Power battery life simulation method, device, equipment, medium and program product
By linearizing and globally optimizing the power battery life degradation model, the problems of low parameter identification accuracy and temperature expansion in power battery life simulation were solved, and higher accuracy simulation results were achieved.
Patent Information
- Application Number
- CN202511125183.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-12
- Publication Date
- 2025-11-28
AI Technical Summary
In existing technologies, the parameter identification accuracy of power battery life simulation models is low, and temperature extension cannot be achieved, resulting in inaccurate simulation results.
By conducting lifetime degradation tests on multiple test cells at different temperatures, test data is obtained, and the lifetime degradation model is linearized to determine the exponential factor and intermediate parameters. The activation energy parameters are optimized using a genetic algorithm or particle swarm optimization algorithm, and the pre-exponential factor is calculated to achieve parameter identification of the model at different temperatures.
This improved the accuracy of model parameter identification, enhanced the accuracy of power battery life simulation and temperature range capability, and ensured that the simulation results were closer to reality.
Smart Images
Figure CN121031252A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of power batteries, and in particular relates to a power battery life simulation method, device, equipment, medium and program product. BACKGROUND
[0002] A power battery is a core component of a new energy vehicle and an energy storage system, and its service life and reliability directly affect the performance, economy and safety of the vehicle. Since complete life testing cannot be completed during the research and development stage, life simulation becomes a key support for design, three-package and warranty links, and life simulation in the industry is based on the Arrhenius formula and requires parameter identification based on battery test data.
[0003] Currently, there are two methods for parameter identification of the Arrhenius formula: one is the equation solving method of different temperature data: through two different temperature test data, one temperature parameter is used to fit the other temperature data to optimize, although it can solve the temperature expansion problem, but the accuracy is low, and different temperature combinations will cause differences in identified parameters; the second is a single data fitting method: using the least square method to fit a group of experimental data, the parameter has high consistency with the experimental data, but because it cannot distinguish the pre-exponential factor in the formula and the temperature related term, the life simulation can only calculate the temperature attenuation of the existing test data, and cannot realize temperature expansion. SUMMARY
[0004] The present application provides a power battery life simulation method, device, equipment, medium and program product to solve the problems of low model parameter identification accuracy and the inability of the model to realize temperature expansion in related technologies.
[0005] The first aspect of the present application provides a power battery life simulation method, comprising the following steps: performing life attenuation tests on a plurality of test batteries at different temperatures to obtain test data of the plurality of test batteries; obtaining a life attenuation model of the power battery, and linearly converting the life attenuation model to obtain a target model; fitting the test data based on the target model to obtain an exponential factor and an intermediate parameter of each test battery, and determining an activation energy parameter of the life attenuation model based on the exponential factor and the intermediate parameter, wherein the intermediate parameter is different at different temperatures; determining a pre-exponential factor parameter of the life attenuation model at different temperatures based on the exponential factor, the intermediate parameter and the activation energy parameter, and performing life simulation on a to-be-tested power battery based on the life attenuation model.
[0006] Optionally, the linear conversion of the life attenuation model to obtain the target model comprises: converting the life attenuation model into a logarithmic form to obtain the target model.
[0007] Optionally, the life attenuation model includes a calendar attenuation model and a cycle attenuation model, and the life attenuation model is linearly converted to obtain a target model, including: linearly converting the calendar attenuation model to obtain a first target model; and linearly converting the calendar attenuation model to obtain a second target model.
[0008] Optionally, the calendar attenuation model is:
[0009]
[0010] wherein Q loss is a current power battery capacity attenuation state; A cal is a calendar attenuation pre-exponential factor; Ea is an activation energy; R is a gas constant; T is a temperature; t is an attenuation time; z cal is a calendar exponential factor.
[0011] The cycle attenuation model is:
[0012]
[0013] wherein Q loss is a current power battery capacity attenuation state; A cyc is a cycle attenuation pre-exponential factor; Ea is an activation energy; R is a gas constant; T is a temperature; cyc is a cycle number; z cyc is a cycle exponential factor.
[0014] Optionally, the activation energy parameter of the life attenuation model is determined based on the exponential factor and the intermediate parameter, including: setting an initial value of the activation energy parameter; and optimizing the initial value based on the exponential factor, the intermediate parameter and a target algorithm to obtain a final value of the activation energy parameter.
