Lithium ion cell reliability evaluation method, device and equipment and storage medium
By constructing an initial reliability model and utilizing the Bayesian fusion method, the problem of existing technologies failing to fully consider the characteristics of the degradation process and individual sample differences was solved, thereby improving the accuracy and safety of lithium-ion cell reliability assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHONGQING JINKANG NEW ENERGY VEHICLE CO LTD
- Filing Date
- 2026-01-09
- Publication Date
- 2026-04-24
AI Technical Summary
Existing lithium-ion cell reliability modeling methods do not fully consider the characteristics of various degradation processes and individual sample differences, resulting in large model errors and making it difficult to comprehensively and accurately reflect the true reliability status of lithium-ion cells.
By acquiring degradation process data from multiple lithium-ion cell samples, an initial reliability model was constructed. Then, the data was fused using a Bayesian fusion method to generate a target reliability model, taking into account various degradation process characteristics and individual sample differences.
This improves the accuracy of lithium-ion cell reliability assessment, enabling a more accurate reflection of the reliability status of lithium-ion cells and helping to optimize cell design and ensure safe use.
Smart Images
Figure CN121920288A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of lithium-ion battery cell design technology, specifically to a lithium-ion battery cell reliability assessment method, apparatus, equipment, and storage medium. Background Technology
[0002] Currently, lithium-ion battery cells are widely used in many fields, such as electric vehicles and mobile electronic devices. However, lithium-ion battery cells gradually degrade during use due to various complex factors, affecting their performance and lifespan. Accurately assessing the reliability of lithium-ion battery cells is crucial for optimizing design and ensuring safe use. Existing reliability modeling methods have many shortcomings. Some methods do not fully consider the characteristics of various degradation processes and individual sample differences, resulting in large model errors. Other methods perform poorly when integrating degradation data from different types or different test times, making it difficult to comprehensively and accurately reflect the true reliability status of lithium-ion battery cells. Summary of the Invention
[0003] In view of the above problems, this application provides a method, apparatus, device and storage medium for assessing the reliability of lithium-ion cells, which solves the problem that the reliability modeling of lithium-ion cells in the prior art relies on data from a single degradation stage or a fixed parameter model, making it difficult to comprehensively and accurately reflect the reliability status of the cells.
[0004] According to one aspect of the embodiments of this application, a method for evaluating the reliability of a lithium-ion battery cell is provided, the method comprising: Degradation process data of target degradation indicators of multiple lithium-ion battery cell samples are obtained to construct a degradation process dataset; wherein, the degradation process data includes process data of multiple preset degradation stages; Based on the degradation process dataset and combined with the process material parameters of the multiple lithium-ion cell samples, an initial reliability model is constructed to evaluate the target degradation index of lithium-ion cells. Using the Bayesian fusion method, the target degradation process data of the degradation process dataset are fused to determine the parameter estimates of the initial reliability model and generate the target reliability model; Using the target reliability model, the reliability of the lithium-ion battery cell under test is evaluated, and the reliability evaluation result of the lithium-ion battery cell under test is obtained.
[0005] In one alternative approach, the degradation process dataset includes: degradation process data of lithium-ion cell samples under different process material parameters and different usage conditions, degradation process data under different process material parameters and the same usage conditions, degradation process data under the same process material parameters and different usage conditions, and degradation process data under the same process material parameters and the same usage conditions.
[0006] In an alternative approach, the method further includes: Based on the curvature abrupt change point of each degradation process data, process data for multiple preset degradation stages corresponding to each degradation process data are determined; wherein, the multiple preset degradation stages include: initial rapid decay stage, linear degradation stage and accelerated failure stage.
[0007] In an alternative approach, the step of constructing an initial reliability model for evaluating the target degradation index of lithium-ion cells based on the degradation process dataset and in conjunction with the process material parameters of the plurality of lithium-ion cell samples further includes: Based on the degradation process dataset, and using the process material parameters of the multiple lithium-ion cell samples as random effect variables, an initial reliability model is constructed to characterize the individual differences of lithium-ion cells and to evaluate the target degradation index of lithium-ion cells.
[0008] In an alternative approach, the step of fusing the target degradation process data of the degradation process dataset using a Bayesian fusion method to determine the parameter estimates of the initial reliability model and generate the target reliability model further includes: Based on the data source and test duration, the target degradation process data in the degradation process dataset are classified to obtain at least two sets of classified degradation process data; The parameter estimates are obtained by fusing the at least two sets of classification degradation process data using the Bayesian fusion method. The initial reliability model is optimized using the parameter estimates to generate the target reliability model.
[0009] In one optional approach, the reliability assessment result is a reliability probability value; the step of using the target reliability model to perform a reliability assessment on the lithium-ion cell under test and obtaining the reliability assessment result of the lithium-ion cell under test further includes: The usage conditions and usage duration of the lithium-ion cell under test are input into the target reliability model to obtain the reliability probability value of the lithium-ion cell under test.
[0010] In one alternative approach, the target degradation metric is: capacity attenuation metric, internal resistance increase metric, power decrease metric, current decrease metric, or voltage increase metric.
