A method and system for generating a battery heat exchange network

Abstract

The present application provides a method and system for generating a battery heat exchange network, which relates to the field of battery technology. The method for generating a battery heat exchange network includes: obtaining heat flow influence data of a battery module to be processed, and constructing a high-dimensional heat source data set; obtaining heat transfer efficiency data of the battery module to be processed, and constructing a micro-environmental condition data set; constructing a graph model corresponding to the battery module to be processed according to the high-dimensional heat source data set and the micro-environmental condition data set; analyzing and processing the graph model according to a preset graph theory algorithm and identifying the optimal path of multi-dimensional heat flow to obtain an analysis result; generating heat exchange network data of the battery module to be processed according to the analysis result. The method for generating a battery heat exchange network can achieve the technical effect of improving heat exchange efficiency and adaptability.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular, to a method and system for generating a battery heat exchange network. Background Art

[0002] At present, in the field of new energy vehicles, the battery module is the core storage and supply unit of energy, and its performance and safety are directly related to the vehicle's range, safety performance and user driving experience. However, with the rapid development of the new energy vehicle industry and the continuous improvement of users' requirements for electric vehicle range and safety performance, the traditional battery module thermal management system has been unable to meet the existing needs. It is mainly reflected in the following aspects:

[0003] First, the traditional thermal management system has obvious deficiencies in heat exchange efficiency. Due to the lack of precise control and adaptation to the internal heat source distribution of the battery module and the changes in the external environment, the heat distribution is often uneven, causing overheating in some areas of the battery module, affecting the performance and life of the battery, and even posing safety hazards;

[0004] Secondly, traditional thermal management systems lack flexibility and adjustability in design. The design of the heat exchange network is often static, and it is difficult to dynamically adjust according to the actual working state of the battery module and changes in the external environment, resulting in poor adaptability of the thermal management system and failure to maximize the heat exchange efficiency and the overall energy efficiency of the battery module;

[0005] Therefore, how to optimize the heat exchange network design of new energy vehicle battery modules, improve battery performance, life and stability, and improve the adaptability of the thermal management system is an issue that needs to be addressed urgently. Summary of the invention

[0006] The purpose of the present application is to provide a method, system, electronic device and computer-readable storage medium for generating a battery heat exchange network, which can achieve the technical effect of improving heat exchange efficiency and adaptability.

[0007] In a first aspect, the present application provides a method for generating a battery heat exchange network, comprising:

[0008] Obtain the heat flow impact data of the battery module to be processed and construct a high-dimensional heat source data set;

[0009] Obtain the heat transfer efficiency data of the battery module to be processed and construct a micro-environmental condition data set;

[0010] Constructing a graphical model corresponding to the battery module to be processed according to the high-dimensional heat source data set and the micro-environmental condition data set;

[0011] Analyze and process the graph model according to a preset graph theory algorithm and identify the optimal path of multi-dimensional heat flow to obtain analysis results;

[0012] The heat exchange network data of the battery module to be processed is generated according to the analysis result.

[0013] In the above implementation process, the method for generating the battery heat exchange network constructs a high-dimensional heat source data set and a micro-environmental condition data set respectively through the heat flow influence data and heat transfer efficiency data of the battery module to be processed, thereby constructing a graphical model corresponding to the battery module to be processed based on the high-dimensional heat source data set and the micro-environmental condition data set, and simulates and optimizes the heat transfer process of the battery module to be processed based on the graphical model, and finally obtains the heat exchange network data; thus, the method for generating the battery heat exchange network obtains the optimized heat exchange network data by accurately simulating and optimizing the heat transfer process inside the battery module and between modules, realizes the balanced distribution of heat in the battery module, effectively reduces the hot spot area, improves the working efficiency and life of the battery, and achieves the technical effect of improving the heat exchange efficiency and adaptability.

[0014] Furthermore, the battery module includes a plurality of battery cells, and the steps of obtaining heat flow influence data of the battery module to be processed and constructing a high-dimensional heat source data set include:

[0015] Acquiring first heat generation rate data of the battery module to be processed under multiple charge and discharge cycles;

[0016] Acquiring second heat generation rate data of the battery module to be processed under multiple power outputs;

[0017] Acquiring thermal interaction force data between each battery cell in the battery module to be processed;

[0018] Obtaining data on the influencing factors of the geometric configuration of the battery module to be processed on the heat flow;

[0019] A high-dimensional heat source data set is constructed based on one or more of the first heat generation rate data, the second heat generation rate data, the thermal interaction force data, and the influencing factor data.

[0020] Furthermore, the step of obtaining the heat transfer efficiency data of the battery module to be processed and constructing a micro-environmental condition data set includes:

[0021] Acquiring temperature gradient data of the battery module to be processed in a microenvironment;

[0022] Acquire data on the effect of humidity change on heat transfer efficiency of the battery module to be processed under a microenvironment;

[0023] A micro-environmental condition data set is constructed according to the temperature gradient data and the impact rate data.

[0024] In the above implementation process, by constructing a micro-environmental condition data set, the impact of changes in environmental conditions on the performance of the heat exchange network is taken into account, so that the heat exchange network finally generated can adapt to different environmental conditions and working conditions to ensure that the battery module is always within the optimal operating temperature range.

[0025] Furthermore, the step of constructing a graphical model corresponding to the battery module to be processed according to the high-dimensional heat source data set and the micro-environmental condition data set includes:

[0026] Acquire an initialization graph model, the initialization graph model comprising nodes and edges, the nodes are used to represent heat sources and micro-environmental conditions in the battery module to be processed, and the edges are used to represent heat exchange paths of the battery module to be processed;

[0027] Performing an attribute assignment operation on the initialization graph model according to the high-dimensional heat source data set and the micro-environment condition data set to obtain an attributed graph model;

[0028] determining an optimal heat flow path according to the attribute graph model;

[0029] According to the structural characteristics of the attribute graph model, identifying key nodes and key paths, wherein the key nodes are key areas where heat is concentrated or dispersed, and the key paths are priority paths for heat transfer;

[0030] A graphical model corresponding to the battery module to be processed is constructed according to the optimal heat flow path, the key nodes and the key path.

[0031] Furthermore, the step of analyzing and processing the graph model according to a preset graph theory algorithm and identifying the optimal path of multi-dimensional heat flow to obtain the analysis result includes:

[0032] defining the optimization problem of the graph model as a total thermal resistance combining the physical properties and thermal conductivity characteristics of the heat exchange path according to a preset graph theory algorithm, wherein the total thermal resistance is associated with the heat transfer capacity of each edge in the graph model;

[0033] Dynamically adjust the path operation according to the graph model to identify the optimal path and the adjusted thermal resistance parameters, wherein the optimal path minimizes the total thermal resistance and the optimal path dynamically adapts to changes in the external environment and changes in the internal heat source;

[0034] According to the optimal path and the adjusted thermal resistance parameters, an analysis result is obtained.