[0015] Optionally, a calculation formula of the pre-exponential factor parameter is:
[0016]
[0017] wherein A is a pre-exponential factor; b is a test cell parameter; Ea is an activation energy; R is a gas constant; and T is a temperature.
[0018] The second aspect embodiment of the application provides a power battery life simulation device, comprising: an acquisition module configured to perform life attenuation tests on a plurality of test cells at different temperatures, and acquire test data of the plurality of test cells; a conversion module configured to acquire a life attenuation model of a power battery, and linearly convert the life attenuation model to obtain a target model; a first determination module configured to fit the test data based on the target model to obtain an exponential factor and an intermediate parameter of each test cell, and determine an activation energy parameter of the life attenuation model based on the exponential factor and the intermediate parameter, wherein the intermediate parameter is different at different temperatures; and a second determination module configured to determine a pre-exponential factor parameter of the life attenuation model at different temperatures based on the exponential factor, the intermediate parameter and the activation energy parameter, and perform life simulation on a to-be-tested power battery based on the life attenuation model.
[0019] Optionally, the conversion module is further configured to convert the life attenuation model into a logarithmic form to obtain the target model.
[0020] Optionally, the life attenuation model comprises a calendar attenuation model and a cycle attenuation model, and the conversion module is further configured to linearly convert the calendar attenuation model to obtain a first target model, and linearly convert the calendar attenuation model to obtain a second target model.
[0021] Optionally, the calendar attenuation model is
[0022]
[0023] wherein Q loss is a current power battery capacity attenuation state; A cal is a calendar attenuation pre-exponential factor; Ea is an activation energy; R is a gas constant; T is a temperature; t is an attenuation time; z cal is a calendar exponential factor.
[0024] The cycle attenuation model is
[0025]
[0026] wherein Q loss is a current power battery capacity attenuation state; A cyc is a cycle attenuation pre-exponential factor; Ea is an activation energy; R is a gas constant; T is a temperature; cyc is a cycle number; z cyc is a cycle exponential factor.
[0027] Optionally, the first determination module is further configured to set an initial value of the activation energy parameter, and optimize the initial value based on the exponential factor, the intermediate parameter and a target algorithm to obtain a final value of the activation energy parameter.
[0028] Optionally, a calculation formula of the pre-exponential factor parameter is
[0029]
[0030] wherein A is a pre-exponential factor; b is a test cell parameter; Ea is an activation energy; R is a gas constant; and T is a temperature.
[0031] The third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the power battery life simulation method of the above-mentioned embodiments.
[0032] The fourth aspect of the present application provides a computer readable storage medium having a computer program or instructions stored thereon, wherein the computer program or instructions are executed by a processor to perform the power battery life simulation method of the above-mentioned embodiments.
[0033] The fifth aspect of the present application provides a computer program product comprising a computer program or instructions, wherein the computer program or instructions are executed to implement the power battery life simulation method of the above-mentioned embodiments.
[0034] Therefore, the present application has at least the following beneficial effects:
[0035] The embodiments of the present application can perform life attenuation tests on multiple test cells at different temperatures and obtain test data, linearly process the life attenuation model, reduce the fitting process and the error of nonlinear fitting, fit the test data based on the target model after linearization to obtain the exponential factor and the intermediate parameter of each test cell, determine the pre-exponential factor parameter of the life attenuation model at different temperatures based on the exponential factor and the intermediate parameter, and obtain the activation energy parameter of the life attenuation model and the pre-exponential factor at different temperatures through the test data fitting and global optimization of the multiple test cells at different temperatures. The embodiments of the present application solve the problem that the activation energy parameter and the pre-exponential factor parameter of the model cannot be accurately identified by single temperature data in the traditional method, improve the accuracy of determining the parameters of the life attenuation model, and further improve the accuracy of the power battery life simulation. Therefore, the technical problems such as low model parameter identification accuracy and the model unable to realize temperature expansion in the related art are solved.
