Heat production prediction method and device of solid-state battery and nonvolatile storage medium

Abstract

The invention discloses a heat production prediction method and device of a solid-state battery and a nonvolatile storage medium. The method comprises the steps that a target electrochemical model and an initial heat transfer model corresponding to the solid-state battery are obtained, and the initial heat transfer model is a three-dimensional model; collecting battery cell heat production corresponding to the target electrochemical model; determining a target heat transfer model by taking the heat production quantity of the battery cell as a heat source of the initial heat transfer model; determining temperature data based on the target heat transfer model; and predicting the heat production condition of the solid-state battery based on the temperature data. According to the method, the technical problem that the real-time monitoring of the internal temperature distribution of the battery is difficult to realize due to certain limitation in the prediction of the heat production of the battery, such as experimental measurement and theoretical calculation at present is solved.

Description

Technical Field

[0001] The present invention relates to the field of simulation technology, and in particular, to a method, device, and non-volatile storage medium for predicting the heat generation of a solid-state battery. Background Art

[0002] Currently, although there are various methods for predicting battery heat generation, such as experimental measurement, theoretical calculation, and simulation, these methods have certain limitations. Experimental measurement can directly obtain heat generation data, but it has high costs, a long cycle, and it is difficult to achieve real-time monitoring of the internal temperature distribution of the battery. Theoretical calculation methods rely on complex mathematical models and assumptions, the calculation process is cumbersome, and the accuracy is limited by the selection of models and parameters.

[0003] In view of the above problems, no effective solution has been proposed yet. Summary of the Invention

[0004] Embodiments of the present invention provide a method, device, and non-volatile storage medium for predicting the heat generation of a solid-state battery, so as to at least solve the technical problem that current methods for predicting battery heat generation, such as experimental measurement and theoretical calculation, have certain limitations and it is difficult to achieve real-time monitoring of the internal temperature distribution of the battery.

[0005] According to one aspect of the embodiments of the present invention, a method for predicting the heat generation of a solid-state battery is provided, including: obtaining a target electrochemical model and an initial heat transfer model corresponding to the solid-state battery, where the initial heat transfer model is a three-dimensional model; collecting the heat generation of the battery core corresponding to the target electrochemical model; using the heat generation of the battery core as the heat source of the initial heat transfer model to determine the target heat transfer model; determining temperature data based on the target heat transfer model; and predicting the heat generation situation of the solid-state battery based on the temperature data.

[0006] Optionally, obtaining the target electrochemical model corresponding to the solid-state battery includes: obtaining the initial electrochemical model corresponding to the solid-state battery; receiving the actual operating conditions of the solid-state battery input based on the target account, where the actual operating conditions include the upper voltage limit and current density of the solid-state battery; and adjusting the initial electrochemical model based on the actual operating conditions to obtain the target electrochemical model.

[0007] Optionally, adjusting the initial electrochemical model based on the actual operating conditions to obtain the target electrochemical model includes: obtaining the historical temperature situation of the solid-state battery; and adjusting the initial electrochemical model based on the historical temperature situation and the actual operating conditions to obtain the target electrochemical model.

[0008] Optionally, obtain the initial heat transfer model corresponding to the solid-state battery, including: receiving the material parameters corresponding to the solid-state battery input based on the target account; establishing the original heat transfer model corresponding to the solid-state battery based on the material parameters; dividing the grid in the original heat transfer model to obtain the initial heat transfer model.

[0009] Optionally, use the heat generation of the battery cell as the heat source of the initial heat transfer model to determine the target heat transfer model, including: receiving the selected target heat transfer method based on the target account; using the heat generation of the battery cell as the heat source of the initial heat transfer model based on the target heat transfer method to determine the target heat transfer model.

[0010] Optionally, based on the target heat transfer model, determine the temperature data, including: obtaining the maximum temperature and the minimum temperature corresponding to the battery cell domain in the target heat transfer model; determining the temperature data based on the maximum temperature and the minimum temperature.

[0011] Optionally, generate a battery cell temperature distribution map of the solid-state battery based on the heat generation situation of the solid-state battery; display the battery cell temperature distribution map on a preset screen.

[0012] According to another aspect of the embodiments of the present invention, there is also provided a device for predicting the heat generation of a solid-state battery, including: an acquisition module for acquiring the target electrochemical model and the initial heat transfer model corresponding to the solid-state battery, wherein the initial heat transfer model is a three-dimensional model; a collection module for collecting the heat generation of the battery cell corresponding to the target electrochemical model; a first determination module for using the heat generation of the battery cell as the heat source of the initial heat transfer model to determine the target heat transfer model; a second determination module for determining the temperature data based on the target heat transfer model; a prediction module for predicting the heat generation situation of the solid-state battery based on the temperature data.

[0013] According to yet another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium, the non-volatile storage medium including a stored program, wherein when the program runs, it controls the device where the non-volatile storage medium is located to execute any one of the above-mentioned methods for predicting the heat generation of a solid-state battery.

[0014] According to still another aspect of the embodiments of the present invention, there is also provided a computer device, the computer device including a processor for running a program, wherein when the program runs, it executes any one of the above-mentioned methods for predicting the heat generation of a solid-state battery.

[0015] According to still another aspect of the embodiments of the present invention, there is also provided a computer program product, including a computer program, and when the computer program is executed by a processor, it implements any one of the above-mentioned methods for predicting the heat generation of a solid-state battery.

