Lithium battery life reliability evaluation method, system and device

Abstract

The embodiment of the invention discloses a lithium battery life reliability evaluation method, system and device. The method comprises the following steps: acquiring design parameters of a battery cell; according to the design parameters, carrying out battery cycle aging tests under different working conditions to obtain measured aging data of the battery cell; establishing a simulation model in which an aging model, a thermal model and an electrochemical model are mutually coupled; thermodynamic parameters and dynamic parameters in the simulation model are optimized according to the measured data and an optimization algorithm, and parameter data sets of different cells under different working conditions are obtained; based on the parameter data set and the simulation model, performing multiple aging simulation on the battery cell under a specified working condition to obtain simulation data of battery cell aging; and counting a battery cell life distribution rule according to the simulation data to obtain the life reliability of the battery cell under a specified working condition. According to the embodiment of the invention, the technical problem of how to efficiently and accurately evaluate the life reliability of the battery cell under the complex working condition is solved.

Description

Technical Field

[0001] This application relates to the field of lithium-ion battery technology, and in particular to a method, system and device for assessing the life reliability of lithium batteries. Background Technology

[0002] With the advancement of global energy transition and vehicle electrification, lithium-ion batteries, as the core power source for new energy vehicles, are experiencing continuous market demand growth. However, the performance degradation of batteries during long-term use, such as capacity decay and increased internal resistance, limits the driving range and power performance of the vehicle, and also affects its safety and reliability. Furthermore, battery costs can account for a significant proportion of the total vehicle cost, and the replacement costs at the end of their lifespan constitute a potential after-sales cost risk for automakers. Therefore, accurately predicting battery lifespan and assessing its reliability under different usage conditions has become a common concern for battery manufacturers, automakers, and even consumers.

[0003] Currently, research on lithium-ion battery life assessment in the industry mainly employs several different technical approaches. A common method relies on extensive physical experiments, acquiring data through long-term cycle testing of battery samples under strictly controlled environments and operating conditions. While this method yields intuitive and reliable results, it is time-consuming, costly, and fails to consider the complex and variable stress issues encountered in real-world use. Another approach is prediction methods based on pure simulation models, utilizing coupled models of electrochemical, thermal, and aging fields to simulate the battery degradation process at a mechanistic level. This method often relies on assumptions or simple calibrations, resulting in significant deviations between predicted and actual values. It fails to reflect inherent performance inconsistencies during cell manufacturing and cannot address probabilistic issues in mass production and application. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a lithium battery life reliability assessment method, system, and apparatus to solve the technical problem of how to efficiently and accurately assess the life reliability of battery cells under complex operating conditions.

[0005] In a first aspect, this application provides a method for evaluating the lifespan reliability of a lithium battery, the method comprising: Obtain the design parameters of the battery cell; Based on the design parameters, battery cycle aging tests were conducted under different operating conditions to obtain measured data on cell aging. A simulation model was established that couples the aging model, the thermal model, and the electrochemical model. Based on the measured data and optimization algorithm, the thermodynamic and kinetic parameters in the simulation model are optimized to obtain parameter datasets for different cells under different operating conditions; Based on the parameter dataset and the simulation model, multiple aging simulations of the battery cell under specified operating conditions are performed to obtain simulation data of battery cell aging. Based on the statistical distribution of cell lifespan using the simulation data, the lifespan reliability of the cells under specified operating conditions is obtained.

[0006] In at least some embodiments of this application, The battery cycle aging test under different operating conditions based on the design parameters is used to obtain the measured data of cell aging, including: The operating conditions for conducting battery cycle aging tests on the battery cells are determined based on the design parameters; wherein, the operating conditions include ambient temperature, charging rate, and cutoff voltage; Battery cycle aging tests were conducted on the cells under different operating conditions, and measured data of the aging of all cells were obtained during the test.

[0007] In at least some embodiments of this application, The aging model, thermal model, and electrochemical model are coupled in the following ways: The thermal model transmits the calculated temperature values ​​to the aging model and the electrochemical model; The aging model calculates the cell structure parameters based on the accumulated side reactions and feeds them back to the electrochemical model to update the relevant parameters of the electrochemical model. The electrochemical model updates all its parameters based on the obtained temperature values; The electrochemical model calculates the heat generation rate of the battery cell based on the current internal state and transfers this information to the thermal model. The electrochemical model provides the aging model with the microscopic driving forces that lead to cell aging, wherein the microscopic driving forces include side reaction rates, stress, and lithium plating potential.

[0008] In at least some embodiments of this application, The thermodynamic and kinetic parameters in the simulation model are optimized based on the measured data and optimization algorithm to obtain parameter datasets for different cells under different operating conditions, including: Obtain initial simulation data of cell aging of the simulation model without parameter optimization under different operating conditions; The error between the measured data and the initial simulation data under the corresponding working conditions is used as the optimization objective function; The parameters and dynamic parameters in the simulation model are optimized based on the optimization algorithm and the optimization objective function to obtain a parameter dataset of simulation parameters for different cells under different operating conditions. The parameter dataset includes design parameters, optimized dynamic parameters, and thermodynamic parameters.

[0009] In at least some embodiments of this application, The optimization algorithm includes at least one of genetic algorithm, particle swarm optimization algorithm, and ant colony optimization algorithm.

