Construction method and device of cycle life prediction model, electronic equipment and medium

Abstract

The invention provides a construction method and device of a cycle life prediction model, electronic equipment and a medium, and the method comprises the steps: constructing a second function between a lithium battery capacity fading rate and the cycle life based on a first function between the lithium battery capacity fading rate and the lithium battery temperature and a first function between the lithium battery discharge current and the lithium battery capacity fading rate; performing curve fitting on the lithium battery capacity fading rate and the cycle life based on the second function to obtain a third function between the lithium battery capacity fading rate and the cycle life; the power parameter and the coefficient parameter of the third function are functions about the lithium battery temperature and the lithium battery discharge current; comparing the second function with the third function to obtain a relation model between the discharge current and the accelerated life of the lithium battery; and determining a first expression of a power parameter and a second expression of a coefficient parameter in the third function based on an Arnius formula and the relation model to obtain a cycle life prediction model. According to the invention, the cycle life of the lithium battery can be accurately predicted.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium batteries, and in particular to a method, device, electronic equipment and medium for constructing a cycle life prediction model. Background Art

[0002] With the continuous development of science and technology, lithium batteries have been widely used in various fields due to their high energy density, long cycle life, and environmental protection. However, with long-term use, lithium batteries will gradually lose energy due to the phenomenon of "lithium intercalation", eventually becoming scrapped prematurely, thus significantly shortening their actual cycle life.

[0003] The cycle life of lithium batteries is crucial to safety, performance, cost, environmental protection, and user experience. Accurately predicting the cycle life of lithium batteries can help optimize battery management systems, reduce operating costs, and improve safety and user experience. Therefore, accurately predicting the cycle life of lithium batteries has become a critical issue that needs to be addressed. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method, device, electronic equipment and medium for constructing a cycle life prediction model, which can accurately predict the cycle life of lithium batteries.

[0005] In a first aspect, an embodiment of the present application provides a method for constructing a cycle life prediction model, the method comprising: Based on the first function between the lithium battery capacity attenuation rate, the lithium battery temperature, and the lithium battery discharge current constructed by the Arrhenius formula, a second function between the lithium battery capacity attenuation rate and the cycle life is constructed; Performing curve fitting on the capacity decay rate and cycle life of the lithium battery based on the second function to obtain a third function between the capacity decay rate and cycle life of the lithium battery; the power parameter and coefficient parameter of the third function are both functions of the lithium battery temperature and the lithium battery discharge current; Comparing the second function and the third function to obtain a relationship model between the lithium battery discharge current and the accelerated life; the model parameters in the relationship model include the power parameter and the coefficient parameter in the third function, and a function with the lithium battery temperature as an exponent; Based on the Arrhenius formula and the relationship model, a first expression of a power parameter and a second expression of a coefficient parameter in the third function are determined to obtain a cycle life prediction model.

[0006] In a possible implementation, the first function is constructed by the following steps: Construct the relationship function between the capacity decay rate of lithium battery and the temperature of lithium battery based on Arrhenius formula; The lithium battery discharge current is added to the relationship function to obtain the first function.

[0007] In a possible implementation, constructing a second function between the lithium battery capacity decay rate and the cycle life based on a first function between the lithium battery temperature, the lithium battery capacity decay rate, and the lithium battery discharge current constructed by the Arrhenius formula includes: Assuming that the lithium battery temperature and the lithium battery discharge current are independent of the lithium battery operating time t, the first function is integrated to obtain a second function between the lithium battery capacity attenuation rate and the cycle life.

[0008] In a possible implementation, the expression of the third function is: ; in, Cr is the capacity decay rate of lithium battery, is the coefficient parameter of the third function, is the lithium battery discharge current, is the lithium battery temperature, is the cycle life, is the power parameter of the third function.

[0009] In a possible implementation, the first expression is: ; in, is the coefficient parameter of the third function, is the lithium battery discharge current, is the lithium battery temperature, is the first coefficient of the first expression, is the second coefficient of the first expression, is the third coefficient of the first expression, is the first argument of the first expression, The second argument of the first expression.

[0010] In a possible implementation, the second expression is: ; in, is the power parameter of the third function, is the lithium battery discharge current, is the lithium battery temperature, is the first coefficient of the third function, is the second coefficient of the second expression, f is the third coefficient of the second expression, is the first argument of the second expression, The second parameter of the second expression.

