Battery module heat dissipation optimization method and system based on twin simulation

Abstract

The present invention discloses a method and system for optimizing heat dissipation of battery modules based on twin simulation, which relates to the field of energy technology. The method includes: performing multi-dimensional data collection to obtain a basic data set of batteries; performing three-dimensional modeling to generate a first twin digital model; performing assignment to generate a second twin digital model; obtaining an external environment data set, performing heat conduction simulation, and determining the heat accumulation area of ​​the target battery module; formulating a first heat dissipation optimization strategy; performing random change optimization to generate a second heat dissipation optimization strategy, performing simulation verification, performing heat dissipation evaluation, and outputting heat dissipation evaluation optimization parameters; and executing the second heat dissipation optimization strategy. The method solves the technical problems of high cost, long cycle, insufficient simulation accuracy, and difficulty in discovering potential heat dissipation hazards in the existing battery module heat dissipation optimization, and achieves the technical effects of reducing costs, shortening cycles, improving simulation accuracy, discovering heat dissipation problems and optimizing them, thereby improving the performance, life, and safety of battery modules.

Description

Technical Field

[0001] The present application relates to fields related to energy technology, and in particular to a battery module heat dissipation optimization method and system based on twin simulation. Background Art

[0002] With the rapid development of battery technology, battery modules are increasingly used in energy storage and supply. However, the heat dissipation problem of battery modules has always been a key factor affecting their performance, life and safety, especially under extreme conditions such as high load and high temperature. The heat dissipation problem of battery modules is more prominent. Therefore, it is of great practical significance and application value to develop an effective battery module heat dissipation optimization method to improve the heat dissipation performance of battery modules. The existing technology mainly relies on traditional experimental methods and empirical formulas to carry out heat dissipation design and optimization of battery modules. This method has high experimental cost and long cycle, and it is difficult to adapt to the rapidly changing market demand. In addition, empirical formulas are often based on simplified assumptions and idealized conditions, and cannot accurately reflect the heat dissipation performance of battery modules in actual working environments. At the same time, due to the lack of in-depth understanding and simulation of the complex heat conduction process inside the battery module, it is difficult to discover potential heat dissipation problems and optimize them.

[0003] Among the current related technologies, battery module heat dissipation optimization methods have technical problems such as high cost, long cycle, insufficient simulation accuracy, and difficulty in discovering potential heat dissipation risks. Summary of the invention

[0004] The present application provides a battery module heat dissipation optimization method and system based on twin simulation, obtains the internal structure data and material property data of the target battery module through multi-dimensional data collection, constructs a twin digital model using basic battery data, determines the heat accumulation area through thermal conduction simulation, formulates a heat dissipation optimization strategy based on the simulation results, obtains heat dissipation evaluation optimization parameters through random change optimization and simulation verification, and executes the heat dissipation optimization strategy on the target battery module according to the heat dissipation evaluation optimization parameters. The application achieves the technical effect of reducing costs, shortening cycles, improving simulation accuracy, timely discovering potential heat dissipation problems and performing targeted optimization, thereby improving the performance, life and safety of the battery module.

[0005] This application provides a battery module heat dissipation optimization method based on twin simulation, including:

[0006] Perform multi-dimensional data collection based on the target battery module to obtain a battery basic data set of the target battery module, wherein the battery basic data set includes internal structure data of the battery module and material property data of the battery module;

[0007] Performing three-dimensional modeling based on the internal structure data of the battery module to generate a first twin digital model;

[0008] Assigning values ​​to the first twin digital model based on the material property data of the battery module, updating the first twin digital model, and generating a second twin digital model;

[0009] Acquire an external environment data set of the target battery module, perform heat conduction simulation using the second twin digital model, and determine the heat accumulation area of ​​the target battery module according to the simulation results;

[0010] Traversing the heat accumulation area, and formulating a first heat dissipation optimization strategy based on the simulation result;

[0011] Perform random change optimization based on the first heat dissipation optimization strategy to generate a second heat dissipation optimization strategy, perform simulation verification according to the second heat dissipation optimization strategy, perform heat dissipation evaluation according to the verification result, and output heat dissipation evaluation optimization parameters;

[0012] The second heat dissipation optimization strategy is executed on the target battery module based on the heat dissipation evaluation optimization parameters.

[0013] In a possible implementation, multi-dimensional data collection is performed based on the target battery module to obtain the battery basic data set of the target battery module, and the following processing is performed:

[0014] Performing multi-dimensional measurement on a plurality of battery cells in a target battery module to obtain a multi-dimensional measurement data set, wherein the multi-dimensional measurement data set includes multi-dimensional measurement data of each battery cell;

[0015] Analyzing the component relationship between the plurality of battery cells to obtain component association information;

[0016] Based on the component association information and the multi-dimensional measurement data set, obtaining the internal structure data of the battery module according to the combination of the plurality of battery cells;

[0017] Extracting a plurality of material samples based on the multi-dimensional measurement data, performing property tests on the plurality of material samples, and obtaining the material property data of the battery module according to the test results;

[0018] The battery module internal structure data and the battery module material property data are added to the battery basic data set.

[0019] In a possible implementation, three-dimensional modeling is performed based on the internal structure data of the battery module to generate a first twin digital model, and the following processing is performed:

[0020] Construct a virtual three-dimensional space coordinate system and create the basic framework of the target battery module;

[0021] Using the internal structure data of the battery module as training data, performing detailed optimization training on the basic framework, and setting a simulation parameter set;

[0022] Constructing a simulation twin model of a target battery module based on the simulation parameter set;

[0023] The simulation twin model is compared and verified with the target battery module in terms of multi-dimensional data until the data verification converges to generate the first twin digital model.

[0024] In a possible implementation, the first twin digital model is assigned a value based on the battery module material property data, the first twin digital model is updated, a second twin digital model is generated, and the following processing is performed:

[0025] Traversing the battery module material property data and performing component matching with the first twin digital model to generate a plurality of component matching information;

[0026] Inputting a material property assignment channel into the first twin digital model according to the plurality of component matching information, and outputting physical property data and chemical property data, wherein the material property assignment channel comprises a physical property assignment branch and a chemical property assignment branch;

[0027] Based on the first twin digital model, the physical property data and the chemical property data are component-aligned, and the first twin digital model is updated according to the alignment result to output as the second twin digital model.

