Lithium ion diffusion coefficient prediction method and system in lithium battery charging and discharging process

Abstract

The invention belongs to the technical field of lithium ion batteries. The invention provides a lithium ion diffusion coefficient prediction method and system in the charging and discharging process of a lithium battery. The current temperature in the charging and discharging process of the lithium battery is obtained; and according to the obtained current temperature, the diffusion coefficient correction constant and the temperature dependency constant, determining a prediction result of the lithium ion diffusion coefficient at the current temperature, and according to the prediction results of the lithium ion diffusion coefficient at different temperatures, determining a law curve of the lithium ion diffusion coefficient changing along with the temperature. According to the method, the temperature index growth dependent diffusion coefficient is adopted, the calculation is simple and convenient, the prediction precision is high, the method has a wide engineering application background, and an effective guiding effect can be provided for the design of the lithium battery.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion batteries, and particularly relates to a method for predicting the lithium-ion diffusion coefficient during the charge and discharge process of a lithium battery, a system for predicting the lithium-ion diffusion coefficient during the charge and discharge process of a lithium battery, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] The statements in this part merely provide background art related to the present invention and do not necessarily constitute prior art.

[0003] Lithium-ion batteries (LIBs) have advantages such as high energy density, low self-discharge rate, nearly zero memory effect, high open-circuit voltage, light weight, and long life. In recent years, they have been regarded as the preferred power source for electric vehicles (EVs) and hybrid electric vehicles (HEVs). The lithium-ion diffusion coefficient is one of the core parameters determining the battery's kinetic performance, directly affecting the charge and discharge speed, polarization degree, energy efficiency, and cycle life. Therefore, the research on the diffusion coefficient during its charge and discharge process has received extensive attention.

[0004] Generally, the monomer voltage and capacity of a lithium battery are generally determined by the positive electrode active material. During the charge and discharge process, lithium ions diffuse in the positive electrode material. The lithium-ion diffusion coefficient is an important parameter describing the migration rate of lithium ions in the electrode material and directly affects the charge and discharge kinetics process and overall performance of the lithium battery. Research has shown that the lithium-ion diffusion coefficient is temperature-sensitive. At low temperatures, the migration rate of lithium ions decreases significantly, resulting in a large drop in battery capacity and power output, and easily causing safety problems. Although high temperatures can increase the diffusion coefficient, they may accelerate the decomposition of the electrolyte and the destruction of the SEI film.

[0005] The traditional methods for describing and predicting the diffusion coefficient mainly follow the Arrhenius equation. The Arrhenius formula quantitatively represents the relationship between the reaction rate constant and temperature, and its differential expression is: k is the reaction rate constant in terms of concentration, T is the absolute temperature, Ea is the activation energy, and R is the molar gas constant. The activation energy in the Arrhenius equation is a stage constant and has a good effect within a certain temperature range, but this range is difficult to estimate, and it is impossible to accurately predict the lithium-ion diffusion coefficient during the charge and discharge process of a lithium battery. Summary of the Invention

[0006] To solve the deficiencies of the prior art, the present invention provides a method and system for predicting the lithium-ion diffusion coefficient during the charge and discharge process of a lithium battery. By using a diffusion coefficient that depends on the exponential growth of temperature, the calculation is simple and the prediction accuracy is high, with a wide range of engineering application backgrounds, and it can provide effective guidance for the design of lithium batteries.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] In the first aspect, the present invention provides a method for predicting the lithium-ion diffusion coefficient during the charge and discharge process of a lithium battery.

[0009] A method for predicting the lithium-ion diffusion coefficient during the charge and discharge process of a lithium battery includes the following steps:

[0010] Obtain the current temperature during the charge and discharge process of the lithium battery;

[0011] According to the obtained current temperature, the diffusion coefficient correction constant, and the temperature dependence constant, determine the prediction result of the lithium-ion diffusion coefficient at the current temperature;

[0012] According to the prediction results of the lithium-ion diffusion coefficient at different temperatures, determine the law curve of the lithium-ion diffusion coefficient changing with temperature.

