A battery module heat spread simulation method and device, electronic equipment and medium

Abstract

The application provides a battery module heat spread simulation method and device, electronic equipment and medium, and relates to the technical field of battery heat management. The method comprises the following steps: obtaining cell heat source data of battery monomer thermal runaway and external heat source data of actual thermal runaway test; calling a pre-constructed solid heat transfer geometric model and configuring boundary conditions and thermal conductivity; the solid heat transfer geometric model follows the energy conservation equation of the cell; loading two types of heat source data, simulating the heat spread process after thermal runaway, and obtaining simulation heat spread data; obtaining the measured heat spread data, correcting the thermal conductivity according to the deviation of the simulation data; based on the corrected model, the battery module heat spread state is simulated again to predict the battery module heat spread state. Through the closed-loop calibration of the measured data and the simulation, the heat spread simulation accuracy is improved, the battery module heat safety characteristics can be efficiently and accurately evaluated, and reliable support is provided for the thermal runaway protection design.

Description

Technical Field

[0001] This application relates to the field of battery thermal management technology, specifically to a method, apparatus, electronic device, and medium for simulating thermal spread of battery modules. Background Technology

[0002] As the core power source for new energy vehicles and energy storage systems, the thermal safety performance of batteries directly determines the overall reliability of the system. Under conditions of high temperature, electrical abuse, or mechanical abuse, individual battery cells are prone to thermal runaway, which can then lead to a chain reaction of thermal propagation across multiple cells within the module, seriously threatening system safety. Therefore, accurately predicting the thermal propagation process of battery modules is of crucial engineering value for the design of thermal insulation structures, optimization of thermal management, and the formulation of safety protection schemes.

[0003] Thermal runaway propagation models are crucial tools for battery system thermal safety design. Once thermal runaway is induced, the heat released from a localized cell propagates to the surrounding area, potentially heating adjacent batteries and causing them to run away as well. Thermal runaway propagation models are based on single-cell thermal runaway models, adding descriptions of energy transfer processes between cells to accurately predict both the occurrence and propagation of thermal runaway. While experimental investigation of thermal runaway to explore thermal safety is costly, requiring simulations to build models and analyze thermal safety boundaries to reduce testing volume and trial-and-error costs, existing models are often based on complex chemical reaction kinetics within the cell, requiring numerous parameters that are difficult to obtain precisely. This results in complex modeling processes, high computational demands, and uncertain accuracy. Furthermore, the traditional "design-manufacturing-testing" cycle is costly and time-consuming, and thermal runaway experiments themselves are inherently dangerous. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method, apparatus, electronic device and medium for simulating thermal propagation of battery modules, in order to solve the problems of existing battery module thermal propagation simulation methods having large computational load, difficulty in obtaining parameters, large deviation between simulation results and actual measurements, and difficulty in balancing simplified modeling and high-precision prediction.

[0005] According to a first aspect of the embodiments of this application, a method for simulating thermal propagation of a battery module is provided, comprising: Acquire cell heat source data and external heat source data. The cell heat source data is the data when a single battery cell experiences thermal runaway, and the external heat source data is the data of the heating device during actual thermal runaway testing. The pre-built solid heat transfer geometry model of the battery module is invoked and the boundary conditions and thermal conductivity are configured. The solid heat transfer geometry model follows the cell energy conservation equation. The cell heat source input and the external heat source input are loaded into the solid heat transfer geometry model to simulate the thermal propagation process after thermal runaway of a single battery cell and obtain simulation thermal propagation data. Obtain measured data on thermal spread of the battery module, and correct the thermal conductivity of the solid heat transfer geometric model based on the deviation between the measured data and the simulated thermal spread data. The heat transfer process after thermal runaway of a single battery cell is simulated based on a solid heat transfer geometry model with modified thermal conductivity to predict the thermal propagation state of the battery module.

[0006] In one optional implementation, the process of acquiring the cell heat source data includes: The measured time-temperature curves of thermal runaway of individual battery cells were obtained through ARC adiabatic testing. Based on the data from the heat generation start temperature to the measured highest temperature in the measured time-temperature curve, a temperature-heat generation power curve is generated. A three-dimensional ARC self-heating simulation model is pre-constructed based on the battery cell size. An event module is added to control the occurrence and end time of self-heating, which is the same as that of the ARC adiabatic test. The temperature-heating power curve is used as the cell heat source data for simulation to obtain the simulation time-temperature curve. If the deviation between the simulated time-temperature curve and the measured time-temperature curve exceeds a preset threshold, the coefficient of the temperature-heat generation power curve is adjusted, and the simulation steps are repeated until the simulated time-temperature curve matches the measured time-temperature curve. The temperature-heat generation power curve at this point is used as the calibrated cell heat source data.

[0007] In one optional implementation, the process of acquiring the external heat source data includes: The temperature rise data of the heating device in actual testing is obtained, and the temperature rise data shows a change pattern of first increasing and then stabilizing. When the power of the heating device is constant, if the variation law of the temperature rise data obtained by simulation does not match the variation law of the temperature rise data of actual test, the curve of the resistance of the heating device changing with temperature is obtained by back fitting. Based on the measured temperature rise data at the center of the heating device, the resistance-temperature change curve is derived. After comparing and verifying the resistance-temperature change curve with the temperature data at the center of the heating device in the actual heat spread test, the external heat source data is obtained.

[0008] In one optional embodiment, the solid heat transfer geometry model is a two-dimensional model reflecting the cross-sectional structure of the battery module in the height direction, including: a cell area, a heating device area, a heat insulation area, an air area, and a cooling device area; The boundary conditions include: the convective heat transfer coefficient and surface emissivity between the outer cell and the environment, the convective heat transfer coefficient of the cooling boundary, and the equivalent thermal resistance between each component. The thermal conductivity includes: the thermal conductivity of the battery cell under normal conditions and the equivalent thermal conductivity of the air region under normal conditions.

[0009] In one optional implementation, the solid heat transfer geometric model is equipped with a physical field for real-time monitoring of the simulated temperature of each battery cell in the battery module. When the simulated temperature of a battery cell reaches a preset thermal runaway trigger threshold, the model controls the cell to undergo thermal runaway and adjusts the thermal parameters of the corresponding thermal runaway battery cell when the energy of the thermal runaway is completely consumed.