[0011] In one possible implementation, the method further includes: Obtain the current discharge current, current temperature, and current capacity decay rate of the target lithium battery; The current discharge current, current temperature and current capacity attenuation rate of the target lithium battery are substituted into the cycle life prediction model to obtain the current cycle life of the target lithium battery.

[0012] In a second aspect, an embodiment of the present application further provides a device for constructing a cycle life prediction model, the device comprising: A construction module is used to construct a second function between the lithium battery capacity decay rate and the cycle life based on the first function between the lithium battery capacity decay rate, the lithium battery temperature and the lithium battery discharge current constructed by the Arrhenius formula; a curve fitting module, configured to perform curve fitting on the capacity decay rate and cycle life of the lithium battery based on the second function to obtain a third function between the capacity decay rate and cycle life of the lithium battery; wherein the power parameter and coefficient parameter of the third function are both functions of the lithium battery temperature and the lithium battery discharge current; a comparison module, configured to compare the second function and the third function to obtain a relationship model between the discharge current of the lithium battery and the accelerated life; wherein the model parameters in the relationship model include the power parameter and the coefficient parameter in the third function, and a function with the lithium battery temperature as an exponent; A determination module is used to determine a first expression of a power parameter and a second expression of a coefficient parameter in the third function based on the Arrhenius formula and the relationship model to obtain a cycle life prediction model.

[0013] In one possible implementation, the building module is further configured to: Construct the relationship function between the capacity decay rate of lithium battery and the temperature of lithium battery based on Arrhenius formula; The lithium battery discharge current is added to the relationship function to obtain the first function.

[0014] In a possible implementation, constructing a second function between the lithium battery capacity decay rate and the cycle life based on a first function between the lithium battery temperature, the lithium battery capacity decay rate, and the lithium battery discharge current constructed by the Arrhenius formula includes: Assuming that the lithium battery temperature and the lithium battery discharge current are independent of the lithium battery operating time t, the first function is integrated to obtain a second function between the lithium battery capacity attenuation rate and the cycle life.

[0015] In a possible implementation, the expression of the third function is: ; in, Cr is the capacity decay rate of lithium battery, is the coefficient parameter of the third function, is the lithium battery discharge current, is the lithium battery temperature, is the cycle life, is the power parameter of the third function.

[0016] In a possible implementation, the first expression is: ; in, is the coefficient parameter of the third function, is the lithium battery discharge current, is the lithium battery temperature, is the first coefficient of the first expression, is the second coefficient of the first expression, is the third coefficient of the first expression, is the first argument of the first expression, The second argument of the first expression.

[0017] In a possible implementation, the second expression is: ; in, is the power parameter of the third function, is the lithium battery discharge current, is the lithium battery temperature, is the first coefficient of the third function, is the second coefficient of the second expression, f is the third coefficient of the second expression, is the first argument of the second expression, The second parameter of the second expression.

[0018] In one possible embodiment, the device also includes: a prediction module; a prediction module specifically used to obtain the current discharge current, current temperature and current capacity decay rate of the target lithium battery; substituting the current discharge current, current temperature and current capacity decay rate of the target lithium battery into the cycle life prediction model to obtain the current cycle life of the target lithium battery.

[0019] In a third aspect, an embodiment of the present application further provides an electronic device comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to perform the steps of the method for constructing a cycle life prediction model as described in any one of the first aspects.

[0020] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for constructing a cycle life prediction model as described in any one of the first aspects are executed.