[0013] As a further limitation of the first aspect of the present invention, when the current temperature is zero, the lithium-ion diffusion coefficient is equal to the diffusion coefficient correction constant.

[0014] As a further limitation of the first aspect of the present invention, the prediction result D of the lithium-ion diffusion coefficient at the current temperature is: D = αe βT , where α is the diffusion coefficient correction constant, β is the temperature dependence constant, and T is the current temperature.

[0015] As a further limitation of the first aspect of the present invention, the determination of the diffusion coefficient correction constant and the temperature dependence constant includes:

[0016] Combining the experimental data of the lithium-ion diffusion coefficient changing with temperature at different temperatures, and using the non-linear least squares method for fitting to obtain the diffusion coefficient correction constant and the temperature dependence constant.

[0017] As a further limitation of the first aspect of the present invention, compare the law curve of the predicted lithium-ion diffusion coefficient changing with temperature with the law curve of the lithium-ion diffusion coefficient changing with temperature predicted according to the Arrhenius formula, and determine the prediction accuracy according to the comparison result.

[0018] In the second aspect, the present invention provides a system for predicting the lithium-ion diffusion coefficient during the charge and discharge process of a lithium battery.

[0019] A system for predicting the lithium-ion diffusion coefficient during the charge and discharge process of a lithium battery includes:

[0020] A data acquisition unit, configured to: acquire the current temperature during the charge and discharge process of a lithium battery;

[0021] A coefficient prediction unit, configured to: determine a prediction result of the lithium ion diffusion coefficient at the current temperature according to the obtained current temperature, diffusion coefficient correction constant, and temperature dependence constant;

[0022] A law generation unit, configured to: determine a law curve of the lithium ion diffusion coefficient varying with temperature according to the prediction results of the lithium ion diffusion coefficient at different temperatures.

[0023] As a further limitation of the second aspect of the present invention, in the coefficient prediction unit, the prediction result D of the lithium ion diffusion coefficient at the current temperature is: D = αe βT , where α is the diffusion coefficient correction constant, β is the temperature dependence constant, and T is the current temperature.

[0024] In a third aspect, the present invention provides a computer device, including: a processor and a computer-readable storage medium;

[0025] The processor is adapted to execute a computer program;

[0026] The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the method for predicting the lithium ion diffusion coefficient during the charge and discharge process of a lithium battery as described in the first aspect of the present invention.

[0027] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores a computer program, and the computer program is adapted to be loaded and executed by a processor to implement the method for predicting the lithium ion diffusion coefficient during the charge and discharge process of a lithium battery as described in the first aspect of the present invention.

[0028] In a fifth aspect, the present invention provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the method for predicting the lithium ion diffusion coefficient during the charge and discharge process of a lithium battery as described in the first aspect of the present invention.

[0029] Compared with the prior art, the beneficial effects of the present invention are:

[0030] The present invention innovatively proposes a method for predicting the lithium-ion diffusion coefficient during the charge and discharge process of a lithium battery. According to the obtained current temperature, diffusion coefficient correction constant, and temperature dependence constant, the prediction result of the lithium-ion diffusion coefficient at the current temperature is determined. According to the prediction results of the lithium-ion diffusion coefficient at different temperatures, the law curve of the lithium-ion diffusion coefficient changing with temperature is determined, thereby accurately grasping the change characteristics of the lithium-ion diffusion coefficient; the diffusion coefficient dependent on temperature exponential growth is adopted, which is simple to calculate and has high prediction accuracy, and has a wide engineering application background, and can provide effective guidance for the design of lithium batteries.