[0010] In one optional implementation, correcting the thermal conductivity of the solid heat transfer geometry model based on the deviation between the measured data and the simulated thermal spread data includes: Key indicators were extracted from the simulated thermal propagation data, including: target cell thermal runaway trigger time, thermal runaway trigger interval between adjacent cells, maximum temperature of each cell, and total thermal propagation duration. Extract the key indicators corresponding to the measured data of thermal spread of the battery module, and calculate the deviation between the key indicators in the simulated thermal spread data and the corresponding key indicators in the measured data. For indicators whose deviation values ​​exceed the preset allowable range, the corresponding thermal conductivity parameters are corrected to preset values ​​that match the state after thermal runaway: if the thermal propagation trigger time deviation is large, the equivalent thermal conductivity of the air region is corrected; if the maximum temperature deviation of the battery cell is large, the thermal conductivity of the battery cell itself or the equivalent thermal conductivity of the air region is corrected. After the correction is completed, run the simulation again and recalculate the deviation between the simulation index and the measured index. Repeat the correction and simulation steps until the deviation of all key indexes is less than or equal to the corresponding allowable range.

[0011] In one optional implementation, the preset thermal runaway trigger threshold is the thermal runaway trigger temperature of a single battery cell; the preset value matching the state after thermal runaway is determined based on the expansion and compression characteristics of the single battery cell after thermal runaway and measured thermal propagation data.

[0012] According to a second aspect of the embodiments of this application, a battery module thermal propagation simulation device is provided, comprising: The heat source input acquisition module is used to acquire cell heat source data and external heat source data. The cell heat source data is the data when the battery cell experiences thermal runaway, and the external heat source data is the data when the heating device is actually conducting thermal runaway tests. The heat transfer geometry model construction module is used to call the pre-built solid heat transfer geometry model of the battery module and configure the boundary conditions and thermal conductivity. The solid heat transfer geometry model follows the cell energy conservation equation. The simulated thermal propagation data acquisition module is used to load the cell heat source input and the external heat source input onto the solid heat transfer geometry model, simulate the thermal propagation process after thermal runaway of a single battery cell, and acquire simulated thermal propagation data. The thermal conductivity correction module is used to acquire measured data of thermal spread of the battery module and correct the thermal conductivity of the solid heat transfer geometric model based on the deviation between the measured data and the simulated thermal spread data. The thermal propagation state prediction module is used to simulate the heat transfer process after thermal runaway of a single battery cell based on a solid heat transfer geometry model with corrected thermal conductivity, in order to predict the thermal propagation state of the battery module.

[0013] According to a third aspect of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method described in the first aspect. According to a fourth aspect of the present application, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method described in the first aspect.

[0014] The battery module thermal propagation simulation method provided by this invention achieves precise input of cell heat source and external heat source based on measured data; relying on a solid heat transfer geometric model that follows the cell energy conservation equation, and combining the deviation between measured thermal propagation data and simulation data to reverse the thermal conductivity to form a closed-loop optimization process, this method ensures a high degree of consistency between simulation results and actual thermal propagation process while reducing input parameters, simplifying model dimensions, and reducing computational load; this method does not rely on complex microscopic reaction models or dedicated simulation modules, has a clear process, low engineering application threshold, can accurately predict key states of thermal propagation, and can be used to evaluate the suppression effect of internal insulation materials, cooling systems, cell layout and other designs on thermal propagation in battery modules, thereby improving the overall safety of battery systems. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of a battery module thermal propagation simulation method provided in an embodiment of this application.

[0017] Figure 2 The diagram provided for the embodiments of this application is a comparison diagram of measured and simulated time-temperature curves.

[0018] Figure 3 This is a schematic diagram of the temperature-heat generation power curve provided in the embodiments of this application.

[0019] Figure 4 The temperature rise comparison curves provided for the embodiments of this application are the actual and simulated temperature rise curves of the heating device.

[0020] Figure 5 This is a schematic diagram of a two-dimensional solid heat transfer geometric model provided in an embodiment of this application.

[0021] Figure 6 This is a schematic diagram of the mesh generation for a two-dimensional solid heat transfer geometric model provided in an embodiment of this application.

[0022] Figure 7 This is a schematic diagram comparing the measured and simulated temperatures of the heating plate and surrounding battery cells, provided for an embodiment of this application.

[0023] Figure 8 The embodiment provided in this application provides a battery module thermal propagation simulation device.

[0024] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] Exemplary methods Please see Figure 1 In one exemplary embodiment, a method for simulating thermal propagation of a battery module 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. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here. Figure 1 As shown, it includes the following steps: Step S101: Obtain cell heat source data and external heat source data. The cell heat source data is the data when the battery cell experiences thermal runaway, and the external heat source data is the data when the heating device is actually conducting thermal runaway tests.

[0027] Specifically, cell heat source data refers to the heat-related data generated when a single battery cell experiences thermal runaway. It is the core input to the "internal heat source" in the simulation model, directly reflecting the heat generation characteristics of the cell's own thermal runaway. It does not depend on external heating conditions, but is only related to the cell's own state and chemical reaction process. The cell heat source data is mainly obtained through ARC adiabatic testing. Combined with experiments on the cell's thermal runaway mechanism, the cell's own heat generation data under different operating conditions is fitted, serving as the core basis for reflecting the cell's "endogenous heat" characteristics.

[0028] External heat source data refers to the thermally relevant data generated by the heating device during actual thermal runaway testing. It serves as the input to the "external excitation heat source" in the simulation model, reflecting the inducing effect of external heating on cell thermal runaway in the test environment and closely matching the boundary conditions of the real test scenario. The external heat source data is directly collected from the operating parameters of the heating device and the test environment parameters during actual thermal runaway testing. It can be recorded in real time through testing equipment such as temperature sensors and power meters, and is key data for reproducing real test conditions.