[0021] The embodiment of the present application provides a method, device, electronic device and medium for constructing a cycle life prediction model, the method comprising: constructing a second function between the lithium battery capacity decay rate and the cycle life based on a first function between the lithium battery capacity decay rate, the lithium battery temperature and the lithium battery discharge current constructed by the Arrhenius formula; performing curve fitting on the lithium battery capacity decay rate and the cycle life based on the second function to obtain a third function between the lithium battery capacity decay rate and the cycle life; the power parameter and coefficient parameter of the third function are both functions of the lithium battery temperature and the lithium battery discharge current; comparing the second function and the third function to obtain a relationship model between the lithium battery discharge current and the accelerated life; based on the Arrhenius formula and the relationship model, determining a first expression of the power parameter and a second expression of the coefficient parameter in the third function to obtain a cycle life prediction model. Through the present application, the cycle life of the lithium battery can be accurately predicted. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A flow chart showing a method for constructing a cycle life prediction model provided in an embodiment of the present application is shown; Figure 2 A schematic diagram showing a fitting curve obtained by curve fitting the capacity decay rate and cycle life of a lithium battery based on a second function provided in an embodiment of the present application is shown; Figure 3 A schematic diagram of a fitting curve of coefficient parameters provided in an embodiment of the present application is shown; Figure 4 A schematic diagram of a fitting curve of power parameters provided in an embodiment of the present application is shown; Figure 5 A first comparison chart showing cycle life prediction results and experimental observation results provided by an embodiment of the present application is shown; Figure 6 A second comparison chart showing cycle life prediction results and experimental observation results provided by an embodiment of the present application is shown; Figure 7 A third comparison chart showing cycle life prediction results and experimental observation results provided by an embodiment of the present application is shown; Figure 8 A schematic diagram of a device for constructing a cycle life prediction model provided in an embodiment of the present application is shown; Figure 9 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of illustration and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowcharts can be implemented out of sequence, and steps without logical context can be reversed or implemented simultaneously. In addition, those skilled in the art, under the guidance of the contents of this application, can add one or more other operations to the flowchart, or remove one or more operations from the flowchart.

[0025] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application.

[0026] To enable those skilled in the art to use the contents of this application, the following embodiments are provided in conjunction with the specific application scenario of "lithium battery technology." It will be apparent to those skilled in the art that the general principles defined herein can be applied to other embodiments and application scenarios without departing from the spirit and scope of this application. Although this application is primarily described in the context of "lithium battery technology," it should be understood that this is merely an exemplary embodiment.

[0027] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0028] The following is a detailed description of a method for constructing a cycle life prediction model provided in an embodiment of the present application.

[0029] Reference Figure 1 FIG. 1 is a flow chart of a method for constructing a cycle life prediction model provided in an embodiment of the present application. The exemplary steps of the embodiment of the present application are described below: S101. Based on a first function between the lithium battery capacity decay rate, the lithium battery temperature, and the lithium battery discharge current constructed by the Arrhenius formula, a second function between the lithium battery capacity decay rate and the cycle life is constructed.

[0030] In the embodiment of the present application, a first function between the lithium battery temperature, the lithium battery capacity decay rate, and the lithium battery discharge current is first constructed using the Arrhenius formula. Then, a second function between the lithium battery capacity decay rate and the cycle life is constructed based on the first function.

[0031] The capacity decay rate of a lithium battery refers to the rate or degree of decrease in capacity relative to its initial capacity during use. This is a key indicator of lithium battery aging and performance degradation. Initial capacity refers to the maximum capacity a new lithium battery can achieve after undergoing standard charge and discharge cycles, typically measured in milliampere-hours (mAh) or ampere-hours (Ah). Lithium battery capacity refers to the amount of charge a lithium battery can deliver under certain conditions (such as discharge current, temperature, and cut-off voltage). Lithium battery capacity is typically expressed in ampere-hours (Ah) or milliampere-hours (mAh). For example, a 2000mAh battery can theoretically be discharged at a current of 2000mA (2A) in one hour, or at a current of 1000mA (1A) in two hours, until it is depleted. Cycle life refers to the total number of charge and discharge cycles a battery can undergo before failure.

[0032] Specifically, the first function is constructed by the following steps: Step 1: Construct a relationship function between the capacity decay rate of the lithium battery and the temperature of the lithium battery based on the Arrhenius formula.

[0033] In the embodiments of this application, it is generally believed that the accelerating effect of temperature stress on product failure follows the Arrhenius equation. Based on this, the relationship function between the lithium battery capacity decay rate and the lithium battery temperature can be expressed as: ; in, is the capacity decay rate of lithium battery, is the lithium battery capacity, is a constant, is the failure activation energy of lithium batteries, is the Boltzmann constant, and T is the temperature of the lithium battery.

[0034] Step 2: Add the lithium battery discharge current to the relationship function to obtain the first function.