[0035] In the above implementation process, by minimizing the total thermal resistance of the heat exchange network and optimizing the heat transfer path, unnecessary heat energy loss can be reduced and the energy efficiency of the battery module can be improved.

[0036] Furthermore, the step of generating the heat exchange network data of the battery module to be processed according to the analysis result includes:

[0037] Based on the analysis results and in combination with the heat flow requirements of the battery module to be processed and the adaptability to changes in the external environment, an initial heat exchange network is obtained, wherein the initial heat exchange network includes a heat pipe and a heat exchanger;

[0038] Determining heat pipe data with adaptive heat transfer capability according to the initial heat exchange network;

[0039] Dynamically adjust the position and specifications of the heat exchanger according to environmental conditions to obtain heat exchanger data;

[0040] Heat exchange network data is obtained according to the heat pipe data and the heat exchanger data.

[0041] Furthermore, after the step of generating the heat exchange network data of the battery module to be processed according to the analysis result, the method further includes:

[0042] Performing real-time dynamic testing on the heat exchange network data based on the simulation platform to obtain dynamic test data;

[0043] Feedback optimization and adjustment are performed on the heat exchange network data according to the dynamic test data to obtain optimized heat exchange network data.

[0044] In the above implementation process, relying on the real-time dynamic testing of the simulation platform, potential thermal management problems can be foreseen and optimized in the design stage, greatly improving the reliability and stability of the thermal management system in practical applications.

[0045] In a second aspect, the present application provides a system for generating a battery heat exchange network, comprising:

[0046] The heat source data module is used to obtain the heat flow impact data of the battery module to be processed and construct a high-dimensional heat source data set;

[0047] The micro-environment data module is used to obtain the heat transfer efficiency data of the battery module to be processed and construct a micro-environment condition data set;

[0048] A graphical model module, used to construct a graphical model corresponding to the battery module to be processed according to the high-dimensional heat source data set and the micro-environment condition data set;

[0049] An analysis module, used to analyze and process the graph model according to a preset graph theory algorithm and identify the optimal path of multi-dimensional heat flow to obtain analysis results;

[0050] The exchange network generation module is used to generate the heat exchange network data of the battery module to be processed according to the analysis result.

[0051] Further, the battery module includes a plurality of battery cells, and the heat source data module is specifically used for:

[0052] Acquiring first heat generation rate data of the battery module to be processed under multiple charge and discharge cycles;

[0053] Acquiring second heat generation rate data of the battery module to be processed under multiple power outputs;

[0054] Acquiring thermal interaction force data between each battery cell in the battery module to be processed;

[0055] Obtaining data on the influencing factors of the geometric configuration of the battery module to be processed on the heat flow;

[0056] A high-dimensional heat source data set is constructed based on one or more of the first heat generation rate data, the second heat generation rate data, the thermal interaction force data, and the influencing factor data.

[0057] Furthermore, the microenvironment data module is specifically used for:

[0058] Acquiring temperature gradient data of the battery module to be processed in a microenvironment;

[0059] Acquire data on the effect of humidity change on heat transfer efficiency of the battery module to be processed under a microenvironment;

[0060] A micro-environmental condition data set is constructed according to the temperature gradient data and the impact rate data.

[0061] Furthermore, the graph model module is specifically used for:

[0062] Acquire an initialization graph model, the initialization graph model comprising nodes and edges, the nodes are used to represent heat sources and micro-environmental conditions in the battery module to be processed, and the edges are used to represent heat exchange paths of the battery module to be processed;

[0063] Performing an attribute assignment operation on the initialization graph model according to the high-dimensional heat source data set and the micro-environment condition data set to obtain an attributed graph model;

[0064] determining an optimal heat flow path according to the attribute graph model;

[0065] According to the structural characteristics of the attribute graph model, identifying key nodes and key paths, wherein the key nodes are key areas where heat is concentrated or dispersed, and the key paths are priority paths for heat transfer;

[0066] A graphical model corresponding to the battery module to be processed is constructed according to the optimal heat flow path, the key nodes and the key path.

[0067] Furthermore, the analysis module is specifically used for:

[0068] defining the optimization problem of the graph model as a total thermal resistance combining the physical properties and thermal conductivity characteristics of the heat exchange path according to a preset graph theory algorithm, wherein the total thermal resistance is associated with the heat transfer capacity of each edge in the graph model;

[0069] Dynamically adjust the path operation according to the graph model to identify the optimal path and the adjusted thermal resistance parameters, wherein the optimal path minimizes the total thermal resistance and the optimal path dynamically adapts to changes in the external environment and changes in the internal heat source;

[0070] According to the optimal path and the adjusted thermal resistance parameters, an analysis result is obtained.

[0071] Furthermore, the switching network generation module is specifically used for:

[0072] Based on the analysis results and in combination with the heat flow requirements of the battery module to be processed and the adaptability to changes in the external environment, an initial heat exchange network is obtained, wherein the initial heat exchange network includes a heat pipe and a heat exchanger;

[0073] Determining heat pipe data with adaptive heat transfer capability according to the initial heat exchange network;

[0074] Dynamically adjust the position and specifications of the heat exchanger according to environmental conditions to obtain heat exchanger data;

[0075] Heat exchange network data is obtained according to the heat pipe data and the heat exchanger data.

[0076] Furthermore, the battery heat exchange network generation system further includes a feedback module, and the feedback module is used to:

[0077] Performing real-time dynamic testing on the heat exchange network data based on the simulation platform to obtain dynamic test data;

[0078] Feedback optimization and adjustment are performed on the heat exchange network data according to the dynamic test data to obtain optimized heat exchange network data.

[0079] In a third aspect, the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in any one of the first aspects when executing the computer program.

[0080] In a fourth aspect, the present application provides a computer-readable storage medium, on which instructions are stored. When the instructions are executed on a computer, the computer executes the method as described in any one of the first aspects.

[0081] In a fifth aspect, the present application provides a computer program product, which, when running on a computer, enables the computer to execute the method as described in any one of the first aspects.

[0082] Other features and advantages disclosed in the present application will be described in the following description, or some features and advantages can be inferred or determined without doubt from the description, or can be learned by implementing the above-mentioned technology disclosed in the present application.

