A parameter-driven simulation method and system for full-link battery heat transfer analysis

Abstract

The application discloses a parameter-driven simulation method and system for full-link battery heat transfer analysis, and belongs to the technical field of battery heat transfer simulation. The method comprises the following steps: acquiring geometric parameters, and generating geometric model data of target area battery heat transfer analysis; acquiring grid density and area grouping information, and generating grid data based on the geometric model data, the grid density and the area grouping information; acquiring material properties and boundary conditions, and performing numerical solution of fluid-solid conjugate heat transfer based on the grid data, the material properties and the boundary conditions to obtain collaborative simulation data; and performing post-processing on the collaborative simulation data to obtain final simulation results. The application can simplify the modeling and simulation steps, and improve the efficiency and flexibility of the battery heat transfer simulation process.

Description

Technical Field

[0001] This invention relates to the field of battery heat transfer simulation technology, and in particular to a parameter-driven simulation method and system for end-to-end battery heat transfer analysis. Background Technology

[0002] Battery heat transfer simulation involves different phases, such as liquids and solids, and various physical processes, including heat and flow. It typically relies on the collaborative work of multiple CFD (Computational Fluid Dynamics) software programs. Traditional simulation processes require calling different functional modules from various CFD software programs to complete the overall simulation task. This process usually includes multiple steps, such as modeling, mesh generation, simulation calculation, and post-processing.

[0003] Specifically, simulation engineers typically operate different software environments manually, performing various simulation tasks in different CFD software programs. For example, they might model in one CFD software, mesh in another, perform simulation calculations using solver software, and finally visualize the results using post-processing software. This involves data transfer and exchange, loading intermediate results from one software program as input into another. Simulation engineers need to utilize graphical interfaces or scripts to transfer data and results between different CFD software programs until the final simulation calculation or visualization analysis results are obtained.

[0004] While this process can achieve simulation analysis of battery heat transfer, its reliance on manual operation makes the method face significant challenges in areas such as data interaction between multiple software programs, software call order, and simulation state management.

[0005] 1) Complex operation and low efficiency

[0006] Because existing CFD software involves collaborative operation, simulation processes are often highly complex and cumbersome. Each software has different requirements, necessitating simulation engineers to switch between multiple tools and manually complete various simulation tasks, leading to low simulation efficiency. Furthermore, the complexity and diversity of models increase the difficulty of the simulation process, preventing effective improvements in efficiency.

[0007] 2) Poor interoperability between CFD software

[0008] The lack of good interoperability between existing CFD software makes data transfer and sharing cumbersome and error-prone. Differences in file formats, computational workflows, and data structures among CFD software programs make data exchange and synchronization a challenging task. In many cases, simulation engineers need to manually check and adjust data compatibility and input / output formats between different software programs, further increasing the complexity and error risk of the simulation process.

[0009] 3) Highly dependent on manual operation

[0010] Existing simulation methods heavily rely on manual intervention. In stages such as modeling, mesh generation, simulation, and post-processing, simulation personnel must manually perform various operations, including data import / export, simulation status monitoring, and result analysis. Due to the lack of automation tools, each stage of the simulation process is susceptible to human error and requires simulation personnel to possess high levels of professional skills and extensive experience. Summary of the Invention

[0011] This invention discloses a parameter-driven simulation method and system for end-to-end battery heat transfer analysis, which can simplify modeling and simulation steps and improve the efficiency and flexibility of battery heat transfer simulation process.

[0012] To achieve the above objectives, the technical solution of the present invention includes the following:

[0013] A parameter-driven simulation method for end-to-end battery heat transfer analysis, the method comprising:

[0014] Obtain geometric parameters and generate geometric model data for battery heat transfer analysis in the target area;

[0015] Obtain grid density and region grouping information, and generate grid data based on geometric model data, grid density, and region grouping information;

[0016] Material properties and boundary conditions are obtained, and numerical solutions for fluid-structure conjugate heat transfer are performed based on mesh data, material properties, and boundary conditions to obtain co-simulation data;

[0017] The co-simulation data is post-processed to obtain the final simulation results.

