters, and only one parameter has a different value.

[0014] Thirdly, this application provides an electronic device, including a memory and a processor; The memory is connected to the processor and is used to store programs; The processor is used to implement the method for predicting battery cycle life data as described in the first aspect by running a program in the memory.

[0015] This application provides a method, system, device, and storage medium for predicting battery cycle life data. The prediction method includes: based on target cycle parameters corresponding to the battery to be predicted, obtaining the cycle life data of the battery under the target cycle parameters through a battery life degradation model corresponding to the target cycle parameters; wherein, the battery life degradation model corresponding to the target cycle parameters is determined based on the cycle life data under the measured cycle parameters; both the target cycle parameters and the measured cycle parameters include multiple parameters, and only one parameter has a different value. The battery life degradation model corresponding to the target cycle parameters predicted in this application is determined based on the cycle life data corresponding to the measured cycle parameters, which eliminates the experimental process of obtaining the measured cycle life data corresponding to the target cycle parameters, thereby effectively reducing experimental costs and time investment, and improving the practicality of the prediction method. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a method for predicting battery cycle life data provided in this application embodiment.

[0018] Figure 2 This is a schematic diagram of battery life degradation curves at different temperatures provided in the embodiments of this application.

[0019] Figure 3 A schematic diagram showing the fitting results of cycle lifetime data for different single-sided surface densities of the negative electrode provided in the embodiments of this application.

[0020] Figure 4 This is a schematic diagram showing the predicted cycle life data for different single-sided areal densities of the negative electrode provided in the embodiments of this application.

[0021] Figure 5 This is a schematic diagram showing the predicted cycle life data at different charge / discharge rates provided in the embodiments of this application.

[0022] Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Battery cycle life is a key indicator for measuring its performance and reliability, directly affecting its application in fields such as electric vehicles and energy storage systems. Battery cycle life degradation is influenced by a combination of various charge and discharge condition parameters, such as temperature, charge / discharge rate / power, and depth of discharge (DOD), as well as various battery design parameters, such as electrode areal density, compaction density, and electrode porosity. The interaction mechanisms among these factors are complex.

[0025] In existing technologies, the following approaches are mainly used to predict the cycle life of batteries under different parameters (such as temperature, charge / discharge rate / charge / discharge power, DOD, etc.): (1) Electrochemical mechanism method: Electrochemical mechanism methods, which establish mathematical models based on the electrochemical reactions and physical processes within a battery, can theoretically reveal the mechanisms of battery performance degradation. However, their drawbacks lie in the complexity of the models and their high dependence on internal battery parameters. Since the chemical reactions and material properties within a battery are difficult to fully quantify, practical applications require obtaining a large number of parameters through experiments. This not only increases experimental costs but also limits the applicability of the models. Furthermore, electrochemical mechanism methods struggle to simultaneously consider the combined effects of multiple parameters (such as temperature, charge / discharge rate / power, and DOD) on battery cycle life, leading to significant discrepancies between predicted and actual results.

[0026] (2) Big Data Approach: Big data methods rely on large amounts of experimental or operational data to build lifespan prediction models through data-driven approaches (such as machine learning). However, their drawback lies in the extremely high requirements for both the quality and quantity of data. In practical applications, obtaining comprehensive and representative experimental data is often difficult, especially under extreme conditions (such as high temperatures and high charge / discharge rates), where data scarcity can lead to insufficient generalization ability of the model. Furthermore, big data methods are often considered "black box models," lacking a clear explanation of battery degradation mechanisms, which may raise issues of trustworthiness and interpretability in engineering applications.

[0027] (3) Traditional testing methods: Traditional testing methods involve conducting numerous experiments in a laboratory environment to obtain battery cycle life data under different parameters (such as temperature, charge / discharge rate / power, DOD, etc.), and then building a life prediction model based on this data. However, this method has the following problems: First, the experimental cost is high, requiring long-term cycle testing under different temperatures, charge / discharge rates, and DOD parameters, which is time-consuming and costly; second, the testing conditions are limited and cannot cover all the complex operating conditions in real-world applications; finally, traditional testing methods are difficult to predict battery life under different parameters (such as temperature, charge / discharge rate / power, DOD, etc.), resulting in poor applicability and generalizability of the model.

[0028] In summary, existing technologies for predicting battery cycle life generally suffer from problems such as high data dependence (i.e., high experimental costs), poor applicability, high computational complexity, and poor prediction accuracy, making it difficult to meet the needs of practical applications for efficient, accurate, and universal prediction methods.

[0029] To address the aforementioned technical problems, embodiments of this application provide a method, system, and electronic device for predicting battery cycle life data; Exemplary methods This application provides a method for predicting battery cycle life data. This method can be executed by an electronic device, which can be any device with data and instruction processing capabilities. For example, it can be a laptop, tablet, desktop computer, mobile device (e.g., mobile phone, personal digital assistant, dedicated messaging device), or any combination of two or more of these electronic devices. It can also be a server, such as... Figure 1 As shown, the prediction methods include: S110: Based on the target cycle parameters corresponding to the battery to be predicted, the cycle life data of the battery to be predicted under the target cycle parameters is obtained through the battery life degradation model corresponding to the target cycle parameters. The battery life degradation model corresponding to the target cycle parameters is determined based on the cycle life data under the measured cycle parameters; both the target cycle parameters and the measured cycle parameters include multiple parameters, and only one parameter has a different value.