[0011] According to another aspect of the embodiments of this application, a lithium-ion battery cell reliability evaluation device is provided, comprising: The acquisition module is used to acquire degradation process data of target degradation indicators of multiple lithium-ion battery cell samples and construct a degradation process dataset; wherein, the degradation process data includes process data of multiple preset degradation stages; A construction module is used to construct an initial reliability model for evaluating the target degradation index of lithium-ion cells based on the degradation process dataset and the process material parameters of the multiple lithium-ion cell samples. The fusion module is used to fuse the target degradation process data of the degradation process dataset using the Bayesian fusion method, determine the parameter estimates of the initial reliability model, and generate the target reliability model. The evaluation module is used to evaluate the reliability of the lithium-ion cell under test using the target reliability model, and obtain the reliability evaluation result of the lithium-ion cell under test.
[0012] According to another aspect of the embodiments of this application, a lithium-ion battery cell reliability evaluation device is provided, comprising: Controller; The memory is used to store one or more programs, which, when executed by the controller, enable the controller to implement the lithium-ion cell reliability evaluation method of this application.
[0013] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, the storage medium storing at least one executable instruction, which, when executed on a lithium-ion cell reliability assessment device / equipment, causes the lithium-ion cell reliability assessment device / equipment to perform operations as described in the lithium-ion cell reliability assessment method of this application.
[0014] This application embodiment constructs a degradation process dataset by acquiring degradation process data of target degradation indicators from multiple lithium-ion battery cell samples. The degradation process data includes process data from multiple preset degradation stages. Based on the degradation process dataset and combined with the process material parameters of the multiple lithium-ion battery cell samples, an initial reliability model for evaluating the target degradation indicators of lithium-ion batteries is constructed. A Bayesian fusion method is used to fuse the target degradation process data from the degradation process dataset to determine the parameter estimates of the initial reliability model, generating a target reliability model. Using the target reliability model, the reliability of the lithium-ion battery cell under test is evaluated, yielding the reliability evaluation result. This approach fully considers the characteristics of various degradation processes and individual sample differences, effectively solving the problems of large model errors and poor fusion of degradation data in existing modeling methods. It improves the accuracy of lithium-ion battery cell reliability evaluation, more realistically reflects the reliability status of lithium-ion batteries, and helps optimize battery cell design and ensure safe use.
[0015] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description
[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.
[0017] Figure 1 A flowchart illustrating an embodiment of the lithium-ion cell reliability assessment method provided in this application is shown.
[0018] Figure 2 A schematic diagram of an embodiment of the lithium-ion cell reliability evaluation device provided in this application is shown.
[0019] Figure 3 A schematic diagram of an embodiment of the lithium-ion cell reliability evaluation equipment provided in this application is shown. Detailed Implementation
[0020] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.
[0021] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor 30 devices and / or microcontroller devices.
[0022] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily need to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.
[0023] In this application, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.
[0024] Currently, lithium-ion battery cells are widely used in many fields, such as electric vehicles and mobile electronic devices. However, lithium-ion battery cells gradually degrade during use due to various complex factors, affecting their performance and lifespan. Accurately assessing the reliability of lithium-ion battery cells is crucial for optimizing design and ensuring safe use. Existing reliability modeling methods have many shortcomings. Some methods do not fully consider the characteristics of various degradation processes and individual sample differences, resulting in large model errors; others perform poorly when integrating degradation data from different types or different test times, making it difficult to comprehensively and accurately reflect the true reliability status of lithium-ion battery cells. Based on this: Figure 1 A flowchart illustrating an embodiment of the lithium-ion cell reliability assessment method provided in this application is shown. This method is performed by a lithium-ion cell reliability assessment device. Please refer to... Figure 1 As shown, the method includes the following steps: S110: Obtain degradation process data of target degradation indicators for multiple lithium-ion cell samples and construct a degradation process dataset.
[0025] The lithium-ion cell samples refer to physical lithium-ion cell units used for data acquisition, including commercial products (different brands / batch) or laboratory-made cells. Target degradation indices are key parameters characterizing the performance degradation of lithium-ion cells, such as capacity decay. Degradation process data refers to the measured data sequence of target degradation indices over time or cycle count within the lifespan of the lithium-ion cell samples. Degradation process data includes process data for multiple preset degradation stages.
[0026] S120: Based on the degradation process dataset and combined with the process material parameters of the multiple lithium-ion cell samples, construct an initial reliability model for evaluating the target degradation index of lithium-ion cells.
[0027] Among them, process and material parameters refer to manufacturing factors that affect the individual differences of lithium-ion cells, including differences in cell manufacturing processes and material microstructure. The initial reliability model refers to a mathematical model built based on a degradation process dataset, and its form is... ; This represents the predicted target degradation index value of the i-th lithium-ion cell sample at time t, which is used to quantify the performance status of the cell at a specific time. Indicates time, used as an independent variable to characterize the cumulative usage or aging process of lithium-ion cells; This represents a fixed-effects parameter vector, used to characterize the systematic impact of common factors such as environment and usage conditions on the degradation process of all battery cell samples; Let represent the random effect variable of the i-th lithium-ion cell sample, used to quantify the individual performance differences caused by the differences in the specific process and material parameters of the cell.