[0028] In a possible implementation, an external environment data set of the target battery module is obtained, and heat conduction simulation is performed using the second twin digital model to perform the following processing:

[0029] Collect parameters based on the deployment scenario of the target battery module to obtain the external environment data set;

[0030] Importing the external environment data set into the second twin digital model, setting simulation parameters, wherein the simulation parameters include simulation initial conditions and simulation boundary conditions;

[0031] Setting operating parameters based on the operating state of the target battery module, wherein the operating parameters include internal heat source data and heat dissipation standard data;

[0032] Based on the simulation initial conditions and the simulation boundary conditions, the internal heat source data and the heat dissipation standard data are input into the heat conduction simulation formula, and a heat conduction simulation is performed on the target battery module according to the heat conduction simulation formula, and the simulation results are output. The simulation results include the internal temperature distribution parameters and internal heat flow parameters of the target battery module.

[0033] In a possible implementation, the heat accumulation area of ​​the target battery module is determined according to the simulation results, and the following processing is performed:

[0034] Analyze the internal temperature distribution parameters based on the simulation results, obtain multiple peak values ​​and extract a first target area;

[0035] Analyzing the internal heat flow parameters based on the simulation results to obtain flow direction information of the heat flow;

[0036] Identify the flow heat value according to the flow direction information, and extract the second target area according to the identification result;

[0037] Based on the comparison between the first target area and the second target area, a plurality of overlapping areas are obtained, and the heat accumulation area is determined by combining the temperature data and the heat flow data in the plurality of overlapping areas.

[0038] In a possible implementation, the second heat dissipation optimization strategy is executed on the target battery module based on the heat dissipation evaluation optimization parameter, and the following processing is performed:

[0039] Using the heat dissipation evaluation optimization parameter as a boundary value, limiting the second heat dissipation optimization strategy, monitoring the execution process of the second heat dissipation optimization strategy in real time, and generating operation monitoring data and heat dissipation monitoring data of the target battery module;

[0040] The operation monitoring data is traversed, the heat dissipation monitoring data is evaluated and fed back, a feedback tag is generated, and a heat dissipation abnormality mark is performed on the target battery module based on the feedback tag.

[0041] This application also provides a battery module heat dissipation optimization system based on twin simulation, including:

[0042] A multidimensional data acquisition module, wherein the multidimensional data acquisition module is used to perform multidimensional data acquisition based on a target battery module to obtain a battery basic data set of the target battery module, wherein the battery basic data set includes battery module internal structure data and battery module material property data;

[0043] A three-dimensional modeling module, wherein the three-dimensional modeling module is used to perform three-dimensional modeling based on the internal structure data of the battery module to generate a first twin digital model;

[0044] A model assignment module, the model assignment module is used to assign a value to the first twin digital model based on the battery module material property data, update the first twin digital model, and generate a second twin digital model;

[0045] A heat conduction simulation module, which is used to obtain an external environment data set of a target battery module, perform heat conduction simulation using the second twin digital model, and determine a heat accumulation area of ​​the target battery module according to the simulation results;

[0046] A first heat dissipation optimization strategy formulation module, the first heat dissipation optimization strategy formulation module is used to traverse the heat accumulation area and formulate a first heat dissipation optimization strategy based on the simulation result;

[0047] a heat dissipation evaluation optimization parameter output module, wherein the heat dissipation evaluation optimization parameter output module is used to perform random change optimization based on the first heat dissipation optimization strategy, generate a second heat dissipation optimization strategy, perform simulation verification according to the second heat dissipation optimization strategy, perform heat dissipation evaluation according to the verification result, and output heat dissipation evaluation optimization parameters;

[0048] A second heat dissipation optimization strategy execution module is used to execute the second heat dissipation optimization strategy on the target battery module based on the heat dissipation evaluation optimization parameters.

[0049] The battery module heat dissipation optimization method and system based on twin simulation proposed in this application first collects multi-dimensional data based on the target battery module to obtain the battery basic data set of the target battery module, wherein the battery basic data set includes the internal structure data of the battery module and the material property data of the battery module, and then performs three-dimensional modeling based on the internal structure data of the battery module to generate a first twin digital model, and then assigns the first twin digital model based on the material property data of the battery module, updates the first twin digital model, generates a second twin digital model, and then obtains the external environment data set of the target battery module, and uses the second twin digital model for heat conduction Simulate, determine the heat accumulation area of ​​the target battery module according to the simulation results, then traverse the heat accumulation area, formulate the first heat dissipation optimization strategy based on the simulation results, and then perform random change optimization based on the first heat dissipation optimization strategy to generate a second heat dissipation optimization strategy, perform simulation verification according to the second heat dissipation optimization strategy, perform heat dissipation evaluation according to the verification results, output heat dissipation evaluation optimization parameters, and finally execute the second heat dissipation optimization strategy for the target battery module based on the heat dissipation evaluation optimization parameters, thereby achieving the technical effect of reducing costs, shortening cycles, improving simulation accuracy, timely discovering potential heat dissipation problems and performing targeted optimization, thereby improving the performance, life and safety of the battery module. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0051] Figure 1A schematic diagram of a process flow of a battery module heat dissipation optimization method based on twin simulation provided in an embodiment of the present application;

[0052] Figure 2 A schematic diagram of the structure of a battery module heat dissipation optimization system based on twin simulation provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0054] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0055] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0056] The present application embodiment provides a battery module heat dissipation optimization method based on twin simulation, such as Figure 1 As shown, the method includes:

[0057] Step S100, multi-dimensional data collection is performed based on the target battery module to obtain a battery basic data set of the target battery module, wherein the battery basic data set includes battery module internal structure data and battery module material property data. Specifically, the target battery module is a specific battery module that needs to optimize heat dissipation performance based on product design requirements, actual application scenarios, performance bottlenecks and other factors. Multi-dimensional data collection of the target battery module is to obtain the basic structural information, material properties, manufacturing process, working environment and other data of the target battery module through various sensors, measuring equipment and laboratory tests. These data together constitute the battery basic data set of the target battery module. The data in the battery basic data set are further divided into battery module internal structure data and battery module material property data. Among them, the battery module internal structure data is the basic data for establishing a twin digital model, including the arrangement of battery cells, the position and number of tabs, the layout of the heat dissipation structure (such as heat sinks, heat pipes, etc.), etc., which can be obtained by disassembling the target battery module and using X-rays or CT scans. The battery module material property data determines the performance of the target battery module in terms of heat conduction, thermal radiation, etc., including the thermal conductivity, specific heat capacity, thermal expansion coefficient of the battery cell material, and the thermal physical properties of the heat dissipation structure material, etc., which can be obtained by consulting the material manual, laboratory tests or data provided by suppliers.