[0036] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS
[0037] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, taken in conjunction with the accompanying drawings, in which:
[0038] Figure 1A flow chart of a power battery life simulation method according to an embodiment of the application is provided;
[0039] Figure 2 A flow chart of a cycle attenuation model parameter identification according to an embodiment of the application is provided;
[0040] Figure 3 A flow chart of a genetic algorithm optimization activation energy parameter according to an embodiment of the application is provided;
[0041] Figure 4 A comparison diagram of data and test data of the method according to an embodiment of the application is provided;
[0042] Figure 5 An example diagram of a power battery life simulation device according to an embodiment of the application is provided;
[0043] Figure 6 A structural diagram of an electronic device according to an embodiment of the application is provided. DETAILED DESCRIPTION
[0044] Embodiments of the application are described in detail below with reference to examples shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the application, and cannot be understood as a limitation of the application.
[0045] Before describing the solutions of the embodiments of the embodiments of the application, the related technologies of the embodiments of the application are introduced to assist the understanding of the solutions of the embodiments of the application.
[0046] Currently, the power battery life simulation industry is based on the Arrhenius formula. The Arrhenius formula is an empirical formula, and different battery cells have different attenuation parameters. Before life simulation, parameter identification needs to be performed according to battery cell test data. For the same battery cell test data, the test temperature is the same, the pre-exponential factor A and the temperature related term in the Arrhenius formula are constants, and the same set of test data cannot be accurately identified.
[0047] There are currently two methods for identifying the Arrhenius formula:
[0048] 1. Take two different temperature test data and identify the parameters by solving equations. The basic principle of this identification method is to use the parameters at one temperature to fit the test data at another temperature, so that the result is optimal. This identification method can better solve the temperature expansion problem, but the accuracy is not high, and the parameters are different after identification of different temperature combinations.
[0049] 2. A set of experimental data is used for parameter identification by a fitting method such as the least square method. The parameters obtained by this method are more consistent with the test data, but A and Ea cannot be accurately distinguished because A and Ea are both constants, which leads to the fact that only the temperature attenuation with test data can be calculated in the life simulation process, and temperature extension cannot be performed.
[0050] To this end, the application provides a power battery life simulation method to solve at least one of the above problems.
[0051] Specifically, Figure 1 A flowchart of a power battery life simulation method provided by an embodiment of the application is shown in the figure.
[0052] As Figure 1 shown, the power battery life simulation method includes the following steps:
[0053] In step S101, life attenuation tests are performed on a plurality of test cells at different temperatures to obtain test data of the plurality of test cells.
[0054] The test data includes capacity attenuation data of the test cells at different temperatures, times, or cycle numbers.
[0055] In step S102, a life attenuation model of the power battery is obtained, and the life attenuation model is linearly converted to obtain a target model.
[0056] The life attenuation model can also be referred to as a battery attenuation model, which includes a calendar attenuation model and a cycle attenuation model. The calendar attenuation model is used to describe a model of the battery attenuation over time when the battery is not in use (storage state), which is affected by temperature, storage time, etc. The cycle attenuation model is used to describe a model of the battery attenuation over cycle number during the charging and discharging cycle, which is affected by cycle number, temperature, etc.
[0057] It can be understood that the life attenuation model of the power battery can be linearly converted to obtain a target model in the embodiment of the application. The linear conversion simplifies the subsequent model fitting process and reduces the error of nonlinear fitting.
[0058] In the embodiment of the application, the calendar attenuation model is:
[0059]
[0060] Q loss is the current power battery capacity attenuation state; A cal is a calendar attenuation pre-exponential factor; Ea is an activation energy; R is a gas constant; T is a temperature; t is an attenuation time; z cal is a calendar exponential factor;
[0061] The cycle attenuation model is:
[0062]
[0063] wherein Q loss is the current power battery capacity attenuation state; A cyc is the cycle attenuation pre-exponential factor; Ea is the activation energy; R is the gas constant; T is the temperature; cyc is the cycle number; z cyc is the cycle exponential factor.
[0064] Specifically, the battery will have capacity attenuation during use, which can be divided into two main attenuation forms according to different influencing factors: calendar attenuation and cycle attenuation.
[0065] Calendar attenuation refers to the gradual loss of battery capacity over time without use. Calendar attenuation is closely related to factors such as temperature, humidity, storage state, etc.
[0066] Cycle attenuation refers to the gradual attenuation of battery capacity with increasing charge and discharge cycles. Cycle attenuation is usually related to factors such as charge and discharge depth, rate, temperature, etc.
[0067] Q loss = 1 - SOH;
[0068] wherein SOH is the state of health of the power battery, which is only the ratio of the current capacity to the initial capacity; Q loss is the current power battery capacity attenuation state, which is (initial capacity - current capacity) / initial capacity.