[0010] In at least some embodiments of this application, The method further includes: performing statistical analysis on the simulation parameters of different cells under different operating conditions to determine the probability distribution corresponding to the simulation parameters.

[0011] In at least some embodiments of this application, The process of performing multiple aging simulations of the battery cell under specified operating conditions based on the parameter dataset and the simulation model to obtain simulation data of battery cell aging includes: A set of parameters is randomly selected from the parameter dataset as the initial parameter values ​​for the simulation model; Under specified operating conditions, aging simulation is performed using the simulation model, and the simulation process is repeated multiple times to obtain multiple sets of simulation data for cell aging; wherein, the simulation data includes the capacity retention rate and cycle number of the corresponding cells.

[0012] In at least some embodiments of this application, The step of statistically analyzing the cell lifespan distribution pattern based on the simulation data to obtain the cell lifespan reliability under specified operating conditions includes: The number of cycles when the cell capacity retention rate is at a preset value is taken as the life simulation result of the corresponding cell. The lifetime simulation results are arranged in ascending order of the number of cycles to obtain the sequence of the number of cycles. The cumulative failure probability is statistically analyzed for each cycle number in the cycle number sequence, and a scatter plot of the cumulative failure probability corresponding to each cycle number is drawn in the order of the cycle number sequence. The distribution pattern of the battery cell lifespan is determined based on the scatter plot. Based on the distribution pattern, the life reliability of the battery cell when it is cycled to a specified number of cycles under specified operating conditions is obtained.

[0013] In a second aspect, this application provides a lithium battery life reliability assessment system, the system comprising: The data acquisition unit is used to obtain the design parameters of the battery cell; The test unit is used to perform battery cycle aging tests under different operating conditions according to the design parameters, and to obtain the test data of cell aging. The modeling unit is used to create a simulation model that couples the aging model, thermal model, and electrochemical model. The optimization unit is used to optimize the thermodynamic and kinetic parameters in the simulation model based on the measured data and optimization algorithm to obtain parameter datasets for different cells under different operating conditions. The simulation unit is used to perform multiple aging simulations of the battery cell under specified operating conditions based on the parameter dataset and the simulation model, so as to obtain simulation data of battery cell aging. The statistical unit is used to statistically analyze the cell life distribution pattern based on the simulation data to obtain the cell life reliability under specified operating conditions.

[0014] In a third aspect, this application provides an electronic device including a memory, one or more processors, and one or more application programs, wherein the one or more application programs are stored in the memory and are configured to, when invoked by the one or more processors, cause the one or more processors to perform the method as described in any one aspect.

[0015] The above-described one or more embodiments of this application have at least one or more of the following beneficial effects: The lithium battery life reliability assessment method provided in this application calibrates and optimizes simulation model parameters by acquiring actual battery aging test data, making the simulation results highly consistent with the measured data, thus improving the accuracy and reliability of life prediction. Compared with simulation methods that rely solely on experiments or simple calibration, this method has smaller prediction bias and higher confidence. At the same time, the optimized model can quickly complete life simulation predictions under a large number of different operating conditions, shortening the battery life assessment cycle and reducing the time and economic costs caused by a large number of repetitive physical tests. Furthermore, the assessment results provided in this application, such as the life distribution under specific operating conditions, can directly provide a basis for warranty assessment and after-sales cost estimation for vehicles using battery cells, and have engineering application value.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The disclosure of this application will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. Furthermore, similar numbers in the drawings are used to denote similar components, wherein: Figure 1 This is a schematic diagram of the main steps of a lithium battery life reliability assessment method in one embodiment of this application; Figure 2 This is a schematic diagram of the simulation model coupling method in one embodiment of this application; Figure 3This is a schematic diagram of an electrochemical model in one embodiment of this application; Figure 4 This is a schematic diagram of the particle size distribution of the positive and negative electrode active materials in one embodiment of this application; Figure 5 This is a schematic diagram of the equilibrium potential of the positive and negative electrode materials in one embodiment of this application; Figure 6 This is a schematic diagram of the entropy thermal coefficients of the positive and negative electrode materials in one embodiment of this application; Figure 7 This is a schematic diagram of the cell capacity retention rate during a specified operating condition cycle in one embodiment of this application; Figure 8 This is a schematic diagram of the discharge voltage variation curve of the battery cell at different cycle numbers in one embodiment of this application; Figure 9 This is a schematic diagram of the temperature change curve of the large surface of the battery cell during the charging and discharging process in one embodiment of this application; Figure 10 This is a comparison chart of simulation and experimental data before and after parameter optimization in one embodiment of this application; Figure 11 This is a diagram showing the SEI film growth reaction rate distribution in one embodiment of this application; Figure 12 This is a schematic diagram of the cycle life simulation results under specified operating conditions in one embodiment of this application; Figure 13 This is a Weibull distribution probability diagram of cycle lifetime in one embodiment of this application. Detailed Implementation

[0018] Some embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application.

[0019] As described in the background section, existing methods for assessing the reliability of lithium battery life mainly rely on two approaches: actual testing and simulation. However, actual testing is time-consuming and costly, while simulations often suffer from significant deviations and inaccurate results. To address these issues, this application proposes a lithium battery life reliability assessment method that combines measured data with a simulation model. This method calibrates and optimizes the simulation model using the measured data and considers lifespan distribution patterns, thereby enabling efficient and accurate assessment of battery lifespan.