[0030] The measured cycle parameters are the actual cycle conditions used by the user during cycle life testing; the target cycle parameters are the cycle conditions desired by the user. Both the target cycle parameters and the measured cycle parameters include multiple parameters, including charge / discharge condition parameters and battery design parameters. Charge / discharge condition parameters include temperature, charge / discharge performance parameters, and depth of discharge (DOD). Charge / discharge performance parameters are the charge / discharge rate / charge / discharge power. Battery design parameters include electrode areal density, compaction density, and electrode porosity. Electrode areal density includes the bifacial density of the positive electrode, the single-sided density of the positive electrode, the bifacial density of the negative electrode, and the single-sided density of the negative electrode. Compaction density includes the compaction density of the positive electrode and the compaction density of the negative electrode. Electrode porosity includes the porosity of the positive electrode and the porosity of the negative electrode. The specific values ​​of each parameter in the target loop are determined by the prediction requirements, and this application does not impose specific limitations.

[0031] Both the target cycle parameters and the measured cycle parameters include multiple parameters, with only one parameter differing in value. This means that the target cycle parameters and the measured cycle parameters differ only in a single parameter. For example, only the temperature value in the charge / discharge condition parameters differs, while the values ​​of the charge / discharge performance parameters, depth of discharge (DOD), and battery design parameters in the charge / discharge condition parameters are the same. Cycle lifetime data consists of a series of data pairs: [number of cycles, capacity retention / capacity loss]. The battery life degradation model corresponding to the target cycling parameters predicted in this application is determined based on the cycle life data corresponding to the measured cycling parameters. This eliminates the need for the experimental process of obtaining the measured cycle life data corresponding to the target cycling parameters, thereby effectively reducing experimental costs and time investment and improving the practicality of the prediction method.

[0032] The target cycling parameters and measured cycling parameters in this application comprehensively consider the influence of multiple external factors (i.e., temperature, charge and discharge performance parameters, DOD, electrode surface density, compaction density and electrode porosity), which can improve the generalization ability of the model.

[0033] In one or more embodiments, the battery life degradation model represents the mapping relationship between capacity loss rate and number of cycles, or the mapping relationship between capacity retention rate and number of cycles; wherein, the capacity loss rate is equal to the product of the pre-exponential factor and the number of cycles; the pre-exponential factor is a power function with the natural base as the base and the pre-exponential factor as the exponent; the number of cycles is a power function with the number of cycles as the base and the degradation coefficient as the exponent; the sum of the capacity loss rate and the capacity retention rate is 1.

[0034] When the battery life degradation model represents the mapping relationship between capacity loss rate and number of cycles, the expression for the battery life degradation model is: ; in, The value represents the capacity loss rate; e represents the natural base; A represents the pre-exponential factor; N represents the number of cycles; and z represents the decay coefficient. When the battery life degradation model represents the mapping relationship between capacity retention and cycle count, the expression for the battery life degradation model is: ; in, Indicates capacity retention rate; The value represents the capacity loss rate; e represents the natural base; A represents the pre-exponential factor; N represents the number of cycles; and z represents the decay coefficient. The pre-exponential factor A and the degradation coefficient z are two key undetermined coefficients in the battery life degradation model. Their values ​​are not fixed but are closely related to the battery's charge and discharge conditions and design parameters. For a given set of charge and discharge conditions and battery design parameters, there is a specific set of (A, z) values.

[0035] The battery life degradation model provided in this application is simple in form and significantly reduces computational complexity compared to the electrochemical mechanism model. Furthermore, the battery life degradation model provided in this application is more consistent with the actual degradation law of batteries, thereby significantly improving the interpretability and prediction accuracy of the model.

[0036] It is understandable that battery life degradation models are not limited to those described above. and Battery life degradation models can also use functions such as polynomials and logarithms to represent the mapping relationship between capacity loss rate / capacity retention rate and cycle count. These alternatives can achieve the same predictive effect, but the prediction accuracy is relatively lower.

[0037] In one or more embodiments, the steps for obtaining the battery life degradation model corresponding to the target cycling parameters are as follows: Obtain cycle life data of the tested battery under at least two measured cycle parameters; wherein the tested battery and the battery to be predicted are the same model; and only one parameter of the two measured cycle parameters differs in value; The tested battery and the battery to be predicted can be lithium iron phosphate system, ternary system, cylindrical battery, VDA battery, or blade battery. That is, the prediction method of this application is applicable to lithium iron phosphate system, ternary system, cylindrical battery, VDA battery, and blade battery, and has wide applicability and promotion value.

[0038] All measured cycle life data of the battery obtained under at least two measured cycle parameters are measured cycle life data. Obtaining cycle life data of a measured battery under a certain measured cycle parameter refers to obtaining a series of data pairs of [number of cycles, capacity retention / capacity loss] of a measured battery under a certain measured cycle parameter; Both measured cycle parameters include multiple parameters, with only one parameter differing in value. This means that the two measured cycle parameters differ only in a single parameter. For example, only the temperature value in the charge / discharge condition parameter differs, while the values ​​of the charge / discharge performance parameter, depth of discharge (DOD), and battery design parameter in the charge / discharge condition parameter are the same. If the parameter with a different value is DOD, then at least the two measured cycle parameters preferably include shallow charge / discharge and deep charge / discharge. In one specific embodiment, the cycle lifetime data under at least two measured cycling parameters are preprocessed cycle lifetime data. Preprocessing includes removing abnormal noise points from the original cycle lifetime data using wavelet thresholding, and interpolating the original cycle lifetime data after removing abnormal noise points using cubic spline interpolation to ensure the continuity and physical rationality of the data sequence, as well as removing cycle lifetime data with capacity spikes (battery capacity retention initially increases and then decreases during early cycles). The preprocessed cycle lifetime data provides cleaner and more reasonable input for subsequent model parameter fitting, thereby improving the accuracy of the pre-exponential factor A and the decay coefficient z estimation, resulting in more accurate and reliable prediction results.