[0031] Advantages of additional aspects of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings forming a part of this specification are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0033] Figure 1 It is a schematic flow chart of the method for predicting the lithium-ion diffusion coefficient during the charge and discharge process of the lithium battery provided in Embodiment 1 of the present invention;

[0034] Figure 2 It is a graph showing the change of the lithium-ion self-diffusion coefficient with temperature provided in Embodiment 1 of the present invention;

[0035] Figure 3 It is a graph showing the change of the lithium-ion diffusion coefficient in the x direction with temperature provided in Embodiment 1 of the present invention;

[0036] Figure 4 It is a graph showing the change of the lithium-ion diffusion coefficient in the y direction with temperature provided in Embodiment 1 of the present invention;

[0037] Figure 5 It is a graph showing the change of the lithium-ion diffusion coefficient in the z direction with temperature provided in Embodiment 1 of the present invention;

[0038] Figure 6 It is a schematic diagram of a system for predicting the lithium-ion diffusion coefficient during the charge and discharge process of the lithium battery provided in Embodiment 2 of the present invention;

[0039] Figure 7 It is a schematic diagram of a computer device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0041] It should be noted that the following detailed description is exemplary and is intended to provide further illustration of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention pertains.

[0042] In the case of no conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0043] Embodiment 1:

[0044] The lithium-ion diffusion coefficient refers to the diffusion rate of lithium ions in battery materials under certain conditions. It reflects the migration ability of lithium ions inside the active material and is one of the key factors affecting the charge and discharge performance of the battery. At the initial stage of charging, as lithium ions are released, the diffusion coefficient usually increases and reaches a maximum value when the lithium content is a certain specific value (such as 0.5); subsequently, as lithium ions continue to be removed, the diffusion coefficient gradually decreases. When the lithium content is lower than a certain threshold (such as 0.2), the diffusion coefficient will drop rapidly. At the initial stage of discharging, the diffusion coefficient is usually high. As lithium ions are inserted, the diffusion coefficient decreases slightly but remains at a high level. When the inserted lithium ion content reaches a certain specific value (such as 0.8), the diffusion coefficient will drop sharply.

[0045] A large number of studies have shown that generally, the diffusion coefficient increases with the increase of temperature, but the diffusion coefficient is not an equal-proportion growth constant but a non-linear function of temperature. Usually, researchers use the Arrhenius equation to describe and predict it, but the activation energy in this equation is a stage constant. In the case of a large temperature scale, the activation energy is not a specific value. To overcome the limitations of the existing methods, this implementation mode proposes a method for predicting the lithium-ion diffusion coefficient during the charge and discharge process of a lithium battery, including the following process:

[0046] S1. Select the lithium-ion diffusion coefficient during the charge and discharge process of a specific lithium battery as the research object, conduct charge and discharge tests on the lithium battery, and obtain the experimental data of the lithium-ion diffusion coefficient varying with temperature, which is specifically manifested as the variation characteristic that the diffusion coefficient of lithium ions increases with the increase of temperature during the charge and discharge process of the battery.

[0047] S2. Establish a variable-temperature diffusion coefficient model.

[0048] The variable-temperature diffusion coefficient model is:

[0049] D = αe βT (1);

[0050] where D is the diffusion coefficient of lithium ions at temperature T, α is a correction constant, and β is a temperature-dependent constant; according to the properties of the exponential function, when the temperature is 0, it means the diffusion coefficient is α.

[0051] S3. Combine the experimental data of the lithium-ion diffusion coefficient varying with temperature at different temperatures in S1, and fit to obtain the parameters of the variable-temperature diffusion coefficient model in step S2.

[0052] Specifically: Adopt the nonlinear least squares method, and through the lsqcurvefit command in the software, obtain the most suitable matching parameters in the variable-temperature diffusion coefficient model. These parameters minimize the mean square error between the variable-temperature diffusion coefficient model and the charge-discharge experimental data. The parameters here include: the correction constant α and the temperature dependence constant β.