[0029] This invention simultaneously acquires two types of data, enabling a dual coupling of "internal heat generation + external excitation": the cell heat source data supports the model in reproducing the chemical reaction exothermic characteristics of the cell's own thermal runaway, while the external heat source data supports the model in reproducing the external heating induction process in actual testing. The combination of the two allows the model's temperature field and heat generation power distribution to be highly consistent with the actual thermal runaway test results, significantly improving the model's accuracy and avoiding errors in purely theoretical modeling.

[0030] It's important to clarify that thermal runaway targets a single battery cell, essentially a self-destructive chain reaction within the cell. Due to internal or external short circuits, or overcharging, the cell's temperature rises sharply, triggering internal chemical reactions (SEI film decomposition, cathode decomposition, electrolyte combustion, etc.). These reactions release a large amount of heat, further driving up the temperature, creating a self-accelerating, unstoppable cycle. Thermal propagation, on the other hand, targets a battery module (or battery pack), essentially the transfer and diffusion of heat between multiple cells. When a cell experiences thermal runaway and emits high-temperature flames and gases, this enormous energy is violently transferred to adjacent cells through thermal radiation, convection, and conduction. If adjacent cells cannot be cooled or insulated in time, they will also subsequently trigger thermal runaway.

[0031] In one example, the process of acquiring the cell heat source data includes: A1, the measured time-temperature curve of the adiabatic thermal runaway of a single battery cell was obtained through ARC adiabatic testing.

[0032] Specifically, ARC (Accelerating Rate Calorimeter) is a specialized adiabatic testing device that monitors temperature changes in samples such as battery cells under near-adiabatic conditions. Through precise temperature control and data acquisition, it captures the entire process from slow temperature rise to thermal runaway. In adiabatic testing, the sample experiences almost no heat exchange with the external environment (i.e., adiabatic conditions) to eliminate interference from external factors such as environmental heat dissipation and heat conduction, accurately capturing the sample's own heat generation and temperature rise characteristics. Combining the equipment (ARC) with precise testing methods based on adiabatic conditions is the most reliable way to obtain battery cell heat source data. The high-precision temperature control of the ARC equipment maximizes adiabatic conditions, reduces external interference, and ensures the accuracy and reliability of the collected battery cell heat generation data. The resulting time-temperature curve is shown in the image. Figure 2 The measured curve shown is shown in the figure.

[0033] A2. Based on the data from the heat generation start temperature to the measured highest temperature stage in the measured time-temperature curve, a temperature-heat generation power curve is generated.

[0034] Specifically, the time-temperature curve is obtained through ARC adiabatic testing, showing the complete temperature change over time from the cell's self-heating initiation temperature (the temperature at which the cell's own chemical reaction begins to generate heat before thermal runaway is triggered) to the measured maximum temperature (the highest temperature the cell can reach during thermal runaway); the temperature-heat generation power curve is based on the time-temperature data of this stage, combined with the heat generation power calculation formula. The temperature rise rate is obtained by differentiating the time-temperature curve, and then substituted into the cell density ρ and specific heat capacity c. p The heat production power corresponding to different temperatures is obtained by converting temperature T and time t, and the resulting temperature-heat production power curve is shown in the figure. Figure 3 As shown.

[0035] A3, based on the battery cell size, pre-constructs a three-dimensional ARC self-heating simulation model, adds an event module to control the self-heating occurrence and end time with the same as the ARC adiabatic test, and uses the temperature-heating power curve as the cell heat source data for simulation to obtain the simulation time-temperature curve.

[0036] Specifically, the battery cell size refers to the actual physical dimensions of the cell to be tested and modeled (e.g., the length, width, and thickness of a square cell; the diameter and height of a cylindrical cell; and the external dimensions and thickness distribution of a pouch cell). The pre-built model is a three-dimensional geometric model constructed in simulation software (e.g., COMSOL, ANSYS) based on these actual dimensions. Simultaneously, the basic model settings are completed by incorporating the adiabatic conditions of the ARC test. Pre-building a three-dimensional model framework that closely matches the actual cell is crucial. Since the occurrence of thermal runaway in different cells is random, and the mass loss and heat generation vary, the degree of thermal runaway for each cell cannot be accurately measured. Therefore, during simulation prediction, the heat generated by a cell experiencing ARC thermal runaway is used to represent the heat of each cell. This prepares the groundwork for subsequent input of temperature-heat generation power curves and simulation, avoiding errors caused by unreasonable model settings during the simulation process.

[0037] A4. If the deviation between the simulated time-temperature curve and the measured time-temperature curve exceeds the preset threshold, adjust the coefficient of the temperature-heat generation power curve and repeat the simulation steps until the simulated time-temperature curve matches the measured time-temperature curve. The temperature-heat generation power curve at this point is used as the calibrated cell heat source data.

[0038] Specifically, by using the acquired temperature-heat generation power curve as the heat source input for the initial model, a pre-built 3D ARC self-heating simulation model is run. The simulation process outputs a set of simulated time-temperature curves, i.e., the law of temperature change over time, which are compared point by point with the real time-temperature curves obtained through ARC experimental measurements. Figure 2 The diagram shown is a comparison of the measured and simulated time-temperature curves. The deviation between the two exceeds the preset threshold, indicating that there is an error between the current heat source data and the actual heat generation characteristics of the battery cell. At this time, the coefficients in the temperature-heat generation power curve are automatically adjusted, including the specific heat capacity correction coefficient, density correction coefficient, temperature rise rate correction coefficient, peak heat generation power scaling coefficient, global scaling coefficient, or piecewise correction coefficient, to adjust the magnitude of the heat generation power, the temperature range distribution, and the rate of change with temperature, until the simulated time-temperature curve matches the measured time-temperature curve.

[0039] The preset threshold refers to the maximum permissible error limit set in advance according to engineering requirements (e.g., temperature deviation not exceeding ±2℃, or time deviation not exceeding ±5%). Repeat the "simulation-comparison-correction" cycle until the deviation between the simulation output time-temperature curve and the measured curve is less than or equal to the preset threshold. At this point, the temperature-heat generation power curve is confirmed as the calibrated cell heat source data, representing the most realistic mapping relationship of thermal runaway heat generation within the cell.