[0028] S130: Using the Bayesian fusion method, the target degradation process data of the degradation process dataset are fused to determine the parameter estimates of the initial reliability model and generate the target reliability model.
[0029] The specific process of fusing target degradation process data using the Bayesian fusion method is as follows: ① The prerequisite for executing S130 is that the classification of the target degradation process data has been completed. This classification is based on the data source and test duration, and at least two sets of classified degradation process data have been obtained. The degradation model on which the fusion calculation relies directly adopts the initial reliability model constructed in S120, specifically the capacity decay model: ; ② When establishing the likelihood function based on the capacity decay model, it is necessary to assume that the observed values in each group of classification degradation process data revolve around the model's predicted values. It follows a normal distribution and has a measurement error variance to be estimated. This describes the degree of dispersion, thus constructing a joint likelihood function for all observed data points. The fixed effects parameter vector Includes model parameters to be estimated and measurement error variance .
[0030] ③ is the fixed effects parameter vector. Define a prior distribution that reflects prior knowledge. Applying Bayes' theorem Calculate the posterior distribution The mean of the posterior distribution is the parameter estimate, for example, the parameter... The posterior mean of 2.5 is the parameter. The parameter estimates.
[0031] ④ Estimate the parameters, such as those in the capacity decay model. Specific values (such as) ), directly substitute into the specific mathematical expression of the initial reliability model, that is, substitute into the formula By determining the corresponding parameter positions, we obtain a mathematical model with all parameters determined that can be directly used for calculation, i.e., the optimized target reliability model.
[0032] It should be noted that, Indicates the first A sample of lithium-ion battery cells at time [time] and temperature The predicted capacity under the given conditions is used to describe the performance degradation state of the battery cell. This indicates the initial capacity of the lithium-ion battery cell, which is used as a benchmark value for capacity decay calculations. A positive scaling parameter is used to control the overall amplitude proportion of the capacity decay curve; This represents the temperature sensitivity coefficient, used to quantify the capacity decay rate as a function of temperature. The exponential dependence of change; Indicates the first The ambient temperature of each lithium-ion battery cell sample was used as a key conditional variable input into the model. This represents the time power parameter, used to determine the nonlinear shape of capacity decay over time.
[0033] S140: Using the target reliability model, perform a reliability assessment on the lithium-ion cell under test to obtain the reliability assessment result of the lithium-ion cell under test.
[0034] In this embodiment, the lithium-ion battery cell under test is the physical unit of the lithium-ion battery cell that requires reliability evaluation. The reliability evaluation result refers to the reliability probability value output by the target reliability model. The calculation formula is as follows: ,in Indicates the probability of failure. Indicates the time of lithium-ion cells The reliability probability value, Indicates the time of lithium-ion cells The actual value of the target degradation index.
[0035] The technical solution of this embodiment fully considers the characteristics of various degradation processes and individual sample differences, effectively solving the problems of large model errors and poor fusion of degradation data in existing modeling methods. It improves the accuracy of lithium-ion cell reliability assessment, can more realistically reflect the reliability status of lithium-ion cells, and helps to optimize cell design and ensure safe use.
[0036] In one alternative approach, the degradation process dataset includes: degradation process data of lithium-ion cell samples under different process material parameters and different usage conditions, degradation process data under different process material parameters and the same usage conditions, degradation process data under the same process material parameters and different usage conditions, and degradation process data under the same process material parameters and the same usage conditions.
[0037] Among them, usage conditions refer to external factors that affect the degradation of lithium-ion cells, including environmental parameters such as temperature, charge / discharge rate, and humidity.
[0038] Specifically: 1) Degradation data for lithium-ion battery cell samples under different process material parameters and usage conditions: Measured performance degradation sequences of multiple lithium-ion battery cells with different manufacturing processes or material compositions under different temperatures, charge / discharge rates, or humidity environments. 2) Degradation data for lithium-ion battery cell samples under different process material parameters and the same usage conditions: Measured performance degradation sequences of multiple lithium-ion battery cells with different manufacturing processes or material compositions under constant temperature, charge / discharge rate, and humidity environments. 3) Degradation data for lithium-ion battery cell samples with the same process material parameters and different usage conditions: Measured performance degradation sequences of multiple lithium-ion battery cells with completely identical manufacturing processes and material compositions under varying temperature, charge / discharge rate, or humidity environments. 4) Degradation data for lithium-ion battery cell samples with the same process material parameters and the same usage conditions: Measured performance degradation sequences of multiple lithium-ion battery cells with completely identical manufacturing processes and material compositions under constant temperature, charge / discharge rate, and humidity environments.
[0039] Among the above-mentioned optional methods, it is further clarified that the degradation process dataset covers data under different combinations of process material parameters and usage conditions, so that the initial reliability model can fully consider the influence of multiple factors, enhance the comprehensiveness and accuracy of the model, and better evaluate the reliability of lithium-ion cells in different scenarios.