[0083] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0085] Figure 1 A schematic diagram of a flow chart of a method for generating a battery heat exchange network provided in an embodiment of the present application;

[0086] Figure 2 A schematic diagram of a process for constructing a high-dimensional heat source data set provided in an embodiment of the present application;

[0087] Figure 3 A schematic diagram of a process for constructing a micro-environmental condition data set provided in an embodiment of the present application;

[0088] Figure 4 A schematic diagram of the process of constructing a graph model provided in an embodiment of the present application;

[0089] Figure 5 A schematic diagram of a process for obtaining analysis results provided in an embodiment of the present application;

[0090] Figure 6 A schematic diagram of a process for generating heat exchange network data provided in an embodiment of the present application;

[0091] Figure 7 A schematic flow chart of another method for generating a battery heat exchange network provided in an embodiment of the present application;

[0092] Figure 8 A structural block diagram of a system for generating a battery heat exchange network provided in an embodiment of the present application;

[0093] Fig. 9 A structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0094] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0095] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0096] The embodiments of the present application provide a method, system, electronic device and computer-readable storage medium for generating a battery heat exchange network, which can be applied to the design process of a battery module heat exchange network of a new energy vehicle; the method for generating a battery heat exchange network constructs a high-dimensional heat source data set and a micro-environmental condition data set respectively through the heat flow influence data and heat transfer efficiency data of the battery module to be processed, thereby constructing a graphical model corresponding to the battery module to be processed based on the high-dimensional heat source data set and the micro-environmental condition data set, and simulates and optimizes the heat transfer process of the battery module to be processed based on the graphical model, and finally obtains the heat exchange network data; thus, the method for generating a battery heat exchange network obtains the optimized heat exchange network data by accurately simulating and optimizing the heat transfer process inside the battery module and between modules, realizes the balanced distribution of heat in the battery module, effectively reduces the hot spot area, improves the working efficiency and life of the battery, and achieves the technical effect of improving the heat exchange efficiency and adaptability.

[0097] See also Figure 1 , Figure 1 A schematic flow chart of a method for generating a battery heat exchange network provided in an embodiment of the present application, wherein the method for generating a battery heat exchange network comprises the following steps:

[0098] S100: Acquire heat flow impact data of the battery module to be processed and construct a high-dimensional heat source data set;

[0099] Exemplarily, by collecting data on different charge and discharge cycles of the battery module to be processed, the heat generation rate at different power outputs, the thermal interaction force between battery cells, and the impact of the battery module's geometric configuration on heat flow, a high-dimensional heat source data set is constructed.

[0100] S200: Acquire heat transfer efficiency data of the battery module to be processed and construct a micro-environmental condition data set;

[0101] Exemplarily, the heat transfer efficiency data includes temperature gradient data and the influence rate of humidity change on heat transfer efficiency; thus, the temperature gradient data and the influence rate of humidity change on heat transfer efficiency of the battery module to be processed in the microenvironment are collected to construct a micro-environmental condition data set.

[0102] Exemplarily, the number of battery modules to be processed provided in the embodiments of the present application may be one or more, and each module to be processed may include multiple battery cells, wherein one battery cell represents a separate battery cell; each battery module or battery cell to be processed may serve as a heat source.

[0103] S300: constructing a graphical model corresponding to the battery module to be processed according to the high-dimensional heat source data set and the micro-environmental condition data set;

[0104] Exemplarily, based on a high-dimensional heat source dataset and a micro-environmental condition dataset, a graphical model representing a battery module and its complex thermal environment is constructed.

[0105] S400: Analyze and process the graph model according to a preset graph theory algorithm and identify the optimal path of multi-dimensional heat flow to obtain analysis results;

[0106] Exemplarily, in an embodiment of the present application, the preset graph theory algorithm is an improved graph theory algorithm; the improved graph theory algorithm is applied to perform in-depth analysis on the constructed graph model to obtain analysis results, which can identify the optimal path of multi-dimensional heat flow. The improved graph theory algorithm is combined with minimizing the total thermal resistance of the system, and dynamically adjusts the path to cope with changes in the external environment, so that heat is transferred from the heat source to the heat dissipation interface.

[0107] S500: Generate heat exchange network data of the battery module to be processed according to the analysis result.

[0108] Exemplarily, the heat exchange network includes heat pipes and heat exchangers; based on the analysis results of the graph theory algorithm, the layout of the heat exchange network is designed, including determining the optimal path, size and distribution of heat pipes with adaptive heat conduction capabilities and the location and specifications of the heat exchangers that are dynamically adjusted according to environmental conditions, and finally obtaining the heat exchange network data.

[0109] In some embodiments, the method for generating a battery heat exchange network constructs a high-dimensional heat source data set and a micro-environmental condition data set respectively through the heat flow influence data and heat transfer efficiency data of the battery module to be processed, thereby constructing a graphical model corresponding to the battery module to be processed based on the high-dimensional heat source data set and the micro-environmental condition data set, and simulates and optimizes the heat transfer process of the battery module to be processed based on the graphical model, and finally obtains the heat exchange network data; thus, the method for generating a battery heat exchange network obtains the optimized heat exchange network data by accurately simulating and optimizing the heat transfer process inside the battery module and between modules, realizes the balanced distribution of heat in the battery module, effectively reduces the hot spot area, improves the working efficiency and life of the battery, and achieves the technical effect of improving the heat exchange efficiency and adaptability.

[0110] See also Figure 2 , Figure 2 A schematic diagram of the process of constructing a high-dimensional heat source dataset provided in an embodiment of the present application.

[0111] Exemplarily, the battery module includes a plurality of battery cells. S100: the step of obtaining heat flow influence data of the battery module to be processed and constructing a high-dimensional heat source data set includes:

[0112] S110: Acquire first heat generation rate data of a battery module to be processed under multiple charge and discharge cycles;

[0113] S120: Acquire second heat generation rate data of the battery module to be processed under multiple power outputs;

[0114] S130: Acquire thermal interaction force data between each battery cell in the battery module to be processed;

[0115] S140: Obtaining data on influencing factors of the geometric configuration of the battery module to be processed on the heat flow;

[0116] S150: Construct a high-dimensional heat source data set according to one or more of the first heat generation rate data, the second heat generation rate data, the thermal interaction force data, and the influencing factor data.

[0117] See also Figure 3 , Figure 3 A schematic diagram of the process of constructing a micro-environmental condition data set provided in an embodiment of the present application.

[0118] Exemplarily, S200: the step of obtaining heat transfer efficiency data of the battery module to be processed and constructing a micro-environmental condition data set includes:

[0119] S210: Acquire temperature gradient data of the battery module to be processed in a microenvironment;

[0120] S220: Obtaining data on the effect of humidity change on heat transfer efficiency of the battery module to be processed under a microenvironment;

[0121] S230: Construct a micro-environmental condition data set according to the temperature gradient data and the impact rate data.