[0020] See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a lithium battery life reliability assessment method according to an embodiment of this application. Figure 1As shown, a lithium battery life reliability assessment method in this embodiment of the invention mainly includes the following steps S101-S106: S101: Obtain the design parameters of the battery cell; S102: Conduct battery cycle aging tests under different operating conditions according to design parameters to obtain actual measurement data of cell aging; S103: Establish a simulation model that couples the aging model, thermal model, and electrochemical model; S104: Optimize the thermodynamic and kinetic parameters in the simulation model based on measured data and optimization algorithms to obtain parameter datasets for different cells under different operating conditions; S105: Based on the parameter dataset and simulation model, perform multiple aging simulations of the battery cell under specified operating conditions to obtain simulation data of battery cell aging. S106: Based on the simulation data, statistically analyze the cell life distribution pattern to obtain the cell life reliability under specified operating conditions.

[0021] It is understood that in this application, measured data of battery aging are obtained by designing tests under different operating conditions. These different operating conditions can cover multiple stress factors such as temperature, rate, and voltage. The main purpose is to comprehensively consider multiple factors under limited conditions to obtain more accurate measured data. A simulation model coupling thermal model, electrochemical model, and aging model is established. This simulation model comprehensively considers the dynamic changes of the battery and can accurately predict the performance evolution and lifespan degradation of the battery throughout its entire life cycle, thereby improving the accuracy of the simulation. Furthermore, this application uses optimization algorithms to identify and calibrate key parameters in the simulation model to ensure a high degree of consistency between the simulation and measured data. Then, based on this dataset, the aging behavior of a batch of battery cells can be simulated through large-scale random sampling and repeated simulations, and finally, the lifespan distribution and reliability of the battery group can be statistically obtained.

[0022] The embodiments of this application calibrate and optimize the simulation model parameters by acquiring actual battery aging test data, so that the simulation results are highly consistent with the test data, improving the accuracy and reliability of life prediction. Compared with simulation methods that rely solely on experiments or simple calibration, this method has smaller prediction deviations and higher confidence. At the same time, the optimized model can quickly complete life simulation predictions under a large number of different operating conditions, shortening the battery life assessment cycle and reducing the time and economic costs caused by a large number of repetitive physical tests. Furthermore, the assessment results provided by this application, such as the life distribution under specific operating conditions, can directly provide a basis for warranty assessment and after-sales cost estimation for vehicles using battery cells, and have engineering application value.

[0023] In one embodiment, the parameters of the battery cell in this application include design parameters, kinetic parameters, and thermodynamic parameters. The design parameters include dimensional parameters, such as electrode length, electrode width, electrode thickness, current collector thickness, and number of electrodes. The design parameters also include: active material particle radius, active material type, porosity, active material volume fraction, capacity, group margin, separator porosity, electrolyte type, electrolyte injection volume, conductive agent volume fraction, binder volume fraction, and voltage range. The kinetic and thermodynamic parameters include electrical conductivity, constant-pressure heat capacity, density, thermal conductivity, equilibrium potential, entropic thermal coefficient, diffusion coefficient, maximum lithium intercalation concentration, exchange current density, diffusion coefficient activation energy, and reaction boundary conditions.

[0024] In one embodiment, in step S102, the battery cell for which reliability evaluation is required is subjected to cyclic aging tests under different operating conditions according to the test scheme listed in Table 1 below. The operating conditions are determined based on the battery cell's design parameters, including the ambient temperature, charging rate, and cutoff voltage. The cutoff voltage includes an upper cutoff voltage and a lower cutoff voltage.

[0025] Table 1: In Table 1, T1, T2, and T3 represent different temperatures; C1 and C2 represent different charging rates; Vmin1 and Vmin2 represent different lower cutoff voltages; Vmax represents the upper cutoff voltage; and N is the number of cell samples, which must be ≥2. The number of cell samples and experimental conditions can be flexibly set according to requirements.

[0026] Cyclic tests were performed under different operating conditions as shown in Table 1 above in a constant temperature and humidity simulated environment test chamber.

[0027] Specifically, taking test number 1 as an example, the cyclic aging test method includes: (1) Use 1 / 3 at room temperature The battery cell was subjected to three charge-discharge tests at a current of the designed capacity, with a 1-hour rest period between charge and discharge cycles. The capacities obtained from the three cycles were C1, C2, and C3, and the average value was taken as the initial capacity C0 of the battery cell. (2) At an ambient temperature of 25±2℃, 1 / 3 C0 charges at a constant current and constant voltage until the upper limit cutoff voltage Vmax1 is reached, at which point the cutoff current reaches 0.05. C0, let stand for 30 minutes; (3) At an ambient temperature of 25±2℃, 1 / 3 C0 constant current discharge to 50% SOC; (4) At an ambient temperature of 25±2℃, 3 After discharging at C0 for 10 seconds and resting for 30 minutes, calculate the discharge DCR at 50% SOC. (5) At an ambient temperature of 25±2℃, 1 / 3 C0 charges at a constant current and constant voltage until it reaches the upper limit voltage Vmax1, at which point the cutoff current reaches 0.05. C0, let stand for 1 hour; (6) At ambient temperature T1, 1 / 3 C0 is discharged at a constant current to the lower limit voltage Vmin1, and then left to stand for 1 hour. (7) At ambient temperature T1, charge at C1 rate to the upper limit voltage Vmax1 and let stand for 1 hour; (8) At ambient temperature T1, discharge at constant current C0 to the lower limit voltage Vmin1 and let stand for 1 hour; (9) Repeat steps (7) to (8) 100 times; (10) Using C0 capacity as the standard capacity, Cn and DCR were tested at room temperature after constant volume determination; (11) During the repeated (6)-(10) tests, DCR and SOC-OCV tests were performed when SOH was 90%, 80% and 70%, respectively.