[0040] In one example, the process of acquiring the external heat source data includes: B1. Obtain the temperature rise data of the heating device in actual testing. The temperature rise data shows a pattern of first increasing and then leveling off.

[0041] Specifically, in the initial stage of heating, the temperature of the heating device rises rapidly (increased temperature rise); however, as the temperature rises, the heat loss (convection and radiation) between the heating device and the environment (such as the test box and the battery cell) gradually increases, eventually reaching a thermal equilibrium state. At this point, the rate of temperature rise no longer increases and tends to stabilize (level off). This step provides real temperature-time observation data as a benchmark for subsequent simulation comparison and correction.

[0042] B2. When the power of the heating device is constant, if the variation law of the temperature rise data obtained by simulation does not match the variation law of the temperature rise data of actual test, the curve of the resistance of the heating device as a function of temperature is obtained by back fitting.

[0043] Specifically, in the simulation model, it is assumed that the power of the heating device is constant. If the simulated temperature rise curve shows a continuous increase, while the measured curve shows an initial increase followed by a flattening, it indicates that the model has neglected the physical characteristic of the heating device's resistance changing with temperature. Based on the deviation between the measured and simulated temperature rise data, an inverse fitting algorithm (such as back-calculating based on the heat balance equation) is used to calculate the functional relationship between the heating device's resistance and temperature, thus enabling the model to possess key parameters that reflect the heating device's own physical characteristics.

[0044] B3. Based on the measured temperature rise data at the center of the heating device, the resistance-temperature change curve is derived. After comparing and verifying the resistance-temperature change curve with the temperature data at the center of the heating device in the actual heat spread test, the external heat source data is obtained.

[0045] Specifically, the measured temperature rise data at the center of the heating device (the center location has the highest and most representative temperature) is collected and combined with the thermophysical property equation of the heating device to derive and construct an accurate resistance-temperature curve. This curve is ensured to be entirely based on measured data, eliminating biases introduced by assumptions and providing precise input parameters for the external heat source. The simulation output of the heating device's center temperature curve is then observed to ensure complete consistency with the center temperature data measured in the actual thermal spread test (i.e., a test closer to real-world conditions). Only when the curve passes the calibration is the external heat source model confirmed to have fully reproduced the thermal behavior of the real test environment, and only then are the obtained parameters considered accurate external heat source data. Figure 4 The figures shown are the temperature rise curves of the heating device as measured and simulated.

[0046] Step S102: Call the pre-built solid heat transfer geometry model of the battery module and configure the boundary conditions and thermal conductivity. The solid heat transfer geometry model follows the cell energy conservation equation.

[0047] Specifically, the solid heat transfer geometric model in this embodiment of the invention is a two-dimensional model reflecting the cross-sectional structure of the battery module in the height direction, including: cell area, heating device area, heat insulation area, air area and cooling device area.

[0048] like Figure 5 The diagram shown is a schematic of a two-dimensional solid heat transfer geometric model provided in an embodiment of the present invention. The top horizontal region includes a cooling device region and an air region. The cooling device region simulates the cooling structure at the top of the module (such as a liquid cooling plate / air cooling channel), which is used for heat dissipation under normal operating conditions. In the event of thermal runaway, it will also affect the upward path of heat transfer. The air region is the air gap between the cooling device and the battery cell, which is an important path for heat transfer. Its equivalent thermal conductivity will directly affect the heat spread rate.

[0049] The central cell area contains battery cells numbered 1# to 6#, serving as the core carrier for thermal runaway and heat propagation. Cell 3# is the target cell (the cell whose thermal runaway is directly induced by the heating device). The bottom heating device area, directly opposite cell 3#, is used to induce thermal runaway in the target cell during testing / simulation and is the core location of the external heat source. Thermal insulation areas (e.g., aerogel) are distributed between cells 1# and 2#, and between cells 4# and 5#. Using aerogel insulation material, these areas delay / block heat propagation between cells and are a key structure for thermal safety protection.

[0050] Furthermore, the two-dimensional solid heat transfer geometric model is meshed as follows: Figure 6 As shown, the grid is divided into eight cell regions corresponding to the battery module. The grid is denser at the boundaries of each region. During simulation, based on this grid, the temperature change of each grid node is calculated by solving the energy conservation equation to simulate the process of thermal runaway spreading from the target cell to the surrounding area. In this embodiment of the invention, the control equation is established based on the law of energy conservation, and the solid heat transfer geometric model follows the cell energy conservation equation as follows: In the formula, ρ For density, C P For specific heat capacity, The total heat released per unit time; λ eff For equivalent thermal conductivity, T For temperature, t For time, Q dec It is the heat generated by the decomposition of internal active materials during battery thermal runaway. h conv The convective heat transfer coefficient between the battery and the environment. T ambLet ε be the ambient temperature, ε be the surface emissivity, and A be the Stefan-Boltzmann constant. The model follows this equation to ensure that the temperature change and heat transfer process of the battery cell strictly follow the law of conservation of physical energy during the simulation, avoiding unreasonable situations such as heat being generated out of thin air or heat transfer being irregular.

[0051] The boundary conditions configured for the two-dimensional model in this embodiment of the invention include: the convective heat transfer coefficient and surface emissivity between the peripheral battery cell and the environment, the convective heat transfer coefficient of the cooling boundary, and the equivalent thermal resistance between each component; for example, the convective heat transfer coefficient between the peripheral battery cell and the environment is... The surface emissivity is 0.6; based on actual cooling test conditions, simulated cooling boundary conditions are added, and an equivalent thin thermal resistance layer is also added between the water-cooled plate and the cell for heat transfer, with the thermal resistance uniformly set to... The thermal conductivity includes the thermal conductivity of the cell under normal conditions and the equivalent thermal conductivity of the air region under normal conditions. Both are set based on test data of actual materials and can be dynamically adjusted according to temperature (to match the actual situation of temperature changes causing changes in the thermal conductivity of materials during thermal runaway), ensuring that the simulation process is consistent with the actual thermal behavior of the cell.