[0039] Based on the measured cycle life data of the battery under at least two measured cycle parameters, obtain the pre-exponential factor and the decay coefficient corresponding to at least two measured cycle parameters; Based on the pre-exponential factor and decay coefficient corresponding to at least two measured cycling parameters, determine the battery life decay model corresponding to the target cycling parameters.

[0040] This application uses a small amount of measured cycle life data under a limited number of actual cycle parameters to determine the battery life degradation model corresponding to the target cycle parameters. This reduces the dependence on the amount of data and eliminates the experimental process of obtaining measured cycle life data corresponding to the target cycle parameters. As a result, it can effectively reduce experimental costs and time investment and improve the practicality of the prediction method.

[0041] In one or more embodiments, based on measured battery cycle life data under at least two measured cycle parameters, the pre-exponential factor and decay coefficient corresponding to at least two measured cycle parameters are obtained, including: The following operations were performed on the cycle life data of the measured battery under each measured cycle parameter: Based on the cycle life data under the measured cycle parameters, the pre-exponential factor and decay coefficient in the battery life decay model are estimated to obtain the pre-exponential factor and decay coefficient corresponding to the measured cycle parameters.

[0042] Obtaining the pre-exponential factor and decay coefficient corresponding to at least two measured cyclic parameters helps to quickly determine the pre-exponential factor and decay coefficient corresponding to the target cyclic parameter.

[0043] In one specific embodiment, the following operations are performed independently for each measured loop parameter: Based on the measured cycle life data (i.e., a series of data pairs of [number of cycles, capacity retention / capacity loss]) obtained under the measured cycle parameters, the pre-exponential factor A and the decay coefficient z in the battery life decay model are estimated using a preset parameter estimation method.

[0044] With model For example, we can take the natural logarithm of both sides to get ln( ) = A +z ln(N). This is about ln(N) and ln( The linear equation of the equation is given. Using all data pairs of [number of cycles, capacity loss rate] under the measured cycling parameters, the pre-exponential factor A and the attenuation coefficient z under the measured cycling parameters can be directly estimated by parameter estimation methods such as the least squares method.

[0045] Using the above method, the pre-exponential factor A and the decay coefficient z can be obtained for each measured cyclic parameter.

[0046] In one or more embodiments, a battery life degradation model corresponding to a target cycling parameter is determined based on the pre-exponential factor and degradation coefficient corresponding to at least two measured cycling parameters, including: Obtain the first and second measured loop parameters that have the smallest difference from the target loop parameters; The difference here refers to the absolute value of the difference between the target loop parameter and the only parameter in the measured loop parameter that has a different value.

[0047] For example, the only parameter that differs in value between the target cycle parameters and the measured cycle parameters is temperature; Assuming the experiment only obtained measured cycle life data at two different temperatures, such as measured cycle life data at 25℃ and 45℃, then the first measured cycle parameter and the second measured cycle parameter are the measured cycle parameters at 25℃ and 45℃. Assuming the experiment yielded measured cycle life data at three or more different temperatures, taking measured cycle life data at three different temperatures as an example, such as 25℃, 35℃, and 45℃, we now need to predict cycle life data at 20℃, 30℃, 50℃, and 60℃. Since the difference between 20℃ and 25℃ / 35℃ is the smallest, the first and second measured cycle parameters are the measured cycle parameters at 25℃ and 35℃. Similarly, since the difference between 30℃ and 25℃ / 35℃ is the smallest, the first and second measured cycle parameters are the measured cycle parameters at 25℃ and 35℃. Furthermore, since the difference between 50℃ and 35℃ / 45℃ is the smallest, the first and second measured cycle parameters are the measured cycle parameters at 35℃ and 45℃. Finally, since the difference between 60℃ and 35℃ / 45℃ is the smallest, the first and second measured cycle parameters are the measured cycle parameters at 35℃ and 45℃.

[0048] Based on the first measured cycle parameter, the second measured cycle parameter, the pre-exponential factor and decay coefficient corresponding to the first measured cycle parameter, and the pre-exponential factor and decay coefficient corresponding to the second measured cycle parameter, determine the pre-exponential factor and decay coefficient corresponding to the target cycle parameter; This application utilizes the principle of proximity to select two measured cyclic parameters and their corresponding pre-exponential factors and attenuation coefficients, which can make the most of the neighbor information, reduce the error of the pre-exponential factors and attenuation coefficients corresponding to the subsequent extrapolation of the target cyclic parameters, and make the prediction results closer to the actual situation.

[0049] Substitute the pre-exponential factor and decay coefficient corresponding to the target cycling parameters into the battery life decay model to obtain the battery life decay model corresponding to the target cycling parameters.