[0016] In an embodiment of the present invention, a method for predicting the heat generation of a solid-state battery is adopted. By obtaining the target electrochemical model and the initial heat transfer model corresponding to the solid-state battery, where the initial heat transfer model is a three-dimensional model; collecting the heat generation of the battery cell corresponding to the target electrochemical model; using the heat generation of the battery cell as the heat source of the initial heat transfer model to determine the target heat transfer model; based on the target heat transfer model, determining the temperature data; and based on the temperature data, predicting the heat generation situation of the solid-state battery, the purpose of determining the temperature distribution inside the solid-state battery is achieved, thereby realizing the technical effect of improving the accuracy of predicting the heat generation of the solid-state battery, and further solving the technical problem that the current prediction methods for battery heat generation, such as experimental measurement and theoretical calculation, have certain limitations and it is difficult to achieve real-time monitoring of the temperature distribution inside the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention, form a part of this application, and the schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 shows a hardware structure block diagram of a computer terminal for implementing the method for predicting the heat generation of a solid-state battery;

[0019] Figure 2 is a schematic flowchart of the method for predicting the heat generation of a solid-state battery according to an embodiment of the present invention;

[0020] Figure 3 is a one-dimensional electrochemical geometric model of the method for predicting the heat generation of a solid-state battery according to an optional embodiment of the present invention;

[0021] Figure 4 is a three-dimensional heat transfer geometric model of the method for predicting the heat generation of a solid-state battery according to an optional embodiment of the present invention;

[0022] Figure 5 is a mesh division diagram of the three-dimensional heat transfer model in the method for predicting the heat generation of a solid-state battery according to an optional embodiment of the present invention;

[0023] Figure 6 is a flowchart of the method for predicting the heat generation of a solid-state battery according to an optional embodiment of the present invention;

[0024] Figure 7 is a structural block diagram of the device for predicting the heat generation of a solid-state battery according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0027] First, some nouns or terms that appear in the process of describing the embodiments of the present application are applicable to the following explanations:

[0028] The electrochemical model focuses on the electrochemical reactions inside the battery, including the intercalation and deintercalation of lithium ions in the electrode material, the transport of electrons in the electrode, the migration of lithium ions in the electrolyte, and the reaction kinetics at the electrode-electrolyte interface. The model is usually based on a series of electrochemical equations, such as the Nernst equation, the Butler-Volmer equation, and Fick's second law, to simulate the dynamic changes of physical quantities such as the electrode potential, current density, and lithium ion concentration.

[0029] The heat transfer model focuses on the heat transfer process inside and outside the battery, including heat conduction, heat convection, and heat radiation. It takes into account factors such as heat generation inside the battery (such as heat generated by electrochemical reactions), heat diffusion, and heat exchange between the battery and the outside world. The heat transfer model is usually based on the principles of thermodynamics and heat transfer, and uses partial differential equations (such as the heat conduction equation) to describe the change of temperature with time and space. Through the heat transfer model, the temperature distribution of the battery under different operating conditions can be predicted.

[0030] According to an embodiment of the present invention, a method embodiment for predicting the heat generation of a solid-state battery is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0031] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal, or a similar computing device. Figure 1 A hardware structure block diagram of a computer terminal for implementing the method for predicting the heat generation of a solid-state battery is shown. As Figure 1 shown, the computer terminal 10 may include one or more (shown as 102a, 102b,..., 102n in the figure) processors (the processor may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), and a memory 104 for storing data. In addition, it may further include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which can be included as one of the ports of the BUS bus), a network interface, a power supply, and / or a camera. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only illustrative and does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 may further include more or fewer components than Figure 1 shown, or have a different configuration from Figure 1 shown.

[0032] It should be noted that the above one or more processors and / or other data processing circuits are generally referred to as "data processing circuits" in this article. The data processing circuit may be embodied in software, hardware, firmware, or any combination thereof, in whole or in part. In addition, the data processing circuit may be a single independent processing module, or be incorporated in whole or in part into any one of the other elements in the computer terminal 10. As involved in the embodiments of the present application, the data processing circuit is used for processor control (such as the selection of a variable resistance terminal path connected to an interface).

[0033] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for predicting the heat generation amount of a solid-state battery in an embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implements the method for predicting the heat generation amount of the solid-state battery of the above application program. The memory 104 may include a high-speed random access memory, and may further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor, and these remote memories can be connected to the computer terminal 10 through a network. Examples of the above network include but are not limited to the Internet, intranet, local area network, mobile communication network, and combinations thereof.

[0034] The display can be, for example, a touch-screen liquid crystal display (LCD), which enables a user to interact with the user interface of the computer terminal 10.

[0035] Figure 2 is a schematic flowchart of the method for predicting the heat generation amount of a solid-state battery provided according to an embodiment of the present invention, as Figure 2 shown, the method includes the following steps:

[0036] Step S202, obtain a target electrochemical model and an initial heat transfer model corresponding to the solid-state battery, where the initial heat transfer model is a three-dimensional model.

[0037] In this step, a geometric structure of a one-dimensional electrochemical model can be established according to the actual structure of the solid-state battery, including a positive electrode, a negative electrode, a solid electrolyte, a current collector, etc. Electrochemical reactions can be set in the geometric structure, and electrochemical reactions can be set in the corresponding regions of the electrode and the electrolyte, including intercalation / deintercalation reactions of lithium ions in the electrode, migration of lithium ions in the electrolyte, etc. And boundary conditions of current and voltage can be set, which may include the applied charge / discharge rate, voltage limit of the battery, etc. Then, the initial state of the electrochemical model is set, including lithium ion concentration, electrode potential, etc.

[0038] Figure 3 is the one-dimensional electrochemical geometric model of the method for predicting the heat generation amount of a solid-state battery provided according to an alternative embodiment of the present invention, as Figure 3 shown, to obtain a one-dimensional electrochemical model. Figure 4 is the three-dimensional heat transfer geometric model of the method for predicting the heat generation amount of a solid-state battery provided according to an alternative embodiment of the present invention, as Figure 4As shown, when establishing the initial heat transfer model, a three-dimensional (3D) spatial dimension can be selected, and then "heat transfer" can be chosen as the main physical field of the model. The "solid heat transfer" or "solid and fluid heat transfer" interface can be considered, and then the boundary conditions can be defined: set the heat transfer conditions on each boundary, such as convection, radiation, or heat conduction. For example, if simulating a battery in an oven, the convective heat exchange conditions of the battery case can be set to obtain the initial heat transfer model.

[0039] Step S204: Collect the heat generation of the battery cell corresponding to the target electrochemical model.