[0028] Acquire all monitoring data during the testing process, including voltage, current, temperature, and capacity. All test data are compiled to form a test dataset, i.e., measured data.

[0029] In one embodiment, the lithium battery life simulation model established in this application is a coupled simulation model of a thermal model, an electrochemical model, and an aging model. The coupling method is described in reference [reference needed]. Figure 2 ,include: The thermal model transmits the calculated temperature values ​​to the aging model and the electrochemical model; The aging model calculates the cell structure parameters based on the accumulated side reactions and feeds them back to the electrochemical model to update the relevant parameters of the electrochemical model; The electrochemical model updates all its parameters based on the obtained temperature values; The electrochemical model calculates the heat generation rate of the battery cell based on the current internal state and transfers it to the thermal model; The electrochemical model provides the aging model with the microscopic driving forces that lead to cell aging, including side reaction rates, stress, and lithium plating potential.

[0030] Specifically, battery operation is a dynamic, self-evolving process. Charge-discharge behavior (electrochemical) generates heat and induces aging; temperature changes drastically affect the rate of electrochemical reactions and the rate of aging; aging (such as loss of active material and increased internal resistance) permanently alters electrochemical and thermal behavior. In specific coupling mechanisms: The electrochemical model and the thermal model are coupled in two ways: At each calculation time step, the electrochemical model calculates the local heat generation rate (W / m³) inside the battery based on the current internal state (lithium ion concentration, overpotential, etc.) using formulas such as the Bernardi heat generation equation. This provides the thermal model with a non-uniform, time-varying internal heat source. After the thermal model calculates the temperature field (T) at various points inside the battery, it feeds this temperature data back to the electrochemical model. Temperature affects almost all the key parameters in the electrochemical model (including reaction rate constant, lithium ion diffusion coefficient, electrolyte conductivity, equilibrium potential, etc.). The electrochemical model uses the new temperature value to update all its parameters, thereby calculating the voltage, current, and reaction distribution at the new temperature. Bidirectional coupling between electrochemical and aging models: The electrochemical model provides the aging model with the microscopic driving forces that lead to aging, mainly including: side reaction rates (e.g., the current density for SEI film growth). j This depends on the potential, temperature, and lithium-ion concentration of the negative electrode surface, stress (lithium ion insertion and extraction in electrode particles causes volume changes and generates stress; the electrochemical model can provide the lithium-ion concentration gradient inside the particles to estimate the stress magnitude), and the negative electrode lithium plating potential (the electrochemical model can calculate the overpotential at the negative electrode / electrolyte interface to determine if lithium plating is imminent). The aging model no longer uses empirical "cycle count" or "ampere-hour throughput," but instead uses physics-based driving forces to quantify aging. Based on accumulated side reactions, the aging model calculates the permanent changes in battery structural parameters and feeds them back to the electrochemical model to update its initial parameters. For example: a decrease in the total amount of cyclic active lithium updates the initial lithium-ion concentration in the electrochemical model; deactivation or structural damage of the positive and negative electrode active materials updates the maximum lithium intercalation concentration and active surface area of ​​the electrodes; increased internal resistance due to SEI film thickening updates the interface resistance parameters in the electrochemical model. The electrochemical model can simulate how battery performance continuously degrades under the combined effects of aging and thermal coupling. The thermal model provides a one-way acceleration factor to the aging model: the thermal model transmits the calculated temperature field (T) to the aging model. Temperature is the strongest accelerating factor for almost all aging mechanisms. For example, in the kinetic equations of side reactions such as SEI growth and active substance dissolution, the rate constants usually follow the Arrhenius equation. Therefore, the aging model uses the real-time temperature provided by the thermal model to dynamically adjust the rates of all aging reactions within it. High temperatures greatly accelerate the aging process.

[0031] The three-in-one coupled simulation model takes into account all key interactions, making more accurate predictions. It can accurately predict the performance evolution and lifespan degradation of batteries throughout their entire life cycle, thereby optimizing design, improving safety, and extending lifespan.

[0032] The aging model includes side reaction models such as the cycle number model, the SEI film growth reaction model, the lithium plating reaction model, and the active material rupture reaction model.

[0033] Among them, reference Figure 3 The electrochemical model mainly includes a solid-phase model, an electrolyte transport model, a charge conservation model, and an electrode kinetics model, encompassing computational domains such as the negative electrode current collector, negative electrode, separator, positive electrode, and positive electrode current collector. These models primarily calculate the solid-phase lithium-ion concentration in the active material particles using Fick's diffusion law, the lithium-ion concentration in the electrolyte using electromigration and diffusion, the solid-phase potential of the electrode and the liquid-phase potential in the electrolyte using Ohm's law, and the electrochemical reactions at the solid-liquid interface using the Butler-Volmer equation.