[0050] In one or more embodiments, the pre-exponential factor and attenuation coefficient corresponding to the target loop parameter are determined based on the values ​​of the parameters that are uniquely different in the first measured loop parameters, the parameters that are uniquely different in the second measured loop parameters, the parameters that are uniquely different in the target loop parameters, the pre-exponential factor and attenuation coefficient corresponding to the first measured loop parameter, and the pre-exponential factor and attenuation coefficient corresponding to the second measured loop parameter. In a specific embodiment, based on the values ​​of the parameters that are uniquely different in the first measured cyclic parameters, the values ​​of the parameters that are uniquely different in the second measured cyclic parameters, the values ​​of the parameters that are uniquely different in the target cyclic parameters, the pre-exponential factor and the attenuation coefficient corresponding to the first measured cyclic parameter, and the pre-exponential factor and the attenuation coefficient corresponding to the second measured cyclic parameter, the pre-exponential factor and the attenuation coefficient corresponding to the second measured cyclic parameter, the pre-exponential factor and the attenuation coefficient corresponding to the target cyclic parameter are determined by linear interpolation. The expression for determining the pre-exponential factor and decay coefficient corresponding to the target cyclic parameters using linear interpolation is as follows: A_target = A1 + (A2 - A1) (X_target - X1) / (X2 - X1) Z_target = Z1 + (Z2 - Z1) (X_target - X1) / (X2 - X1) Where A_target and Z_target represent the pre-exponential factor and attenuation coefficient corresponding to the target loop parameter, respectively; A1 and Z1 represent the pre-exponential factor and attenuation coefficient corresponding to the first measured loop parameter, respectively; A2 and Z2 represent the pre-exponential factor and attenuation coefficient corresponding to the second measured loop parameter, respectively; X1, X2, and X_target represent the values ​​of the only parameter that is different among the first measured loop parameter, the second measured loop parameter, and the target loop parameter, respectively. It should be noted that the linear interpolation method is only one embodiment provided in this application. Other interpolation methods (such as logarithmic interpolation, exponential interpolation, etc.) can also be used in this application, which will not be elaborated here.

[0051] Substitute the calculated pre-exponential factor A_target and decay coefficient Z_target corresponding to the target cycle parameters into the battery life decay model (e.g.) Thus, the battery life degradation model corresponding to the target cycling parameters is obtained. ; This application extrapolates the pre-exponential factor and decay coefficient corresponding to the target cycle parameter by using a small number of measured cycle parameters, thereby obtaining the battery life decay model corresponding to the target cycle parameter. This eliminates the need for the experimental process of obtaining measured cycle life data corresponding to the target cycle parameter, thus effectively reducing experimental costs and time investment and improving the practicality of the prediction method.

[0052] In one or more embodiments, based on the target cycle parameters corresponding to the battery to be predicted, the cycle life data of the battery to be predicted under the target cycle parameters is obtained through the battery life degradation model corresponding to the target cycle parameters, including: The target cycle parameters and target capacity loss rate / target capacity retention rate of the battery to be predicted are input into the battery life degradation model corresponding to the target cycle parameters to obtain the predicted number of cycles of the battery under the target cycle parameters and target capacity loss rate / target capacity retention rate.

[0053] If the battery life degradation model represents the mapping relationship between capacity loss rate and number of cycles, then the target cycle parameters and target capacity loss rate of the battery to be predicted are input into the battery life degradation model corresponding to the target cycle parameters to obtain the predicted number of cycles of the battery to be predicted under the target cycle parameters and target capacity loss rate. If the battery life degradation model represents the mapping relationship between capacity retention rate and number of cycles, then the target cycle parameters and target capacity retention rate of the battery to be predicted are input into the battery life degradation model corresponding to the target cycle parameters to obtain the predicted number of cycles of the battery to be predicted under the target cycle parameters and target capacity retention rate. The above process enables the task of quickly predicting the number of cycles given the cycle parameters and the target capacity loss rate / target capacity retention rate.

[0054] In one or more embodiments, based on the target cycle parameters corresponding to the battery to be predicted, the cycle life data of the battery to be predicted under the target cycle parameters is obtained through the battery life degradation model corresponding to the target cycle parameters, including: Input the target cycle parameters and target number of cycles corresponding to the battery to be predicted into the battery life degradation model corresponding to the target cycle parameters to obtain the predicted capacity loss rate / capacity retention rate of the battery under the target cycle parameters and target number of cycles.

[0055] If the battery life degradation model represents the mapping relationship between capacity loss rate and number of cycles, then the target cycle parameters and target number of cycles corresponding to the battery to be predicted are input into the battery life degradation model corresponding to the target cycle parameters to obtain the predicted capacity loss rate of the battery to be predicted under the target cycle parameters and target number of cycles. If the battery life degradation model represents the mapping relationship between capacity retention rate and number of cycles, then the target cycle parameters and target number of cycles corresponding to the battery to be predicted are input into the battery life degradation model corresponding to the target cycle parameters to obtain the predicted capacity retention rate of the battery to be predicted under the target cycle parameters and target number of cycles. The above process enables the task of quickly predicting the capacity loss rate / target capacity retention rate when the loop parameters and target number of loops are known.

[0056] In one or more embodiments, the model is calibrated using cycle lifetime data corresponding to measured cycle parameters, and the accuracy and generalization ability of the model are evaluated using cross-validation.