[0040] In an alternative approach, the method further includes: Based on the curvature abrupt change point of each degradation process data, process data for multiple preset degradation stages corresponding to each degradation process data are determined.
[0041] The pre-defined degradation stages include: an initial rapid decay stage, a linear degradation stage, and an accelerated failure stage.
[0042] Among them, the curvature abrupt change point refers to the moment when the curvature of the degradation process data curve changes significantly, used to delineate the boundaries of the preset degradation stages. The initial rapid degradation stage represents the stage in which the performance of a lithium-ion cell drops sharply in the early stages of degradation due to the rapid formation of the solid electrolyte interphase (SEI) film; its characteristic is that the degradation rate decreases sharply over time. The linear degradation stage represents the stable degradation stage in the middle stage of lithium-ion cell degradation, dominated by side reactions; its characteristic is that the degradation index has an approximately linear relationship with time or the number of cycles. The accelerated failure stage represents the stage in the late stage of lithium-ion cell degradation where the performance collapses due to a sudden increase in internal impedance; its characteristic is that the degradation rate increases exponentially over time.
[0043] Specifically: 1) Obtain continuous monitoring data of the target degradation index of lithium-ion battery cell samples over time or cycle count to form the original degradation curve (i.e., degradation process data). 2) Calculate the first derivative (rate of change) and second derivative (acceleration) of the original degradation curve, and identify the moment when the curvature value changes abruptly using mathematical methods. 3) Mark the moment when the curvature value exceeds a preset threshold as the curvature abrupt change point, which represents the location where the degradation trend changes significantly. The preset threshold can be set according to the actual situation and is not limited here. 4) Divide the degradation process data into three preset degradation stages according to the curvature abrupt change points: ① Initial rapid decay stage: from the degradation start point to the first curvature abrupt change point; ② Linear degradation stage: from the first curvature abrupt change point to the second curvature abrupt change point; ③ Accelerated failure stage: from the second curvature abrupt change point to the degradation end point.
[0044] Among the above-mentioned optional methods, further determining the data of multiple preset degradation stages based on the curvature abrupt change points of the degradation process data can accurately capture the characteristics of lithium-ion cells at different degradation stages, providing strong support for building a more realistic reliability model, thereby improving the reliability of the evaluation results.
[0045] In an alternative approach, the step of constructing an initial reliability model for evaluating the target degradation index of lithium-ion cells based on the degradation process dataset and in conjunction with the process material parameters of the plurality of lithium-ion cell samples further includes: Based on the degradation process dataset, and using the process material parameters of the multiple lithium-ion cell samples as random effect variables, an initial reliability model is constructed to characterize the individual differences of lithium-ion cells and to evaluate the target degradation index of lithium-ion cells.
[0046] Among them, random effect variables refer to variables in the initial reliability model that follow a specific distribution (such as a normal distribution). ) variables It is used to quantify individual differences caused by process material parameters.
[0047] Specifically: 1) Obtain measured data sequences of target degradation indices over time or cycle number for lithium-ion battery cell samples based on degradation process datasets. 2) Construct mathematical expressions. As the basic framework of the initial reliability model, Indicates the first Each battery cell at any time 3) Determine the target degradation index value. The physical meaning of the fixed-effect parameters is related to environmental factors and usage conditions, including temperature, charge / discharge rate, or humidity variables. 4) Map the process and material parameters of multiple lithium-ion cell samples to random-effect variables. 5) Define the random effects variable. To quantify individual differences, a specific probability distribution is adopted, specifically the normal distribution. 6) Describe the performance fluctuations caused by process material parameters. Generate a distribution parameter library of random effect variables by statistically analyzing the degradation process dataset. This parameter library includes variance. 7) Integrating fixed effects parameters and random effect variables An initial reliability model is generated, which has the function of assessing target degradation indicators and characterizing individual differences.
[0048] Among the above-mentioned optional methods, it is further clarified that using process material parameters as random effect variables to construct an initial reliability model can effectively characterize the individual differences of lithium-ion cells, enabling the model to more accurately assess the reliability of each cell and reduce the assessment error caused by individual differences.
[0049] In an alternative approach, the step of fusing the target degradation process data of the degradation process dataset using a Bayesian fusion method to determine the parameter estimates of the initial reliability model and generate the target reliability model further includes: Based on the data source and test duration, the target degradation process data in the degradation process dataset are classified to obtain at least two sets of classified degradation process data.
[0050] Here, "data source" refers to the classification of channels through which degradation process data is acquired, including different manufacturers, different production process batches, or different experimental sources. "Test duration" refers to the time span or number of cycles for collecting degradation process data. "Target degradation process data" refers to the degradation process data actually used for Bayesian fusion within the degradation process dataset.
[0051] For example, the categorized degradation process data includes: heterogeneous data, such as data of battery cells from different manufacturers but with similar specifications; data of battery cells produced by the same manufacturer but with slightly adjusted production processes; and results of similar data at different test times, such as data of a batch of battery cells at different accelerated aging test stages.