[0034] Specifically, when a current density of... is applied to the battery cell... When the current is applied, it flows from the Cu current collector into the battery cell, passes through the negative electrode active material, and then flows into the electrolyte. The closer the negative electrode active material is to the membrane, the lower its solid-phase current density gradually decreases until it reaches zero. In the electrolyte, the current is entirely converted into an ion flow. When the ion flow passes through the membrane and reaches the surface of the positive electrode active material, it is converted into a solid-phase current that flows into the positive electrode active material and finally flows out through the Al current collector. The solid-phase current gradually decays to zero because an electrochemical reaction occurs, converting the electron flow into an ion flow that flows into the electrolyte. The electrochemical reaction current density... j It can be calculated using the following formula (1): (1) in, It is the overpotential; Specific surface area of ​​the active substance; The positive electrode charge transfer coefficient; The negative electrode charge transfer coefficient; It is the reaction rate constant; This represents the maximum lithium intercalation concentration for the active material. The concentration of lithium ions on the surface of the active material; This refers to the lithium ion concentration in the electrolyte. F It is Faraday's constant; This is an overpotential; R It is the ideal gas constant; The equilibrium potential of the active material at 25℃; This is the solid-state potential; The potential of the electrolyte; The entropic heat coefficient is used to reflect the relationship between equilibrium potential and temperature. T This refers to the temperature of the battery cell.

[0035] During the electrochemical reaction, the concentration of solid-phase lithium ions within the active material... It can be calculated using the following formula (2): (2) (3) in, t For time; r The direction of the active material particle radius; is the solid-phase diffusion coefficient of lithium ions; soc The lithium intercalation ratio of the active material is given by equation (4).

[0036] (4) The lithium ion concentrations in the electrolyte in the positive and negative electrode regions satisfy the partial differential equation shown in equation (5): (5) In the formula, For Brugmann correction coefficients; Porosity; Let be the lithium-ion transference number; where when and When the lithium ion concentration in the electrolyte in the diaphragm region is 5, the formula (5) can be used to describe the concentration of lithium ions in the electrolyte in the diaphragm region.

[0037] In lithium-ion batteries, the liquid phase potential consists of the ohmic potential generated by ion current and the migration potential generated by ion migration. The liquid phase potential and solid phase potential of lithium-ion batteries can be calculated by the following equations (6) and (7), respectively: (6) (7) In the formula, The ionic conductivity of the electrolyte; The liquid phase current density; The conductivity of the active material; This refers to the solid-state current density. is the solid-phase conductivity correction coefficient. In equation (6), the first term on the right side represents the migration potential generated by ion migration, and the second term represents the ohmic potential generated by lithium-ion current.

[0038] According to the law of conservation of charge, at any location inside the battery cell... and The sum equals the charging and discharging current density of the battery cell during operation, that is: (8) In the formula, A Let be the effective area of ​​the electrode. For this simulation model, the terminal voltage of the simulated lithium battery cell can be calculated using the following formula (9): (9) During long-term cycling, the negative electrode will undergo side reactions such as SEI film growth and lithium plating as shown in equation (10), leading to irreversible capacity decay of the battery cell.

[0039] (10) The loss of active lithium ions due to the lithium plating side reaction can be calculated using the following formula (11): (11) In the formula, This represents the lithium plating current density. This represents the lithium plating exchange current density. and The charge transfer coefficient for lithium deposition; This is the rate constant for the lithium plating reaction; This is the lithium plating potential; The thickness of the lithium deposition; The conductivity of the lithium deposition layer; is the molar mass of lithium; The density of lithium; Porosity; This represents the initial porosity.

[0040] The loss of active lithium due to SEI film growth can be calculated using the following formula (12): (12) In the formula, The kinetic rate constant for SEI film growth reaction is denoted as . The membrane resistance of the SEI membrane; The reactive electromotive force generated by the SEI film; EC concentration indicated by active particles; Let be the diffusion coefficient of EC; The thickness of the SEI film; This refers to the EC concentration in the electrolyte. The molar mass of the SEI film; The molar concentration of SEI; The density of the SEI film; The volume fraction of SEI; The ionic conductivity of the SEI membrane; A and B For parameters; C This refers to the rate of increase during the cell testing process. For reference testing ratio.

[0041] The thermal model mainly includes the heat generation and heat dissipation of the battery cell, and the battery cell temperature can be calculated using the following formula (13): (13) In the formula, Total heat production rate; This is the heat generated by the electrochemical reaction inside the battery cell; For Ohm heat; It is the heat of polarization; The current flowing through the active material; The current in the electrolyte; This is the solid-state potential; The potential of the electrolyte; It is the overpotential; Specific surface area of ​​the active substance; Cell density; Specific heat capacity of the battery cell; Thermal conductivity; The ambient temperature; This refers to the surface temperature of the battery cell.

[0042] In one embodiment, optimization algorithms, such as genetic algorithms, particle swarm optimization algorithms, and ant colony optimization algorithms, are used based on the voltage and cycling data of each cell in the measured data to identify relevant parameters in the SEI film growth, lithium plating, and electrochemical models under different cycle numbers, thereby forming a parameter dataset. D Parameter dataset D It includes design parameters, kinetic parameters, and thermodynamic parameters. , , , , Taking parameters as an example, after optimization, we obtain... , , , , The dataset, , , as well as .