[0057] Next, the technical solution of the present invention will be illustrated through specific application scenario examples: Example 1: Given the early-stage degradation curves (i.e., a series of [number of cycles, capacity retention] data pairs) of a certain square-cased battery cell at 25℃ (0.5P charge / discharge power) and 45℃ (0.5P charge / discharge power), predict the number of cycles required to achieve 80% capacity retention under the conditions of 25℃ (0.5P charge / discharge power) and 45℃ (0.5P charge / discharge power); and predict the number of cycles required to achieve 80% capacity retention under the conditions of 30℃ (0.5P charge / discharge power), 35℃ (0.5P charge / discharge power), 40℃ (0.5P charge / discharge power), 45℃ (0.5P charge / discharge power), and 55℃ (0.5P charge / discharge power).

[0058] First, the attenuation curve at 25℃ was fitted to obtain the attenuation coefficient Z_25=0.701 and the pre-exponential factor A_25=0.000567 corresponding to 25℃; the attenuation curve at 45℃ was fitted to obtain the attenuation coefficient Z_45=0.6472 and the pre-exponential factor A_45=0.00101 corresponding to 45℃. The attenuation coefficient Z_30 and pre-exponential factor A_30 corresponding to 30℃ were calculated using linear interpolation: The specific calculation formula is as follows: A_30 = A_25 + (A_45 - A_25) (30-25) / (45-25); Z_30 = Z_25 + (Z_45 - Z_25) (30-25) / (45-25); The attenuation coefficient Z_35 and the pre-exponential factor A_35 corresponding to 35℃ were calculated using linear interpolation. The specific calculation formula is as follows: A_35 = A_25 + (A_45 - A_25) (35-25) / (45-25); Z_35 = Z_25 + (Z_45 - Z_25) (35-25) / (45-25); The attenuation coefficient Z_40 and the pre-exponential factor A_40 corresponding to 40℃ were calculated using linear interpolation. The specific calculation formula is as follows: A_40 = A_25 + (A_45 - A_25) (40-25) / (45-25); Z_40 = Z_25 + (Z_45 - Z_25) (40-25) / (45-25); The attenuation coefficient Z_55 and the pre-exponential factor A_55 corresponding to 55℃ were calculated using linear interpolation. The specific calculation formula is as follows: A_55 = A_25 + (A_45 - A_25) (55-25) / (45-25); Z_55 = Z_25 + (Z_45 - Z_25) (55-25) / (45-25); Substituting the attenuation coefficients and pre-exponential factors corresponding to 25℃, 30℃, 35℃, 40℃, 45℃ and 55℃ into the battery life attenuation model, we obtain the life attenuation models corresponding to 25℃, 30℃, 35℃, 40℃, 45℃ and 55℃. Simulation predictions were performed based on lifetime decay models corresponding to 25℃, 30℃, 35℃, 40℃, 45℃ and 55℃. The numerical simulation results of capacity retention at different temperatures (i.e. 25℃, 30℃, 35℃, 40℃, 45℃ and 55℃) are shown in Table 1. Table 1 Numerical simulation results of capacity retention at different temperatures Battery life degradation curves at different temperatures (25℃, 30℃, 35℃, 40℃, 45℃, and 55℃) are shown below. Figure 2As shown, the number of cycles required to achieve 80% capacity retention at 25℃ (0.5P charge / discharge power), 30℃ (0.5P charge / discharge power), 35℃ (0.5P charge / discharge power), 40℃ (0.5P charge / discharge power), 45℃ (0.5P charge / discharge power), and 55℃ (0.5P charge / discharge power) are 4600, 4200, 3800, 3500, 3000, and 2600, respectively. Example 2: For example, the areal density of the negative electrode of a certain pouch cell is known to be 8.01 g / cm³. 2 (Temperature: 25℃, Charge / Discharge Rate: 1C), Anode single-sided surface density: 7.74 g / cm³ 2 The early decay curve (i.e., a series of [cycle count, capacity retention] data pairs) at 25℃ and a charge / discharge rate of 1C predicts that the single-sided areal density of the negative electrode is 8.01 g / cm³. 2 (Temperature: 25℃, Charge / Discharge Rate: 1C), Anode single-sided surface density: 7.74 g / cm³ 2 (At 25℃ and a charge / discharge rate of 1C) Calculate the number of cycles required to achieve 80% capacity retention for this pouch cell, and determine its negative electrode surface density to be 7.875 g / cm³. 2 (At a temperature of 25°C and a charge / discharge rate of 1C) Cycle until the capacity retention rate is 80%.

[0059] First, the surface density of the negative electrode was obtained by fitting the data, which was 8.01 g / cm³. 2 The surface density of the negative electrode is 7.74 g / cm³. 2 The corresponding attenuation coefficients are Z1=0.9115, Z2=0.84571, and pre-exponential factor A1=2.471e. -4 A2 = 3.039e -4 ; Then, the areal density of the negative electrode was calculated to be 7.875 g / cm³ using linear interpolation. 2 The corresponding attenuation coefficient Z3 = 0.8786 and pre-exponential factor A3 = 2.755e -4 (The specific calculation process is the same as in Example 1, and will not be repeated here.)