[0058] In a possible implementation, multi-dimensional data collection is performed based on the target battery module to obtain a battery basic data set of the target battery module. Step S100 further includes step S110, performing multi-dimensional measurements on multiple battery cells in the target battery module to obtain a multi-dimensional measurement data set, wherein the multi-dimensional measurement data set contains multi-dimensional measurement data of each battery cell. Specifically, each battery cell in the target battery module is measured, and the measurement content includes the size, weight, appearance inspection, electrical performance parameters (such as voltage, internal resistance, etc.) and thermal performance parameters (such as thermal resistance, heat capacity, etc.) of the battery cell. The multi-dimensional measurement data of each battery cell is obtained by measurement, and the multiple multi-dimensional measurement data corresponding to the multiple battery cells are collected together to form the multi-dimensional measurement data set. Step S120, analyze the component relationship between the multiple battery cells and obtain component association information. Specifically, the connection mode between each battery cell in the target battery module is determined through the electrical connection structure between the battery cells, such as series connection, parallel connection or mixed connection; the spatial layout and contact mode between the battery cell and the heat dissipation structure (such as heat sink, heat pipe, etc.) are analyzed, including how the heat dissipation structure covers the battery cell, the material selection of the heat dissipation structure, the layout design of the heat dissipation structure, etc.; the relationship between the battery cell and other components in the target battery module (such as connecting pieces, insulating materials, sensors, etc.) is analyzed. These relationship information are integrated to form component association information, that is, the component association information includes the connection sequence between battery cells, the layout diagram of the heat dissipation structure, the relative position relationship between components, etc. Step S130, based on the component association information and the multi-dimensional measurement data set, the internal structure data of the battery module is obtained according to the combination of the multiple battery cells. Specifically, according to the component association information obtained in step S120, the interaction and dependency between the components (including battery cells, heat dissipation structures, connecting pieces, etc.) in the target battery module are determined, and the overall structural framework of the target battery module is constructed. Then, the key data of the internal structure of the target battery module is obtained in combination with the multidimensional measurement data set collected in step S110. Then, according to the combination mode of the battery cells (such as series connection, parallel connection, etc.), the component association information and the multidimensional measurement data are combined to construct the internal structure model of the target battery module. The internal structure model includes information such as the connection mode between the battery cells, the layout of the heat dissipation structure, and the position and function of other components in the target battery module. Through data integration and processing, the above information is extracted in the form of data to form the internal structure data of the battery module. Step S140, extract multiple material samples based on the multidimensional measurement data, perform property tests on the multiple material samples, and obtain the material property data of the battery module according to the test results.Specifically, according to the multi-dimensional measurement data collected in step S110, the different material types and their positions used in the target battery module are determined, and representative material samples are extracted from the target battery module, and the representative material samples cover various key materials in the target battery module, such as positive electrode materials, negative electrode materials, electrolytes, diaphragms, and shell materials. Using testing equipment and instruments such as X-ray diffractometers, scanning electron microscopes, electron probe X-ray microanalyzers, transmission electron microscopes, infrared spectrometers, etc., the extracted material samples are subjected to property tests, including chemical composition analysis, crystal structure analysis, morphology observation, chemical bond and functional group analysis, etc., and the corresponding relationship between the material property data obtained by the analysis and the material samples is integrated to form complete battery module material property data. Step S150, the battery module internal structure data and the battery module material property data are added to the battery basic data set. That is, the data obtained in step S130 and step S140 are integrated together to form a complete battery basic data set, which contains detailed information on the structure and materials of the target battery module, providing comprehensive data support for subsequent digital modeling, simulation analysis and heat dissipation optimization. Through the above series of steps, the battery basic data set of the target battery module is comprehensively and accurately obtained, achieving the technical effect of providing basic data support for subsequent digital twin modeling and heat dissipation optimization.

[0059] Step S200, three-dimensional modeling is performed based on the internal structure data of the battery module to generate a first twin digital model. Specifically, the internal structure data of the battery module is extracted from the battery basic data set, and the three-dimensional modeling software is determined according to actual needs. According to the internal structure data of the battery module, the three-dimensional geometric shape of the battery cell is created in the three-dimensional modeling software, including defining the size, shape and position of the battery cell, and establishing the connection relationship between the battery cells according to the component association information, such as series connection, parallel connection, etc., adding other components such as heat dissipation structure, connecting sheet, insulating material, and adjusting their position and layout to reflect the internal structure of the target battery module. After completing the three-dimensional modeling, the first twin digital model is exported, and the first twin digital model contains the complete internal structure of the target battery module, component relationship and other attribute information.

[0060] In a possible implementation, step S200 further includes step S210, constructing a virtual three-dimensional space coordinate system and creating a basic framework of the target battery module. Specifically, first, a virtual three-dimensional space coordinate system is established as a spatial reference for modeling, and then, according to the approximate size and structure of the target battery module, a basic framework is created in the virtual space. This basic framework is the preliminary form of the target battery module, including the main geometric features and spatial layout of the target battery module. Step S220, using the internal structure data of the battery module as training data, performing detailed optimization training on the basic framework, and setting a simulation parameter set. Specifically, after having the basic framework, according to the actual battery cell size, position, connection method, heat dissipation structure and other information, the basic framework is optimized in detail. By fine-tuning and optimizing the basic framework, the parameters and structure of the digital model are continuously adjusted to enable it to better fit the actual data, and the digital model is ensured to be structurally consistent with the target battery module through fine-tuning. At the same time, a simulation parameter set is set, which is a set of parameters used to describe various physical and electrical characteristics of the target battery module during operation, including electrical parameters such as resistance, capacitance, and inductance of the battery cell, as well as thermal parameters such as thermal conductivity coefficient and convection heat transfer coefficient. By setting the simulation parameter set, the behavior of the target battery module in actual operation can be accurately simulated. Step S230, a simulation twin model of the target battery module is constructed based on the simulation parameter set. Specifically, based on the basic framework completed by the detailed optimization training and the set simulation parameter set, a simulation twin model of the target battery module is constructed, and the simulation twin model has physical and electrical characteristics similar to those of the target battery module, and can perform various simulation tests in a virtual environment. Step S240, the simulation twin model is compared and verified with the target battery module for multi-dimensional data until the data verification converges, and the first twin digital model is generated. Specifically, the multi-dimensional data types used for comparison and verification are determined, including temperature distribution, voltage change, current distribution, thermal conductivity efficiency, etc. At the same time, the actual operation data of the target battery module is obtained through experimental measurement or actual operation records. Run the simulation twin model in the simulation environment to simulate the behavior of the target battery module under various working conditions. During the simulation process, collect simulation data corresponding to the multidimensional data type, compare the simulation data with the actual operation data of the target battery module through charts, curves or statistical analysis, find out the inconsistencies between the simulation twin model and the target battery module, and evaluate their impact. According to the results of the data comparison, adjust and optimize the simulation twin model, including modifying the physical parameters, electrical parameters or thermal parameters of the simulation twin model, improving the simulation algorithm or adding new simulation modules, etc. Through the iterative adjustment and optimization process, gradually reduce the difference between the simulation twin model and the target battery module.After each adjustment of the simulation twin model, the simulation is re-run and data is compared to determine whether the data verification has converged. The convergence judgment is based on the preset convergence criteria, such as the threshold of data difference or the stability of the change trend. When the data verification converges to meet the preset criteria, the simulation twin model is considered to be close enough to the target battery module and can be used as the first twin digital model. This implementation method ensures that the generated first twin digital model can accurately reflect the performance and behavior of the target battery module through multi-dimensional data comparison and verification, thereby achieving the technical effect of providing a reliable foundation for subsequent digital twin analysis, simulation and optimization.