[0040] In this step, domain point probes can be inserted at the boundary positions of the electrochemical model to obtain real-time current and voltage change data. Then, intercalation of porous electrode particles and porous electrode reactions can be added to the electrochemical model to calculate the heat generation of the bare battery cell. Domain Point Probes is a tool in COMSOL for monitoring the changes in physical quantities at specific points in the model, which can help you collect real-time data of key electrochemical parameters such as current and voltage. Among them, COMSOL Multiphysics is a software widely used in multi-physics field simulation analysis. It can couple and simulate multiple physical processes such as electrochemistry and heat conduction through the finite element analysis method. Select the position where the probe is to be monitored, usually at the boundary of the electrochemical model, such as the surface boundary of the positive or negative electrode. Input the coordinates of this position to ensure that the probe is placed at the corresponding position. In the settings of the domain point probe, select the physical quantities to be monitored, usually the current density and potential here. After the model is solved, view the probe data through the postprocessing function. These data can be output as a time series chart to observe the changes of current and voltage over time. Add intercalation reactions of porous electrode particles and porous electrode reactions to calculate the heat generation of the bare battery cell. Because the physical and chemical properties of the porous electrode need to be considered in the electrochemical model, including porosity, diffusion coefficient, conductivity, kinetic parameters of lithium ion intercalation / deintercalation reactions, etc., which can be input through the material library or experimental data. In the physical interface settings, add the intercalation reaction term to set the lithium ion intercalation / deintercalation and electron conduction processes on the porous electrode to ensure that the model can accurately reflect the heat effects of these reactions. The heat generation calculation is based on the thermodynamics and kinetics of the electrochemical reaction. The physical interface of COMSOL usually automatically calculates the ohmic heat (Joule heat), reaction heat, and polarization heat.

[0041] Step S206: Use the heat generation of the battery cell as the heat source of the initial heat transfer model to determine the target heat transfer model.

[0042] In this step, the heat generation of the battery cell is used as the heat source of the initial heat transfer model, and the target heat transfer model is further determined to understand the distribution and transfer of heat inside the battery. The heat source data is imported into the initial heat transfer model. It is necessary to ensure that the data format of the battery cell heat generation is compatible with the data format of the heat source required in the heat transfer model, which can be in the form of a text file or a data set. Specifically, in the physical field settings of the heat transfer model, select the "Heat Source" or "Source Term" function, and import the battery cell heat generation data as part of the heat source distribution into the model. If the heat generation data varies with time, ensure it is imported in the form of "Time Dependent" or "Data Set" so that the model can dynamically adjust the heat source intensity according to the output of the electrochemical model. If the heat generation data also includes spatial distribution, through the "Spatial Distribution" option, associate the heat source with the grid nodes of the battery cell to ensure the accurate distribution of the heat source in three-dimensional space. Integrate the battery cell heat generation as the heat source into the target heat transfer model to obtain a more comprehensive simulation result of the battery's thermal behavior. The establishment of this model is of great significance for optimizing the performance of solid-state batteries and ensuring their safe operation.

[0043] Step S208: Determine the temperature data based on the target heat transfer model.

[0044] In this step, to determine the temperature data based on the target heat transfer model, mainly by solving the heat transfer equation to calculate the temperature changes and distributions inside the battery and its surrounding environment under the action of the given heat source. The "Time Dependent" solver can be selected because the temperature change of the battery is dynamic over time and its evolution process over time needs to be simulated. In the settings of the solver, define the time range and time step for the solution. The time step needs to be small enough to ensure the stability of the model and capture rapid temperature changes. After the solution is completed, enter the "Postprocessing" module to visualize and analyze the results of the heat transfer model. For example, through visualization tools such as "Slice" or "Contour", view the temperature distributions inside and on the surface of the battery. Different time points and spatial sections can be set to comprehensively understand the temperature changes over time and space. Use "Domain Point Probes" or "Mesh Point Probes" to collect temperature data at specific locations, and then create charts to show the temperature change trend over time. Calculate the average value, maximum value, and minimum value of the temperature within the battery cell domain through tools such as "Global Average" or "Integration", which is crucial for evaluating the thermal stability of the battery and the thermal management strategy.

[0045] Through the above steps, the temperature data of the solid-state battery under high-rate charge and discharge conditions can be determined based on the target heat transfer model, which provides an important reference for formulating the battery's thermal management strategy and battery design. Ensuring the accuracy of the model parameters and verifying them through experiments or theoretical calculations are the keys to improving the model's prediction ability.

[0046] Step S210: Predict the heat generation of the solid-state battery based on the temperature data.

[0047] In this step, the heat generation of the solid-state battery is predicted based on the temperature data calculated by the heat transfer model. Among them, the temperature data can be used to verify and fine-tune the accuracy of the heat generation model, or to evaluate the battery's thermal response when the heat source is known.

[0048] Through the above steps, the purpose of determining the temperature distribution inside the solid-state battery can be achieved, thus realizing the technical effect of improving the accuracy of predicting the heat generation of the solid-state battery, and further solving the technical problem that there are certain limitations in the current prediction of battery heat generation, such as experimental measurement, theoretical calculation, etc., and it is difficult to achieve real-time monitoring of the internal temperature distribution of the battery.

[0049] As an optional embodiment, obtaining the target electrochemical model corresponding to the solid-state battery includes: obtaining the initial electrochemical model corresponding to the solid-state battery; receiving the actual operating conditions of the solid-state battery input based on the target account, where the actual operating conditions include the upper voltage limit and current density of the solid-state battery; adjusting the initial electrochemical model based on the actual operating conditions to obtain the target electrochemical model.