[0052] The parameter estimates are obtained by fusing the at least two sets of classification degradation process data using the Bayesian fusion method.
[0053] The initial reliability model is optimized using the parameter estimates to generate the target reliability model.
[0054] The process of data fusion using the Bayesian fusion method is as follows: 1) Determine the likelihood function and choose an appropriate form based on the distribution characteristics of the degraded data. For example, a normal distribution likelihood function can be used for continuous degraded data. Specifically: ① Data Distribution Type: For degradation process data, it is usually assumed that it follows a normal distribution. Therefore, the likelihood function can take the form of a normal distribution likelihood function. Its parameters include the mean and variance of the data. The mean reflects the average degree of degradation of the battery at a certain stage, while the variance represents the dispersion of the data.
[0055] ② Degradation Model Parameters: Typical stages of multi-stage battery degradation include the initial rapid degradation stage (e.g., SEI formation), the linear degradation stage (dominated by side reactions), and the accelerated failure stage (sudden increase in impedance). Based on the mechanism and characteristics of battery degradation, an appropriate degradation model, such as a linear degradation model or a nonlinear degradation model, should be selected. For nonlinear degradation models, parameters may include coefficients of the exponential term, which determine the shape and trend of the degradation curve.
[0056] 2) Set a prior distribution. Based on historical experience or knowledge, make preliminary assumptions about the range of values and probability distribution of model parameters. Specifically: ① Range of parameter values: Based on historical experience, knowledge, or relevant literature, the possible range of values for model parameters is initially set. For example, for a certain parameter in the battery capacity degradation model, based on previous research and experimental data, it can be assumed that its value range is within a certain interval.
[0057] ② Type of Probability Distribution: Choose an appropriate prior probability distribution to describe the uncertainty of the parameter. Common prior distributions include uniform distribution, normal distribution, and gamma distribution. Uniform distribution is suitable for situations where there is no preference for the parameter value and specific prior information is lacking; normal distribution is suitable for situations where there is some understanding of the parameter, and its value is believed to be concentrated around a certain mean with a certain range of fluctuation; gamma distribution is suitable for situations where the parameter value is positive, such as certain rate parameters in the battery degradation process.
[0058] 3) Calculate the posterior distribution using Bayes' theorem to obtain the fused parameter estimates. For example, for the model parameter vector θ, where D is the observed data, the likelihood function is L(θ|D), and the prior distribution is π(θ), then the posterior distribution π(θ|D) ∝ L(θ|D)π(θ). The posterior distribution contains the mean and variance. The mean represents the best estimate of the parameters after considering the observed data, while the variance reflects the degree of uncertainty in the estimate. Compared to the prior distribution, the variance of the posterior distribution is usually smaller, indicating that data fusion reduces the uncertainty of the parameters.
[0059] It should be noted that, taking the fusion of two sets of classification degradation process data as an example: 1) Two sets of accelerated degradation process data obtained in the laboratory under different temperature conditions were fused. One set of data was obtained from experiments conducted at 45℃, and the other set was obtained from experiments conducted at 60℃.
[0060] 2) First, determine the likelihood function, assuming the capacity data follows a normal distribution N(Yi(t,Ti). ),in This represents the measurement error variance.
[0061] 3) Set the prior distribution. Based on previous research and experience, assume that the prior distribution of parameters a, b, and c is a uniform distribution U(0,10).
[0062] 4) Then, the posterior distribution is calculated according to Bayes' theorem, and numerical sampling is performed using the Markov chain Monte Carlo method to obtain the fused parameter estimates. For example, after 10,000 iterations of sampling, the posterior mean of parameter a is 2.5 and the variance is 0.3.
[0063] 5) Validate the degradation process data of the remaining lithium-ion cell samples not included in the modeling through actual use or simulation testing, comparing the actual failure scenarios with the reliability probability values. If the two match well, the model is proven effective; otherwise, further adjust the model structure and parameters until satisfactory accuracy is achieved. For example, divide the degradation process data of the remaining lithium-ion cell samples in the laboratory into a validation group and a control group. The validation group performs reliability assessments according to the above-mentioned fused model, predicting the cell lifespan and reliability under different temperatures and charge / discharge conditions; the control group records the actual cell failure scenarios in actual testing. The average cell lifespan predicted by the validation group differs from the actual observation results of the control group by no more than 5%, and the reliability curve trend is consistent, proving the effectiveness of the model.
[0064] In the above-mentioned optional methods, after further classifying the degradation process dataset according to data source and test duration, the Bayesian fusion method is used to fuse the classified data to determine the parameter estimates. This can fully explore the information in different types and durations of data, optimize the initial reliability model, and improve the accuracy and adaptability of the model for assessing the reliability of lithium-ion cells.
[0065] In one optional approach, the reliability assessment result is a reliability probability value; the step of using the target reliability model to perform a reliability assessment on the lithium-ion cell under test and obtaining the reliability assessment result of the lithium-ion cell under test further includes: The usage conditions and usage duration of the lithium-ion cell under test are input into the target reliability model to obtain the reliability probability value of the lithium-ion cell under test.