[0061] Step S300, assign values ​​to the first twin digital model based on the material property data of the battery module, update the first twin digital model, and generate a second twin digital model. Specifically, by identifying the material type of each component in the first twin digital model, the material property data of the battery module is matched with the corresponding component in the first twin digital model, that is, the material property data is assigned to the corresponding component. For example, if there is a component in the first twin digital model that represents the positive electrode material of the battery, then the conductivity, density and other properties of the positive electrode material are assigned to this component. After completing the data assignment, each component in the first twin digital model has the same material properties as the corresponding component in the target battery module. Through data assignment, the update of the first twin digital model is realized. The updated model is the second twin digital model. The second twin digital model not only has a three-dimensional structure consistent with the target battery module, but also has characteristics that match the actual material properties.

[0062] In a possible implementation, step S300 further includes step S310, traversing the material attribute data of the battery module and matching the components with the first twin digital model to generate multiple component matching information. Specifically, by identifying each component (such as a battery cell, a connecting piece, a shell, etc.) in the first twin digital model and searching for the corresponding material attribute data, traversing and matching the components, multiple component matching information is generated by traversing and matching, and each component matching information contains the corresponding relationship between the model component and the material attribute data. Step S320, according to the multiple component matching information, the material attribute assignment channel is input into the first twin digital model, and the physical attribute data and the chemical attribute data are output. The material attribute assignment channel includes a physical attribute assignment branch and a chemical attribute assignment branch. Specifically, after obtaining the component matching information, the component matching information is input into the material attribute assignment channel. The material attribute assignment channel is a channel for processing different types of material attribute data and outputting physical attribute data and chemical attribute data respectively, wherein the physical attribute data includes density, thermal conductivity, elastic modulus, etc., and the chemical attribute data includes electrochemical reaction rate, ion diffusion coefficient, etc. The material property assignment channel includes a physical property assignment branch and a chemical property assignment branch, which are used to process different types of data respectively. Step S330, based on the first twin digital model, the physical property data and the chemical property data are aligned as components, and the first twin digital model is updated according to the alignment result as the second twin digital model for output. Specifically, after obtaining the physical property data and the chemical property data, the physical property data and the chemical property data of each component are matched with the position and structure of the component in the first twin digital model to ensure that the property data is accurately aligned with the corresponding component. After the alignment is completed, the first twin digital model is updated according to the updated property data to generate a second twin digital model. The second twin digital model not only has the same structure as the first twin digital model, but also contains more accurate physical and chemical property data, which can more realistically reflect the performance and behavior of the target battery module. This implementation method accurately assigns the first twin digital model based on the material property data of the battery module, generates a more accurate second twin digital model, thereby achieving a technical effect of providing a more reliable and accurate basis for subsequent simulation analysis, optimization design, etc.

[0063] Step S400, obtain the external environment data set of the target battery module, use the second twin digital model to perform heat conduction simulation, and determine the heat accumulation area of ​​the target battery module according to the simulation results. Specifically, collect the external environment data that the target battery module may encounter in actual use through on-site measurement, historical records or simulation prediction, including ambient temperature, humidity, air flow, operating conditions, etc., input the external environment data set into the second twin digital model, set the parameters required for heat conduction simulation according to the material properties, structural characteristics and working environment of the target battery module, such as initial temperature, boundary conditions, thermal conductivity coefficient, etc., perform heat conduction simulation, simulate the temperature distribution and heat conduction process of the target battery module at different times and in different environments, and observe the distribution and accumulation of heat in the target battery module during operation through simulation. According to the simulation results, by comparing the temperature differences in different areas, the heat accumulation area is identified. The heat accumulation area refers to an area with high temperature or obvious heat accumulation due to excessive heat generation, insufficient heat dissipation or uneven heat conduction.

[0064] In a possible implementation, an external environment data set of the target battery module is obtained, and the heat conduction simulation is performed using the second twin digital model. Step S400 further includes step S410, which performs parameter collection based on the layout scene of the target battery module to obtain the external environment data set. Specifically, according to the specific layout scene of the target battery module (such as indoors, outdoors, inside a vehicle, a specific industrial environment, etc.), relevant environmental parameters are collected by field measurement, using a sensor network or referring to historical data, including ambient temperature, humidity, wind speed, wind direction, solar radiation intensity, external heat source, etc., so as to obtain an external environment data set that matches the actual working conditions of the target battery module. Step S420, import the external environment data set into the second twin digital model, set simulation parameters, and the simulation parameters include simulation initial conditions and simulation boundary conditions. Specifically, the external environment data set is imported into the second twin digital model, and then, the simulation parameters are set based on the actual working environment and the characteristics of the target battery module, including simulation initial conditions (such as initial temperature distribution, initial thermal state, etc.) and simulation boundary conditions (such as boundary temperature, boundary heat flow, etc.). Step S430, setting operating parameters based on the operating state of the target battery module, the operating parameters include internal heat source data and heat dissipation standard data. Specifically, in addition to external environmental factors, the operating state of the target battery module will also affect the thermal conductivity performance. Therefore, in this step, the operating parameters are set according to the actual operating state of the target battery module, including internal heat source data (such as the heat generated by the battery cell during operation) and heat dissipation standard data (such as the performance parameters of the heat dissipation system, the design of the heat dissipation channel, etc.), and the operating parameters are set to more accurately simulate the thermal conduction behavior of the target battery module during actual operation. Step S440, based on the simulation initial conditions and the simulation boundary conditions, the internal heat source data and the heat dissipation standard data are input into the heat conduction simulation formula, and the heat conduction simulation of the target battery module is performed according to the heat conduction simulation formula, and the simulation results are output, and the simulation results include the internal temperature distribution parameters and internal heat flow parameters of the target battery module. Specifically, the simulation initial conditions and simulation boundary conditions set in step S420 and the operating parameters (internal heat source data and heat dissipation standard data) set in step S430 are input into the heat conduction simulation formula. The heat conduction simulation formula is established based on the heat conduction theory and describes the heat conduction process through the material. The mathematical expression of the heat conduction simulation formula is:

[0065]

[0066] Among them, q is the heat flow, which represents the amount of heat passing through a certain area per unit time, and is used to represent the heat transfer inside the target battery module; k is the thermal conductivity, which is a physical property of the material and represents the ability of the material to conduct heat. The higher the thermal conductivity, the stronger the thermal conductivity of the material. Different materials (such as battery cells, heat sinks, packaging materials, etc.) have different thermal conductivities; A is the cross-sectional area of ​​heat transfer, that is, the physical cross-section through which heat passes, for example, the area where the battery cell contacts the heat sink, or the surface area where the target battery module contacts the surrounding environment. The size of the cross-sectional area directly affects the efficiency of heat transfer; It is the temperature gradient, which indicates the rate of change of temperature in the direction of heat transfer, that is, the degree of temperature change with position. The temperature gradient determines the direction and rate of heat transfer, which is obtained by simulating the temperature distribution of the target battery module under different working conditions. By running the heat conduction simulation formula, the target battery module is simulated for heat conduction, and the simulation results are output. The simulation results include the internal temperature distribution parameters (such as temperature field, temperature gradient, etc.) and internal heat flow parameters (such as heat flux density, heat flow distribution, etc.) of the target battery module. This implementation method accurately simulates the heat conduction behavior of the target battery module in the actual working environment by running the heat conduction simulation formula, thereby achieving the technical effect of accurately obtaining the generation, transfer and distribution of heat inside the target battery module.

[0067] In another possible implementation, the heat accumulation area of ​​the target battery module is determined according to the simulation results, and step S400 further includes step S450, analyzing the internal temperature distribution parameters based on the simulation results, obtaining multiple peaks and extracting the first target area. Specifically, the internal temperature distribution parameters are analyzed by data processing and visualization technology to obtain a temperature distribution diagram or temperature field data. In the temperature distribution diagram or temperature field data, a region with a higher temperature, that is, a temperature peak, is found. The temperature peak is distributed at different positions of the target battery module, representing a potential area for heat accumulation, and the region containing the temperature peak is marked as the first target area. The first target area is a preliminary candidate area for the heat accumulation area. Step S460, analyzing the internal heat flow parameters based on the simulation results to obtain the flow direction information of the heat flow. Specifically, the internal heat flow parameters are analyzed by methods such as vector field visualization or streamline diagrams, and the flow direction information of the heat flow is obtained by analyzing the flow path and direction of the heat inside the target battery module. The flow direction information describes the heat transfer process. Step S470, identifying the flow heat value according to the flow direction information, and extracting the second target area according to the identification result. Specifically, after obtaining the flow direction information of the heat flow, the heat value in the flow direction is further analyzed, and by comparing the heat flow intensity on different paths, the areas where the heat flow is more significant or concentrated are identified. These areas are the key paths or nodes of heat transfer, and therefore are also potential locations of heat accumulation areas. These areas are marked as second target areas, and the second target areas are another group of candidate areas for heat accumulation areas. Step S480, based on the comparison of the first target area and the second target area, a plurality of overlapping areas are obtained, and the heat accumulation area is determined by combining the temperature data and heat flow data in the plurality of overlapping areas. Specifically, the first target area and the second target area are compared to find the overlapping part between them. The overlapping area is both an area with a higher temperature and an area with significant heat flow. The overlapping area is used as a preliminary heat accumulation area, and the temperature data and heat flow data in the overlapping area are further combined for quantitative analysis and evaluation, and finally the specific location and range of the heat accumulation area are determined. This implementation method more accurately identifies the area where heat actually accumulates in the target battery module by comparing the first target area determined based on the internal temperature distribution parameters and the second target area determined based on the internal heat flow parameters. This double verification method achieves the technical effect of improving the accuracy of identifying heat accumulation areas.

[0068] Step S500, traverse the heat accumulation area, and formulate a first heat dissipation optimization strategy based on the simulation results. Specifically, traversing the heat accumulation area includes checking the position, size, temperature distribution and heat flow of each heat accumulation area, and determining the heat transfer and accumulation in the target battery module by traversing the heat accumulation area. Based on the simulation results and the analysis of the heat accumulation area and the heat transfer mechanism, formulate a first heat dissipation optimization strategy, including heat dissipation structure design optimization, material optimization, thermal management control strategy optimization, battery module layout optimization, active heat dissipation technology introduction optimization, etc. For example, according to the position and size of the heat accumulation area, optimize the layout and parameters of the heat sink, heat dissipation channel or heat dissipation fan to improve the heat dissipation efficiency; select materials with better thermal conductivity for key components such as battery cells and heat sinks to speed up the heat conduction speed; by adjusting the operating parameters of the heat dissipation system, such as fan speed, heat dissipation liquid flow rate, etc., to achieve precise control of the temperature of the target battery module; re-plan the layout of the target battery module to avoid the bottleneck area of ​​heat accumulation and transfer and reduce the overall temperature gradient; introduce active heat dissipation technologies such as liquid cooling and heat pipes to improve the performance of the heat dissipation system.