[0069] For the attenuation behavior of the battery, the following attenuation formula can be used:
[0070] Calendar attenuation formula:
[0071]
[0072] Cycle attenuation formula:
[0073]
[0074] wherein A cal and A cyc are the calendar attenuation and cycle attenuation pre-exponential factors, respectively; Ea is the activation energy; R is the gas constant; T is the temperature; z cal and z cyc are the calendar and exponential factors, respectively; t is the attenuation time; cyc is the cycle number.
[0075] Through the formulas, the attenuation law of the battery under different conditions can be described, and a basis for battery life prediction is provided.
[0076] In the embodiment of the present application, the life attenuation model is linearly converted to obtain a target model, including: converting the life attenuation model into a logarithmic form to obtain the target model.
[0077] The logarithmic form is a form obtained by taking the natural logarithm on both sides of the attenuation model, converting the multiplication and division relationship into an addition and subtraction relationship, and converting the power function relationship into a linear relationship.
[0078] It can be understood that the life attenuation model can be converted into a logarithmic form to obtain the target model in the embodiment of the present application, that is, the nonlinear model is converted into a linear model, the complexity of subsequent parameter fitting is reduced, the fitting process is more stable, and the result is more accurate.
[0079] In the embodiment of the present application, the life attenuation model is linearly converted to obtain a target model, including: linearly converting the calendar attenuation model to obtain a first target model; linearly converting the calendar attenuation model to obtain a second target model.
[0080] The first target model is a model obtained by linearly converting the calendar attenuation model, and the second target model is a model obtained by cyclically attenuating.
[0081] Taking linearization of the cyclic attenuation model as an example, the logarithm of the cyclic attenuation formula is taken, the formula is converted into a linear relationship, and subsequent fitting is facilitated:
[0082]
[0083] Let Then:
[0084] ln Q loss = z cyc * ln cyc + b;
[0085] This transformation converts a nonlinear problem into a linear problem, which is convenient for solving.
[0086] In step S103, the test data is fitted based on the target model to obtain the fitting parameters of each test cell, and the activation energy parameter of the life attenuation model is determined based on the exponential factor and the intermediate parameter, wherein the intermediate parameter is different at different temperatures.
[0087] The exponential factor includes a calendar exponential factor z cal and a cycle exponential factor z cycThe intermediate parameter b is different at different temperatures; the activation energy parameter Ea is a key parameter in the Arrhenius formula, and reflects the sensitivity of the reaction to temperature, which is a core factor affecting the battery attenuation rate.
[0088] It can be understood that the embodiments of the present application can fit the test data based on the target model to obtain the exponential factor and the intermediate parameter of each test battery, and determine the activation energy parameter of the life attenuation model based on the exponential factor and the intermediate parameter.
[0089] It should be noted that the exponential factor of the embodiments of the present application remains the same at different temperatures, and the intermediate parameter differs at different temperatures, which not only ensures consistent description of the inherent characteristics of the battery model, but also adapts the attenuation law at different temperatures through the temperature specificity of b, and accurately identifies the activation energy parameter independent of temperature based on the exponential factor and the intermediate parameter. Thus, the activation energy parameter can be used as a general parameter at different temperatures, providing a basis for subsequent life attenuation model expansion at different temperatures.
[0090] Taking cycle attenuation as an example, for all test batteries, the parameters z cyc and b of each battery are obtained by linear fitting of the attenuation data. The principle of fitting is to find the optimal parameters by minimizing the sum of squares of errors:
[0091]
[0092] Save all test battery fitting parameters z cyc [z1, z2, …, z n ], and the parameter b is [[b1, b2, …, b n ]; as an input parameter of the Arrhenius formula, b is further fitted to obtain A cyc and Ea.
[0093] In the embodiments of the present application, the activation energy parameter of the life attenuation model is determined based on the exponential factor and the intermediate parameter, which includes setting an initial value of the activation energy parameter; and optimizing the initial value based on the exponential factor, the intermediate parameter and a target algorithm to obtain a final value of the activation energy parameter.
[0094] The target algorithm can be a genetic algorithm or a particle swarm optimization algorithm, and is not limited in this regard.