[0066] Among them, the usage conditions and usage duration of the lithium-ion battery cell under test are variables input into the target reliability model, corresponding to the environmental parameters of the cell and the cumulative usage time / number of cycles, respectively. The reliability probability value refers to the output of the target reliability model. This indicates that the battery cell is in time. The probability that it has not failed.
[0067] In the above-mentioned optional methods, the usage conditions and usage time of the battery cell under test are further input into the target reliability model to obtain the reliability probability value. This realizes an intuitive and quantitative assessment of the reliability of lithium-ion battery cells, providing users with a clear reliability reference and facilitating their decision-making in practical applications.
[0068] In one alternative approach, the target degradation metric is: capacity attenuation metric, internal resistance increase metric, power decrease metric, current decrease metric, or voltage increase metric.
[0069] Among them, the capacity decay index represents the actual capacity of the battery cell relative to its initial capacity. The percentage decrease (e.g.) The internal resistance increase index indicates the increase in the cell's internal impedance over time. The power decrease index indicates the decrease in the cell's maximum output power. The current decrease index indicates the decrease in the cell's output current at a fixed voltage. The voltage increase index indicates the abnormal increase in the cell's terminal voltage at the same charge level (reflecting polarization).
[0070] Among the above-mentioned optional methods, it is even more possible to comprehensively evaluate the reliability of lithium-ion cells from multiple key performance dimensions, ensuring the comprehensiveness and accuracy of the evaluation results.
[0071] Figure 2 A schematic diagram of an embodiment of the lithium-ion cell reliability evaluation device provided in this application is shown. Please refer to... Figure 2 As shown, the device 300 includes: an acquisition module 310, a construction module 320, a fusion module 330, and an evaluation module 340.
[0072] The acquisition module 310 is used to acquire degradation process data of target degradation indicators of multiple lithium-ion cell samples and construct a degradation process dataset; wherein, the degradation process data includes process data of multiple preset degradation stages; The construction module 320 is used to construct an initial reliability model for evaluating the target degradation index of lithium-ion cells based on the degradation process dataset and in combination with the process material parameters of the multiple lithium-ion cell samples. The fusion module 330 is used to fuse the target degradation process data of the degradation process dataset using the Bayesian fusion method, determine the parameter estimates of the initial reliability model, and generate the target reliability model. The evaluation module 340 is used to evaluate the reliability of the lithium-ion cell under test using the target reliability model, and obtain the reliability evaluation result of the lithium-ion cell under test.
[0073] In one alternative approach, the degradation process dataset includes: degradation process data of lithium-ion cell samples under different process material parameters and different usage conditions, degradation process data under different process material parameters and the same usage conditions, degradation process data under the same process material parameters and different usage conditions, and degradation process data under the same process material parameters and the same usage conditions.
[0074] In an alternative embodiment, the apparatus further includes: a determining module; the determining module is configured to: Based on the curvature abrupt change point of each degradation process data, process data for multiple preset degradation stages corresponding to each degradation process data are determined; wherein, the multiple preset degradation stages include: initial rapid decay stage, linear degradation stage and accelerated failure stage.
[0075] In an alternative embodiment, the building module 320 is specifically used for: Based on the degradation process dataset, and using the process material parameters of the multiple lithium-ion cell samples as random effect variables, an initial reliability model is constructed to characterize the individual differences of lithium-ion cells and to evaluate the target degradation index of lithium-ion cells.
[0076] In an alternative embodiment, the fusion module 330 is specifically used for: Based on the data source and test duration, the target degradation process data in the degradation process dataset are classified to obtain at least two sets of classified degradation process data; The parameter estimates are obtained by fusing the at least two sets of classification degradation process data using the Bayesian fusion method. The initial reliability model is optimized using the parameter estimates to generate the target reliability model.
[0077] In one optional approach, the reliability assessment result is a reliability probability value; the assessment module 340 is specifically used for: The usage conditions and usage duration of the lithium-ion cell under test are input into the target reliability model to obtain the reliability probability value of the lithium-ion cell under test.
[0078] In one alternative approach, the target degradation metric is: capacity attenuation metric, internal resistance increase metric, power decrease metric, current decrease metric, or voltage increase metric.
[0079] The technical solution of this embodiment fully considers the characteristics of various degradation processes and individual sample differences, effectively solving the problems of large model errors and poor fusion of degradation data in existing modeling methods. It improves the accuracy of lithium-ion cell reliability assessment, can more realistically reflect the reliability status of lithium-ion cells, and helps to optimize cell design and ensure safe use.
[0080] It should be noted that the lithium-ion cell reliability evaluation device provided in the above embodiments and the lithium-ion cell reliability evaluation method provided in the foregoing embodiments belong to the same concept. The specific way in which each module and unit performs its operation has been described in detail in the method embodiments, and will not be repeated here.