[0069] Step S600, based on the first heat dissipation optimization strategy, random change optimization is performed to generate a second heat dissipation optimization strategy, simulation verification is performed according to the second heat dissipation optimization strategy, heat dissipation evaluation is performed according to the verification results, and heat dissipation evaluation optimization parameters are output. Specifically, one or more optimization algorithms (such as genetic algorithm, particle swarm optimization algorithm, etc.) are used to randomly change the key parameters in the first heat dissipation optimization strategy, including the design parameters of the heat dissipation structure, material selection, adjustment of the control strategy, etc., and multiple candidate second heat dissipation optimization strategies are generated through random changes. Then, simulation verification is performed according to each candidate second heat dissipation optimization strategy, that is, each candidate second heat dissipation optimization strategy is applied to the thermal management simulation of the target battery module, and its heat dissipation effect under different working conditions is analyzed, so as to evaluate the feasibility and effectiveness of these candidate second heat dissipation optimization strategies in actual applications. After the simulation verification is completed, heat dissipation evaluation is performed according to the verification results, including heat dissipation efficiency, temperature distribution uniformity, cost-effectiveness, etc. By comparing the performance indicators of different candidate second heat dissipation optimization strategies, the strategy with the best heat dissipation performance is selected as the second heat dissipation optimization strategy, and the heat dissipation evaluation optimization parameters are output. The heat dissipation evaluation optimization parameters include key design parameters and control parameters in the second heat dissipation optimization strategy, such as the best heat dissipation material type, the most suitable heat dissipation structure design, and the most effective cooling system configuration. The heat dissipation evaluation optimization parameters are used as part of the heat dissipation optimization solution to guide the actual design and optimization of the target battery module heat dissipation system.

[0070] Step S700, execute the second heat dissipation optimization strategy for the target battery module based on the heat dissipation evaluation optimization parameters. Specifically, according to the heat dissipation evaluation optimization parameters output by step S600, clarify which design or control parameters need to be adjusted to achieve a better heat dissipation effect, and then apply these heat dissipation evaluation optimization parameters to the target battery module, such as adjusting the layout and size of the heat sink, replacing materials with better thermal conductivity, modifying the control logic of the heat dissipation system, etc. These changes are precisely performed according to the specific requirements of the heat dissipation evaluation optimization parameters to ensure that the expected heat dissipation effect can be achieved. In the process of executing the second heat dissipation optimization strategy, pay close attention to the changes in the heat dissipation performance of the target battery module, verify the effectiveness of the second heat dissipation optimization strategy through actual operation and testing, and collect relevant heat dissipation data for further evaluation and adjustment. If it is found in actual application that the heat dissipation effect does not meet expectations, or new problems and challenges arise, return to the simulation stage for further optimization and adjustment. The embodiment of the present application acquires the internal structure data and material property data of the target battery module through multi-dimensional data collection, constructs a twin digital model using basic battery data, determines the heat accumulation area through heat conduction simulation, formulates a heat dissipation optimization strategy based on the simulation results, obtains heat dissipation evaluation optimization parameters through random change optimization and simulation verification, and executes the heat dissipation optimization strategy on the target battery module according to the heat dissipation evaluation optimization parameters. The technical effects of reducing costs, shortening cycles, improving simulation accuracy, timely discovering potential heat dissipation problems and performing targeted optimization, thereby improving the performance, life and safety of the battery module are achieved.

[0071] In a possible implementation, step S700 further includes step S710, using the heat dissipation evaluation optimization parameter as a boundary value, limiting the second heat dissipation optimization strategy, monitoring the execution process of the second heat dissipation optimization strategy in real time, and generating operation monitoring data and heat dissipation monitoring data of the target battery module. Specifically, the heat dissipation evaluation optimization parameter is not only used as an optimization guide, but also as an execution boundary to ensure that the implementation of the second heat dissipation optimization strategy does not exceed a predetermined safety or performance range. By setting these boundary values, unnecessary damage or performance degradation to the target battery module is prevented during the actual execution process. During the execution of the second heat dissipation optimization strategy, various data of the target battery module are collected in real time to understand the heat dissipation effect and system status, including the temperature distribution of the target battery module, the operating status of the heat dissipation system, and external factors that may affect the heat dissipation effect (such as ambient temperature, humidity, etc.). By collecting real-time monitoring data, the operation monitoring data and heat dissipation monitoring data of the target battery module are generated, wherein the operation monitoring data records various parameters and status information of the target battery module during operation, including the voltage, current, temperature, etc. of the target battery module, which is the basis for evaluating the heat dissipation effect and system stability. The heat dissipation monitoring data focuses on the performance of the heat dissipation system, including heat sink temperature, fan speed, heat dissipation liquid flow, etc., which is used to directly evaluate the effectiveness of the second heat dissipation optimization strategy. Step S720, traverse the operation monitoring data, evaluate and feedback the heat dissipation monitoring data, generate feedback tags, and identify the heat dissipation abnormality of the target battery module based on the feedback tags. Specifically, the collected operation monitoring data is comprehensively analyzed to determine the overall operation status of the target battery module, and potential performance problems or safety hazards are discovered by analyzing the operation monitoring data. The evaluation of the heat dissipation monitoring data is used to determine whether the second heat dissipation optimization strategy has achieved the expected effect. By comparing the heat dissipation monitoring data with the preset performance indicators or thresholds, it is determined whether the heat dissipation effect is good, and a corresponding feedback label is generated. The feedback label can be qualitative (such as "normal", "abnormal", etc.) or quantitative (such as specific heat dissipation efficiency values, temperature ranges, etc.). The feedback label identifies the performance status of the target battery module in terms of heat dissipation. If the evaluation results of the heat dissipation monitoring data show that the heat dissipation effect is poor or abnormal, the heat dissipation abnormality mark of the target battery module is based on the feedback label. The heat dissipation abnormality mark is used to promptly remind the operator or system to intervene and deal with the heat dissipation problem to avoid possible safety hazards or performance degradation. This implementation method ensures that the second heat dissipation optimization strategy is effectively implemented in actual applications, and through real-time monitoring and evaluation of operation and heat dissipation data, potential heat dissipation problems are discovered and resolved in a timely manner, thereby achieving the technical effect of improving the stability and safety of the target battery module.

[0072] In the above, refer to Figure 1The battery module heat dissipation optimization method based on twin simulation according to an embodiment of the present invention is described in detail. Figure 2 A battery module heat dissipation optimization system based on twin simulation according to an embodiment of the present invention is described.

[0073] The battery module heat dissipation optimization system based on twin simulation according to an embodiment of the present invention is used to solve the technical problems of high cost, long cycle, insufficient simulation accuracy, and difficulty in discovering potential heat dissipation risks in existing battery module heat dissipation optimization methods, so as to reduce costs, shorten cycles, improve simulation accuracy, timely discover potential heat dissipation problems and perform targeted optimization, thereby improving the performance, life and safety of battery modules. The battery module heat dissipation optimization system based on twin simulation includes: a multi-dimensional data acquisition module 10, a three-dimensional modeling module 20, a model assignment module 30, a heat conduction simulation module 40, a first heat dissipation optimization strategy formulation module 50, a heat dissipation evaluation optimization parameter output module 60, and a second heat dissipation optimization strategy execution module 70.