[0053] Principle of the nonlinear least squares method: In the present invention, (D, T) is a pair of experimental observables, and the optimal estimated values of the correction constant α and the temperature dependence constant β that satisfy D = exp(T, α, β) are obtained. For the given experimental data, the minimum value of the following formula is obtained to get the corresponding correction constant α and temperature dependence constant β:

[0054]

[0055] where D i represents the diffusion coefficient corresponding to the i-th group of experimental observation data, T i represents the temperature corresponding to the i-th group of experimental observation data, α i represents the correction constant corresponding to the i-th group of experimental observation data, β i represents the temperature dependence constant corresponding to the i-th group of experimental observation data, and n represents the total number of groups of experimental observation data.

[0056] Use the variable-temperature diffusion coefficient model to fit the experimental data of the lithium-ion diffusion coefficient varying with temperature at different times obtained in step S1, and obtain the curve of the lithium-ion diffusion coefficient varying with temperature of the experimental data.

[0057] S4. According to the parameter values of the variable-temperature diffusion coefficient model obtained by fitting in step S3, use the model in S2 to change the lithium-ion diffusion direction, and obtain the lithium-ion diffusion coefficient curve in the new direction. This curve is the image of the law of the lithium-ion diffusion coefficient varying with temperature predicted by the variable-temperature diffusion coefficient model, which can be used to guide engineering practical applications.

[0058] As Figure 2 , Figure 3 , Figure 4 and Figure 5 shown, the present invention proposes a specific implementation method:

[0059] Molecular dynamics simulations were employed to study the diffusion of lithium ions in LiCoPO4 from an atomic perspective. Then, the Einstein equation was used to calculate the lithium ion diffusion coefficient, and the data of the diffusion coefficients in the x, y, and z directions and the total diffusion coefficient as a function of temperature were obtained. According to the predicted law of the lithium ion diffusion coefficient at the positive electrode of the battery, the experimental data were fitted using a variable-temperature diffusion coefficient model. The temperature range was 300 K - 800 K, as Figure 2 shown. By the nonlinear least squares method, the calculated model parameters and mean square error are listed in Table 1. It can be seen from Table 1 that the feasibility of this prediction method

[0060] Table 1: Parameters and mean square error of the total self-diffusion coefficient of lithium ions obtained by fitting.

[0061]

[0062] According to the above process and the variable-temperature diffusion coefficient model, the variable-temperature diffusion coefficient and the Arrhenius equation were used to describe the trends of the diffusion coefficients of lithium ions in the x, y, and z directions as a function of temperature, as shown in Table 2, Table 3, Table 4, and Figure 3 (The left figure is the prediction result of the present invention, and the right figure is the prediction result of the Arrhenius equation), Figure 4 (The left figure is the prediction result of the present invention, and the right figure is the prediction result of the Arrhenius equation), Figure 5 (The left figure is the prediction result of the present invention, and the right figure is the prediction result of the Arrhenius equation). It can be found that the variable-temperature diffusion coefficient model can replace the Arrhenius equation to describe the trend of the diffusion coefficient as a function of temperature.

[0063] Table 2: Mean square error of the diffusion coefficient of lithium ions along the x direction fitted by different models.

[0064]

[0065] Table 3: Mean square error of the diffusion coefficient of lithium ions along the y direction fitted by different models.

[0066]

[0067] Table 4: Mean square error of the diffusion coefficient of lithium ions along the z direction fitted by different models.

[0068]

[0069] In summary, the present invention takes the lithium-ion diffusion coefficient during the charge and discharge process of a lithium battery as the research object, obtains the experimental data of the lithium-ion diffusion coefficient varying with temperature, then establishes a variable-temperature diffusion coefficient model, combines the experimental data to determine the parameters of the variable-temperature diffusion coefficient model, quantifies the lithium-ion diffusion coefficient curve, and predicts the variation law of the lithium-ion diffusion coefficient. The present invention adopts a diffusion coefficient that depends on the exponential growth of temperature, which is simple to calculate, has a high prediction accuracy, has a wide engineering application background, and can provide an effective guiding role for the design of lithium batteries.