[0122] Exemplarily, by constructing a micro-environmental condition data set, the impact of changes in environmental conditions on the performance of the heat exchange network is taken into account, so that the ultimately generated heat exchange network can adapt to different environmental conditions and operating states to ensure that the battery module is always within the optimal operating temperature range.

[0123] See also Figure 4 , Figure 4 A schematic diagram of the process of constructing a graph model provided in an embodiment of the present application.

[0124] Exemplarily, S300: the step of constructing a graphical model corresponding to the battery module to be processed according to the high-dimensional heat source data set and the micro-environment condition data set includes:

[0125] S310: Acquire an initialization graph model, where the initialization graph model includes nodes and edges, where the nodes are used to represent heat sources and micro-environmental conditions in the battery module to be processed, and the edges are used to represent heat exchange paths of the battery module to be processed;

[0126] S320: performing an attribute assignment operation on the initialized graph model according to the high-dimensional heat source data set and the micro-environment condition data set to obtain an attributed graph model;

[0127] S330: determining an optimal heat flow path according to the attribute graph model;

[0128] S340: identifying key nodes and key paths according to the structural characteristics of the attribute graph model, wherein the key nodes are key areas where heat is concentrated or dispersed, and the key paths are priority paths for heat transfer;

[0129] S350: Constructing a graphical model corresponding to the battery module to be processed according to the optimal heat flow path, key nodes and key paths.

[0130] See also Figure 5 , Figure 5 A schematic diagram of a process for obtaining analysis results provided in an embodiment of the present application.

[0131] Exemplarily, S400: the step of analyzing and processing the graph model according to a preset graph theory algorithm and identifying the optimal path of multi-dimensional heat flow to obtain the analysis result includes:

[0132] S410: defining the optimization problem of the graph model as a total thermal resistance combining the physical properties and thermal conductivity characteristics of the heat exchange path according to a preset graph theory algorithm, wherein the total thermal resistance is associated with the heat transfer capacity of each edge in the graph model;

[0133] S420: dynamically adjusting the path operation according to the graph model to identify the optimal path and the adjusted thermal resistance parameters, wherein the optimal path minimizes the total thermal resistance and the optimal path dynamically adapts to changes in the external environment and changes in the internal heat source;

[0134] S430: Obtain analysis results according to the optimal path and the adjusted thermal resistance parameters.

[0135] For example, by minimizing the total thermal resistance of the heat exchange network and optimizing the heat transfer path, unnecessary heat energy loss can be reduced and the energy efficiency of the battery module can be improved.

[0136] See also Figure 6 , Figure 6 A schematic diagram of a process for generating heat exchange network data provided in an embodiment of the present application.

[0137] Exemplarily, S500: the step of generating heat exchange network data of the battery module to be processed according to the analysis result includes:

[0138] S510: Based on the analysis results and in combination with the heat flow requirements of the battery module to be processed and the adaptability to changes in the external environment, an initial heat exchange network is obtained, where the initial heat exchange network includes a heat pipe and a heat exchanger;

[0139] S520: Determine heat pipe data with adaptive heat conduction capability according to the initial heat exchange network;

[0140] S530: dynamically adjusting the position and specification of the heat exchanger according to environmental conditions, and obtaining heat exchanger data;

[0141] S540: Obtain heat exchange network data according to the heat pipe data and the heat exchanger data.

[0142] See also Figure 7 , Figure 7 A schematic flow chart of another method for generating a battery heat exchange network provided in an embodiment of the present application.

[0143] Exemplarily, after the step of S500: generating heat exchange network data of the battery module to be processed according to the analysis result, the method for generating a battery heat exchange network further includes the following steps:

[0144] S600: Perform real-time dynamic testing on the heat exchange network data based on the simulation platform to obtain dynamic test data;

[0145] S700: Feedback optimization and adjustment are performed on the heat exchange network data according to the dynamic test data to obtain optimized heat exchange network data.

[0146] For example, by relying on real-time dynamic testing on a simulation platform, potential thermal management problems can be foreseen and optimized during the design phase, greatly improving the reliability and stability of the thermal management system in actual applications.

[0147] In some embodiments, in combination Figures 1 to 7 The method for generating the battery heat exchange network shown in the figure has the following specific process steps:

[0148] S1. Collect data on different charge and discharge cycles of battery modules, heat generation rate at different power outputs, thermal interaction forces between battery cells, and the impact of the geometric configuration of battery modules on heat flow, and construct a high-dimensional heat source data set;

[0149] In this implementation, S1 specifically includes:

[0150] S11. Collect heat generation rate data of battery modules under different charge and discharge cycles , where t represents the time point in the charge-discharge cycle, represents the heat generation rate at that point in time;

[0151] S12. Collect heat generation rate data of battery modules at different power outputs , where p represents the power output level, represents the heat generation rate at that power level;

[0152] S13. Collect thermal interaction force data between battery cells , where i and j represent the indices of different battery cells, represents the thermal interaction force of unit i on unit j;

[0153] S14. Collect data on the effect of battery module geometry on heat flow and construct geometric influence factors ,in Represents the spatial coordinates of a point inside the battery module, Indicates the heat flow influence factor of the point;

[0154] S14, combining the data collected from S11-S14 to construct a high-dimensional heat source dataset:

[0155] ;

[0156] in, It is a high-dimensional heat source dataset that contains the comprehensive thermal characteristics information of the battery module under different working conditions.

[0157] S2. Collect the temperature gradient data of the battery module in the micro-environment and the influence rate of humidity change on the heat transfer efficiency, and construct a micro-environment condition data set;

[0158] In this implementation, S2 specifically includes:

[0159] S21. Collect temperature gradient data of the microenvironment where the battery module is located ,in Represents the coordinates of the space where the battery module is located, represents the temperature gradient at that point;

[0160] S22. Collect data on the effect of humidity changes in the microenvironment where the battery module is located on the heat transfer efficiency , where h represents the ambient humidity, Indicates the effect rate of humidity on heat transfer efficiency;

[0161] S23. Combine the data collected in S21-S22 to construct a micro-environmental condition data set:

[0162] ;

[0163] in, It is a micro-environmental condition dataset, which contains the comprehensive impact information of the temperature gradient and humidity changes in the micro-environment where the battery module is located on the heat transfer efficiency.