[0074] The multi-dimensional data acquisition module 10 is used to perform multi-dimensional data acquisition based on the target battery module to obtain a battery basic data set of the target battery module, wherein the battery basic data set includes battery module internal structure data and battery module material property data;

[0075] The three-dimensional modeling module 20 is used to perform three-dimensional modeling based on the internal structure data of the battery module to generate a first twin digital model;

[0076] The model assignment module 30 is used to assign a value to the first twin digital model based on the material property data of the battery module, update the first twin digital model, and generate a second twin digital model;

[0077] The heat conduction simulation module 40 is used to obtain an external environment data set of the target battery module, perform heat conduction simulation using the second twin digital model, and determine the heat accumulation area of ​​the target battery module according to the simulation results;

[0078] The first heat dissipation optimization strategy formulation module 50 is used to traverse the heat accumulation area and formulate a first heat dissipation optimization strategy based on the simulation result;

[0079] The heat dissipation evaluation optimization parameter output module 60 is used to perform random change optimization based on the first heat dissipation optimization strategy, generate a second heat dissipation optimization strategy, perform simulation verification according to the second heat dissipation optimization strategy, perform heat dissipation evaluation according to the verification result, and output the heat dissipation evaluation optimization parameter;

[0080] The second heat dissipation optimization strategy execution module 70 is used to execute the second heat dissipation optimization strategy on the target battery module based on the heat dissipation evaluation optimization parameters.

[0081] The specific configuration of the multidimensional data acquisition module 10 will be described in detail below. As described above, based on the target battery module, multidimensional data acquisition is performed to obtain the battery basic data set of the target battery module. The multidimensional data acquisition module 10 may further include: a multidimensional measurement data set acquisition unit for performing multidimensional measurement on multiple battery cells in the target battery module to obtain a multidimensional measurement data set, wherein the multidimensional measurement data set contains multidimensional measurement data of each battery cell; a component association information acquisition unit for analyzing the component relationship between the multiple battery cells to obtain component association information; a battery module internal structure data acquisition unit for obtaining the battery module internal structure data according to the combination of the multiple battery cells based on the component association information and the multidimensional measurement data set; a battery module material attribute data acquisition unit for extracting multiple material samples based on the multidimensional measurement data, performing attribute tests on the multiple material samples, and obtaining the battery module material attribute data according to the test results; and a battery basic data set acquisition unit for adding the battery module internal structure data and the battery module material attribute data to the battery basic data set.

[0082] The specific configuration of the three-dimensional modeling module 20 will be described in detail below. As described above, three-dimensional modeling is performed based on the internal structure data of the battery module to generate a first twin digital model. The three-dimensional modeling module 20 may further include: a basic framework creation unit for constructing a virtual three-dimensional space coordinate system to create a basic framework of the target battery module; a simulation parameter set setting unit for using the internal structure data of the battery module as training data, performing detailed optimization training on the basic framework, and setting a simulation parameter set; a simulation twin sub-model construction unit for constructing a simulation twin sub-model of the target battery module based on the simulation parameter set; and a first twin digital model generation unit for performing multi-dimensional data comparison and verification between the simulation twin sub-model and the target battery module until the data verification converges to generate the first twin digital model.

[0083] The specific configuration of the model assignment module 30 will be described in detail below. As described above, the first twin digital model is assigned based on the material property data of the battery module, the first twin digital model is updated, and the second twin digital model is generated. The model assignment module 30 may further include: a component matching unit is used to traverse the material property data of the battery module and match the first twin digital model with the component to generate multiple component matching information; a material property assignment channel input unit is used to input the material property assignment channel to the first twin digital model according to the multiple component matching information, and output physical property data and chemical property data. The material property assignment channel includes a physical property assignment branch and a chemical property assignment branch; a model update unit is used to align the physical property data with the chemical property data based on the first twin digital model, and update the first twin digital model according to the alignment result as the second twin digital model for output.

[0084] The specific configuration of the heat conduction simulation module 40 will be described in detail below. As described above, the external environment data set of the target battery module is obtained, and the heat conduction simulation is performed using the second twin digital model. The heat conduction simulation module 40 may further include: an external environment data set acquisition unit is used to collect parameters based on the layout scenario of the target battery module and obtain the external environment data set; a simulation parameter setting unit is used to import the external environment data set into the second twin digital model, set simulation parameters, and the simulation parameters include simulation initial conditions and simulation boundary conditions; an operation parameter setting unit is used to set operation parameters based on the operation state of the target battery module, and the operation parameters include internal heat source data and heat dissipation standard data; a simulation result output unit is used to input the internal heat source data and the heat dissipation standard data into the heat conduction simulation formula based on the simulation initial conditions and the simulation boundary conditions, perform heat conduction simulation on the target battery module according to the heat conduction simulation formula, and output the simulation result, and the simulation result includes the internal temperature distribution parameters and internal heat flow parameters of the target battery module.

[0085] Among them, the heat accumulation area of ​​the target battery module is determined according to the simulation results, and the thermal conduction simulation module 40 may further include: a first target area extraction unit is used to analyze the internal temperature distribution parameters based on the simulation results, obtain multiple peaks and extract the first target area; a flow direction information acquisition unit is used to analyze the internal heat flow parameters based on the simulation results, and obtain the flow direction information of the heat flow; a second target area extraction unit is used to identify the flow heat value according to the flow direction information, and extract the second target area according to the identification result; a heat accumulation area determination unit is used to compare the first target area and the second target area, obtain multiple overlapping areas, and determine the heat accumulation area in combination with the temperature data and heat flow data in the multiple overlapping areas.

[0086] The specific configuration of the second heat dissipation optimization strategy execution module 70 will be described in detail below. As described above, the second heat dissipation optimization strategy is executed on the target battery module based on the heat dissipation evaluation optimization parameter, and the second heat dissipation optimization strategy execution module 70 may further include: a monitoring data generation unit is used to use the heat dissipation evaluation optimization parameter as a boundary value, limit the second heat dissipation optimization strategy, monitor the execution process of the second heat dissipation optimization strategy in real time, and generate operation monitoring data and heat dissipation monitoring data of the target battery module; a heat dissipation abnormality identification unit is used to traverse the operation monitoring data, evaluate and feedback the heat dissipation monitoring data, generate a feedback label, and identify the heat dissipation abnormality of the target battery module based on the feedback label.