[0095] It can be understood that the present application can give an initial value of the activation energy parameter, and optimize the initial value of the activation energy parameter based on the exponential factor, the intermediate parameter and the target algorithm.
[0096] In step S104, a pre-exponential factor parameter of the life attenuation model at different temperatures is determined based on the exponential factor, the intermediate parameter and the activation energy parameter, and life simulation is performed on the power battery to be tested based on the life attenuation model.
[0097] wherein the pre-exponential factor parameter A is a proportional coefficient in the life attenuation model, and specifically includes A cyc and A cal The pre-exponential factor parameters at different temperatures are different.
[0098] It can be understood that the embodiments of the present application can determine the pre-exponential factor parameters of the life attenuation model at different temperatures based on the exponential factor, the intermediate parameter and the activation energy parameter, perform life simulation on the power battery to be tested based on the life attenuation model, and obtain the activation energy parameter of the life attenuation model and the pre-exponential factor at different temperatures through test data fitting of multiple test cell data at different temperatures and global optimization, thereby solving the problem that a single temperature data cannot accurately identify the model parameters in the traditional method, improving the parameter identification accuracy, and further improving the accuracy of subsequent power battery life simulation.
[0099] Specifically, the embodiments of the present application accurately identify the activation energy parameter Ea in the life attenuation model which is irrelevant to temperature, and when globally optimizing Ea, the compatibility for multiple temperature data is included, so that Ea can still accurately reflect the influence law of temperature on attenuation at untested temperatures, and the pre-exponential factor which is exclusive to temperature is combined, so that the model can be flexibly adapted to any temperature. For the tested temperature, the intermediate parameter b is fitted through the measured data, and the corresponding pre-exponential factor A is calculated by combining the determined Ea. For the untested temperature, no additional testing is required, and only the target temperature T needs to be substituted, and the unified Ea and b under similar conditions are combined, so that the exclusive A at the temperature can be calculated, and then the life of the power battery is simulated through the attenuation model to obtain the predicted life of the power battery.
[0100] In the embodiments of the present application, the calculation formula of the pre-exponential factor parameter is:
[0101]
[0102] wherein A is the pre-exponential factor; b is the test cell parameter; Ea is the activation energy; R is the gas constant; and T is the temperature.
[0103] The power battery life simulation method of the embodiments of the present application will be described below through a specific embodiment, and the flow is as shown in Figure 2 For example, taking the cycle attenuation of the power battery as an example, the method includes:
[0104] S1: Logarithmic linearization
[0105] First, take the logarithm of the cycle decay formula, convert the formula to linear relationship, which is convenient for subsequent fitting:
[0106]
[0107] Let Then:
[0108] ln Q loss = z cyc * ln cyc + b;
[0109] This transformation converts the nonlinear problem into a linear problem, which is convenient for solving.
[0110] S2: Linear fitting
[0111] For all test batteries, by linear fitting of the decay data, the parameters z cyc and b of each battery are obtained. The principle of fitting is to find the optimal parameters by minimizing the sum of squares of errors:
[0112]
[0113] Save all test battery fitting parameters z cyc as [z1, z2, …, z n ], and parameter b as [[b1, b2, …, b n ]; As Arrhenius formula input parameters, further fitting of b obtains A cyc and Ea.
[0114] S3: Genetic algorithm or particle swarm optimization
[0115] According to the parameters z cyc and b of all test batteries obtained in S2, the genetic algorithm or particle swarm optimization method is used to optimize Ea. Taking the genetic algorithm optimization as an example, the optimization process is shown in Figure 3 .
[0116] Given the initial value Ea0, according to z cyc , b as fixed parameters, optimize Ea, the method is as follows:
[0117]
[0118] In the formula, A, b, T are arrays, respectively representing all test battery parameter combinations.
[0119] Q loss = exp(b)*(cyc)^z cyc ;
[0120]
[0121]
[0122]
[0123]
[0124] Wherein, k is the total number of test batteries; m is the number of test batteries except the current battery test temperature; n is the total number of cycles; x is the cycle number of fitting start; j is the battery number; i is the cycle number.
[0125] The optimal activation energy Ea can be obtained by minimizing sigma.
[0126] For the Ea optimization method, similar results can be obtained by using other optimization algorithms (such as particle swarm, simulated annealing, differential evolution, etc.).