[0052] In this embodiment of the invention, the solid heat transfer geometric model is equipped with a physical field to monitor the simulated temperature of each battery cell in the battery module in real time. When the simulated temperature of the battery cell reaches the preset thermal runaway trigger threshold, the model controls the cell to undergo thermal runaway and adjusts the thermal parameters of the corresponding thermal runaway battery cell when the energy of the thermal runaway is completely consumed.

[0053] In one example, the solid-state heat transfer geometry model includes an event module and an ODE (Ordinary Differential Equation) module. The event module pre-sets a thermal runaway trigger threshold (calibrated based on actual ARC test data, i.e., the threshold corresponding to the thermal runaway trigger temperature in the calibrated cell heat source data mentioned earlier) and receives simulated temperature data from the ODE module in real time. When the simulated temperature of a certain battery cell reaches the preset threshold, the event module immediately triggers a control command to control the cell to enter a thermal runaway state. At this time, the calibrated cell heat source data (temperature-heat generation power curve) is automatically called, and the heat generation power density of the cell is updated synchronously through the ODE module to simulate the violent heat generation behavior during its thermal runaway process. At the same time, the heat generation is transferred to adjacent cells through the solid-state heat transfer physical field, providing a trigger condition that conforms to actual laws for module-level thermal propagation simulation. The event module presets a thermal runaway termination judgment condition (i.e., the internal chemical reaction of the cell terminates and the energy is completely consumed, corresponding to the scenario where the cell stops generating heat in the later stage of actual thermal runaway, which can be judged by the heat generation power change trend calculated by the ODE module).

[0054] When it is determined that the energy of a single thermally runaway battery cell has been completely depleted, the event module triggers a parameter adjustment command. The ODE module then dynamically adjusts various thermal parameters of the cell (such as specific heat capacity and thermal conductivity), ensuring the adjustment logic perfectly matches the actual characteristics of the cell after thermal runaway. For example, after air gap thermal runaway, the thermal conductivity changes from an initial 0.024 W / (m²) to... K) thermal runaway on one side transforms into a W / (m K), after thermal runaway on the other side, it transforms into b W / (m After thermal runaway occurs in the battery cell, the temperature rises sharply at various locations within the cell. This is equivalent to a sudden change in the cell's thermal conductivity after localized thermal runaway, altering from the normal value to c W / (m²). K). The heating plate power is a constant value (so the temperature rise increases linearly), but in reality, the temperature rise first increases and then remains flat. Therefore, the resistance R changes with temperature T by backfitting the temperature rise. In one example, a is 5, b is 10, and c is 200.

[0055] Step S103: The cell heat source input and the external heat source input are loaded into the solid heat transfer geometry model to simulate the thermal propagation process after thermal runaway of the battery cell and obtain simulation thermal propagation data.

[0056] In one example, the simulation process includes the following steps: C1, Load heat source input data. In the simulation software, associate the calibrated cell heat source input (temperature-heat generation power curve) with all cells in the solid heat transfer geometry model, and set the cell heat generation to be linked with temperature; at the same time, load the external heat source input to the corresponding position of the heating device in the model, bind it to the surface of the central cell, and set parameters such as heating power and contact thermal resistance to ensure that the heat source input is consistent with the actual test.

[0057] C2, initiate thermal propagation simulation. Start the solid heat transfer geometric model simulation. The ODE module calculates the temperature change of each cell in real time based on the cell energy conservation equation, and the event module monitors the temperature of the central cell synchronously. With continuous heating from the external heat source, the temperature of the central cell gradually increases, while the remaining cells receive a small amount of heat through solid heat transfer, and their temperatures rise slowly.

[0058] C3, thermal runaway triggering and thermal propagation process, acquiring simulation thermal propagation data. During the simulation process, all cells in the module complete thermal runaway and cease heat generation, stopping the simulation; exporting all thermal propagation data during the simulation process to form a complete dataset.

[0059] Through the above steps C1-C3, a complete link for reconstructing the thermal propagation process through simulation input of test data is realized. This not only accurately reproduces the physical laws of actual thermal runaway and thermal propagation, but also provides reliable quantitative data support for subsequent model calibration and battery module thermal safety design (such as optimizing the thermal insulation structure and adjusting the cell arrangement).

[0060] Step S104: Obtain the measured data of thermal spread of the battery module, and correct the thermal conductivity of the solid heat transfer geometric model based on the deviation between the measured data and the simulated thermal spread data.

[0061] Specifically, in battery thermal propagation simulations, the thermal conductivity coefficients in the model (such as the cell thermal conductivity and the equivalent thermal conductivity of the air gap) are often set based on theoretical or empirical values, which deviate from actual conditions. These deviations can lead to discrepancies between simulation results (such as thermal runaway trigger time and temperature peak) and measured results. Therefore, this invention employs a closed-loop process of "measured data comparison - deviation analysis - thermal conductivity correction" to calibrate model parameters, ensuring high reliability and engineering applicability of the model simulation results. In one example, the following steps are included: D1, extract key indicators from the simulated thermal propagation data, including: target cell thermal runaway trigger time, thermal runaway trigger interval between adjacent cells, maximum temperature of each cell, and total thermal propagation time.

[0062] Among them, the target cell thermal runaway trigger time refers to the time from the start of the simulation or experiment to the point in time when the temperature of the cell (target cell) that is actively heated or first reaches the thermal runaway condition exceeds the critical threshold. This indicator reflects the response speed of the initial thermal abuse conditions triggering runaway.

[0063] Adjacent cell thermal runaway trigger interval: This refers to the time it takes for heat to transfer to and trigger thermal runaway in the next adjacent cell after the first cell experiences thermal runaway. This metric directly characterizes the propagation rate of thermal runaway within the module.

[0064] Maximum temperature of each cell: This records the peak temperature reached by each cell during simulation or experiment. This metric reflects the severity of the thermal runaway event and the total energy released.

[0065] Total duration of thermal propagation: This refers to the total time from the onset of thermal runaway in the first battery cell to the end of the thermal propagation process (i.e., all cells have experienced runaway or the propagation has been suppressed). This metric provides an overall description of the duration of a thermal safety event.

[0066] D2 extracts the key indicators corresponding to the measured data of thermal spread of the battery module, and calculates the deviation between the key indicators in the simulated thermal spread data and the corresponding key indicators in the measured data.