[0087] The battery module heat dissipation optimization system based on twin simulation provided in an embodiment of the present invention can execute the battery module heat dissipation optimization method based on twin simulation provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0088] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0089] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A battery module heat dissipation optimization method based on twin simulation, characterized in that: The method comprises: Perform multi-dimensional data collection based on the target battery module to obtain a battery basic data set of the target battery module, wherein the battery basic data set includes internal structure data of the battery module and material property data of the battery module; Performing three-dimensional modeling based on the internal structure data of the battery module to generate a first twin digital model; Assigning values ​​to the first twin digital model based on the material property data of the battery module, updating the first twin digital model, and generating a second twin digital model; Acquire an external environment data set of the target battery module, perform heat conduction simulation using the second twin digital model, and determine the heat accumulation area of ​​the target battery module according to the simulation results; Traversing the heat accumulation area, and formulating a first heat dissipation optimization strategy based on the simulation result; Perform random change optimization based on the first heat dissipation optimization strategy to generate a second heat dissipation optimization strategy, perform simulation verification according to the second heat dissipation optimization strategy, perform heat dissipation evaluation according to the verification result, and output heat dissipation evaluation optimization parameters; Executing the second heat dissipation optimization strategy on the target battery module based on the heat dissipation evaluation optimization parameter; Based on the target battery module, multi-dimensional data collection is performed to obtain a battery basic data set of the target battery module, and the method includes: Performing multi-dimensional measurement on a plurality of battery cells in a target battery module to obtain a multi-dimensional measurement data set, wherein the multi-dimensional measurement data set includes multi-dimensional measurement data of each battery cell; Analyzing the component relationship between the plurality of battery cells to obtain component association information; Based on the component association information and the multi-dimensional measurement data set, obtaining the internal structure data of the battery module according to the combination of the plurality of battery cells; Extracting a plurality of material samples based on the multi-dimensional measurement data, performing property tests on the plurality of material samples, and obtaining the material property data of the battery module according to the test results; Adding the battery module internal structure data and the battery module material property data to the battery basic data set; Performing three-dimensional modeling based on the internal structure data of the battery module to generate a first twin digital model, the method comprising: Construct a virtual three-dimensional space coordinate system and create the basic framework of the target battery module; Using the internal structure data of the battery module as training data, performing detailed optimization training on the basic framework, and setting a simulation parameter set; Constructing a simulation twin model of a target battery module based on the simulation parameter set; Comparing and verifying the multi-dimensional data of the simulation twin model with the target battery module until the data verification converges, thereby generating the first twin digital model; Assigning a value to the first twin digital model based on the battery module material property data, updating the first twin digital model, and generating a second twin digital model, the method comprising: Traversing the battery module material property data and performing component matching with the first twin digital model to generate a plurality of component matching information; Inputting a material property assignment channel into the first twin digital model according to the plurality of component matching information, and outputting physical property data and chemical property data, wherein the material property assignment channel comprises a physical property assignment branch and a chemical property assignment branch; Based on the first twin digital model, align the physical property data with the chemical property data, and update the first twin digital model according to the alignment result to output it as the second twin digital model; Acquiring an external environment data set of a target battery module and performing heat conduction simulation using the second twin digital model, the method comprising: Collect parameters based on the deployment scenario of the target battery module to obtain the external environment data set; Importing the external environment data set into the second twin digital model, setting simulation parameters, wherein the simulation parameters include simulation initial conditions and simulation boundary conditions; Setting operating parameters based on the operating state of the target battery module, wherein the operating parameters include internal heat source data and heat dissipation standard data; Based on the simulation initial conditions and the simulation boundary conditions, the internal heat source data and the heat dissipation standard data are input into a heat conduction simulation formula, a heat conduction simulation is performed on the target battery module according to the heat conduction simulation formula, and the simulation result is output, wherein the simulation result includes an internal temperature distribution parameter and an internal heat flow parameter of the target battery module; Determine the heat accumulation area of ​​the target battery module according to the simulation results, the method includes: Analyze the internal temperature distribution parameters based on the simulation results, obtain multiple peak values ​​and extract a first target area; Analyzing the internal heat flow parameters based on the simulation results to obtain flow direction information of the heat flow; Identify the flow heat value according to the flow direction information, and extract the second target area according to the identification result; Based on the comparison between the first target area and the second target area, a plurality of overlapping areas are obtained, and the heat accumulation area is determined by combining the temperature data and the heat flow data within the plurality of overlapping areas; Executing the second heat dissipation optimization strategy on the target battery module based on the heat dissipation evaluation optimization parameter, the method comprising: Using the heat dissipation evaluation optimization parameter as a boundary value, limiting the second heat dissipation optimization strategy, monitoring the execution process of the second heat dissipation optimization strategy in real time, and generating operation monitoring data and heat dissipation monitoring data of the target battery module; The operation monitoring data is traversed, evaluation feedback is performed on the heat dissipation monitoring data, and a feedback tag is generated.

2. The battery module heat dissipation optimization system based on twin simulation is characterized by: The system is used to implement the battery module heat dissipation optimization method based on twin simulation according to claim 1, and the system includes: A multidimensional data acquisition module, wherein the multidimensional data acquisition module is used to perform multidimensional data acquisition based on a target battery module to obtain a battery basic data set of the target battery module, wherein the battery basic data set includes battery module internal structure data and battery module material property data; A three-dimensional modeling module, wherein the three-dimensional modeling module is used to perform three-dimensional modeling based on the internal structure data of the battery module to generate a first twin digital model; A model assignment module, the model assignment module is used to assign a value to the first twin digital model based on the battery module material property data, update the first twin digital model, and generate a second twin digital model; A heat conduction simulation module, which is used to obtain an external environment data set of a target battery module, perform heat conduction simulation using the second twin digital model, and determine a heat accumulation area of ​​the target battery module according to the simulation results; A first heat dissipation optimization strategy formulation module, the first heat dissipation optimization strategy formulation module is used to traverse the heat accumulation area and formulate a first heat dissipation optimization strategy based on the simulation result; a heat dissipation evaluation optimization parameter output module, wherein the heat dissipation evaluation optimization parameter output module is used to perform random change optimization based on the first heat dissipation optimization strategy, generate a second heat dissipation optimization strategy, perform simulation verification according to the second heat dissipation optimization strategy, perform heat dissipation evaluation according to the verification result, and output heat dissipation evaluation optimization parameters; A second heat dissipation optimization strategy execution module is used to execute the second heat dissipation optimization strategy on the target battery module based on the heat dissipation evaluation optimization parameters.

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