[0127] S4: Calculate the pre-exponential factor A
[0128] According to the optimal Ea obtained in S3, calculate the pre-exponential factor A of all test batteries:
[0129]
[0130] S1-S4 take cycle decay as an example, and the calendar decay parameter identification method is similar to cycle decay. According to the identification method, all test battery decay parameters are obtained as battery life simulation input parameters to participate in battery life simulation.
[0131] In S3, the application is specifically given from x cycles for fitting, which improves the simulation accuracy, and x is recommended to be 200 of the total cycle number corresponding to the warranty mileage, and n is the total cycle number corresponding to the warranty mileage.
[0132] The application identifies the Arrhenius formula (i.e. life decay model) to obtain the predicted data and experimental data as shown in Figure 4 .
[0133] Specifically, the application proposes an Arrhenius formula identification method, which processes the Arrhenius formula, and the entire identification process is based on linear fitting, with higher fitting accuracy. In the identification process, Ea is optimized by using all test results, which improves the Ea identification accuracy. Different pre-exponential factors A are used for different test results to ensure that the life simulation input parameters and test results are basically consistent, thereby providing a more reliable theoretical basis for the life prediction of the power battery.
[0134] In summary, the application converts the nonlinear fitting into linear fitting by taking the derivative of the Arrhenius formula, and the fitting result is more accurate. The application proposes to optimize the activation energy Ea of the formula, and all test data are considered in the optimization process, so that the identified Ea is as close to the true value as possible. The application proposes to use different pre-exponential factors for fitting for each test result, and the fitting result is closer to the test value. Therefore, the capacity decay input in the life simulation process is closer to the measured value, and the life simulation accuracy is further improved. By optimizing Ea, the true Ea is obtained as much as possible according to the measured data, which provides conditions for temperature expansion of life simulation, and ensures that the fitting result is consistent with the measured result.
[0135] The power battery life simulation method proposed in the embodiments of the application can perform life decay tests on multiple test cells at different temperatures and obtain test data, linearize the life decay model, reduce the fitting process and the error of nonlinear fitting, fit the test data of each test cell based on the linearized target model to obtain the exponential factor and the intermediate parameter, determine the pre-exponential factor parameter of the life decay model at different temperatures based on the exponential factor and the intermediate parameter, and obtain the activation energy parameter of the life decay model and the pre-exponential factor at different temperatures through the test data fitting and global optimization of multiple test cells at different temperatures. The simulation capability of the model at different temperatures is improved, the problem that the activation energy parameter and the pre-exponential factor parameter of the model cannot be accurately identified in the traditional method is solved, the accuracy of determining the life decay model parameters is improved, and then the accuracy of the power battery life simulation is improved.
[0136] Secondly, the power battery life simulation device proposed in the embodiments of the application is described with reference to the accompanying drawings.
[0137] Figure 5 is a block schematic diagram of the power battery life simulation device in the embodiments of the application.
[0138] As shown in Figure 5 , the power battery life simulation device 10 includes an acquisition module 100, a conversion module 200, a first determination module 300, and a second determination module 400.
[0139] The obtaining module 100 is configured to perform life attenuation tests on the plurality of test battery cells at different temperatures, and obtain test data of the plurality of test battery cells; the conversion module 200 is configured to obtain a life attenuation model of the power battery, and perform linear conversion on the life attenuation model to obtain a target model; the first determining module 300 is configured to fit the test data based on the target model to obtain an exponential factor and an intermediate parameter of each test battery cell, and determine an activation energy parameter of the life attenuation model based on the exponential factor and the intermediate parameter, wherein the intermediate parameter is different at different temperatures; and the second determining module 400 is configured to determine a pre-exponential factor parameter of the life attenuation model at different temperatures based on the exponential factor, the intermediate parameter and the activation energy parameter, and perform life simulation on the power battery to be tested based on the life attenuation model.
[0140] In the embodiment of the present application, the conversion module 200 is further configured to convert the life attenuation model into a logarithmic form to obtain the target model.
[0141] In the embodiment of the present application, the life attenuation model includes a calendar attenuation model and a cycle attenuation model.
[0142] In the embodiment of the present application, the calendar attenuation model is linearly converted to obtain a first target model; and the calendar attenuation model is linearly converted to obtain a second target model.