[0067] Specifically, using battery modules and test conditions completely consistent with the simulation scenario, actual tests of thermal runaway and thermal propagation are conducted. By calculating the deviation values ​​(such as absolute error and relative error) between the simulation indicators and the corresponding measured indicators, the accuracy of the model prediction is objectively and quantitatively evaluated.

[0068] D3, for indicators whose deviation values ​​exceed the preset allowable range, corrects the corresponding thermal conductivity parameters to preset values ​​that match the state after thermal runaway: if the thermal propagation trigger time deviation is large, corrects the equivalent thermal conductivity of the air region; if the maximum temperature deviation of the battery cell is large, corrects the thermal conductivity of the battery cell itself or the equivalent thermal conductivity of the air region.

[0069] Specifically, a large deviation in the heat spread trigger time (or interval) indicates that the simulation of the rate of heat transfer between batteries (i.e., the thermal resistance of the heat transfer path) is inaccurate. Since heat transfer between batteries is mainly dominated by gaps (such as air or insulation layers), the equivalent thermal conductivity of the air region or insulation material should be corrected first. Increasing this value simulates the actual gap compression and tighter contact caused by expansion; decreasing it simulates a better insulation effect.

[0070] If the maximum temperature deviation of the battery cell is large, it indicates that the simulation of heat generation or dissipation inside the battery is inaccurate. This may be due to: an inaccurate equivalent thermal conductivity of the battery under thermal runaway conditions, affecting the rate of heat diffusion to the surface; or inaccurate parameters of the heat transfer path between batteries, affecting heat dissipation to adjacent batteries or the environment. Therefore, it is necessary to correct the thermal conductivity of the battery cell itself or the equivalent thermal conductivity of the air region.

[0071] It should be noted that the preset values ​​matching the state after thermal runaway are determined based on the expansion and compression characteristics of the battery cell after thermal runaway and the measured thermal propagation data.

[0072] D4. After the correction is completed, run the simulation again and recalculate the deviation between the simulation index and the measured index. Repeat the correction and simulation steps until the deviation of all key indexes is less than or equal to the corresponding allowable range.

[0073] This step defines the termination criteria for the calibration process, ensuring that the model ultimately meets the accuracy standards required for engineering applications. It is a standard "simulation-comparison-calibration" iterative loop. By repeatedly running this loop until the deviations of all key indicators meet the preset allowable ranges (e.g., time deviation ±5%, temperature deviation ±3%), the model is considered to be fully calibrated.

[0074] Step 105: Simulate the heat transfer process after thermal runaway of a single battery cell based on the solid heat transfer geometry model with corrected thermal conductivity, in order to predict the thermal propagation state of the battery module.

[0075] In one example, taking the internal heating (built-in heating element) of a fully charged 6-cell module with a long 113 Ah battery cell as an example to trigger the cell thermal runaway test, the actual test showed that target cell #3 experienced thermal runaway at 47 seconds. The cell did not catch fire or explode, but thermal spread occurred. The thermal spread from #3 to #4 took 2 minutes and 12 seconds. Water cooling conditions of 25℃ and 15L / min were introduced for the first 15 seconds and continued for 30 minutes. Figure 7 This is a diagram comparing the measured and simulated temperatures of the battery's heat spread heating plate and surrounding battery cells. Figure 7 It can be seen that the actual measured maximum temperature of the heating plate and the time to reach the maximum temperature rise are relatively well matched with the simulated maximum temperature and the time to reach the maximum temperature rise. Among them, T1 is the heating center point of the heating plate, T2 is the heating center point on the opposite side of the #3 target cell, and T3 is the heating center point on the opposite side of the #4 cell. Moreover, the simulation prediction of whether the #4 cell will experience heat spread is relatively accurate, which confirms that the model prediction is relatively consistent with the actual heat diffusion process of the battery.

[0076] Exemplary device Accordingly, embodiments of this application also provide a battery module thermal propagation simulation device, such as... Figure 8 As shown, it includes: The heat source input acquisition module 801 is used to acquire cell heat source data and external heat source data. The cell heat source data is the data when the battery cell experiences thermal runaway, and the external heat source data is the data when the heating device is actually conducting thermal runaway tests. The heat transfer geometry model construction module 802 is used to call the pre-built solid heat transfer geometry model of the battery module and configure the boundary conditions and thermal conductivity. The solid heat transfer geometry model follows the cell energy conservation equation. The simulation thermal propagation data acquisition module 803 is used to load the cell heat source input and the external heat source input onto the solid heat transfer geometry model, simulate the thermal propagation process after thermal runaway of a single battery cell, and acquire simulation thermal propagation data. The thermal conductivity correction module 804 is used to acquire measured data of thermal spread of the battery module and correct the thermal conductivity of the solid heat transfer geometric model based on the deviation between the measured data and the simulated thermal spread data. The thermal propagation state prediction module 805 is used to simulate the heat transfer process after thermal runaway of a single battery cell based on a solid heat transfer geometry model with corrected thermal conductivity, so as to predict the thermal propagation state of the battery module.

[0077] In one or more embodiments of this application, the process of acquiring the cell heat source data in the heat source input acquisition module 801 includes: The measured time-temperature curves of thermal runaway of individual battery cells were obtained through ARC adiabatic testing. Based on the data from the heat generation start temperature to the measured highest temperature in the measured time-temperature curve, a temperature-heat generation power curve is generated. A three-dimensional ARC self-heating simulation model is pre-constructed based on the battery cell size. An event module is added to control the occurrence and end time of self-heating, which is the same as that of the ARC adiabatic test. The temperature-heating power curve is used as the cell heat source data for simulation to obtain the simulation time-temperature curve. If the deviation between the simulated time-temperature curve and the measured time-temperature curve exceeds a preset threshold, the coefficient of the temperature-heat generation power curve is adjusted, and the simulation steps are repeated until the simulated time-temperature curve matches the measured time-temperature curve. The temperature-heat generation power curve at this point is used as the calibrated cell heat source data.