[0070] Embodiment 2:

[0071] As Figure 6 shown, this implementation provides a lithium-ion diffusion coefficient prediction system during the charge and discharge process of a lithium battery, including:

[0072] A data acquisition unit, configured to: acquire the current temperature during the charge and discharge process of the lithium battery;

[0073] A coefficient prediction unit, configured to: determine the prediction result of the lithium-ion diffusion coefficient at the current temperature according to the obtained current temperature, diffusion coefficient correction constant, and temperature dependence constant;

[0074] A law generation unit, configured to: determine the law curve of the lithium-ion diffusion coefficient varying with temperature according to the prediction results of the lithium-ion diffusion coefficient at different temperatures.

[0075] For the specific working processes of the data acquisition unit, the coefficient prediction unit, and the law generation unit, refer to the introduction in Embodiment 1, which will not be elaborated here.

[0076] It can be understood that the above-mentioned respective units can be separately or all combined into one or several other units to form, or some of them can be further split into multiple smaller units with functional division to form, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above units are divided based on logical functions. In actual applications, the function of one unit can also be realized by multiple units, or the functions of multiple units can be realized by one unit. In other embodiments of the present application, the system may also include other units. In actual applications, these functions can also be assisted by other units and can be realized by the cooperation of multiple units.

[0077] According to another embodiment of the present application, the system described in this embodiment can be constructed, and the method of Embodiment 1 of the present application can be implemented by running a computer program (including program code) capable of executing the respective steps involved in the corresponding method described in Embodiment 1 on a general computing device such as a computer including processing elements and storage elements such as a Central Processing Unit (CPU), a Random Access Memory (RAM), and a Read-Only Memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, loaded into the above computing device through the computer-readable recording medium, and run therein.

[0078] Embodiment 3:

[0079] As Figure 7 shown, this implementation provides an electronic device, which includes a processor 1001, a communication interface 1002, and a computer-readable storage medium 1003. Among them, the processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 can be connected through a bus or other means.

[0080] Among them, the communication interface 1002 is used to receive and send data. The computer-readable storage medium 1003 can be stored in the memory of the electronic device. The computer-readable storage medium 1003 is used to store a computer program, and the computer program includes program instructions. The processor 1001 is used to execute the program instructions stored in the computer-readable storage medium 1003.

[0081] The processor 1001 (or CPU (Central Processing Unit, central processor)) is the computing core and control core of the electronic device, and is adapted to implement one or more instructions, specifically adapted to load and execute one or more instructions to implement the corresponding method flow or corresponding function.

[0082] The processor 1001 is configured to execute the following process:

[0083] Obtain the current temperature during the charge and discharge process of the lithium battery;

[0084] According to the obtained current temperature, the diffusion coefficient correction constant, and the temperature dependence constant, determine the prediction result of the lithium ion diffusion coefficient at the current temperature;

[0085] According to the prediction results of the lithium ion diffusion coefficient at different temperatures, determine the law curve of the lithium ion diffusion coefficient changing with temperature.

[0086] For the specific working process, see the introduction in Embodiment 1, which will not be elaborated here.

[0087] Example 4:

[0088] This implementation provides a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in an electronic device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the electronic device and, of course, the extended storage medium supported by the electronic device. The computer-readable storage medium provides a storage space, and this storage space stores the processing system of the electronic device.

[0089] Moreover, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory; optionally, it can also be at least one computer-readable storage medium located far from the aforementioned processor.

[0090] In one embodiment, one or more instructions are stored in the computer-readable storage medium; the processor loads and executes one or more instructions stored in the computer-readable storage medium to implement the following process:

[0091] Obtain the current temperature during the charge and discharge process of the lithium battery;

[0092] According to the obtained current temperature, the diffusion coefficient correction constant, and the temperature dependence constant, determine the prediction result of the lithium-ion diffusion coefficient at the current temperature;

[0093] According to the prediction results of the lithium-ion diffusion coefficient at different temperatures, determine the law curve of the lithium-ion diffusion coefficient changing with temperature.

[0094] For the specific working process, see the introduction in Example 1 and will not be elaborated here.