[0018] Furthermore, the geometric model data for generating the target region battery heat transfer analysis includes:

[0019] A Python script is used to read the geometry parameter file of the target region, wherein the target region includes a fluid region and a solid region. The geometry parameter file of the fluid region includes the size and position of the fluid region, and the geometry parameter file of the solid region includes the size and position of the solid region.

[0020] Based on the geometric parameter file of the target region, a Python script drives the Salome software to construct the corresponding geometric model in order to obtain geometric model data.

[0021] Further, the step of obtaining grid density and region grouping information, and generating grid data based on geometric model data, grid density, and region grouping information, includes:

[0022] The geometric model path is obtained based on the location where the geometric model data is saved;

[0023] The Python script reads a parameter file containing geometric model paths, mesh density, region grouping information, and mesh data storage paths, drives the mesh generation software to generate meshes, and outputs mesh data in UNV and MESH formats.

[0024] Furthermore, obtaining material properties includes:

[0025] The configuration file is read using a Python script to obtain material properties, including the density, viscosity, and thermal conductivity of the fluid and solid.

[0026] Furthermore, the numerical solution for fluid-structure conjugate heat transfer based on mesh data, material properties, and boundary conditions yields co-simulation data, including:

[0027] The Python script converts the read mesh data, material properties, boundary conditions, etc., into the format required by OpenFOAM, and generates the input file in the format required for OpenFOAM solving;

[0028] Call the OpenFOAM solver via shell script;

[0029] The OpenFOAM solver is used to perform numerical solutions for fluid-structure interaction heat transfer on the input file, resulting in co-simulation data.

[0030] Furthermore, the post-processing of the co-simulation data to obtain the final simulation results includes:

[0031] Extract parameter data that supports visualization software from the co-simulation data. The parameter data that supports visualization software includes: temperature, speed, and pressure.

[0032] The post-processing parameters are read using a Python script. These parameters include: the resolution of the visualized image, the video frame rate, and the save path of the final simulation results.

[0033] The ParaView software is invoked via a Python script, and post-processing parameters are used to post-process the co-simulation data and the parameter data of the visualization software to obtain the final simulation results.

[0034] A parameter-driven simulation system for end-to-end battery heat transfer analysis, the system comprising:

[0035] The geometric modeling module is used to acquire geometric parameters and generate geometric model data for battery heat transfer analysis in the target area;

[0036] The grid data generation module is used to obtain grid density and region grouping information, and generate grid data based on geometric model data, grid density, and region grouping information;

[0037] The co-simulation module is used to obtain material properties and boundary conditions, and to perform numerical solutions for fluid-structure conjugate heat transfer based on mesh data, material properties, and boundary conditions to obtain co-simulation data.

[0038] The post-processing module is used to post-process the co-simulation data to obtain the final simulation results.

[0039] An electronic device, characterized in that the electronic device comprises: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the parameter-driven simulation method for end-to-end battery heat transfer analysis as described above.

[0040] A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the parameter-driven simulation method for end-to-end battery heat transfer analysis as described above.

[0041] A computer program product, characterized in that, when the computer program product is run on a computer device, it causes the computer device to execute the parameter-driven simulation method for full-link battery heat transfer analysis as described above.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1) The operation process has been simplified, and the complexity of simulation has been reduced.

[0044] Traditional battery heat transfer simulation methods often require significant manual intervention for collaboration and data transfer between multiple CFD software programs, resulting in cumbersome and complex operations. This invention addresses this by modularizing the battery heat transfer simulation process into multiple workflows with specific functions. A predefined calling sequence enables these workflows to automatically collaborate, reducing manual steps and simplifying the entire simulation process. This modular design makes the simulation process more standardized, modular, and easier to operate.

[0045] 2) Improved simulation efficiency and flexibility.

[0046] This invention employs a parameter-driven approach, allowing users to dynamically set key parameters such as battery geometry, material properties, and operating conditions. The system can then automatically generate a matching simulation model and perform calculations based on these input parameters. This process eliminates the need for repetitive and tedious manual modeling and adjustments, enabling users to quickly generate simulation models under different operating conditions and significantly improving simulation efficiency. Furthermore, this parameter-driven approach enhances simulation flexibility, allowing for rapid adaptation and adjustment to varying design requirements and engineering conditions, thus meeting diverse simulation needs.