[0078] In one or more embodiments of this application, the process of acquiring the external heat source data in the heat source input acquisition module 801 includes: The temperature rise data of the heating device in actual testing is obtained, and the temperature rise data shows a change pattern of first increasing and then stabilizing. When the power of the heating device is constant, if the variation law of the temperature rise data obtained by simulation does not match the variation law of the temperature rise data of actual test, the curve of the resistance of the heating device changing with temperature is obtained by back fitting. Based on the measured temperature rise data at the center of the heating device, the resistance-temperature change curve is derived. After comparing and verifying the resistance-temperature change curve with the temperature data at the center of the heating device in the actual heat spread test, the external heat source data is obtained.

[0079] In one or more embodiments of this application, the solid heat transfer geometry model in the heat transfer geometry model construction module 802 is a two-dimensional model reflecting the cross-sectional structure of the battery module in the height direction, including: cell area, heating device area, heat insulation area, air area and cooling device area; The boundary conditions include: the convective heat transfer coefficient and surface emissivity between the outer cell and the environment, the convective heat transfer coefficient of the cooling boundary, and the equivalent thermal resistance between each component. The thermal conductivity includes: the thermal conductivity of the battery cell under normal conditions and the equivalent thermal conductivity of the air region under normal conditions.

[0080] In one or more embodiments of this application, the solid heat transfer geometric model in the heat transfer geometric model construction module 802 is equipped with a physical field to monitor the simulated temperature of each battery cell in the battery module in real time, and when the simulated temperature of the battery cell reaches the preset thermal runaway trigger threshold, control the cell to undergo thermal runaway and adjust the thermal parameters of the corresponding thermal runaway battery cell when the energy of thermal runaway is completely consumed.

[0081] In one or more embodiments of this application, the thermal conductivity correction module 804 is specifically used for: Key indicators were extracted from the simulated thermal propagation data, including: target cell thermal runaway trigger time, thermal runaway trigger interval between adjacent cells, maximum temperature of each cell, and total thermal propagation duration. Extract the key indicators corresponding to the measured data of thermal spread of the battery module, and calculate the deviation between the key indicators in the simulated thermal spread data and the corresponding key indicators in the measured data. For indicators whose deviation values ​​exceed the preset allowable range, the corresponding thermal conductivity parameters are corrected to preset values ​​that match the state after thermal runaway: if the thermal propagation trigger time deviation is large, the equivalent thermal conductivity of the air region is corrected; if the maximum temperature deviation of the battery cell is large, the thermal conductivity of the battery cell itself or the equivalent thermal conductivity of the air region is corrected. After the correction is completed, run the simulation again and recalculate the deviation between the simulation index and the measured index. Repeat the correction and simulation steps until the deviation of all key indexes is less than or equal to the corresponding allowable range.

[0082] In one or more embodiments of this application, the preset thermal runaway trigger threshold is the thermal runaway trigger temperature of a single battery cell; the preset value matching the state after thermal runaway is determined based on the expansion and compression characteristics of the single battery cell after thermal runaway and measured thermal propagation data.

[0083] The apparatus provided in this embodiment belongs to the same concept as the method provided in the above embodiments of this application, and can execute the method provided in any of the above embodiments of this application, possessing the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the specific processing content of the method provided in the above embodiments of this application, and will not be repeated here.

[0084] The functions implemented by the above modules can be implemented by the same or different processors, and this application embodiment does not limit this.

[0085] It should be understood that the modules in the above device can be implemented by a processor calling software. For example, the device includes a processor connected to a memory containing instructions. The processor calls the instructions stored in the memory to implement any of the above methods or to implement the functions of each unit of the device. The processor can be a general-purpose processor, such as a CPU or microprocessor, and the memory can be internal or external to the device. Alternatively, the units in the device can be implemented as hardware circuits. By designing the hardware circuits, some or all of the unit functions can be implemented. The hardware circuits can be understood as one or more processors. For example, in one implementation, the hardware circuit is an ASIC, and the functions of some or all of the above units are implemented by designing the logical relationships between the components within the circuit. In another implementation, the hardware circuit can be implemented by a PLD, such as an FPGA, which can include a large number of logic gates. The connection relationships between the logic gates are configured through configuration files to implement the functions of some or all of the above units. All units of the above device can be implemented entirely by a processor calling software, entirely by hardware circuits, or partially by a processor calling software with the remaining parts implemented by hardware circuits.

[0086] In this application embodiment, a processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a CPU, microprocessor, GPU, or DSP. In another implementation, the processor can implement certain functions through the logical relationships of hardware circuits. These logical relationships are fixed or reconfigurable. For example, the processor may be a hardware circuit implemented as an ASIC or PLD, such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the processor loading instructions to implement the functions of some or all of the above units. Furthermore, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as an NPU, TPU, or DPU.

[0087] As can be seen, each unit in the above device can be one or more processors (or processing circuits) configured to implement the above methods, such as: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.

[0088] Furthermore, the units in the above devices can be integrated in whole or in part, or they can be implemented independently. In one implementation, these units are integrated together and implemented in the form of a System-on-Chip (SoC). The SoC may include at least one processor for implementing any of the above methods or implementing the functions of the units in the device. The at least one processor may be of different types, such as CPU and FPGA, CPU and artificial intelligence processor, CPU and GPU, etc.

[0089] Exemplary electronic devices This application provides an electronic device, and a schematic diagram of the device's hardware structure is shown below. Figure 9 As shown, the electronic device of this embodiment includes a processor 901 and a memory 902.

[0090] The memory 902 stores computer execution instructions; the processor 901 executes the computer execution instructions stored in the memory to implement the various steps performed by the electronic device in the above embodiments. For details, please refer to the relevant descriptions in the foregoing method embodiments.

[0091] Alternatively, the memory 902 can be either standalone or integrated with the processor 901.

[0092] When the memory 902 is set up independently, the electronic device also includes a bus 903 for connecting the memory 902 and the processor 901.

[0093] Exemplary computer program products and storage media In addition to the methods and devices described above, this application also provides a computer storage medium storing computer execution instructions. When the processor executes the computer execution instructions, the above-described battery module thermal propagation simulation method is implemented.