[0043] Specifically, initial simulation data of cell aging under different operating conditions without parameter optimization can be obtained. The error between the measured data and the initial simulation data under the corresponding operating conditions is used as the optimization objective function. Based on the optimization algorithm and the optimization objective function, the parameters and dynamic parameters in the simulation model are optimized. For example, the error between the discharge capacity change data of the cell under a certain operating condition in the measured data and the discharge capacity change data of the cell under the corresponding operating condition simulated by the simulation model is used as the optimization objective. Optimization algorithms such as genetic algorithms, particle swarm optimization (PSO), and ant colony optimization are used to optimize the dynamic and thermophysical parameters of the cell. The parameter datasets obtained from optimizing each measured data set together form the simulation parameters of the cell under that operating condition. This step is repeated to obtain the simulation parameters of different cells under different operating conditions. The simulation parameters of different cells together form the parameter dataset. D .

[0044] In one embodiment, the model is calibrated using partial measured data. The calibration target can be a set of averaged parameters used to predict the lifespan of an ideal battery cell. However, individual battery cells vary, and using averaged parameters cannot solve the probabilistic problems in mass production and application. Therefore, this application proposes to construct a parameter set that reflects the individual differences of battery cells by statistically analyzing the calibrated parameters to determine their probability distribution characteristics.

[0045] Specifically, through statistical analysis, the distributions that different parameters satisfy are determined, including but not limited to the Weibull distribution and the normal distribution, as follows: In the formula x They are different parameters; It is a scaling factor and ; It is a shape factor and ; For variance; This is the mean.

[0046] In one possible implementation, due to inconsistencies in the battery cells during testing, a set of parameters is randomly selected from the distributions of different parameters obtained from the aforementioned statistical analysis to simulate the inconsistencies in cell aging during the simulation process. This set of parameters serves as the initial values ​​for the simulation model. Aging simulation is then performed using the simulation model under specified operating conditions. For example, the charge / discharge rate, ambient temperature, upper cutoff voltage, and lower cutoff voltage from the cycle test are input into the simulation model to complete the simulation. This yields a set of aging simulation data for cell capacity retention and cycle count. Repeating this simulation process NUM times results in NUM sets of cell aging simulation data. .

[0047] In one possible implementation, assuming that the reliability of the cell capacity retention rate needs to be evaluated through simulation at X%, take... The number of cycles when the capacity retention rate is X% can be used as the cell cycle life under a specified cycle regime to obtain the cell cycle life. The obtained cell cycle lives are arranged in ascending order, resulting in the following sequence: Fill in Table 2 below to statistically analyze the cycle life of the battery cells.

[0048] Table 2: In other words, the cumulative failure probability is statistically analyzed for each cycle number in the cycle number sequence using Table 2, and a scatter plot of the cumulative failure probability corresponding to each cycle number is drawn in the order of the cycle number sequence; it is then determined whether the following functional relationship is satisfied: In the formula, N It is the number of cycles in the cell simulation; These are the scale factor and shape parameter, respectively. Based on the scatter plot, the scale factor and shape parameter can be obtained, thus allowing the lifetime reliability corresponding to a specified number of cycles to be calculated. .

[0049] The following example will illustrate a method for assessing the lifespan reliability of lithium batteries.

[0050] Taking ternary lithium batteries as an example, the size parameters and design parameters of the battery cell obtained from the specifications and experiments are shown in Table 3 below, and the kinetic and thermodynamic parameters are shown in Table 4.

[0051] Table 3: Table 4: Based on the contents of Tables 3 and 4, the upper limit of the cell's operating temperature, maximum charging rate, upper limit voltage, and lower limit voltage were determined, and the designed test table is shown in Table 5 below.

[0052] Table 5: Combining the battery cycle life test method in GB / T18287-2000 and the test procedures described in this application, cycle tests under the different conditions listed in the table above were completed in a constant temperature and humidity simulated environment test chamber. When the battery cell reached 300 cycles, all monitoring data during the test, including voltage, current, temperature, and capacity, were acquired. All test data were compiled to form a test dataset.

[0053] Some measured data during the test are as follows: Figures 7-9 As shown, the main factors include voltage, capacity decay, and temperature.

[0054] The test temperature data of the first battery cell under the conditions of 45℃, 1C, 2.8V, and 4.3V were used in the test data. Cell temperature data simulated by the simulation model under corresponding operating conditions The error was used as the objective function for optimization, and the voltage data from the first cell under the experimental conditions of 45℃, 1C, 2.8V, and 4.3V were also used. Voltage data simulated by the simulation model under the corresponding operating conditions The error between them is used as the objective function for optimization as shown in Equation (14). Using the Particle Swarm Optimization (PSO) algorithm, the particle swarm size is set to 100, the dimension is set to 13, the learning factor is set to 1.49, and the inertia weight strategy is set to 0.729, until the MSE-sum is less than 0.01. The kinetic parameters and thermophysical parameters of the battery cell are optimized. Based on the optimization of the kinetic parameters and thermophysical parameters, the kinetic parameters of the aging model and the thermodynamic parameters of the battery cell are optimized. The aging model parameters to be optimized mainly include the SEI film reaction rate, SEI film conductivity, lithium plating reaction rate constant, diffusion coefficient, etc.

[0055] The discharge capacity change data of the first cell during the test process under the conditions of 45℃, 1C, 2.8V, and 4.3V were used as the basis for the experimental data. Data on the change in discharge capacity of the battery cell under the corresponding operating conditions simulated by the simulation model. The error between them is used as the optimization target, as shown in Equation (15). The dynamic parameters and thermal properties of the battery cell are optimized using the Particle Swarm Optimization (PSO) algorithm.