[0050] Optionally, obtaining the target electrochemical model corresponding to the solid-state battery and adjusting it according to the actual operating conditions is a key step in simulating battery performance and thermal behavior. This process can be divided into three main stages: obtaining the initial electrochemical model, receiving the actual operating conditions, and adjusting the model to match the actual conditions. Based on the physical and electrochemical characteristics of the solid-state battery, after constructing an initial electrochemical model, in the model, corresponding physical parameters, including conductivity, diffusion coefficient, reaction kinetic parameters, etc., are input for each component of the solid-state battery (such as the positive electrode, negative electrode, solid electrolyte). And set the electrochemical reaction mechanism, such as the intercalation and deintercalation process of lithium ions, to ensure that the model can accurately reflect the thermal effect and kinetics of the electrochemical reaction. Then, according to the usage of the battery, define the actual operating conditions, which may include environmental conditions such as charge and discharge rates, temperature, and pressure. Input the key parameters (such as the upper voltage limit value and current density) in the actual operating conditions into the model as boundary conditions or operating parameters. The upper voltage limit value is usually used to control the charge and discharge cut-off voltage of the battery, while the current density determines the charge and discharge rate and mode of the battery. According to the received actual operating conditions, adjust the parameters of the model. For example, if the current density is high, it may be necessary to adjust the rate constant, diffusion coefficient, etc. of the electrochemical reaction to reflect the electrochemical behavior under high-rate charge and discharge.

[0051] Through the above process, a target electrochemical model of the solid-state battery based on the actual operating conditions can be constructed, thereby providing a scientific basis for battery performance optimization, thermal behavior prediction, and fault diagnosis. Ensuring that each step is based on accurate physical principles and experimental data is the key to improving the prediction accuracy of the model.

[0052] As an alternative embodiment, based on the actual operating conditions, adjusting the initial electrochemical model to obtain the target electrochemical model includes: obtaining the historical temperature situation of the solid-state battery; based on the historical temperature situation and the actual operating conditions, adjusting the initial electrochemical model to obtain the target electrochemical model.

[0053] Optionally, adjusting the initial electrochemical model based on the actual operating conditions and the historical temperature situation to obtain a more accurate target electrochemical model is a process involving parameter calibration and model optimization. This process ensures that the model can accurately reflect the electrochemical behavior of the battery under actual use conditions, including heat generation and temperature change. The target electrochemical parameters can be determined by obtaining the previous heat generation situation and adjusting the parameters in the electrochemical model based on the previous heat generation situation.

[0054] Coupling the electrochemical model with the heat transfer model and using the results of the heat transfer model to correct the temperature-related parameters in the electrochemical model is a key step in accurately simulating the thermoelectrochemical behavior of the battery. This process ensures that the heat generation in the electrochemical process and the temperature changes in the heat transfer process can affect each other, forming a closed-loop system, and the simulation results are closer to the actual battery operation. The electrochemical model and the heat transfer model can be coupled, and then at the end of each calculation time step or cycle, the temperature data of different regions of the battery are read from the heat transfer model, and these data are passed to the electrochemical model through preset coupling variables. Parameters in the electrochemical model, such as reaction rate constants, diffusion coefficients, conductivity, etc., are usually temperature-dependent. Based on the temperature data fed back by the heat transfer model, use the corresponding temperature-dependent formula or look-up table method to update the values of these parameters. For example, the lithium-ion diffusion coefficient may vary exponentially with temperature and needs to be recalculated according to the current temperature. After the parameters are corrected, the electrochemical model is re-run to reflect the electrochemical behavior under the new temperature conditions. This process may require multiple iterations until the model reaches the thermoelectrochemical equilibrium state.

[0055] Using the temperature information of the heat transfer model to correct the temperature-related parameters in the electrochemical model to form a dynamic and interacting thermoelectrochemical coupling model. This coupling model is of great value for the design, performance evaluation, and thermal management strategy formulation of solid-state batteries. Ensure that the update of all model parameters is based on accurate physical equations and experimental data to improve the prediction accuracy of the model.

[0056] As an alternative embodiment, obtaining the initial heat transfer model corresponding to the solid-state battery includes: receiving the material parameters corresponding to the solid-state battery input based on the target account; establishing the original heat transfer model corresponding to the solid-state battery based on the material parameters; dividing the grid in the original heat transfer model to obtain the initial heat transfer model.

[0057] Optionally, Figure 5 is the grid division diagram of the three-dimensional heat transfer model in the heat generation prediction method of the solid-state battery provided by the optional embodiment of the present invention, as Figure 5As shown, the process of obtaining the initial heat transfer model for a solid-state battery involves receiving material parameters, establishing the original heat transfer model, and meshing. This process ensures that the model can accurately reflect the heat transfer characteristics inside the battery. First, the thermophysical properties of the solid-state battery materials can be collected, including but not limited to specific heat capacity, thermal conductivity, density, etc. Then, based on the material parameters, the original heat transfer model corresponding to the solid-state battery is established and the boundary conditions of the model are defined. For example, the convective boundary condition is defined to describe the heat exchange between the battery and the environment. The radiative boundary condition, if radiative heat transfer needs to be considered. The conductive boundary condition, if there are thermal contacts or heat conduction interfaces inside the battery. Mesh the original heat transfer model to obtain the initial heat transfer model. Specifically, perform meshing. The fineness of the mesh directly affects the calculation accuracy and efficiency of the model. "Free Tetrahedral" or "Structured" meshes can be used, and the appropriate mesh type is selected according to the model complexity and heat source distribution. Finer meshes can also be used in areas with concentrated heat sources or large temperature gradients to improve the accuracy of local calculations.

[0058] Based on these parameters, the original heat transfer model is established, and the initial heat transfer model is obtained through meshing. This lays the foundation for subsequent heat prediction and thermal behavior analysis and is an important tool for battery thermal management research and optimization. Ensure that all steps are based on accurate physical principles and experimental data to improve the prediction accuracy of the model.

[0059] As an alternative embodiment, the heat generation of the battery cell is used as the heat source of the initial heat transfer model to determine the target heat transfer model, including: receiving the target heat transfer method selected based on the target account; based on the target heat transfer method, using the heat generation of the battery cell as the heat source of the initial heat transfer model to determine the target heat transfer model.