[0047] 3) Multi-software collaborative simulation has been optimized.

[0048] Traditional battery heat transfer simulation requires cumbersome data transfer and coordination between multiple CFD software programs. However, this invention solves the data transfer problem during collaborative simulation between multiple software programs by defining a clear workflow sequence and standardized input / output file formats. It achieves efficient automatic coupling between software programs, greatly reduces the risk of software incompatibility and data loss, and improves the efficiency and accuracy of collaborative simulation. Attached Figure Description

[0049] Figure 1 This is the overall flowchart of the present invention. Detailed Implementation

[0050] The technical solutions in the embodiments of the present invention will now be fully described with reference to the accompanying drawings.

[0051] The parameter-driven simulation method for end-to-end battery heat transfer analysis proposed in this invention comprises four steps, such as... Figure 1 The diagrams show the workflows for geometric modeling, mesh generation, simulation calculation, and post-processing, respectively.

[0052] 1) Geometric modeling workflow

[0053] In the process of battery heat transfer simulation, the task of the geometric modeling step is to generate geometric model data for battery heat transfer analysis.

[0054] Battery heat transfer involves both fluid and solid regions. This invention uses a Python script to automatically read the geometric parameter files of the fluid and solid regions, including the dimensions and locations of the fluid and solid regions, and the save path of the generated files. By parsing the input parameters, the script drives the Salome software to construct the corresponding geometric model and saves the generated geometric model data to the specified path, thus obtaining the geometric model path.

[0055] 2) Grid-based workflow

[0056] The task of the mesh generation step is to generate mesh data based on the geometric model data.

[0057] This invention employs an automated mesh generation method. It uses a Python script to read a parameter file containing geometric model paths, mesh density, region grouping information, and mesh data storage paths, and drives the mesh generation software GMSH to generate meshes, automatically outputting mesh data in UNV and MESH formats.

[0058] 3) Simulation calculation workflow

[0059] The task of the simulation calculation step is to solve the model numerically based on mesh data, material properties, boundary conditions, etc., and output the simulation calculation results data.

[0060] This invention utilizes a Python script to read configuration files and obtain necessary material properties, such as density, viscosity, and thermal conductivity of fluids and solids. The script then converts the read parameters (mesh data, material properties, set boundary conditions, etc.) into the format required by OpenFOAM and generates an input file in the format needed for OpenFOAM solving. Next, a shell script calls the OpenFOAM solver to perform numerical solutions for fluid-structure interaction heat transfer. This solver can handle multi-region heat conduction and fluid flow problems and is suitable for fluid-structure interaction analysis. After the numerical solution is completed, the co-simulation data is saved in the OpenFOAM output directory.

[0061] 4) Post-processing workflow

[0062] The post-processing workflow first processes the co-simulation data to extract parameter data that supports visualization software. This parameter data includes data such as temperature, speed, and pressure. Then, a Python script reads the post-processing parameters, such as the visualization image resolution, video frame rate, and save path. Finally, the Python script calls ParaView software to perform post-processing on the co-simulation data and the parameter data supporting the visualization software, saves the results to the specified path, and obtains the final simulation result.

[0063] It should be noted that, depending on the needs, simulation can be a full-process simulation, including four workflows: geometric modeling, mesh generation, simulation calculation, and post-processing. Alternatively, it can run only some workflows, such as geometric modeling + mesh generation or geometric modeling + mesh generation + simulation calculation. The workflow invocation settings are configured through a shell script. During simulation, simply setting the input parameters for the corresponding workflow and calling the simulation workflow configuration script will execute the desired simulation process and obtain the simulation results.

[0064] In summary, addressing the problems of complex operation and low simulation efficiency in traditional battery heat transfer simulation methods, particularly the difficulties in collaborative work and data transfer between multiple CFD software programs, this invention proposes a parameter-driven software coupling method. This method modularizes the battery heat transfer simulation process, breaking it down into multiple workflows. Each workflow corresponds to a specific CFD simulation function, and different workflows are called sequentially according to a predefined order. Workflows interact and collaborate through input / output files. Users can dynamically set different input parameters during the simulation process, such as battery geometry, material properties, and operating conditions. Based on the set parameters, the system can automatically generate corresponding simulation models and dynamically adjust the geometry and physical properties of the battery model, thereby achieving personalized simulations for different operating conditions and design requirements. Users only need to input a set of parameters, and the system can automatically generate the corresponding model and perform simulation calculations, greatly improving simulation efficiency and flexibility.