[0060] The areal density of the negative electrode is 8.01 g / cm³. 2 The surface density of the negative electrode is 7.74 g / cm³. 2 The surface density of the negative electrode is 7.875 g / cm³. 2 Substituting the corresponding attenuation coefficients and pre-exponential factors into the battery life attenuation model, the areal density of the negative electrode was found to be 8.01 g / cm³. 2 The surface density of the negative electrode is 7.74 g / cm³.2 The surface density of the negative electrode is 7.875 g / cm³. 2 The corresponding lifetime decay model; Based on the single-sided areal density of the negative electrode of 8.01 g / cm³ 2 The surface density of the negative electrode is 7.74 g / cm³. 2 The surface density of the negative electrode is 7.875 g / cm³. 2 The corresponding lifetime decay model yielded prediction results for cycle lifetime data of different anode single-sided surface densities.

[0061] Figure 3 For different single-sided areal densities of the negative electrode (i.e., single-sided areal density of the negative electrode 8.01 g / cm³), 2 The surface density of the negative electrode on one side is 7.74 g / cm³. 2 The fitting results of the cycle life data, Figure 4 For different single-sided areal densities of the negative electrode (i.e., single-sided areal density of the negative electrode 8.01 g / cm³), 2 The surface density of the negative electrode is 7.74 g / cm³. 2 The surface density of the negative electrode is 7.875 g / cm³. 2 The cycle life prediction results are shown in the data. The areal density of this pouch cell on the negative electrode single side is 8.01 g / cm³. 2 (Temperature: 25℃, Charge / Discharge Rate: 1C), Anode single-sided surface density: 7.74 g / cm³ 2 (Temperature: 25℃, Charge / Discharge Rate: 1C) and the single-sided areal density of the negative electrode is 7.875 g / cm³. 2 (At a temperature of 25°C and a charge / discharge rate of 1C) the number of cycles required to maintain 80% capacity is 1532, 2213, and 1800.

[0062] Implementation Case 3: Given the early decay curves (i.e., a series of [cycle count, capacity retention] data pairs) of a certain square-cased battery cell at charge / discharge rates of 0.5C (temperature 25℃) and 1C (temperature 25℃), predict the capacity retention rate after 4000 cycles at charge / discharge rates of 0.5C (temperature 25℃), 1C (temperature 25℃), and 0.33C (temperature 25℃).

[0063] First, the attenuation coefficients Z1=0.69 and Z2=0.808, and the pre-exponential factor A1=5.208e were obtained by fitting the data at 0.5C (temperature 25℃) and 1C (temperature 25℃), respectively. -4 A2 = 2.457e -4 ; Then, linear interpolation was used to calculate the attenuation coefficient Z3 = 0.654 and the pre-exponential factor A3 = 6.619e corresponding to a charge / discharge rate of 0.33C.-4 (The specific calculation process is the same as in Example 1, and will not be repeated here.)

[0064] Substituting the attenuation coefficients and pre-exponential factors corresponding to charge / discharge rates of 0.5C (temperature 25℃), 1C (temperature 25℃), and 0.33C (temperature 25℃) into the battery life attenuation model, we obtain the life attenuation models corresponding to charge / discharge rates of 0.5C (temperature 25℃), 1C (temperature 25℃), and 0.33C (temperature 25℃). Based on the lifetime decay model corresponding to charge / discharge rates of 0.5C (temperature of 25℃), 1C (temperature of 25℃), and 0.33C (temperature of 25℃), the cycle lifetime data prediction results for different charge / discharge rates (i.e., charge / discharge rates of 0.5C, 1C, and 0.33C) were obtained. Figure 5 The predicted cycle life data for different charge / discharge rates are presented. When the prismatic cell has been cycled to 4000 cycles, the capacity retention rate is 86% at 0.5C (temperature 25℃), 81% at 1C (temperature 25℃), and 87% at 0.33C (temperature 25℃).

[0065] Exemplary System This application also provides a battery cycle life data prediction system, including: The cycle life data prediction module is used to obtain the cycle life data of the battery under the target cycle parameters based on the target cycle parameters of the battery to be predicted and through the battery life degradation model corresponding to the target cycle parameters. The battery life degradation model corresponding to the target cycle parameters is determined based on the cycle life data under the measured cycle parameters; both the target cycle parameters and the measured cycle parameters include multiple parameters, and only one parameter has a different value.

[0066] In one or more embodiments, the battery life degradation model represents the mapping relationship between capacity loss rate and number of cycles, or the mapping relationship between capacity retention rate and number of cycles; wherein, the capacity loss rate is equal to the product of the pre-exponential factor and the number of cycles; the pre-exponential factor is a power function with the natural base as the base and the pre-exponential factor as the exponent; the number of cycles is a power function with the number of cycles as the base and the degradation coefficient as the exponent; the sum of the capacity loss rate and the capacity retention rate is 1.

[0067] In one or more embodiments, it further includes a first acquisition module, a second acquisition module, and a third acquisition module; The first acquisition module is used to acquire the cycle life data of the measured battery under at least two measured cycle parameters; wherein the measured battery and the battery to be predicted are the same model of battery; and only one parameter of the two measured cycle parameters has a different value; The second acquisition module is used to obtain the pre-exponential factor and decay coefficient corresponding to at least two measured cycle parameters based on the measured cycle life data of the battery under at least two measured cycle parameters. The third acquisition module is used to determine the battery life degradation model corresponding to the target cycle parameter based on the pre-exponential factor and degradation coefficient corresponding to at least two measured cycle parameters.