[0060] Optionally, taking the heat generation of the battery cell as the heat source of the initial heat transfer model and determining the target heat transfer model according to the target heat transfer method is a key step in accurately simulating the thermal behavior of a solid-state battery. This process ensures that the model can accurately reflect how the heat generated by the battery during operation is distributed within the battery and the surrounding environment. Receive the selection input by the user (target account) to determine which heat transfer mechanisms will be adopted by the target heat transfer model, such as convection, radiation, or conduction. This is usually based on the actual application environment and design parameters of the battery, such as whether the battery operates in a forced convection environment or whether the battery needs to consider radiative heat exchange with the surrounding environment. According to the selected heat transfer method, collect or set the corresponding heat transfer conditions and parameters, such as the convective heat transfer coefficient, radiation coefficient, ambient temperature, etc. Add the heat generation of the battery cell calculated by the electrochemical model as the heat source to the heat transfer model. This is usually done in the "Internal Heat Source" option of the "Solid Thermal" physics interface to ensure that the distribution of the heat source matches the heat generation location and heat generation rate in the electrochemical model. According to the target heat transfer method, select and set the corresponding heat transfer physics interface. For example, if the goal is conduction and convective heat transfer, then the "Solid Thermal" and "Convective Heat Transfer" interfaces will be used. If the goal includes radiative heat transfer, then the "Thermal Radiation" interface should also be added. In the "Boundary Conditions", set the correct boundary conditions according to the target heat transfer method. For example, for forced convective heat transfer, convective boundary conditions need to be set, including the convective heat transfer coefficient and ambient temperature.

[0061] Through the above steps, taking the heat generation of the battery cell as the heat source, based on the selected heat transfer method, determine the target heat transfer model. This model can be used to accurately predict the temperature behavior of a solid-state battery during operation, which is of great significance for formulating the battery's thermal management strategy and optimizing the battery performance. Ensure that each setting of the model is based on accurate physical principles and experimental data to improve the model's prediction ability.

[0062] As an alternative embodiment, based on the target heat transfer model, determine the temperature data, including: obtaining the maximum temperature and minimum temperature corresponding to the battery cell domain in the target heat transfer model; based on the maximum temperature and minimum temperature, determine the temperature data.

[0063] Optionally, the temperature change within the battery cell domain can be directly monitored by adding "Domain Point Probes" or "Domain Integration Probes". To obtain the maximum and minimum temperature values at specific points within the battery cell domain, use "Domain Point Probes"; if you need to calculate the maximum and minimum temperature values for the entire battery cell domain, use "Domain Integration Probes". For "Domain Point Probes", the probe positions can be set at specific locations within the battery cell domain, such as the center or edge of the cell, which may have an important impact on the battery's thermal performance. Then, run the target heat transfer model to obtain the temperature distribution within the battery cell domain, and built-in functions such as max() and min() can be used to calculate the global maximum and minimum temperature values within the battery cell domain. Based on the obtained maximum and minimum temperature values, determine the temperature range during battery operation, which is crucial for understanding the battery's thermal performance and identifying potential thermal risks. Calculate the difference (thermal gradient) between the maximum and minimum temperature values in the battery cell domain, which helps to evaluate the heat distribution and thermal stress conditions inside the battery. A high thermal gradient may indicate local overheating or uneven heat dissipation.

[0064] Through the above steps, the maximum and minimum temperature values within the battery cell domain can be obtained based on the target heat transfer model, and then the temperature data can be determined for in-depth analysis of the battery's thermal behavior and formulation of thermal management strategies. Ensure that all relevant factors are considered during model setup and result analysis, including the thermal conductivity of the battery materials, heat generation from electrochemical reactions, environmental conditions, and battery operating conditions, etc.

[0065] As an alternative embodiment, based on the heat generation of the solid-state battery, generate a temperature distribution map of the solid-state battery cell; display the temperature distribution map of the cell on a preset screen.

[0066] Optionally, generating a temperature distribution map of the battery cell based on the heat generation of the solid-state battery and displaying it on a preset screen is an important part of evaluating the thermal behavior of the solid-state battery. Temperature distribution data of the battery under specific operating conditions can be obtained based on a coupled electrochemical and heat transfer model. Multiple image forms can be selected to display the temperature distribution. For example, a slice plot: shows the temperature distribution of the battery cell at a specific cross-section. An isosurface plot: displays the regions within the battery cell where the temperature is uniform, which helps to understand the temperature gradient. A surface plot: for a 3D model, a temperature distribution map of the cell surface can be created to visually show the temperature change of the entire cell. Arrow and streamline plots: if the model includes fluid flow, these plots can show the direction of heat flow and the change of temperature with fluid flow. Display the generated images on a preset screen for relevant personnel to view.

[0067] Through the above steps, a cell temperature distribution map can be generated based on the heat generation of the solid-state battery and displayed on a preset screen, providing an intuitive visual aid for battery thermal management to facilitate the quick identification and understanding of the temperature change trend inside the battery.

[0068] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0069] Next, a specific embodiment is given. Figure 6 is a flowchart of a method for predicting the heat generation of a solid-state battery according to an optional embodiment of the present invention, as Figure 6 shown:

[0070] 1. Use software to establish a one-dimensional solid-state battery electrochemical model:

[0071] Specifically, in the negative electrode thickness (87 μm), electrolyte thickness (200 μm), and positive electrode thickness (75 μm), line segments can be drawn at intervals and combined.

[0072] 2. Confirm the battery material system in the electrochemical model and input the basic battery parameters:

[0073] Specifically, in step 2, confirm that the battery material system is a graphite negative electrode, an NCM811 positive electrode, and an LPSC solid electrolyte, and input parameters such as electrode particle size (0.5 μm), electrolyte conductivity (0.5 μm), charge-discharge rate (1C), and cycle time (1800 s) into the model.

[0074] 3. Define the battery operating conditions and initial conditions in the electrochemical model:

[0075] Specifically, in step 3, use a waveform function wv1 with a period equal to the cycle time, a function phase of 0, a baseline of 0, a magnitude of 1, and a duty cycle of 0.5 to define the application direction of the current; set the initial current density I0 = I_1C * wv1 and the voltage upper limit of 4.25 V at the lithium-ion battery interface.

[0076] 4. Insert domain point probes at the boundary positions of the electrochemical model to obtain real-time current and voltage change data:

[0077] Specifically, in step 4, add domain point probes, which act on the positive electrode boundary position.