[0164] S3. Based on the high-dimensional heat source dataset and micro-environmental condition dataset, a graphical model representing the battery module and its complex thermal environment is constructed;

[0165] In this implementation, S3 specifically includes:

[0166] S31. Initialize the graph model :

[0167] ;

[0168] Among them, the graph model By node collection and edge set The components are used to represent the battery module and its complex thermal environment. The nodes are used to represent the heat sources and micro-environmental conditions in the battery module, and the edges are used to represent the heat exchange paths between the heat sources and between the heat sources and the environment.

[0169] S32. Based on the high-dimensional heat source dataset and micro-environmental conditions dataset , respectively, as graph models The nodes and edges in the , thermal interaction force Geometry Impact Factor and ambient temperature gradient , Humidity influence rate ,Edge attributes include the heat exchange efficiency between two nodes, which is determined by the physical distance between the nodes and the environmental conditions;

[0170] S32. Apply physical rules and graph theory principles to each node, calculate the heat exchange efficiency between nodes in combination with node attributes and the relative position relationship between nodes, and determine the optimal heat flow path:

[0171] ;

[0172] in, represents the heat exchange efficiency between node i and node j, represents the distance between nodes, Used to prevent division by zero. It represents the heat exchange influencing factor after comprehensive consideration of geometric influence and environmental conditions;

[0173] S34. Using the graph model The structural characteristics of the system are used to identify key nodes and key paths. Key nodes are key areas where heat is concentrated or dispersed, and key paths are priority paths for heat transfer.

[0174] S35. Complete the graphical model representing the battery module and its complex thermal environment Construction of graph model Reflects the heat flow inside and outside the battery module.

[0175] In this implementation, the node attribute of the heat generation rate is expressed as:

[0176] ;

[0177] in, and is a coefficient used to adjust the weight of different heat source data. and Represent the time point and power output level in the charge and discharge cycle respectively;

[0178] The nodal properties of the thermal interaction force are expressed as:

[0179] ;

[0180] in, It is the coefficient that adjusts the influence of thermal interaction forces between different battery cells;

[0181] The composite properties of geometric influencing factors and environmental conditions are expressed as:

[0182] ;

[0183] in, , and It is the weight coefficient, which is used to adjust the influence of geometric factors, temperature gradient and humidity change on heat transfer efficiency.

[0184] In this implementation, S34 specifically includes:

[0185] S341. Define the graph model The capacity of each edge in is the maximum heat value that can pass through the edge, which is determined by the heat exchange efficiency of the edge and the heat capacity of the nodes at both ends:

[0186] ;

[0187] in, represents the heat exchange efficiency of edge e, and Represent the heat capacity of the nodes at both ends of edge e;

[0188] S342, select graph model The two nodes representing the heat input source and heat output source of the battery module are used as the source point S and the sink point T, and the minimum cut algorithm is applied to find the minimum cut set from S to T. , the minimum cut set The graph is divided into two parts, one of which contains the source point S and the other contains the sink point T, and the cut set The minimum total capacity is expressed as:

[0189] ;

[0190] Identify the weakest link in heat transfer in the battery module thermal management system, i.e. the path with the lowest heat transfer efficiency;

[0191] S343, according to the minimum cut set The recognition results of the graph model Optimize, adjust the layout of heat exchange paths, enhance the heat exchange capacity of key nodes, or improve the heat exchange efficiency of certain edges;

[0192] S344. After optimization, the minimum cut algorithm is applied again to evaluate the graph model performance.

[0193] S4. Apply the improved graph theory algorithm to conduct in-depth analysis on the constructed graph model to identify the optimal path of multi-dimensional heat flow. The improved graph theory algorithm is combined with minimizing the total thermal resistance of the system and dynamically adjusting the path to cope with changes in the external environment, so that heat is transferred from the heat source to the heat dissipation interface;

[0194] In this implementation, S4 specifically includes:

[0195] S41. Apply improved graph theory algorithms to the constructed graph model Conduct in-depth analysis and convert the graph model The optimization problem is defined as an objective function that combines the physical properties of the heat exchange path and the thermal conductivity characteristics to minimize the total thermal resistance of the system. , while ensuring that heat is effectively transferred from the heat source to the heat dissipation interface;

[0196] ;

[0197] in, represents the length of side e, is the thermal conductivity of the material on edge e, is the cross-sectional area of ​​side e;

[0198] S42. Define the total thermal resistance and the heat transfer capacity of each edge in the associated graph model:

[0199] ;

[0200] in, It is a graph model The set of middle edges;

[0201] S43, the function of dynamically adjusting the path is integrated into it by monitoring the temperature gradient and humidity , define the adjustment factor , which is used to update the heat conduction capacity of the edge in real time:

[0202] ;

[0203] ;

[0204] in, and Represent the changes in temperature gradient and humidity respectively. and is the adjustment parameter;

[0205] S44. Adjust the parameters of nodes and edges in the graph model according to the actual distribution of heat flow in the battery module. If it is found that the actual heat flow of some heat exchange paths is lower than expected, the algorithm will increase the priority of the path, otherwise it will decrease:

[0206] ;

[0207] Where P(e) is the current heat flow of edge e, is the expected heat flux on edge e, is the actual measured heat flow, is the adjustment coefficient;

[0208] S45. Identify the optimal path to ensure that the heat inside and between battery modules can be distributed according to preset parameters under various working and environmental conditions. The path not only minimizes the total thermal resistance, but also dynamically adapts to changes in the external environment and changes in internal heat sources:

[0209] ；

[0210] ；

[0211] ;

[0212] in, and Represents the temperature gradient and humidity changes Adjusted thermal conductivity and cross-sectional area changes.

[0213] S5. Based on the analysis results of the graph theory algorithm, design the layout of the heat exchange network, including determining the optimal path, size and distribution of heat pipes with adaptive heat transfer capabilities and the location and specifications of heat exchangers that are dynamically adjusted according to environmental conditions;

[0214] In this implementation, S5 specifically includes:

[0215] S51, based on the identified optimal path and adjusted thermal resistance parameters , design the preliminary layout of the heat exchange network based on the heat flow requirements within and between battery modules, as well as the adaptability to external environmental changes;

[0216] S52. Determine the optimal path, size and distribution of heat pipes with adaptive heat conduction capabilities, and use optimization formulas to calculate the layout of heat pipes and diameter parameter:

[0217] ;

[0218] in, is the length of side e, is the thermal conductivity of the material on edge e, is the cross-sectional area of ​​side e, which depends on the pipe diameter D;

[0219] S53, dynamically adjusting the position and specifications of the heat exchanger according to environmental conditions, monitoring environmental parameters in real time and adjusting the working state of the heat exchanger accordingly:

[0220] ;

[0221] ;

[0222] in, Indicates the adjusted heat exchanger specifications, is the initial specification, Based on the temperature gradient and humidity The amount of specification change adjusted by the change in and is the adjustment parameter;

[0223] S54. Based on the above parameters, the final design of the heat exchange network is completed.