[0095] Example 5:

[0096] This implementation provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and these computer instructions are stored in a computer-readable storage medium. The processor of the electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, enabling the electronic device to perform the following process:

[0097] Obtain the current temperature during the charge and discharge process of the lithium battery;

[0098] Based on the obtained current temperature, diffusion coefficient correction constant, and temperature dependence constant, determine the predicted result of the lithium-ion diffusion coefficient at the current temperature;

[0099] Based on the predicted results of the lithium-ion diffusion coefficient at different temperatures, determine the law curve of the lithium-ion diffusion coefficient varying with temperature.

[0100] For the specific working process, see the introduction in Embodiment 1 and will not be elaborated here.

[0101] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this application can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0102] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of this application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data processing device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0103] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting the lithium ion diffusion coefficient during the charging and discharging process of a lithium battery, characterized in that: The process includes: Get the current temperature of the lithium battery during charging and discharging; Determine a prediction result of the lithium ion diffusion coefficient at the current temperature according to the obtained current temperature, diffusion coefficient correction constant and temperature dependence constant; According to the prediction results of the lithium ion diffusion coefficient at different temperatures, a regular curve of the lithium ion diffusion coefficient changing with temperature is determined.

2. The method for predicting the lithium ion diffusion coefficient during the charge and discharge process of a lithium battery according to claim 1, characterized in that: When the current temperature is zero, the lithium ion diffusion coefficient is equal to the diffusion coefficient correction constant.

3. The method for predicting the lithium ion diffusion coefficient during the charge and discharge process of a lithium battery according to claim 1, characterized in that: The predicted result D of the lithium ion diffusion coefficient at the current temperature is: D = αe βT , where α is the diffusion coefficient correction constant, β is the temperature dependence constant, and T is the current temperature.

4. The method for predicting the lithium ion diffusion coefficient during the charge and discharge process of a lithium battery according to any one of claims 1 to 3, characterized in that: Determination of diffusion coefficient correction constants and temperature dependence constants, including: Combining the experimental data of the lithium ion diffusion coefficient changing with temperature at different temperatures, the nonlinear least squares method was used to fit the diffusion coefficient correction constant and the temperature dependence constant.

5. The method for predicting the lithium ion diffusion coefficient during the charge and discharge process of a lithium battery according to any one of claims 1 to 3, characterized in that: The predicted curve of lithium ion diffusion coefficient changing with temperature is compared with the curve of lithium ion diffusion coefficient changing with temperature predicted by Arrhenius formula, and the prediction accuracy is determined according to the comparison result.

6. A lithium ion diffusion coefficient prediction system for lithium battery charging and discharging process, characterized in that: include: The data acquisition unit is configured to: acquire the current temperature of the lithium battery during the charging and discharging process; The coefficient prediction unit is configured to: determine a prediction result of the lithium ion diffusion coefficient at the current temperature according to the obtained current temperature, the diffusion coefficient correction constant and the temperature dependence constant; The rule generating unit is configured to determine a rule curve of the lithium ion diffusion coefficient changing with temperature according to the prediction results of the lithium ion diffusion coefficient at different temperatures.

7. The lithium ion diffusion coefficient prediction system for the lithium battery charge and discharge process as claimed in claim 6, characterized in that: In the coefficient prediction unit, the predicted result D of the lithium ion diffusion coefficient at the current temperature is: D = αe βT , where α is the diffusion coefficient correction constant, β is the temperature dependence constant, and T is the current temperature.

8. A computer device, characterized in that: include: a processor and a computer readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the method for predicting the lithium ion diffusion coefficient during the charging and discharging process of a lithium battery according to any one of claims 1 to 5 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing the method for predicting the lithium ion diffusion coefficient in the lithium battery charging and discharging process as described in any one of claims 1 to 5.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the method for predicting the lithium ion diffusion coefficient during the charging and discharging process of a lithium battery as claimed in any one of claims 1 to 5 is implemented.

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