[0078] 5. Add the intercalation of porous electrode particles and the porous electrode reaction to the electrochemical model to calculate the heat generation of the bare battery cell:

[0079] Specifically, the heat generation of the battery cell calculated in step 5 includes: the Joule heat liion.Ilx*philx generated by the mass transfer process of the electrolyte, where liion refers to lithium ions, Ilx is the lithium ion current, and philx is the electrochemical potential corresponding to the lithium ion migration; the Joule heat liion.Ilx*philx + liion.Isx*phisx generated by ion transfer and electron transfer in the electrode, where Isx is the electron current and phisx is the electrochemical potential corresponding to electron migration; the reversible heat and irreversible heat liion.Qrevv_per1 + liion.Qirrevv_per1 generated by the electrode reaction, where liion.Qrevv_per1 represents the reversible heat generated in the electrode reaction and liion.Qirrevv_per1 represents the irreversible heat generated in the electrode reaction; among them, the reversible heat is the entropy production heat liion.iv_per1*liion.pce1.per1.minput_temperature*liion.dEeqdT_per1, which is jointly determined by the current density iv_per1, the volume specific heat capacity pce1.per1 of the electrode material, the operating temperature minput_temperature of the battery, and the partial derivative dEeqdT_per1 of the Gibbs free energy of the electrode reaction with respect to temperature; the irreversible heat is the electrode polarization heat liion.iv_per1*liion.eta_per1, which is jointly determined by the current density iv_per1 and the overpotential eta_per1 of the electrode reaction.

[0080] 6. Use software to establish a three-dimensional solid-state battery heat transfer model:

[0081] Specifically, in step 6, establish a 3D heat transfer model with the actual geometric dimensions of the battery cell, including the bare battery cell, the positive electrode tab, and the negative electrode tab of the target solid-state battery, and form a union of these geometric bodies.

[0082] 7. Divide the grid of the three-dimensional heat transfer model:

[0083] Specifically, in step 7, divide the three-dimensional heat transfer model with free tetrahedral meshes, and the meshes at the connection between the tab and the battery cell are denser for subsequent calculations.

[0084] 8. Input material parameters into the heat transfer model:

[0085] Specifically, in step 8, input the thermal performance parameters of the copper pole ear, aluminum pole ear, and bare battery cell. Among them, the constant-pressure heat capacity Cp_Cu = 385 J / kg·K, Cp_Al = 900 J / kg·K, the thermal conductivity k_Cu = 385 W / m, k_Al = 238 W / m. The constant-pressure specific heat capacity Cp_cell of the bare battery cell is defined as (Cp_pos * L_pos + Cp_neg * L_neg + Cp_pos_cc * L_pos_cc + Cp_neg_cc * L_neg_cc + Cp_ele * L_ele) / L_cell. The thermal conductivity k_cell_xy in the plane direction is defined as (k_pos * L_pos + k_neg * L_neg + k_pos_cc * L_pos_cc + k_neg_cc * L_neg_cc + k_ele * L_ele) / L_cell. The thermal conductivity k_cell_z in the thickness direction is defined as L_cell / (L_pos / k_pos + L_neg / k_neg + L_pos_cc / k_pos_cc + L_neg_cc / k_neg_cc + L_ele / k_ele). Among them, Cp_pos is the constant-pressure specific heat capacity of the positive electrode material, Cp_neg is the constant-pressure specific heat capacity of the negative electrode material, Cp_pos_cc is the constant-pressure specific heat capacity of the positive current collector, Cp_neg_cc is the constant-pressure specific heat capacity of the negative current collector, Cp_ele is the constant-pressure specific heat capacity of the electrolyte, k_pos is the thermal conductivity of the positive electrode material, k_neg is the thermal conductivity of the negative electrode material, k_pos_cc is the thermal conductivity of the positive current collector, k_neg_cc is the thermal conductivity of the negative current collector, k_ele is the thermal conductivity of the electrolyte, L_pos is the thickness of the positive electrode material, L_neg is the thickness of the negative electrode material, L_pos_cc is the thickness of the positive current collector, L_neg_cc is the thickness of the negative current collector, L_ele is the thickness of the electrolyte, and L_cell is the total thickness of the bare battery cell.

[0086] 9. Confirm the heat source and heat transfer mode of the heat transfer model:

[0087] Specifically, in step 9, add solid and fluid heat transfer to the three-dimensional model. The heat source is the total heat generation Q of the battery, where Q is the heat generation of the bare battery cell described in step 5. The initial temperature of the battery is set to room temperature 298.15 K, and the external temperature of the heat flux is an oven at 50 degrees.

[0088] 10. Insert domain probes in the battery cell domain of the heat transfer model to obtain the maximum, minimum, and average temperatures:

[0089] Couple the electrochemical model and the heat transfer model, and return the temperature to the one-dimensional model to correct the temperature-related parameters therein.

[0090] Specifically, in step 10, the temperature-related parameters include the equilibrium potential, the temperature coefficient of the equilibrium potential, the diffusion coefficient, etc. The domain probe obtains the changes of the maximum temperature, minimum temperature, and average temperature with the charge and discharge time to determine the heat generation situation.

[0091] Through the description of the above embodiments, those skilled in the art can clearly understand that the method for predicting the heat generation amount of the solid-state battery according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases, the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions to enable a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0092] According to an embodiment of the present invention, there is also provided a device for predicting the heat generation amount of a solid-state battery for implementing the above method for predicting the heat generation amount of a solid-state battery. Figure 7 It is a structural block diagram of the device for predicting the heat generation amount of a solid-state battery provided according to an embodiment of the present invention. As Figure 7 shown, the device for predicting the heat generation amount of a solid-state battery includes: an acquisition module 702, a collection module 704, a first determination module 706, a second determination module 708, and a prediction module 710. The device for predicting the heat generation amount of a solid-state battery will be described below.

[0093] The acquisition module 702 is configured to acquire a target electrochemical model and an initial heat transfer model corresponding to the solid-state battery, where the initial heat transfer model is a three-dimensional model.