[0081] Figure 3 The diagram shows a structural schematic of an embodiment of the lithium-ion cell reliability evaluation device provided in this application. It also shows a structural schematic of a computer system suitable for implementing the lithium-ion cell reliability evaluation device of this application. The specific embodiments of this application do not limit the specific implementation of the lithium-ion cell reliability evaluation device.
[0082] Please see Figure 3 As shown, the lithium-ion cell reliability assessment device includes: a controller; and a memory for storing one or more programs, which, when executed by the controller, enable the controller to implement the aforementioned lithium-ion cell reliability assessment method.
[0083] Please continue reading. Figure 3 As shown, the computer system 500 of the lithium-ion cell reliability evaluation device includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from storage section 508 into random access memory (RAM) 503, such as executing the methods in the above embodiments. Various programs and data required for system operation are also stored in RAM 503. The CPU 501, ROM 502, and RAM 503 are interconnected via bus 504. An input / output (I / O) interface 505 is also connected to bus 504.
[0084] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 510 as needed so that computer programs read from them can be installed into storage section 508 as needed.
[0085] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs various functions defined in the system of this application.
[0086] Another aspect of this application provides a computer-readable storage medium storing at least one executable instruction that, when executed on a lithium-ion cell reliability assessment device / equipment, causes the lithium-ion cell reliability assessment device / equipment to perform the operation of the lithium-ion cell reliability assessment method as described above. This computer-readable storage medium may be included in the lithium-ion cell reliability assessment device described in the above embodiments, or it may exist independently and not assembled into the electronic device.
[0087] Another aspect of this application provides a computer program product or computer program that includes at least one executable instruction that, when executed on a lithium-ion cell reliability assessment device / equipment, causes the lithium-ion cell reliability assessment device / equipment to perform the lithium-ion cell reliability assessment method as described above.
[0088] Specifically, the executable instructions can be used to cause the lithium-ion cell reliability assessment equipment / device to perform the following operations: Degradation process data of target degradation indicators of multiple lithium-ion battery cell samples are obtained to construct a degradation process dataset; wherein, the degradation process data includes process data of multiple preset degradation stages; Based on the degradation process dataset and combined with the process material parameters of the multiple lithium-ion cell samples, an initial reliability model is constructed to evaluate the target degradation index of lithium-ion cells. Using the Bayesian fusion method, the target degradation process data of the degradation process dataset are fused to determine the parameter estimates of the initial reliability model and generate the target reliability model; Using the target reliability model, the reliability of the lithium-ion battery cell under test is evaluated, and the reliability evaluation result of the lithium-ion battery cell under test is obtained.
[0089] In one alternative approach, the degradation process dataset includes: degradation process data of lithium-ion cell samples under different process material parameters and different usage conditions, degradation process data under different process material parameters and the same usage conditions, degradation process data under the same process material parameters and different usage conditions, and degradation process data under the same process material parameters and the same usage conditions.
[0090] In an alternative approach, the method further includes: Based on the curvature abrupt change point of each degradation process data, process data for multiple preset degradation stages corresponding to each degradation process data are determined; wherein, the multiple preset degradation stages include: initial rapid decay stage, linear degradation stage and accelerated failure stage.
[0091] In an alternative approach, the step of constructing an initial reliability model for evaluating the target degradation index of lithium-ion cells based on the degradation process dataset and in conjunction with the process material parameters of the plurality of lithium-ion cell samples further includes: Based on the degradation process dataset, and using the process material parameters of the multiple lithium-ion cell samples as random effect variables, an initial reliability model is constructed to characterize the individual differences of lithium-ion cells and to evaluate the target degradation index of lithium-ion cells.
[0092] In an alternative approach, the step of fusing the target degradation process data of the degradation process dataset using a Bayesian fusion method to determine the parameter estimates of the initial reliability model and generate the target reliability model further includes: Based on the data source and test duration, the target degradation process data in the degradation process dataset are classified to obtain at least two sets of classified degradation process data; The parameter estimates are obtained by fusing the at least two sets of classification degradation process data using the Bayesian fusion method. The initial reliability model is optimized using the parameter estimates to generate the target reliability model.
[0093] In one optional approach, the reliability assessment result is a reliability probability value; the step of using the target reliability model to perform a reliability assessment on the lithium-ion cell under test and obtaining the reliability assessment result of the lithium-ion cell under test further includes: The usage conditions and usage duration of the lithium-ion cell under test are input into the target reliability model to obtain the reliability probability value of the lithium-ion cell under test.
[0094] In one alternative approach, the target degradation metric is: capacity attenuation metric, internal resistance increase metric, power decrease metric, current decrease metric, or voltage increase metric.
[0095] The technical solution of this embodiment fully considers the characteristics of various degradation processes and individual sample differences, effectively solving the problems of large model errors and poor fusion of degradation data in existing modeling methods. It improves the accuracy of lithium-ion cell reliability assessment, can more realistically reflect the reliability status of lithium-ion cells, and helps to optimize cell design and ensure safe use.
[0096] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.
[0097] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0098] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.
[0099] According to one aspect of the embodiments of this application, a computer system is also provided, including a Central Processing Unit (CPU), which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) or a program loaded from storage into random access memory (RAM), such as performing the methods described above. Various programs and data required for system operation are also stored in the RAM. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.