[0143] In the embodiment of the present application, the calendar attenuation model is:
[0144]
[0145] wherein Q loss is a current power battery capacity attenuation state; A cal is a calendar attenuation pre-exponential factor; Ea is an activation energy; R is a gas constant; T is a temperature; t is an attenuation time; z cal is a calendar exponential factor.
[0146] The cycle attenuation model is:
[0147]
[0148] wherein Q loss is a current power battery capacity attenuation state; A cyc is a cycle attenuation pre-exponential factor; Ea is an activation energy; R is a gas constant; T is a temperature; cyc is a cycle number; z cyc is a cycle exponential factor.
[0149] In the embodiment of the present application, the first determining module 300 is further configured to set an initial value of the activation energy parameter; and optimize the initial value based on the exponential factor, the intermediate parameter and a target algorithm to obtain a final value of the activation energy parameter.
[0150] In the embodiment of the present application, the calculation formula of the pre-exponential factor parameter is:
[0151]
[0152] Wherein, A is the pre-exponential factor; b is the test battery parameter; Ea is the activation energy; R is the gas constant; and T is the temperature.
[0153] It should be noted that the aforementioned explanation of the power battery life simulation method embodiment is also applicable to the power battery life simulation device of this embodiment, which will not be described here.
[0154] The power battery life simulation device according to the embodiment of the present application can perform life attenuation tests on multiple test batteries at different temperatures and obtain test data, linearly process the life attenuation model, reduce the fitting process and the error of nonlinear fitting, obtain the exponential factor and the intermediate parameter of each test battery based on the target model after linearization, determine the pre-exponential factor parameter of the life attenuation model at different temperatures based on the exponential factor and the intermediate parameter, and obtain the activation energy parameter of the life attenuation model and the pre-exponential factor at different temperatures through test data fitting and global optimization of multiple test batteries at different temperatures. The simulation capability of the model at different temperatures is improved, the problem that the activation energy parameter and the pre-exponential factor parameter of the model cannot be accurately identified by single temperature data in the traditional method is solved, the accuracy of the life attenuation model parameter determination is improved, and the accuracy of the subsequent power battery life simulation is improved.
[0155] Figure 6 The structure schematic diagram of the electronic device provided in the embodiment of the present application is shown. The electronic device can include:
[0156] The memory 601, the processor 602, and the computer program stored in the memory 601 and executable on the processor 602.
[0157] The processor 602 implements the power battery life simulation method provided in the above embodiments when executing the program.
[0158] Further, the electronic device further includes:
[0159] The communication interface 603 is used for communication between the memory 601 and the processor 602.
[0160] The memory 601 is used to store the computer program executable on the processor 602.
[0161] The memory 601 can include a high-speed RAM memory, and can also include a non-volatile memory, such as at least one disk memory.
[0162] If the memory 601, the processor 602 and the communication interface 603 are implemented independently, the communication interface 603, the memory 601 and the processor 602 can be connected with each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 6 In the figure, only one thick line is used to represent the communication interface 603, the memory 601 and the processor 602, but it does not mean that there is only one bus or only one type of bus.
[0163] Optionally, in a specific implementation, if the memory 601, the processor 602 and the communication interface 603 are integrated on a chip, the memory 601, the processor 602 and the communication interface 603 can complete communication between each other through an internal interface.
[0164] The processor 602 can be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.
[0165] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program or instructions, and the computer program or instructions are executed by a processor to implement the power battery life simulation method.
[0166] The embodiments of the present application also provide a computer program product, which includes a computer program or instructions, and the computer program or instructions are executed to implement the power battery life simulation method.
[0167] In the description of the application, reference to "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" etc. means that a particular feature, structure, material or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. The illustrative appearances of the above-mentioned terms in various places in the specification are not necessarily referred to the same embodiment or example. Moreover, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. Furthermore, in non-contradictory cases, those skilled in the art can combine and combine the features of different embodiments or examples described in the specification, and the features of different embodiments or examples.
[0168] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically limited.
[0169] Any process or method descriptions or descriptions of the flow diagrams described herein or otherwise described in this application can be understood as representing the steps of a method implemented by one or N executable instructions, code segments, or portions of code, for implementing custom logic or a process, and the scope of the preferred embodiments of the application includes additional implementation involving structural computer hardware, software, firmware, or combinations thereof, where the steps of the method are performed by one or more hardware components, software components, firmware components, or combinations thereof, and where the functions performed by the various components can be performed in different order, in parallel, or in reverse order, where appropriate, and where the steps of the method can be performed by the same or different components, and where appropriate, the steps of the method can be performed by the same or different components.