[0224] S6. Use the simulation platform to conduct real-time dynamic testing to verify the performance of the designed heat exchange network under variable environments and working conditions, including the adaptability of thermal management, the steady-state and dynamic temperature response of the battery module, and the overall energy efficiency performance of the system;

[0225] In this implementation, S6 specifically includes:

[0226] S61. Use the simulation platform to set up a simulation environment for a battery module thermal management system, including variable environmental conditions and different charge and discharge cycles and power output levels;

[0227] S62. Deploy the designed heat exchange network in a simulation environment, including the layout, dimensions, and material property parameters of heat pipes and heat exchangers;

[0228] S63. Start the simulation platform to perform real-time dynamic testing, record the temperature response data of the battery module under various simulated environments and working conditions, including steady-state temperature and dynamic temperature changes, and calculate the steady-state and dynamic temperature responses:

[0229] ;

[0230] ;

[0231] in, represents the temperature of edge e under steady-state conditions, represents the dynamic temperature of edge e at time point t, is the ambient temperature, and They represent the steady-state and dynamic temperature changes on edge e due to the action of the heat source;

[0232] S64. Analyze the test data, evaluate the thermal management adaptability of the heat exchange network, the steady-state and dynamic temperature response of the battery module, and the overall energy efficiency performance of the system, paying attention to the adaptability of the heat exchange network to changes in environmental conditions and the efficiency of heat management under different working conditions.

[0233] S7. Optimizing the heat exchange network design based on the feedback from the simulation test, including adjusting the adaptive characteristics of the heat pipe, the dynamic adjustment mechanism of the heat exchanger, or other relevant parameters;

[0234] In this implementation, S7 specifically includes:

[0235] S71. Analyze the steady-state and dynamic temperature responses of the battery modules and the adaptability of the heat exchange network to changes in environmental conditions during simulation tests, and identify areas of insufficient performance or optimization potential, including heat pipes and heat exchangers with low heat exchange efficiency;

[0236] S72. For the identified optimization potential area, adjust the adaptive characteristics of the heat pipe, including changing the layout, size or material of the heat pipe, and use the following formula to calculate the improved heat transfer capacity of the heat pipe during the adjustment process:

[0237] ;

[0238] in, represents the thermal conductivity of the heat pipe after improvement, is the original thermal conductivity, Based on the temperature gradient ,humidity and other related parameters The adjustment value of

[0239] S73. Adjust the dynamic adjustment mechanism of the heat exchanger, change the position and size of the heat exchanger or use a different working fluid:

[0240] ;

[0241] in, Indicates the adjusted heat exchanger specifications, is the original specification, Based on performance feedback The amount of adjustment;

[0242] S74. Adjust other relevant parameters, including improving thermal interface materials and optimizing hot runner design.

[0243] S8. Complete the final design of the heat exchange network and integrate the design into the thermal management system of the new energy vehicle battery module.

[0244] In this embodiment, the problems existing in the prior art are addressed by accurately simulating and optimizing the heat transfer process inside the battery module and between modules to achieve balanced distribution of heat in the battery module, effectively reduce hot spots, and improve the working efficiency and life of the battery. At the same time, a dynamic adjustment mechanism is introduced to dynamically adjust the configuration of the heat exchange network according to the changes in battery usage status and external environmental conditions to achieve the best thermal management effect. In addition, by using a simulation platform for real-time dynamic testing, this method can fully verify and optimize the performance of the heat exchange network in the design stage, ensuring that the battery module can achieve the best thermal management effect in actual applications.

[0245] In some implementation scenarios, the main challenge facing engineers in new energy vehicle battery module thermal management projects is how to ensure that the temperature of the battery module is always kept within the ideal range under various environmental conditions and workloads to maximize the performance and life of the battery while ensuring safety. Traditional thermal management methods often lead to a decrease in thermal management performance under extreme conditions or at different power outputs due to the lack of flexibility and precise control, which not only affects the efficiency and life of the battery, but also may bring safety hazards.

[0246] In order to solve this problem, in the embodiment of the present application, a new energy vehicle battery module heat exchange network design method based on a graph theory algorithm is adopted, and a new energy vehicle test scenario in a hot desert environment is used as an example to illustrate.

[0247] In this scenario, new energy vehicles are tested under continuous high temperatures (average daily temperature reaches 40°C) and different driving conditions (including highway cruising and urban congestion). The battery modules equipped in the car need to maintain optimal working conditions under these extreme conditions to ensure that the vehicle's range and overall performance are not affected.

[0248] First, sensors are used to collect data on the heat generation rate of the battery module under different charge and discharge cycles and different power outputs, as well as the thermal interaction data between battery cells. At the same time, data on the temperature gradient and humidity changes in the surrounding environment are collected.

[0249] Using the collected data, a graphical model reflecting the battery module and its thermal environment was constructed. The model contains nodes and edges representing heat sources, heat flow paths, and environmental conditions.

[0250] An improved graph theory algorithm is applied to analyze the graph model, identify the optimal paths for heat transfer, and dynamically adjust these paths according to environmental changes.

[0251] Based on the results of the algorithm analysis, a heat pipe layout with adaptive heat transfer capabilities and dynamically adjusted heat exchanger specifications were designed.

[0252] The designed heat exchange network was subjected to real-time dynamic testing through a simulation platform. The test results showed that under extremely high temperatures and different driving conditions, the battery module can effectively maintain an ideal temperature range.

[0253] In tests in desert environments, the average temperature of the battery module under the traditional thermal management system reached 50.1°C during high-speed cruising. After adopting the heat exchange network designed based on graph theory algorithm, the average temperature of the battery module was maintained at 37.3°C, which is significantly lower than the traditional system.

[0254] Under urban congestion conditions, the average temperature of the battery module of the traditional system is 45.5°C, while the new system can control the temperature at 35.8°C. This temperature reduction directly affects the performance and life of the battery. Tests show that the battery efficiency has increased by 15.13%, and due to more precise temperature control, the expected service life of the battery has also increased by 20.32%.

[0255] The present invention constructs a graphical model that reflects the complexity of the battery thermal environment by accurately collecting heat source data and condition data inside the battery module and its microenvironment. It uses an improved graph theory algorithm to deeply analyze and identify the optimal path of multi-dimensional heat flow, significantly optimizing the distribution and transfer efficiency of heat inside the battery module and between modules, thereby effectively improving the working efficiency of the battery module and extending its service life.