[0065] It should be clarified that the specific embodiments and accompanying drawings of the present invention disclosed above are intended to help understand the content of the present invention and to implement it accordingly. For those skilled in the art, it will be understood that various substitutions, variations, and modifications are within the scope of the present invention without departing from its principles and spirit, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A parameter-driven simulation method for full-link battery heat transfer analysis, characterized in that, The method includes: Obtain geometric parameters and generate geometric model data for battery heat transfer analysis in the target area; Obtain grid density and region grouping information, and generate grid data based on geometric model data, grid density, and region grouping information; Material properties and boundary conditions are obtained, and numerical solutions for fluid-structure conjugate heat transfer are performed based on mesh data, material properties, and boundary conditions to obtain co-simulation data; The co-simulation data is post-processed to obtain the final simulation results.

2. The method of claim 1, wherein, The geometric model data for generating the target region battery heat transfer analysis includes: A Python script is used to read the geometry parameter file of the target region, wherein the target region includes a fluid region and a solid region, the geometry parameter file of the fluid region includes the size and position of the fluid region, and the geometry parameter file of the solid region includes the size and position of the solid region; Based on the geometric parameter file of the target region, a Python script drives the Salome software to construct the corresponding geometric model in order to obtain geometric model data.

3. The method of claim 1, wherein, The process of acquiring grid density and region grouping information, and generating grid data based on geometric model data, grid density, and region grouping information, includes: The geometric model path is obtained based on the location where the geometric model data is saved; The Python script reads a parameter file containing geometric model paths, mesh density, region grouping information, and mesh data storage paths, drives the mesh generation software to generate meshes, and outputs mesh data in UNV and MESH formats.

4. The method according to claim 1, characterized in that, The acquisition of material properties includes: The configuration file is read using a Python script to obtain material properties, including the density, viscosity, and thermal conductivity of the fluid and solid.

5. The method according to claim 1, characterized in that, The numerical solution for fluid-structure conjugate heat transfer based on mesh data, material properties, and boundary conditions yields co-simulation data, including: The Python script converts the read mesh data, material properties, boundary conditions, etc., into the format required by OpenFOAM, and generates the input file in the format required for OpenFOAM solving; Call the OpenFOAM solver via shell script; The OpenFOAM solver is used to perform numerical solutions for fluid-structure interaction heat transfer on the input file, resulting in co-simulation data.

6. The method according to claim 1, characterized in that, The post-processing of the co-simulation data to obtain the final simulation results includes: Extract parameter data that supports visualization software from the co-simulation data. The parameter data that supports visualization software includes: temperature, speed, and pressure. The post-processing parameters are read using a Python script. These parameters include: the resolution of the visualized image, the video frame rate, and the save path of the final simulation results. The ParaView software is invoked via a Python script, and post-processing parameters are used to post-process the co-simulation data and the parameter data of the visualization software to obtain the final simulation results.

7. A parameter-driven simulation system for end-to-end battery heat transfer analysis, characterized in that, The system includes: The geometric modeling module is used to acquire geometric parameters and generate geometric model data for battery heat transfer analysis in the target area; The grid data generation module is used to obtain grid density and region grouping information, and generate grid data based on geometric model data, grid density, and region grouping information; The co-simulation module is used to obtain material properties and boundary conditions, and to perform numerical solutions for fluid-structure conjugate heat transfer based on mesh data, material properties, and boundary conditions to obtain co-simulation data. The post-processing module is used to post-process the co-simulation data to obtain the final simulation results.

8. An electronic device, characterized in that, The electronic device includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the parameter-driven simulation method for end-to-end battery heat transfer analysis as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the parameter-driven simulation method for end-to-end battery heat transfer analysis as described in any one of claims 1-6.

10. A computer program product, characterized in that, When the computer program product is run on a computer device, the computer device performs the parameter-driven simulation method for end-to-end battery heat transfer analysis as described in any one of claims 1-6.

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