[0094] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described battery module thermal propagation simulation method.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0096] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0097] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0098] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0099] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0100] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0101] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0102] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0103] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. Both the processor and the storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic device or host device.

[0104] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0105] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for simulating thermal propagation in a battery module, characterized in that, include: Acquire cell heat source data and external heat source data. The cell heat source data is the data when a single battery cell experiences thermal runaway, and the external heat source data is the data of the heating device during actual thermal runaway testing. The pre-built solid heat transfer geometry model of the battery module is invoked and the boundary conditions and thermal conductivity are configured. The solid heat transfer geometry model follows the cell energy conservation equation. The cell heat source input and the external heat source input are loaded into the solid heat transfer geometry model to simulate the thermal propagation process after thermal runaway of a single battery cell and obtain simulation thermal propagation data. Obtain measured data on thermal spread of the battery module, and correct the thermal conductivity of the solid heat transfer geometric model based on the deviation between the measured data and the simulated thermal spread data. The heat transfer process after thermal runaway of a single battery cell is simulated based on a solid heat transfer geometry model with modified thermal conductivity to predict the thermal propagation state of the battery module.

2. The method according to claim 1, characterized in that, The process of acquiring the cell heat source data includes: The measured time-temperature curves of thermal runaway of individual battery cells were obtained through ARC adiabatic testing. Based on the data from the heat generation start temperature to the measured highest temperature in the measured time-temperature curve, a temperature-heat generation power curve is generated. A three-dimensional ARC self-heating simulation model is pre-constructed based on the battery cell size. An event module is added to control the occurrence and end time of self-heating, which is the same as that of the ARC adiabatic test. The temperature-heating power curve is used as the cell heat source data for simulation to obtain the simulation time-temperature curve. If the deviation between the simulated time-temperature curve and the measured time-temperature curve exceeds a preset threshold, the coefficient of the temperature-heat generation power curve is adjusted, and the simulation steps are repeated until the simulated time-temperature curve matches the measured time-temperature curve. The temperature-heat generation power curve at this point is used as the calibrated cell heat source data.

3. The method according to claim 1, characterized in that, The process of acquiring the external heat source data includes: The temperature rise data of the heating device in actual testing is obtained, and the temperature rise data shows a change pattern of first increasing and then stabilizing. When the power of the heating device is constant, if the variation law of the temperature rise data obtained by simulation does not match the variation law of the temperature rise data of actual test, the curve of the resistance of the heating device changing with temperature is obtained by back fitting. Based on the measured temperature rise data at the center of the heating device, the resistance-temperature change curve is derived. After comparing and verifying the resistance-temperature change curve with the temperature data at the center of the heating device in the actual heat spread test, the external heat source data is obtained.

4. The method according to claim 1, characterized in that, The solid heat transfer geometric model is a two-dimensional model reflecting the cross-sectional structure of the battery module in the height direction, including: cell area, heating device area, heat insulation area, air area and cooling device area; The boundary conditions include: the convective heat transfer coefficient and surface emissivity between the outer cell and the environment, the convective heat transfer coefficient of the cooling boundary, and the equivalent thermal resistance between each component. The thermal conductivity includes: the thermal conductivity of the battery cell under normal conditions and the equivalent thermal conductivity of the air region under normal conditions.

5. The method according to claim 4, characterized in that, The solid heat transfer geometric model is equipped with a physical field to monitor the simulated temperature of each battery cell in the battery module in real time. When the simulated temperature of a battery cell reaches a preset thermal runaway trigger threshold, the model controls the cell to undergo thermal runaway and adjusts the thermal parameters of the corresponding thermal runaway battery cell when the energy of thermal runaway is completely consumed.

6. The method according to claim 1 or 5, characterized in that, The step of correcting the thermal conductivity of the solid heat transfer geometry model based on the deviation between the measured data and the simulated thermal spread data includes: Key indicators were extracted from the simulated thermal propagation data, including: target cell thermal runaway trigger time, thermal runaway trigger interval between adjacent cells, maximum temperature of each cell, and total thermal propagation duration. Extract the key indicators corresponding to the measured data of thermal spread of the battery module, and calculate the deviation between the key indicators in the simulated thermal spread data and the corresponding key indicators in the measured data. For indicators whose deviation values ​​exceed the preset allowable range, the corresponding thermal conductivity parameters are corrected to preset values ​​that match the state after thermal runaway: if the thermal propagation trigger time deviation is large, the equivalent thermal conductivity of the air region is corrected; if the maximum temperature deviation of the battery cell is large, the thermal conductivity of the battery cell itself or the equivalent thermal conductivity of the air region is corrected. After the correction is completed, run the simulation again and recalculate the deviation between the simulation index and the measured index. Repeat the correction and simulation steps until the deviation of all key indexes is less than or equal to the corresponding allowable range.

7. The method according to claim 6, characterized in that, The preset thermal runaway trigger threshold is the thermal runaway trigger temperature of a single battery cell; the preset value that matches the state after thermal runaway is determined based on the expansion and compression characteristics of the single battery cell after thermal runaway and the measured thermal propagation data.

8. A battery module thermal propagation simulation device, characterized in that, include: The heat source input acquisition module is used to acquire cell heat source data and external heat source data. The cell heat source data is the data when the battery cell experiences thermal runaway, and the external heat source data is the data when the heating device is actually conducting thermal runaway tests. The heat transfer geometry model construction module is used to call the pre-built solid heat transfer geometry model of the battery module and configure the boundary conditions and thermal conductivity. The solid heat transfer geometry model follows the cell energy conservation equation. The simulated thermal propagation data acquisition module is used to load the cell heat source input and the external heat source input onto the solid heat transfer geometry model, simulate the thermal propagation process after thermal runaway of a single battery cell, and acquire simulated thermal propagation data. The thermal conductivity correction module is used to acquire measured data of thermal spread of the battery module and correct the thermal conductivity of the solid heat transfer geometric model based on the deviation between the measured data and the simulated thermal spread data. The thermal propagation state prediction module is used to simulate the heat transfer process after thermal runaway of a single battery cell based on a solid heat transfer geometry model with corrected thermal conductivity, in order to predict the thermal propagation state of the battery module.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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