[0056] The parameter datasets obtained from the two optimizations together form the simulation parameters of the first battery cell under operating conditions of 45℃, 1C, 2.8V, and 4.3V. This step is repeated to obtain the simulation parameters for different battery cells under different operating conditions. The simulation parameters of different battery cells are collectively used to form parameter dataset D. The simulation comparison before and after optimization is shown in the figure below. Figure 10 As shown.

[0057] (14) (15) The kinetic, thermodynamic, and side reaction parameters of the optimized cells were statistically analyzed to determine their distribution types. The SEI film growth reaction kinetic rate constant was used as the basis for this analysis. For example, the optimized Statistical analysis was performed on it to determine that it follows a mean distribution, such as Figure 11 As shown.

[0058] A set of parameters was randomly selected from the distributions of different parameters obtained through statistical analysis as the initial values ​​for the simulation model. The simulation model was then used to simulate the lifespan of the battery cells under operating conditions of 45℃, 2C, 2.8V, and 4.3V. Repeating the simulation 300 times yielded 300 capacity decay curves for the battery cells. Some simulation results are shown below. Figure 12 As shown, a cell is considered to fail when its capacity retention rate is less than 80%. The number of cycles at which the simulated capacity decay curve reaches 80% is taken as the lifespan simulation result for the corresponding cell. .

[0059] Calculation of cell life reliability under specified operating conditions: The simulated life results are used to calculate the reliability of the cell life. Arranged in ascending order of the number of cycles, and counted every 50 cycles, the results are shown in Table 6 below: According to Table 6 above, "Number of cycles" and "Cumulative failure probability" "Draw a Weibull probability diagram as follows." Figure 13 As shown, through Figure 12 It can be seen that the cell life under operating conditions of 45℃, 2C, 2.8V, and 4.3V follows a Weibull distribution with a shape factor of 13.01656 and a scale factor of 1742.40909.

[0060] Based on the following distribution formula, the reliability of this batch of battery cells after 1800 cycles at 45℃, 2C, 2.8V, and 4.3V is: It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effects of the present invention, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these variations are all within the scope of protection of the present invention.

[0061] Furthermore, this application provides a lithium battery life reliability assessment system, the system comprising: The data acquisition unit is used to obtain the design parameters of the battery cell; The test unit is used to perform battery cycle aging tests under different operating conditions according to the design parameters, and to obtain the test data of cell aging. The modeling unit is used to create a simulation model that couples the aging model, thermal model, and electrochemical model. The optimization unit is used to optimize the thermodynamic and kinetic parameters in the simulation model based on the measured data and optimization algorithm to obtain parameter datasets for different cells under different operating conditions. The simulation unit is used to perform multiple aging simulations of the battery cell under specified operating conditions based on the parameter dataset and the simulation model, so as to obtain simulation data of battery cell aging. The statistical unit is used to statistically analyze the cell life distribution pattern based on the simulation data to obtain the cell life reliability under specified operating conditions.

[0062] Furthermore, this application also provides an electronic device including a memory, one or more processors, and one or more application programs, wherein the one or more application programs are stored in the memory, and the one or more application programs are configured to cause the one or more processors to perform the method described in any of the preceding technical solutions when invoked by the one or more processors.

[0063] The apparatus in this embodiment of the invention mainly includes a memory and a processor. The memory can be configured to store a program for executing the methods of the above-described method embodiments, and the processor can be configured to execute the program in the memory. This program includes, but is not limited to, a program for executing the methods of the above-described method embodiments. For ease of explanation, only the parts related to the embodiments of the invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the invention.

[0064] In embodiments of the present invention, the apparatus may be a control device comprising various electronic components. In some possible implementations, the electronic device may include multiple storage devices and multiple processors. The program executing the methods of the above method embodiments may be divided into multiple subroutines, each subroutine being loaded and run by a processor to perform different steps of the methods of the above method embodiments. Specifically, each subroutine may be stored in a different memory, and each processor may be configured to execute programs in one or more memories to jointly implement the methods of the above method embodiments; that is, each processor executes different steps of the methods of the above method embodiments to jointly implement the methods of the above method embodiments.

[0065] The above-mentioned device is used for performing Figure 1 The method embodiments shown are similar in technical principle, technical problem solved and technical effect produced. Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic device and related descriptions can be referred to the content described in the method embodiments, and will not be repeated here.

[0066] Those skilled in the art will understand that all or part of the processes in the method of the above embodiment of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable storage medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0067] Furthermore, the present invention also provides a computer-readable storage medium. In one embodiment of the computer-readable storage medium according to the present invention, the computer-readable storage medium can be configured to store a program that performs the above-described method embodiments, the program of which can be loaded and run by a processor to implement the above-described methods. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. The computer-readable storage medium can be a storage device comprising various electronic devices. Optionally, in the embodiments of the present invention, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0068] Furthermore, it should be understood that the various modules are merely illustrative of the functional modules of the device of the present invention. The physical devices corresponding to these modules may be the processor itself, or a part of the processor's software, hardware, or a combination of software and hardware. Therefore, the number of modules is only illustrative. Those skilled in the art will understand that the various modules in the system can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principles of the present invention; therefore, the technical solutions after splitting or merging will fall within the protection scope of the present invention.