[0094] The collection module 704 is connected to the acquisition module 702 and is configured to collect the heat generation amount of the battery cell corresponding to the target electrochemical model.

[0095] The first determination module 706 is connected to the collection module 704 and is configured to use the heat generation amount of the battery cell as the heat source of the initial heat transfer model to determine the target heat transfer model.

[0096] The second determination module 708 is connected to the first determination module 706 and is configured to determine temperature data based on the target heat transfer model.

[0097] The prediction module 710 is connected to the second determination module 708 and is configured to predict the heat generation situation of the solid-state battery based on the temperature data.

[0098] It should be noted here that the above-mentioned acquisition module 702, acquisition module 704, first determination module 706, second determination module 708, and prediction module 710 correspond to steps S202 to S210 in the embodiment. The instances and application scenarios implemented by the multiple modules and the corresponding steps are the same, but are not limited to the content disclosed in the above-mentioned embodiment. It should be noted that the above-mentioned modules, as part of the device, can run in the computer terminal 10 provided in the embodiment.

[0099] Embodiments of the present invention can provide a computer device. Optionally, in this embodiment, the above-mentioned computer device can be located in at least one of multiple network devices in a computer network. The computer device includes a memory and a processor.

[0100] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the heat generation amount prediction method and device of the solid-state battery in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implements the above-mentioned heat generation amount prediction method of the solid-state battery. The memory can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory can further include a memory remotely set relative to the processor, and these remote memories can be connected to the computer terminal through a network. Examples of the above-mentioned network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and combinations thereof.

[0101] The processor can call the information and application programs stored in the memory through a transmission device to execute the following steps: obtain a target electrochemical model and an initial heat transfer model corresponding to the solid-state battery, where the initial heat transfer model is a three-dimensional model; collect the heat generation amount of the battery core corresponding to the target electrochemical model; use the heat generation amount of the battery core as the heat source of the initial heat transfer model to determine the target heat transfer model; determine temperature data based on the target heat transfer model; and predict the heat generation situation of the solid-state battery based on the temperature data.

[0102] Optionally, the above-mentioned processor can also execute the program code of the following steps: obtaining a target electrochemical model corresponding to the solid-state battery, including: obtaining an initial electrochemical model corresponding to the solid-state battery; receiving the actual operating conditions of the solid-state battery input based on the target account, where the actual operating conditions include the upper voltage limit and current density of the solid-state battery; and adjusting the initial electrochemical model based on the actual operating conditions to obtain the target electrochemical model.

[0103] Optionally, the above-mentioned processor can also execute the program code of the following steps: Based on the actual operating conditions, adjust the initial electrochemical model to obtain a target electrochemical model, including: obtaining the historical temperature conditions of the solid-state battery; based on the historical temperature conditions and the actual operating conditions, adjust the initial electrochemical model to obtain a target electrochemical model.

[0104] Optionally, the above-mentioned processor can also execute the program code of the following steps: Obtain the initial heat transfer model corresponding to the solid-state battery, including: receiving the material parameters corresponding to the solid-state battery input based on the target account; based on the material parameters, establish the original heat transfer model corresponding to the solid-state battery; divide the grid in the original heat transfer model to obtain the initial heat transfer model.

[0105] Optionally, the above-mentioned processor can also execute the program code of the following steps: Use the heat generation of the battery cell as the heat source of the initial heat transfer model to determine the target heat transfer model, including: receiving the target heat transfer method selected based on the target account; based on the target heat transfer method, use the heat generation of the battery cell as the heat source of the initial heat transfer model to determine the target heat transfer model.

[0106] Optionally, the above-mentioned processor can also execute the program code of the following steps: Based on the target heat transfer model, determine the temperature data, including: obtaining the maximum temperature and the minimum temperature corresponding to the battery cell domain in the target heat transfer model; based on the maximum temperature and the minimum temperature, determine the temperature data.

[0107] Optionally, the above-mentioned processor can also execute the program code of the following steps: Based on the heat generation situation of the solid-state battery, generate the battery cell temperature distribution map of the solid-state battery; display the battery cell temperature distribution map on the preset screen.

[0108] By adopting the embodiment of the present invention, a method for predicting the heat generation of a solid-state battery is provided. By obtaining the target electrochemical model and the initial heat transfer model corresponding to the solid-state battery, wherein the initial heat transfer model is a three-dimensional model; collecting the heat generation of the battery cell corresponding to the target electrochemical model; using the heat generation of the battery cell as the heat source of the initial heat transfer model to determine the target heat transfer model; based on the target heat transfer model, determining the temperature data; based on the temperature data, predicting the heat generation situation of the solid-state battery, the purpose of determining the temperature distribution inside the solid-state battery is achieved, thereby realizing the technical effect of improving the accuracy of predicting the heat generation of the solid-state battery, and further solving the technical problem that the current predictions for battery heat generation, such as experimental measurement, theoretical calculation, etc., have certain limitations and it is difficult to realize the real-time monitoring of the temperature distribution inside the battery.

[0109] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by a program instructing the relevant hardware of the terminal device. This program can be stored in a non-volatile storage medium, and the storage medium can include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disc, etc.

[0110] An embodiment of the present invention also provides a non-volatile storage medium. Optionally, in this embodiment, the above non-volatile storage medium can be used to store the program code executed by the method for predicting the heat generation amount of the solid-state battery provided in the above embodiment.

[0111] Optionally, in this embodiment, the above non-volatile storage medium can be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

[0112] Optionally, in this embodiment, the non-volatile storage medium is set to store the program code for performing the following steps: obtaining the target electrochemical model and the initial heat transfer model corresponding to the solid-state battery, where the initial heat transfer model is a three-dimensional model; collecting the heat generation amount of the battery cell corresponding to the target electrochemical model; using the heat generation amount of the battery cell as the heat source of the initial heat transfer model to determine the target heat transfer model; determining the temperature data based on the target heat transfer model; and predicting the heat generation situation of the solid-state battery based on the temperature data.