[0035] In the embodiment of the present application, in addition to temperature stress, there is also the influence of electrical stress during the lithium ion cycle operation. It is generally believed that the temperature of the lithium battery and the operating current (charging and discharging current) are two important stresses that accelerate the capacity decay of the lithium ion battery. However, in actual applications, the charging mode of lithium batteries is usually fixed. Therefore, the effect of charging current on the performance of lithium batteries during use is basically unchanged. However, due to differences in the working environment, the accelerating effect of temperature and discharge current on the capacity decay of lithium ion batteries varies. Therefore, the constant in the above relationship function is changed to Through a function whose independent variable is the discharge current of the lithium battery By adding the lithium battery discharge current into the relationship function, the first function is obtained. The expression of the first function is: .

[0036] Specifically, based on the first function between the lithium battery temperature, the lithium battery capacity decay rate, and the lithium battery discharge current constructed by the Arrhenius formula, a second function between the lithium battery capacity decay rate and the cycle life is constructed, including: In the embodiment of the present application, assuming that the lithium battery temperature and the lithium battery discharge current are independent of the lithium battery operating time t, the first function is integrated to obtain a second function between the lithium battery capacity attenuation rate and the cycle life; the specific process is as follows: First, assuming that the lithium battery temperature and lithium battery discharge current are independent of the lithium battery operating time t, integrate the first function to obtain the following expression: ; ; in, For lithium batteries at all times capacity, When the lithium battery is first used capacity.

[0037] Then, let , , then the expression of the second function is: ; in, is the capacity decay rate of lithium battery, For the cycle life.

[0038] S102: Perform curve fitting on the capacity decay rate and cycle life of the lithium battery based on the second function to obtain a third function between the capacity decay rate and cycle life of the lithium battery.

[0039] In the embodiment of the present application, the power parameter and coefficient parameter of the third function are functions of the lithium battery temperature and the lithium battery discharge current. The specific process is: from the second function, it can be seen that the lithium battery capacity decay rate and the cycle life have a nonlinear relationship. This can be verified by fitting the curve of the lithium battery capacity decay rate and the cycle life based on the second function. Figure 2 As shown, it is a schematic diagram of the fitting curve after the lithium battery capacity decay rate and cycle life are curve-fitted based on the second function provided in the embodiment of the present application; specifically, under the conditions of the lithium battery temperature of 338K and the lithium battery discharge current of 0.8C, the actual curve between the lithium battery capacity decay rate and the cycle life is fitted based on the second function, and the observation curve between the lithium battery capacity decay rate and the cycle life is obtained by observing the actual state of the lithium battery, and the curve equation and determination coefficient of the second function are given. .

[0040] Furthermore, from Figure 2, it can be seen that the battery capacity decay rate and cycle life conform to a power function relationship. In addition, temperature and electrical stress are important factors that accelerate the decay of lithium-ion battery life. Therefore, the power parameter and coefficient parameter are set as functions of the lithium battery temperature and the lithium battery discharge current. Therefore, the following expression of the third function between the lithium battery capacity decay rate and cycle life is constructed: ; in, Cr is the capacity decay rate of lithium battery, is the coefficient parameter of the third function, is the lithium battery discharge current, is the lithium battery temperature, is the cycle life, is the power parameter of the third function.

[0041] Here, because Figure 2 The fitting coefficient is above 0.98, indicating that the third function has a high goodness of fit.

[0042] S103: Compare the second function and the third function to obtain a relationship model between the discharge current and the accelerated life of the lithium battery.

[0043] In the embodiment of the present application, the model parameters in the relational model include the power parameter and coefficient parameter in the third function, and the function with the lithium battery temperature as the exponent. Specifically, the third function is first transformed to obtain the following expression: .

[0044] Then, by comparing the second function with the above-mentioned transformed expression of the third function, the following relationship model between the discharge current and accelerated life of the lithium battery is obtained: = .

[0045] S104 . Based on the Arrhenius formula and the relational model, determine a first expression of the power parameter and a second expression of the coefficient parameter in the third function to obtain a cycle life prediction model.

[0046] In the embodiments of the present application, first, battery attenuation rate and cycle life data are obtained through testing, and the third function is concretely expressed using least squares fitting and nonlinear regression analysis. To facilitate analysis, the values ​​of the power parameters and coefficient parameters in the third function are sorted according to temperature and discharge current; refer to Table 1, which shows the values ​​of the power parameters and coefficient parameters in the third function under the conditions of a lithium battery temperature of 338K and a lithium battery discharge current of 0.8C provided in the embodiments of the present application.