[0256] The present invention takes into account the impact of changing environmental conditions on the performance of the heat exchange network. By dynamically adjusting the layout, size and other key parameters of heat pipes and heat exchangers in real time, the heat exchange network can adapt to different environmental conditions and working conditions, ensuring that the battery module is always within the optimal operating temperature range.

[0257] The present invention reduces unnecessary heat energy loss and improves the energy efficiency of the battery module by minimizing the total thermal resistance of the heat exchange network and optimizing the heat transfer path.

[0258] The present invention relies on real-time dynamic testing of the simulation platform to foresee and optimize potential thermal management problems in the design stage, greatly improving the reliability and stability of the thermal management system in practical applications. At the same time, through the analysis and feedback of the simulation test results, the design of the heat exchange network is further improved and adjusted to ensure that efficient thermal management performance can be maintained under various extreme or unstable environmental conditions.

[0259] See also Figure 8 , Figure 8 A structural block diagram of a battery heat exchange network generation system provided in an embodiment of the present application, wherein the battery heat exchange network generation system includes:

[0260] The heat source data module 100 is used to obtain the heat flow impact data of the battery module to be processed and construct a high-dimensional heat source data set;

[0261] The micro-environment data module 200 is used to obtain the heat transfer efficiency data of the battery module to be processed and construct a micro-environment condition data set;

[0262] A graphical model module 300, for constructing a graphical model corresponding to the battery module to be processed according to the high-dimensional heat source data set and the micro-environment condition data set;

[0263] The analysis module 400 is used to analyze and process the graph model according to a preset graph theory algorithm and identify the optimal path of multi-dimensional heat flow to obtain analysis results;

[0264] The exchange network generation module 500 is used to generate heat exchange network data of the battery module to be processed according to the analysis results.

[0265] Exemplarily, the battery module includes a plurality of battery cells, and the heat source data module 100 is specifically used for:

[0266] Acquiring first heat generation rate data of a battery module to be processed under multiple charge and discharge cycles;

[0267] Acquiring second heat generation rate data of the battery module to be processed under multiple power outputs;

[0268] Acquire thermal interaction force data between each battery cell in the battery module to be processed;

[0269] Obtaining data on the influencing factors of the geometric configuration of the battery module to be processed on the heat flow;

[0270] A high-dimensional heat source data set is constructed based on one or more of the first heat generation rate data, the second heat generation rate data, the thermal interaction force data, and the influencing factor data.

[0271] Exemplarily, the microenvironment data module 200 is specifically used for:

[0272] Obtain temperature gradient data of the battery module to be processed in the microenvironment;

[0273] Obtain data on the effect of humidity changes on heat transfer efficiency of the battery module to be processed in a microenvironment;

[0274] The micro-environmental condition data set is constructed based on the temperature gradient data and impact rate data.

[0275] Exemplarily, the graph model module 300 is specifically used for:

[0276] Obtaining an initialization graph model, the initialization graph model includes nodes and edges, the nodes are used to represent heat sources and micro-environmental conditions in the battery module to be processed, and the edges are used to represent heat exchange paths of the battery module to be processed;

[0277] According to the high-dimensional heat source data set and the micro-environmental condition data set, an attribute-assigned graph model is performed to obtain an attributed graph model;

[0278] Determine the best heat flow path based on the attribute graph model;

[0279] According to the structural characteristics of the attribute graph model, key nodes and key paths are identified, where key nodes are key areas where heat is concentrated or dispersed, and key paths are priority paths for heat transfer;

[0280] According to the optimal heat flow path, key nodes and critical paths, a graphical model corresponding to the battery module to be processed is constructed.

[0281] Exemplarily, the analysis module 400 is specifically used for:

[0282] The optimization problem of the graph model is defined as a total thermal resistance combining the physical properties and thermal conductivity characteristics of the heat exchange path according to a preset graph theory algorithm, wherein the total thermal resistance is related to the heat transfer capacity of each edge in the graph model;

[0283] Dynamically adjust the path operation according to the graph model to identify the optimal path and the adjusted thermal resistance parameters, where the optimal path minimizes the total thermal resistance and dynamically adapts to changes in the external environment and changes in the internal heat source;

[0284] According to the optimal path and the adjusted thermal resistance parameters, the analysis results are obtained.

[0285] Exemplarily, the switching network generation module 500 is specifically used for:

[0286] Based on the analysis results and the heat flow requirements of the battery module to be processed and the adaptability to changes in the external environment, an initial heat exchange network is obtained, the initial heat exchange network including heat pipes and heat exchangers;

[0287] Determine heat pipe data with adaptive heat transfer capability based on an initial heat exchange network;

[0288] Dynamically adjust the location and specifications of the heat exchanger according to environmental conditions and obtain heat exchanger data;

[0289] The heat exchange network data is obtained based on the heat pipe data and the heat exchanger data.

[0290] Exemplarily, the generation system of the battery heat exchange network further includes a feedback module, and the feedback module is used to:

[0291] Conduct real-time dynamic testing on heat exchange network data based on the simulation platform to obtain dynamic test data;

[0292] The heat exchange network data is feedback optimized and adjusted according to the dynamic test data to obtain optimized heat exchange network data.

[0293] It should be noted that the generation system of the battery heat exchange network provided in the embodiment of the present application is Figures 1 to 7 The method embodiments shown correspond to each other and will not be described again here to avoid repetition.

[0294] This application also provides an electronic device, see Fig. 9 , Fig. 9 A block diagram of an electronic device provided in an embodiment of the present application. The electronic device may include a processor 510, a communication interface 520, a memory 530, and at least one communication bus 540. The communication bus 540 is used to realize direct connection and communication between these components. The communication interface 520 of the electronic device in the embodiment of the present application is used to communicate signaling or data with other node devices. The processor 510 may be an integrated circuit chip with signal processing capabilities.

[0295] The processor 510 mentioned above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor 510 can also be any conventional processor, etc.

[0296] The memory 530 may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. The memory 530 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 510, the electronic device may execute the above-mentioned Figures 1 to 7 The method embodiment involves various steps.

[0297] Optionally, the electronic device may further include a storage controller and an input / output unit.

[0298] The memory 530, storage controller, processor 510, peripheral interface, input and output unit components are directly or indirectly electrically connected to each other to achieve data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses 540. The processor 510 is used to execute executable modules stored in the memory 530, such as software function modules or computer programs included in the electronic device.

[0299] The input and output unit is used to provide users with the task creation and to create a start optional time period or preset execution time for the task to realize the interaction between the user and the server. The input and output unit can be, but is not limited to, a mouse and a keyboard.