[0069] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0070] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0071] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for evaluating reliability of lithium battery life, characterized by, The method comprises: obtaining design parameters of the battery cell; performing battery cycle aging tests under different working conditions according to the design parameters to obtain measured data of the battery cell aging; establishing a simulation model in which an aging model, a thermal model and an electrochemical model are coupled with each other; optimizing thermodynamic parameters and kinetic parameters in the simulation model according to the measured data and an optimization algorithm to obtain parameter data sets of different battery cells under different working conditions; performing multiple aging simulations of the battery cell under a specified working condition based on the parameter data sets and the simulation model to obtain simulation data of the battery cell aging; statistically analyzing the simulation data to obtain a lifetime distribution rule of the battery cell and a lifetime reliability of the battery cell under the specified working condition.

2. The lithium battery lifetime reliability evaluation method according to claim 1, characterized in that, The battery cycle aging tests under different working conditions according to the design parameters to obtain measured data of the battery cell aging comprise: determining working conditions for the battery cycle aging tests of the battery cell according to the design parameters, wherein the working conditions include environmental temperature, charging rate and cutoff voltage; performing the battery cycle aging tests of the battery cell under different working conditions and obtaining measured data of all the battery cells during the tests.

3. The lithium battery life reliability evaluation method of claim 1, wherein The coupling of the aging model, the thermal model and the electrochemical model comprises: the thermal model transmits calculated temperature values to the aging model and the electrochemical model; the aging model calculates battery structure parameters according to accumulated side reactions and feeds back the battery structure parameters to the electrochemical model to update relevant parameters of the electrochemical model; the electrochemical model updates all parameters thereof based on the obtained temperature values; the electrochemical model transmits a heat generation rate of the battery cell calculated according to a current internal state to the thermal model; the electrochemical model provides microscopic driving forces causing the battery cell aging to the aging model, wherein the microscopic driving forces include side reaction rate, stress and lithium precipitation potential.

4. The lithium battery life reliability evaluation method of claim 1, wherein The optimization of the thermodynamic parameters and the kinetic parameters in the simulation model according to the measured data and the optimization algorithm to obtain the parameter data sets of different battery cells under different working conditions comprises: obtaining initial simulation data of the battery cell aging of the simulation model under different working conditions without parameter optimization; taking errors between the measured data and the initial simulation data under corresponding working conditions as an optimization objective function; optimizing the parameters and the kinetic parameters in the simulation model based on an optimization algorithm and the optimization objective function to obtain a parameter data set formed by simulation parameters of different battery cells under different working conditions, wherein the parameter data set includes design parameters, optimized kinetic parameters and thermodynamic parameters.

5. The lithium battery lifetime reliability assessment method of claim 4, wherein, The optimization algorithm includes at least one of a genetic algorithm, a particle swarm optimization algorithm and an ant colony optimization algorithm.

6. The lithium battery lifetime reliability evaluation method according to claim 4, characterized in that, The method further comprises statistically analyzing the simulation parameters of different battery cells under different working conditions to determine a probability distribution corresponding to the simulation parameters.

7. The lithium battery lifetime reliability evaluation method according to any one of claims 1 to 6, characterized in that, The multiple aging simulations of the battery cell under a specified working condition based on the parameter data sets and the simulation model to obtain simulation data of the battery cell aging comprise: randomly extracting a group of parameters from the parameter data set as initial parameter values of the simulation model; Aging simulation is performed on the simulation model under a specified working condition, and the simulation process is repeated multiple times to obtain multiple sets of simulation data of cell aging; wherein the simulation data includes the capacity retention rate and the cycle number corresponding to the cell.

8. The lithium battery life reliability evaluation method of claim 1, wherein, The simulation data is used to statistically analyze the cell life distribution rule to obtain the life reliability of the cell under the specified working condition, including: The cycle number when the capacity retention rate of the cell reaches a preset value is taken as the life simulation result of the corresponding cell; The life simulation results are arranged in ascending order of cycle number to obtain a sequence of arranged cycle numbers; The cumulative failure probability of each cycle number in the sequence of cycle numbers is statistically analyzed, and a scatter plot of the cumulative failure probability corresponding to each cycle number is drawn in the order of the sequence of cycle numbers; The distribution rule satisfied by the life of the cell is determined based on the scatter plot; The life reliability of the cell under the specified working condition when cycled to a specified cycle number is obtained based on the distribution rule.

9. A lithium battery lifetime reliability assessment system, characterized by, The system includes: A collection unit configured to obtain design parameters of a cell; A measurement unit configured to perform battery cycle aging tests under different working conditions based on the design parameters to obtain measured data of cell aging; A modeling unit configured to establish a simulation model in which a thermal model, an aging model, and an electrochemical model are coupled with each other; An optimization unit configured to optimize thermodynamic parameters and kinetic parameters in the simulation model based on the measured data and an optimization algorithm to obtain parameter data sets of different cells under different working conditions; A simulation unit configured to perform multiple aging simulations of a cell under a specified working condition based on the parameter data sets and the simulation model to obtain simulation data of cell aging; A statistical unit configured to statistically analyze a cell life distribution rule based on the simulation data to obtain the life reliability of the cell under the specified working condition.

10. An electronic device, comprising: An application including a memory, one or more processors, and one or more application programs stored in the memory, wherein the one or more application programs are configured to be invoked by the one or more processors to cause the one or more processors to perform the method of any one of claims 1-8.

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