[0047] Table 1

[0048] Then, after curve fitting the values ​​of the power parameters and coefficient parameters obtained, it can be seen that the coefficient parameters Sum power parameter It should conform to the regression curve equation.

[0049] Finally, under the discharge condition of 0.8C, T is taken as the independent variable, and the exponential function model is used to perform curve fitting on the coefficient parameter A and the power parameter B respectively and standardize the coefficients. Figure 3 The figure shows a schematic diagram of the fitting curve of the coefficient parameters provided in the embodiment of the present application; Figure 4 As shown, it is a schematic diagram of the fitting curve of the power parameter provided in the embodiment of the present application; wherein, exponential fitting refers to the fitting curve obtained by using the exponential function model, and observation refers to the curve obtained by the actual observation data of the lithium battery; Figure 3 and Figure 4 It can be seen that the fitting curve obtained by using the exponential function model basically coincides with the curve obtained by observation, so it can be confirmed that the coefficient parameter A Sum power parameter B Fits the exponential function model.

[0050] Therefore, referring to the Arrhenius model and the relational model, let , , it can be considered that the coefficient parameter A is related to , Approximately linear relationship. , is the independent variable, and the coefficient parameter A Performing plane regression, we get the first expression: .

[0051] in, is the coefficient parameter of the third function, is the lithium battery discharge current, is the lithium battery temperature, is the first coefficient of the first expression, is the second coefficient of the first expression, is the third coefficient of the first expression, is the first argument of the first expression, The second argument of the first expression.

[0052] Similarly, referring to the Arrhenius model and the relational model, let , , it can be considered that the coefficient parameter A is related to , Approximately linear relationship. , is the independent variable, the coefficient parameter B Performing plane regression, we get the first expression: ; in, is the power parameter of the third function, is the lithium battery discharge current, is the lithium battery temperature, is the first coefficient of the third function, is the second coefficient of the second expression, f is the third coefficient of the second expression, is the first argument of the second expression, The second parameter of the second expression.

[0053] Furthermore, the current discharge current, current temperature and current capacity decay rate of the target lithium battery are obtained; the current discharge current, current temperature and current capacity decay rate of the target lithium battery are substituted into the cycle life prediction model to obtain the current cycle life of the target lithium battery.

[0054] In an embodiment of the present application, before substituting the current discharge current, current temperature and current capacity attenuation rate of the target lithium battery into the cycle life prediction model, the method also includes: determining the coefficient values ​​(the numerical values ​​of the first coefficient, the second coefficient and the third coefficient) and parameter values ​​(the numerical values ​​of the first parameter and the second parameter) in the first expression and the second expression in the cycle life prediction model through the actual discharge current, actual temperature, actual capacity and actual cycle life of the lithium battery fed back by the lithium battery user.

[0055] Example 1: Applying the above method to a sample lithium battery (18650 battery, standard capacity 2200mAh, rated voltage 3-4.2V), the algebraic expression of the cycle life prediction model is: ; The cycle life of the sample battery (under the conditions of 25℃ and 0.5C) is predicted by using the algebraic expression of the cycle life prediction model mentioned above. Figure 5 As shown, this is the first comparison diagram of the cycle life prediction results and the experimental observation results provided in the embodiment of the present application. The error between the cycle life prediction results and the experimental observation results is extremely small, indicating that the model basically conforms to the facts.

[0056] Example 2: Use the algebraic expression of the above cycle life prediction model to predict the cycle life of the sample battery (under the conditions of 25℃ and 0.8C). Figure 6 As shown, this is a second comparison chart of the cycle life prediction results and the experimental observation results provided in the embodiment of the present application. The error between the cycle life prediction results and the experimental observation results is extremely small, indicating that the model basically conforms to the facts.

[0057] Example 3: Use the algebraic expression of the above cycle life prediction model to predict the cycle life of the sample battery (at 45°C and 0.8C). Figure 7 As shown, this is a third comparison diagram of the cycle life prediction results and the experimental observation results provided in the embodiment of the present application. The error between the cycle life prediction results and the experimental observation results is extremely small, indicating that the model is basically consistent with the facts.