[0068] In one or more embodiments, the second acquisition module is used to perform the following operations on the cycle life data of the measured battery under each measured cycle parameter: Based on the cycle life data under the measured cycle parameters, the pre-exponential factor and decay coefficient in the battery life decay model are estimated to obtain the pre-exponential factor and decay coefficient corresponding to the measured cycle parameters.

[0069] In one or more embodiments, the third acquisition module includes a first submodule, a second submodule, and a third submodule; The first submodule is used to obtain the first and second measured loop parameters that have the smallest difference from the target loop parameters; The second submodule is used to determine the pre-exponential factor and attenuation coefficient corresponding to the target cycle parameter based on the first measured cycle parameter, the second measured cycle parameter, the pre-exponential factor and attenuation coefficient corresponding to the first measured cycle parameter, and the pre-exponential factor and attenuation coefficient corresponding to the second measured cycle parameter. The third submodule is used to substitute the pre-exponential factor and decay coefficient corresponding to the target cycling parameters into the battery life decay model to obtain the battery life decay model corresponding to the target cycling parameters.

[0070] In one or more embodiments, the cycle life data prediction module includes a cycle count prediction module; The cycle count prediction module is used to input the target cycle parameters and target capacity loss rate / target capacity retention rate of the battery to be predicted into the battery life degradation model corresponding to the target cycle parameters, so as to obtain the predicted cycle count of the battery under the target cycle parameters and target capacity loss rate / target capacity retention rate.

[0071] In one or more embodiments, the cycle lifetime data prediction module includes a capacity loss rate / capacity retention rate prediction module; The capacity loss rate / capacity retention rate prediction module is used to input the target cycle parameters and target number of cycles corresponding to the battery to be predicted into the battery life degradation model corresponding to the target cycle parameters, so as to obtain the predicted capacity loss rate / capacity retention rate of the battery under the target cycle parameters and target number of cycles.

[0072] In one or more embodiments, the parameters include charge / discharge condition parameters and battery design parameters.

[0073] The prediction system provided in this application belongs to the same concept as the prediction method provided in this application, and has the corresponding functional modules and beneficial effects for performing the above prediction method. Technical details not described in detail in the embodiments can be found in the specific processing content of the above prediction method in the embodiments, and will not be repeated here.

[0074] Exemplary electronic devices This application also provides an electronic device, see [link to relevant documentation] Figure 6 As shown, the device includes: Memory 600 and processor 610; The memory 600 is connected to the processor 610 and is used to store programs; The processor 610 is configured to implement the battery cycle life data prediction method disclosed in any of the above embodiments by running a program stored in the memory 600.

[0075] Specifically, the aforementioned electronic device may also include: a bus, a communication interface 620, an input device 630, and an output device 640.

[0076] The processor 610, memory 600, communication interface 620, input device 630, and output device 640 are interconnected via a bus. Among them: A bus can include a pathway for transmitting information between various components of a computer system.

[0077] The processor 610 can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present invention. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0078] The processor 610 may include a main processor, as well as a baseband chip, modem, etc.

[0079] The memory 600 stores a program that executes the technical solution of this invention, and may also store an operating system and other key business functions. Specifically, the program may include program code, which includes computer operation instructions. More specifically, the memory 600 may include read-only memory (ROM), other types of static storage devices capable of storing static information and instructions, random access memory (RAM), other types of dynamic storage devices capable of storing information and instructions, disk storage, flash memory, etc.

[0080] Input device 630 may include a device for receiving user input data and information, such as a keyboard, mouse, camera, scanner, light pen, voice input device, touch screen, pedometer, or gravity sensor.

[0081] Output device 640 may include devices that allow information to be output to a user, such as a display screen, printer, speaker, etc.

[0082] The communication interface 620 may include a device that uses any transceiver to communicate with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0083] The processor 610 executes the program stored in the memory 600 and calls other devices, which can be used to implement the various steps of any of the battery cycle life data prediction methods provided in the above embodiments of this application.

[0084] Exemplary computer program products and storage media In addition to the methods and apparatus described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the battery cycle life data prediction methods according to various embodiments of this application as described in any of the foregoing embodiments of this specification.

[0085] Computer program products can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0086] Furthermore, embodiments of this application may also be storage media storing a computer program. When the computer program is run by a processor, the processor executes the steps in the battery cycle life data prediction method according to various embodiments of this application described in any of the above embodiments of this specification. Specifically, the following steps can be implemented: Based on the target cycle parameters corresponding to the battery to be predicted, the cycle life data of the battery to be predicted under the target cycle parameters is obtained through the battery life degradation model corresponding to the target cycle parameters. The battery life degradation model corresponding to the target cycle parameters is determined based on the cycle life data under the measured cycle parameters; both the target cycle parameters and the measured cycle parameters include multiple parameters, and only one parameter has a different value.

[0087] For the foregoing method embodiments, in order to simplify the description, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0088] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For apparatus embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0089] The steps in the methods of the various embodiments of this application can be adjusted, merged, or deleted in order according to actual needs, and the technical features described in each embodiment can be replaced or combined.

[0090] The modules and sub-modules in the various embodiments of the present application's devices and terminals can be merged, divided, and deleted according to actual needs.

[0091] It should be understood that the disclosed terminals, devices, and methods can be implemented in other ways, given the several embodiments provided in this application. For example, the terminal embodiments described above are merely illustrative. For instance, the division of modules or sub-modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple sub-modules or modules may be combined or integrated into another module, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0092] The modules or submodules described as separate components may or may not be physically separate. The components that constitute a module or submodule may or may not be physical modules or submodules; that is, they may be located in one place or distributed across multiple network modules or submodules. Some or all of the modules or submodules can be selected to achieve the purpose of this embodiment's solution, depending on actual needs.