[0300] Understandably, Fig. 9 The structure shown is for illustration only, and the electronic device may also include Fig. 9 More or fewer components as shown, or with Fig. 9 Different configurations are shown. Fig. 9 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0301] An embodiment of the present application further provides a storage medium having instructions stored thereon. When the instructions are run on a computer, the computer program is executed by a processor to implement the method described in the method embodiment. To avoid repetition, the method will not be described here.

[0302] The present application also provides a computer program product, which, when executed on a computer, enables the computer to execute the method described in the method embodiment.

[0303] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0304] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0305] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0306] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0307] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0308] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A method for generating a battery heat exchange network, characterized in that: include: Obtain the heat flow impact data of the battery module to be processed and construct a high-dimensional heat source data set; Obtain the heat transfer efficiency data of the battery module to be processed and construct a micro-environmental condition data set; Constructing a graphical model corresponding to the battery module to be processed according to the high-dimensional heat source data set and the micro-environmental condition data set; Analyze and process the graph model according to a preset graph theory algorithm and identify the optimal path of multi-dimensional heat flow to obtain analysis results; Generate heat exchange network data of the battery module to be processed according to the analysis result; The battery module includes a plurality of battery cells. The steps of obtaining heat flow influence data of the battery module to be processed and constructing a high-dimensional heat source data set include: Acquiring first heat generation rate data of the battery module to be processed under multiple charge and discharge cycles; Acquiring second heat generation rate data of the battery module to be processed under multiple power outputs; Acquiring thermal interaction force data between each battery cell in the battery module to be processed; Obtaining data on the influencing factors of the geometric configuration of the battery module to be processed on the heat flow; constructing a high-dimensional heat source data set according to one or more of the first heat generation rate data, the second heat generation rate data, the thermal interaction force data, and the influencing factor data; The steps of obtaining the heat transfer efficiency data of the battery module to be processed and constructing a micro-environmental condition data set include: Acquiring temperature gradient data of the battery module to be processed in a microenvironment; Acquire data on the effect of humidity change on heat transfer efficiency of the battery module to be processed under a microenvironment; A micro-environmental condition data set is constructed according to the temperature gradient data and the impact rate data.

2. The method for generating a battery heat exchange network according to claim 1, characterized in that: The step of constructing a graphical model corresponding to the battery module to be processed according to the high-dimensional heat source data set and the micro-environmental condition data set includes: Acquire an initialization graph model, the initialization graph model comprising nodes and edges, the nodes are used to represent heat sources and micro-environmental conditions in the battery module to be processed, and the edges are used to represent heat exchange paths of the battery module to be processed; Performing an attribute assignment operation on the initialization graph model according to the high-dimensional heat source data set and the micro-environment condition data set to obtain an attributed graph model; determining an optimal heat flow path according to the attribute graph model; According to the structural characteristics of the attribute graph model, identifying key nodes and key paths, wherein the key nodes are key areas where heat is concentrated or dispersed, and the key paths are priority paths for heat transfer; A graphical model corresponding to the battery module to be processed is constructed according to the optimal heat flow path, the key nodes and the key path.

3. The method for generating a battery heat exchange network according to claim 1, characterized in that: The step of analyzing and processing the graph model according to a preset graph theory algorithm and identifying the optimal path of multi-dimensional heat flow to obtain the analysis result includes: defining the optimization problem of the graph model as a total thermal resistance combining the physical properties and thermal conductivity characteristics of the heat exchange path according to a preset graph theory algorithm, wherein the total thermal resistance is associated with the heat transfer capacity of each edge in the graph model; Dynamically adjust the path operation according to the graph model to identify the optimal path and the adjusted thermal resistance parameters, wherein the optimal path minimizes the total thermal resistance and the optimal path dynamically adapts to changes in the external environment and changes in the internal heat source; According to the optimal path and the adjusted thermal resistance parameters, an analysis result is obtained.

4. The method for generating a battery heat exchange network according to claim 1, characterized in that: The step of generating heat exchange network data of the battery module to be processed according to the analysis result includes: Based on the analysis results and in combination with the heat flow requirements of the battery module to be processed and the adaptability to changes in the external environment, an initial heat exchange network is obtained, wherein the initial heat exchange network includes a heat pipe and a heat exchanger; Determining heat pipe data with adaptive heat transfer capability according to the initial heat exchange network; Dynamically adjust the position and specifications of the heat exchanger according to environmental conditions to obtain heat exchanger data; Heat exchange network data is obtained according to the heat pipe data and the heat exchanger data.

5. The method for generating a battery heat exchange network according to claim 1, characterized in that: After the step of generating the heat exchange network data of the battery module to be processed according to the analysis result, the method further includes: Performing real-time dynamic testing on the heat exchange network data based on a simulation platform to obtain dynamic test data; Feedback optimization and adjustment are performed on the heat exchange network data according to the dynamic test data to obtain optimized heat exchange network data.

6. A system for generating a battery heat exchange network, characterized in that: include: The heat source data module is used to obtain the heat flow impact data of the battery module to be processed and construct a high-dimensional heat source data set; The micro-environment data module is used to obtain the heat transfer efficiency data of the battery module to be processed and construct a micro-environment condition data set; A graphical model module, used to construct a graphical model corresponding to the battery module to be processed according to the high-dimensional heat source data set and the micro-environment condition data set; An analysis module, used to analyze and process the graph model according to a preset graph theory algorithm and identify the optimal path of multi-dimensional heat flow to obtain analysis results; A heat exchange network generation module, used to generate heat exchange network data of the battery module to be processed according to the analysis result; The battery module includes a plurality of battery cells, and the heat source data module is specifically used for: Acquiring first heat generation rate data of the battery module to be processed under multiple charge and discharge cycles; Acquiring second heat generation rate data of the battery module to be processed under multiple power outputs; Acquiring thermal interaction force data between each battery cell in the battery module to be processed; Obtaining data on the influencing factors of the geometric configuration of the battery module to be processed on the heat flow; constructing a high-dimensional heat source data set according to one or more of the first heat generation rate data, the second heat generation rate data, the thermal interaction force data, and the influencing factor data; The microenvironment data module is specifically used for: Acquiring temperature gradient data of the battery module to be processed in a microenvironment; Acquire data on the effect of humidity change on heat transfer efficiency of the battery module to be processed under a microenvironment; A micro-environmental condition data set is constructed according to the temperature gradient data and the impact rate data.

7. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for generating a battery heat exchange network as claimed in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the method for generating a battery heat exchange network according to any one of claims 1 to 5.

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