[0113] Optionally, in this embodiment, the non-volatile storage medium is set to store the program code for performing the following steps: obtaining the target electrochemical model corresponding to the solid-state battery, including: obtaining the initial electrochemical model corresponding to the solid-state battery; receiving the actual operating conditions of the solid-state battery input based on the target account, where the actual operating conditions include the upper voltage limit value and the current density of the solid-state battery; and adjusting the initial electrochemical model based on the actual operating conditions to obtain the target electrochemical model.

[0114] Optionally, in this embodiment, the non-volatile storage medium is set to store the program code for performing the following steps: adjusting the initial electrochemical model based on the actual operating conditions to obtain the target electrochemical model, including: obtaining the historical temperature situation of the solid-state battery; and adjusting the initial electrochemical model based on the historical temperature situation and the actual operating conditions to obtain the target electrochemical model.

[0115] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining an initial heat transfer model corresponding to a solid-state battery, including: receiving material parameters corresponding to the solid-state battery input based on a target account; establishing an original heat transfer model corresponding to the solid-state battery based on the material parameters; dividing the grid in the original heat transfer model to obtain the initial heat transfer model.

[0116] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: using the heat generation of the battery cell as the heat source of the initial heat transfer model to determine the target heat transfer model, including: receiving a target heat transfer method selected based on a target account; using the heat generation of the battery cell as the heat source of the initial heat transfer model based on the target heat transfer method to determine the target heat transfer model.

[0117] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining temperature data based on the target heat transfer model, including: obtaining the maximum temperature value and the minimum temperature value corresponding to the battery cell domain in the target heat transfer model; determining the temperature data based on the maximum temperature value and the minimum temperature value.

[0118] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: generating a battery cell temperature distribution map of the solid-state battery based on the heat generation condition of the solid-state battery; displaying the battery cell temperature distribution map on a preset screen.

[0119] An embodiment of the present invention further provides a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can implement: obtaining a target electrochemical model and an initial heat transfer model corresponding to a solid-state battery, where the initial heat transfer model is a three-dimensional model; collecting the heat generation of the battery cell corresponding to the target electrochemical model; using the heat generation of the battery cell as the heat source of the initial heat transfer model to determine the target heat transfer model; determining temperature data based on the target heat transfer model; predicting the heat generation condition of the solid-state battery based on the temperature data.

[0120] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0121] In the above embodiments of the present invention, the descriptions of the various embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0122] In several embodiments provided by this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the division of the units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces. The indirect couplings or communication connections of units or modules can be in electrical or other forms.

[0123] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0124] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0125] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this 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 to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. And the aforementioned storage medium includes: USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs and other various media that can store program codes.

[0126] The above is only the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for predicting heat generation of a solid-state battery, characterized in that: include: Obtaining a target electrochemical model and an initial heat transfer model corresponding to the solid-state battery, wherein the initial heat transfer model is a three-dimensional model; Collecting the heat generated by the battery cell corresponding to the target electrochemical model; Using the heat generated by the battery core as the heat source of the initial heat transfer model to determine the target heat transfer model; Determining temperature data based on the target heat transfer model; Based on the temperature data, the heat generation of the solid-state battery is predicted.

2. The method according to claim 1, characterized in that: The obtaining of a target electrochemical model corresponding to the solid-state battery includes: Obtaining an initial electrochemical model corresponding to the solid-state battery; Receiving an actual operating condition of the solid-state battery input based on a target account, wherein the actual operating condition includes an upper voltage limit and a current density of the solid-state battery; Based on the actual operating conditions, the initial electrochemical model is adjusted to obtain the target electrochemical model.

3. The method according to claim 2, characterized in that The adjusting the initial electrochemical model based on the actual operating condition to obtain the target electrochemical model includes: Obtaining historical temperature conditions of the solid-state battery; Based on the historical temperature conditions and the actual operating conditions, the initial electrochemical model is adjusted to obtain the target electrochemical model.

4. The method according to claim 1, characterized in that The obtaining of an initial heat transfer model corresponding to the solid-state battery includes: Receiving material parameters corresponding to the solid-state battery input based on the target account; Based on the material parameters, establishing an original heat transfer model corresponding to the solid-state battery; The grid in the original heat transfer model is divided to obtain the initial heat transfer model.

5. The method according to claim 1, characterized in that The step of using the heat generated by the battery core as the heat source of the initial heat transfer model to determine the target heat transfer model includes: receiving a target heat transfer method selected based on a target account; Based on the target heat transfer mode, the heat generated by the battery core is used as the heat source of the initial heat transfer model to determine the target heat transfer model.

6. The method according to claim 1, characterized in that The step of determining temperature data based on the target heat transfer model comprises: Obtaining the maximum temperature and the minimum temperature corresponding to the cell domain in the target heat transfer model; The temperature data is determined based on the maximum temperature value and the minimum temperature value.

7. The method according to any one of claims 1 to 6, characterized in that: Also includes: Based on the heat generation of the solid-state battery, generating a cell temperature distribution diagram of the solid-state battery; The battery core temperature distribution diagram is displayed on a preset screen.

8. A heat generation prediction device for a solid-state battery, characterized in that: include: An acquisition module, used to acquire a target electrochemical model and an initial heat transfer model corresponding to the solid-state battery, wherein the initial heat transfer model is a three-dimensional model; A collection module, used to collect the heat generated by the battery cell corresponding to the target electrochemical model; A first determination module is used to determine a target heat transfer model by taking the heat generated by the battery core as a heat source of the initial heat transfer model; A second determination module, configured to determine temperature data based on the target heat transfer model; A prediction module is used to predict the heat generation of the solid-state battery based on the temperature data.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the heat generation prediction method of the solid-state battery according to any one of claims 1 to 7.

10. A computer device, characterized in that: include: Memory and processor, The memory stores a computer program; The processor is used to execute the computer program stored in the memory, and when the computer program is running, the processor executes the heat generation prediction method of the solid-state battery according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the heat generation prediction method of the solid-state battery according to any one of claims 1 to 7 is implemented.

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