[0170] It should be understood that parts of the application can be implemented in hardware, software, firmware, or combinations thereof. In the above-described embodiments, N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. As in another embodiment, if implemented in hardware, any of the following technologies known in the art or a combination thereof can be used: discrete logic circuit with logic gate circuit for implementing logic functions on data signals, application specific integrated circuit with suitable combination logic gate circuit, programmable gate array, field programmable gate array, etc.
[0171] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-mentioned embodiment method can be completed by a program instructing the relevant hardware, and the program can be stored in a computer readable storage medium. The program, when executed, includes one or a combination of steps of the method embodiments.
Claims
1. A method for simulating the lifespan of a power battery, characterized in that, Includes the following steps: Lifetime degradation tests were conducted on multiple test cells at different temperatures to obtain test data for the multiple test cells; Obtain the life degradation model of the power battery, and perform a linearization transformation on the life degradation model to obtain the target model; The test data is fitted based on the target model to obtain the exponential factor and intermediate parameters for each test cell. The activation energy parameters of the lifetime degradation model are determined based on the exponential factor and the intermediate parameters, wherein the intermediate parameters are different at different temperatures. Based on the exponential factor, the intermediate parameter, and the activation energy parameter, the pre-exponential factor parameter of the lifetime decay model at different temperatures is determined, and the lifetime of the power battery under test is simulated based on the lifetime decay model.
2. The power battery life simulation method according to claim 1, characterized in that, The linearization transformation of the lifetime decay model to obtain the target model includes: The lifetime decay model is converted into logarithmic form to obtain the target model.
3. The power battery life simulation method according to claim 1 or 2, characterized in that, The lifetime decay model includes a calendar decay model and a cyclic decay model. The linearization transformation of the lifetime decay model to obtain the target model includes: The calendar decay model is linearized to obtain the first target model; The calendar decay model is linearized to obtain the second target model.
4. The power battery life simulation method according to claim 3, characterized in that, The calendar decay model is as follows: Among them, Q loss This represents the current state of battery capacity degradation; A cal t is the calendar decay pre-exponential factor; Ea is the activation energy; R is the gas constant; T is the temperature; t is the decay time; z cal Calendar index factor; The cyclic decay model is as follows: Among them, Q loss This represents the current state of battery capacity degradation; A cyc yc is the pre-exponential factor for cyclic decay; Ea is the activation energy; R is the gas constant; T is the temperature; cyc is the number of cycles; z cyc It is a cyclic exponential factor.
5. The power battery life simulation method according to claim 1, characterized in that, The determination of the activation energy parameter of the lifetime decay model based on the exponential factor and the intermediate parameter includes: Set the initial value of the activation energy parameter; The final value of the activation energy parameter is obtained by optimizing the initial value based on the exponential factor, the intermediate parameters, and the target algorithm.
6. The power battery life simulation method according to claim 5, characterized in that, The formula for calculating the pre-exponential factor parameter is as follows: Where A is the pre-index factor; b is the intermediate parameter of the test cell; Ea is the activation energy; R is the gas constant; and T is the temperature.
7. A power battery life simulation device, characterized in that, include: The acquisition module is used to perform life degradation tests on multiple test cells at different temperatures and acquire test data of the multiple test cells. The conversion module is used to obtain the life degradation model of the power battery and perform a linearization conversion on the life degradation model to obtain the target model. The first determining module is used to fit the test data based on the target model to obtain the exponential factor and intermediate parameters of each test cell, and to determine the activation energy parameter of the lifetime decay model based on the exponential factor and the intermediate parameters, wherein the intermediate parameters are different at different temperatures; The second determining module is used to determine the pre-exponential factor parameters of the lifetime decay model at different temperatures based on the exponential factor, the intermediate parameters, and the activation energy parameters, and to perform lifetime simulation of the power battery under test based on the lifetime decay model.
8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the power battery life simulation method as described in any one of claims 1-6.
9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, The computer program or instructions are executed by a processor to implement the power battery life simulation method as described in any one of claims 1-7.
10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed, they implement the power battery life simulation method as described in any one of claims 1-6.