[0058] Here, an embodiment of the present application provides a method for constructing a cycle life prediction model, which includes: constructing a second function between the lithium battery capacity decay rate and the cycle life based on the first function between the lithium battery capacity decay rate and the lithium battery temperature and the lithium battery discharge current constructed by the Arrhenius formula; performing curve fitting on the lithium battery capacity decay rate and the cycle life based on the second function to obtain a third function between the lithium battery capacity decay rate and the cycle life; the power parameter and coefficient parameter of the third function are both functions of the lithium battery temperature and the lithium battery discharge current; comparing the second function and the third function to obtain a relationship model between the lithium battery discharge current and the accelerated life; based on the Arrhenius formula and the relationship model, determining the first expression of the power parameter and the second expression of the coefficient parameter in the third function to obtain a cycle life prediction model. The beneficial effects of this method are: (1) Based on the complex physical and chemical processes occurring at the interfaces between the current collector and electrode, electrode and electrolyte, and electrolyte and solvent, the effects of these interfacial reactions on the cycle life of lithium-ion batteries are analyzed through the deduction of failure mechanism theory, and a lithium battery cycle life prediction model is derived. That is, by conducting a preliminary analysis of the failure activation energy of lithium-ion batteries and taking into account interfacial reactions, the model has better battery dynamic simulation characteristics, making the cycle life prediction model closer to the actual working conditions of the battery.

[0059] (2) The construction process of the cycle life prediction model mentioned above has low computational complexity, good adaptive effect, and is widely applicable. While estimating the cycle life, the system noise is adaptively estimated, which improves the estimation accuracy of the cycle life. At the same time, the prediction model is optimized to improve the prediction accuracy.

[0060] (3) The model establishment process reduces the impact of inaccurate prior estimates on posterior estimates and has good practicality. The above method can directly guide the design and improvement of electric vehicles and power batteries from a mechanistic perspective, helping to improve the lifespan and safety of electric vehicles and batteries, and further promote their commercial application. The above method can also form an empirical analysis model that can be applied to the design and optimization of electric vehicles and power batteries and other related fields, showing originality and scalability.

[0061] Based on the same inventive concept, the embodiments of the present application also provide a device for constructing a cycle life prediction model corresponding to the method for constructing a cycle life prediction model. Since the principle of solving the problem by the device in the embodiments of the present application is similar to the method for constructing the cycle life prediction model in the embodiments of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0062] Reference Figure 8 FIG. 1 is a schematic diagram of a device for constructing a cycle life prediction model provided in an embodiment of the present application, the device comprising: A construction module 801 is used to construct a second function between the lithium battery capacity decay rate and the cycle life based on the first function between the lithium battery capacity decay rate, the lithium battery temperature, and the lithium battery discharge current constructed by the Arrhenius formula; a curve fitting module 802 for performing curve fitting on the capacity decay rate and cycle life of the lithium battery based on the second function to obtain a third function between the capacity decay rate and cycle life of the lithium battery; wherein the power parameter and coefficient parameter of the third function are both functions of the lithium battery temperature and the lithium battery discharge current; a comparison module 803, configured to compare the second function and the third function to obtain a relationship model between the lithium battery discharge current and the accelerated lifespan; wherein the model parameters in the relationship model include the power parameter and the coefficient parameter in the third function, and a function with the lithium battery temperature as an exponent; The determination module 804 is configured to determine a first expression of a power parameter and a second expression of a coefficient parameter in the third function based on the Arrhenius formula and the relationship model, so as to obtain a cycle life prediction model.

[0063] like Figure 9 As shown, an electronic device 900 provided in an embodiment of the present application includes: a processor 901, a memory 902 and a bus, wherein the memory 902 stores machine-readable instructions executable by the processor 901. When the electronic device is running, the processor 901 communicates with the memory 902 through the bus, and the processor 901 executes the machine-readable instructions to perform the steps of the method for constructing a cycle life prediction model as described above.

[0064] Specifically, the above-mentioned memory 902 and processor 901 can be general-purpose memory and processor, which are not specifically limited here. When the processor 901 runs the computer program stored in the memory 902, it can execute the above-mentioned method for constructing the cycle life prediction model.

[0065] Corresponding to the above-mentioned method for constructing a cycle life prediction model, an embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the steps of the method for constructing the above-mentioned cycle life prediction model are executed.