[0100] The following components are connected to the I / O interface: input components including keyboards, mice, etc.; output components including cathode ray tubes (CRTs), liquid crystal displays (LCDs), and speakers; storage components including hard drives; and communication components including network interface cards such as LAN (Local Area Network) cards and modems. The communication components perform communication processing via networks such as the Internet. Drives are also connected to the I / O interface as needed. Removable media, such as disks, optical discs, magneto-optical discs, semiconductor memories, etc., are installed on the drive as needed so that computer programs read from them can be installed into the storage components as required.
[0101] The above content is merely a preferred exemplary embodiment of this application and is not intended to limit the implementation of this application. Those skilled in the art can easily make corresponding modifications or alterations based on the main concept and spirit of this application. Therefore, the scope of protection of this application should be determined by the scope of protection claimed in the claims.
Claims
1. A method for evaluating the reliability of lithium-ion battery cells, characterized in that, The method includes: Degradation process data of target degradation indicators of multiple lithium-ion battery cell samples are obtained to construct a degradation process dataset; wherein, the degradation process data includes process data of multiple preset degradation stages; Based on the degradation process dataset and combined with the process material parameters of the multiple lithium-ion cell samples, an initial reliability model is constructed to evaluate the target degradation index of lithium-ion cells. Using the Bayesian fusion method, the target degradation process data of the degradation process dataset are fused to determine the parameter estimates of the initial reliability model and generate the target reliability model; Using the target reliability model, the reliability of the lithium-ion battery cell under test is evaluated, and the reliability evaluation result of the lithium-ion battery cell under test is obtained.
2. The method according to claim 1, characterized in that, The degradation process dataset includes: degradation process data of lithium-ion cell samples under different process material parameters and different usage conditions, degradation process data under different process material parameters and the same usage conditions, degradation process data under the same process material parameters and different usage conditions, and degradation process data under the same process material parameters and the same usage conditions.
3. The method according to claim 1, characterized in that, The method further includes: Based on the curvature abrupt change point of each degradation process data, process data for multiple preset degradation stages corresponding to each degradation process data are determined; wherein, the multiple preset degradation stages include: initial rapid decay stage, linear degradation stage, and accelerated failure stage.
4. The method according to claim 2, characterized in that, The step of constructing an initial reliability model for evaluating the target degradation index of lithium-ion cells based on the degradation process dataset and in conjunction with the process and material parameters of the multiple lithium-ion cell samples further includes: Based on the degradation process dataset, and using the process material parameters of the multiple lithium-ion cell samples as random effect variables, an initial reliability model is constructed to characterize the individual differences of lithium-ion cells and to evaluate the target degradation index of lithium-ion cells.
5. The method according to claim 2, characterized in that, The step of fusing the target degradation process data of the degradation process dataset using the Bayesian fusion method to determine the parameter estimates of the initial reliability model and generate the target reliability model further includes: Based on the data source and test duration, the target degradation process data in the degradation process dataset are classified to obtain at least two sets of classified degradation process data; The parameter estimates are obtained by fusing the at least two sets of classification degradation process data using the Bayesian fusion method. The initial reliability model is optimized using the parameter estimates to generate the target reliability model.
6. The method according to claim 2, characterized in that, The reliability assessment result is a reliability probability value; the step of using the target reliability model to perform a reliability assessment on the lithium-ion cell under test and obtaining the reliability assessment result of the lithium-ion cell under test further includes: The usage conditions and usage duration of the lithium-ion cell under test are input into the target reliability model to obtain the reliability probability value of the lithium-ion cell under test.
7. The method according to any one of claims 1 to 6, characterized in that, The target degradation indicators are: capacity attenuation indicator, internal resistance increase indicator, power decrease indicator, current decrease indicator, or voltage increase indicator.
8. A lithium-ion battery cell reliability evaluation device, characterized in that, The device includes: The acquisition module is used to acquire degradation process data of target degradation indicators of multiple lithium-ion cell samples and construct a degradation process dataset; wherein, the degradation process data includes process data of multiple preset degradation stages; A construction module is used to construct an initial reliability model for evaluating the target degradation index of lithium-ion cells based on the degradation process dataset and the process material parameters of the multiple lithium-ion cell samples. The fusion module is used to fuse the target degradation process data of the degradation process dataset using the Bayesian fusion method, determine the parameter estimates of the initial reliability model, and generate the target reliability model. The evaluation module is used to evaluate the reliability of the lithium-ion cell under test using the target reliability model, and obtain the reliability evaluation result of the lithium-ion cell under test.
9. A lithium-ion battery cell reliability evaluation device, characterized in that, include: Controller; A memory for storing one or more programs, which, when executed by a controller, cause the controller to implement the lithium-ion cell reliability assessment method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, which, when executed on the lithium-ion cell reliability assessment device / equipment, causes the lithium-ion cell reliability assessment device / equipment to perform the operation of the lithium-ion cell reliability assessment method as described in any one of claims 1-7.