[0093] Furthermore, the functional modules or sub-modules in the various embodiments of this application can be integrated into one processing module, or each module or sub-module can exist physically separately, or two or more modules or sub-modules can be integrated into one module. The integrated modules or sub-modules described above can be implemented in hardware or in the form of software functional modules or sub-modules.

[0094] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0095] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software unit executed by a processor, or a combination of both. The software unit can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0096] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0097] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting battery cycle life data, characterized in that, include: Based on the target cycle parameters corresponding to the battery to be predicted, the cycle life data of the battery to be predicted under the target cycle parameters is obtained through the battery life degradation model corresponding to the target cycle parameters. The battery life degradation model corresponding to the target cycle parameter is determined based on the cycle life data under the measured cycle parameter; both the target cycle parameter and the measured cycle parameter include multiple parameters, and only one parameter has a different value.

2. The method for predicting battery cycle life data according to claim 1, characterized in that, The battery life degradation model represents the mapping relationship between capacity loss rate and number of cycles, or the mapping relationship between capacity retention rate and number of cycles; wherein, the capacity loss rate is equal to the product of the pre-exponential factor and the number of cycles; the pre-exponential factor is a power function with the natural base as the base and the pre-exponential factor as the exponent; the number of cycles is a power function with the number of cycles as the base and the degradation coefficient as the exponent; the sum of the capacity loss rate and the capacity retention rate is 1.

3. The method for predicting battery cycle life data according to claim 2, characterized in that, The steps to obtain the battery life degradation model corresponding to the target cycling parameters are as follows: Obtain cycle life data of a measured battery under at least two measured cycle parameters; wherein the measured battery and the battery to be predicted are of the same model; and the two measured cycle parameters differ in only one parameter value; Based on the measured cycle life data of the battery under at least two measured cycle parameters, obtain the pre-exponential factor and decay coefficient corresponding to at least two measured cycle parameters; Based on the pre-exponential factor and decay coefficient corresponding to the at least two measured cycling parameters, the battery life decay model corresponding to the target cycling parameter is determined.

4. The method for predicting battery cycle life data according to claim 3, characterized in that, The step of obtaining the pre-exponential factor and decay coefficient corresponding to at least two measured cycle parameters based on the measured cycle life data of the battery under at least two measured cycle parameters includes: The following operations were performed on the cycle life data of the measured battery under each measured cycle parameter: Based on the cycle life data under the measured cycle parameters, the pre-exponential factor and the decay coefficient in the battery life decay model are estimated to obtain the pre-exponential factor and the decay coefficient corresponding to the measured cycle parameters.

5. The method for predicting battery cycle life data according to claim 4, characterized in that, The step of determining the battery life degradation model corresponding to the target cycling parameter based on the pre-exponential factor and degradation coefficient corresponding to the at least two measured cycling parameters includes: Obtain the first and second measured loop parameters that have the smallest difference from the target loop parameters; Based on the first measured cycle parameter, the second measured cycle parameter, the pre-exponential factor and decay coefficient corresponding to the first measured cycle parameter, and the pre-exponential factor and decay coefficient corresponding to the second measured cycle parameter, the pre-exponential factor and decay coefficient corresponding to the target cycle parameter are determined. Substitute the pre-exponential factor and decay coefficient corresponding to the target cycling parameters into the battery life decay model to obtain the battery life decay model corresponding to the target cycling parameters.

6. The method for predicting battery cycle life data according to claim 1, characterized in that, The process of obtaining cycle life data of the battery under the target cycle parameters based on the target cycle parameters, using the battery life degradation model corresponding to the target cycle parameters, includes: The target cycle parameters and target capacity loss rate / target capacity retention rate of the battery to be predicted are input into the battery life degradation model corresponding to the target cycle parameters to obtain the predicted cycle number of the battery to be predicted under the target cycle parameters and the target capacity loss rate / target capacity retention rate.

7. The method for predicting battery cycle life data according to claim 1, characterized in that, The process of obtaining cycle life data of the battery under the target cycle parameters based on the target cycle parameters, using the battery life degradation model corresponding to the target cycle parameters, includes: The target cycle parameters and target number of cycles corresponding to the battery to be predicted are input into the battery life degradation model corresponding to the target cycle parameters to obtain the predicted capacity loss rate / capacity retention rate of the battery to be predicted under the target cycle parameters and the target number of cycles.

8. The method for predicting battery cycle life data according to claim 1, characterized in that, The parameters include charge / discharge condition parameters and battery design parameters.

9. A system for predicting battery cycle life data, characterized in that, include: The cycle life data prediction module is used to obtain the cycle life data of the battery under the target cycle parameters based on the target cycle parameters of the battery to be predicted, through the battery life degradation model corresponding to the target cycle parameters. The battery life degradation model corresponding to the target cycle parameter is determined based on the cycle life data under the measured cycle parameter; both the target cycle parameter and the measured cycle parameter include multiple parameters, and only one parameter has a different value.

10. An electronic device, characterized in that, Including memory and processor; The memory is connected to the processor and is used to store programs; The processor is used to implement the method for predicting battery cycle life data as described in any one of claims 1 to 8 by running a program in the memory.

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