[0066] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the method embodiment, and will not be repeated in this application. In the several embodiments provided in this application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0067] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0068] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0069] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the information processing method described in each embodiment of this application. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0070] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for constructing a cycle life prediction model, characterized in that: The method comprises: Based on the first function between the lithium battery capacity attenuation rate, the lithium battery temperature, and the lithium battery discharge current constructed by the Arrhenius formula, a second function between the lithium battery capacity attenuation rate and the cycle life is constructed; Performing curve fitting on the capacity decay rate and cycle life of the lithium battery based on the second function to obtain a third function between the capacity decay rate and cycle life of the lithium battery; the power parameter and coefficient parameter of the third function are both functions of the lithium battery temperature and the lithium battery discharge current; Comparing the second function and the third function to obtain a relationship model between the lithium battery discharge current and the accelerated life; the model parameters in the relationship model include the power parameter and the coefficient parameter in the third function, and a function with the lithium battery temperature as an exponent; Based on the Arrhenius formula and the relationship model, a first expression of a power parameter and a second expression of a coefficient parameter in the third function are determined to obtain a cycle life prediction model.

2. The method for constructing a cycle life prediction model according to claim 1, wherein: The first function is constructed by the following steps: Construct the relationship function between the capacity decay rate of lithium battery and the temperature of lithium battery based on Arrhenius formula; The lithium battery discharge current is added to the relationship function to obtain the first function.

3. The method for constructing a cycle life prediction model according to claim 1, wherein: The method of constructing a second function between the lithium battery capacity decay rate and the cycle life based on a first function between the lithium battery temperature, the lithium battery capacity decay rate, and the lithium battery discharge current constructed by the Arrhenius formula includes: Assuming that the lithium battery temperature and the lithium battery discharge current are independent of the lithium battery operating time t, the first function is integrated to obtain a second function between the lithium battery capacity attenuation rate and the cycle life.

4. The method for constructing a cycle life prediction model according to claim 1, wherein: The expression of the third function is: ； in, Cr is the capacity decay rate of lithium battery, is the coefficient parameter of the third function, is the lithium battery discharge current, is the lithium battery temperature, is the cycle life, is the power parameter of the third function.

5. The method for constructing a cycle life prediction model according to claim 1, wherein: The first expression is: ； in, is the coefficient parameter of the third function, is the lithium battery discharge current, is the lithium battery temperature, is the first coefficient of the first expression, is the second coefficient of the first expression, is the third coefficient of the first expression, is the first argument of the first expression, The second argument of the first expression.

6. The method for constructing a cycle life prediction model according to claim 1, wherein: The second expression is: ； in, is the power parameter of the third function, is the lithium battery discharge current, is the lithium battery temperature, is the first coefficient of the third function, is the second coefficient of the second expression, f is the third coefficient of the second expression, is the first argument of the second expression, The second parameter of the second expression.

7. The method for constructing a cycle life prediction model according to any one of claims 1 to 6, characterized in that: The method further comprises: Obtain the current discharge current, current temperature, and current capacity decay rate of the target lithium battery; The current discharge current, current temperature and current capacity attenuation rate of the target lithium battery are substituted into the cycle life prediction model to obtain the current cycle life of the target lithium battery.

8. A device for constructing a cycle life prediction model, characterized in that: The device comprises: A construction module is used to construct a second function between the lithium battery capacity decay rate and the cycle life based on the first function between the lithium battery capacity decay rate, the lithium battery temperature and the lithium battery discharge current constructed by the Arrhenius formula; a curve fitting module, configured to perform curve fitting on the capacity decay rate and cycle life of the lithium battery based on the second function to obtain a third function between the capacity decay rate and cycle life of the lithium battery; wherein the power parameter and coefficient parameter of the third function are both functions of the lithium battery temperature and the lithium battery discharge current; a comparison module, configured to compare the second function and the third function to obtain a relationship model between the discharge current of the lithium battery and the accelerated life; wherein the model parameters in the relationship model include the power parameter and the coefficient parameter in the third function, and a function with the lithium battery temperature as an exponent; A determination module is used to determine a first expression of a power parameter and a second expression of a coefficient parameter in the third function based on the Arrhenius formula and the relationship model to obtain a cycle life prediction model.

9. An electronic device, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the storage medium communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for constructing a cycle life prediction model as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for constructing a cycle life prediction model according to any one of claims 1 to 7 are executed.

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