Thermal management method and system for liquid-cooled battery pack

Abstract

The invention relates to the technical field of battery management, in particular to a liquid-cooled battery pack thermal management method and system. The method comprises the following steps: acquiring information data of a battery pack; performing unit area thermal load analysis on the battery pack information data to generate unit area thermal load data; performing cooling liquid multi-stage shunting pipeline layout according to the thermal load data of the battery unit region, and generating initial cooling liquid pipeline layout data; and performing local thermal aggregation index calculation on the initial cooling liquid pipeline layout data to obtain a local area thermal aggregation index. Through accurate thermal load analysis, cooling liquid pipeline layout optimization, flow control and backflow control, the problems of uneven cooling, low efficiency and inaccurate control in thermal management of a traditional liquid-cooled battery pack are solved, and a more efficient and intelligent thermal management system is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a liquid-cooled battery pack thermal management method and system. Background Art

[0002] With the rapid development of electric vehicle technology, the increasing energy density of batteries has brought about thermal management challenges. Traditional air cooling methods, due to their poor cooling performance, are unable to meet the growing heat dissipation requirements, especially in high-power, high-density battery applications. To address this issue, liquid cooling systems have gradually emerged as a more efficient thermal management method. Liquid cooling technology originated in early industrial applications and has gradually evolved into battery pack cooling. Initially, liquid cooling mainly removed heat from the battery through direct contact with a liquid, which then transferred heat through a heat exchanger to maintain a stable operating temperature. With advances in battery technology, liquid cooling systems have also continued to improve, optimizing coolant selection, fluid dynamics design, and the integration of thermal management modules. Currently, the application of liquid cooling systems in battery thermal management has reached a relatively mature stage. By utilizing a highly thermally conductive liquid and innovative cooling plate design, liquid cooling systems can effectively control battery temperature fluctuations, preventing overheating and overcooling, thereby improving battery charge and discharge efficiency and cycle life. However, the current traditional coolant pipeline layout often relies on empirical design, which has problems such as poor coolant flow and low efficiency. At the same time, coolant flow control is usually difficult to accurately adjust, which can easily lead to local overheating or coolant waste. Summary of the Invention

[0003] Based on this, it is necessary to provide a liquid-cooled battery pack thermal management method and system to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a liquid-cooled battery pack thermal management method is provided, the method comprising the following steps: Step S1: Obtaining battery pack information data; performing unit area heat load analysis on the battery pack information data to generate unit area heat load data; performing multi-stage coolant diversion pipeline layout based on the battery unit area heat load data to generate initial coolant pipeline layout data; Step S2: Calculating a local heat concentration index on the initial coolant pipeline layout data to obtain a local area heat concentration index; optimizing the initial coolant pipeline layout data using the local area heat concentration index to generate annular flow coolant layout optimization data or directional flow coolant layout optimization data; and performing multi-stage flow splitting pipeline layout linkage on the initial coolant pipeline layout data based on the annular flow coolant layout optimization data or the directional flow coolant layout optimization data to generate battery pack cooling pipeline layout optimization data; Step S3: Mapping the battery pack cooling line layout optimization data to generate cooling line mapping data; predicting the remaining coolant amount in the cooling line mapping data to generate coolant line remaining data; performing normal coolant flow control on the cooling line mapping data based on the unit area heat load data to generate battery pack coolant flow control data; and performing coolant reflux control on the battery pack coolant flow control data using the coolant line remaining data to generate coolant reflux control data. Step S4: Using the battery pack coolant flow control data and the coolant reflux control data to evaluate the battery thermal management cooling effect on the unit area heat load data, generate a battery thermal management report, and perform liquid-cooled battery pack thermal management operations.

[0005] By acquiring detailed battery pack information and performing unit-area heat load analysis, this method accurately identifies the heat requirements of different regions. Based on this data, a more optimal coolant piping layout can be developed, ensuring that the initial design of the thermal management system meets the battery pack's heat dissipation requirements. By calculating the local heat concentration index and optimizing the coolant piping layout (circular or directional flow), the system effectively improves coolant flow efficiency within the battery pack, reduces hotspots, enhances cooling efficiency, and ensures uniform heat distribution, thereby enhancing the battery pack's thermal management effectiveness. By mapping the cooling piping and predicting the remaining coolant volume, the system precisely controls coolant flow and recirculation within the piping, ensuring optimal coolant flow in each region. This not only improves coolant utilization efficiency but also optimizes the recirculation system, avoiding coolant waste and enhancing the stability of the overall thermal management system. By evaluating coolant flow and recirculation control data, the system can comprehensively understand the battery pack's thermal management effectiveness, identify potential issues, and optimize control strategies. The resulting thermal management report provides data support for further improvements to the battery pack's heat dissipation design and operation, thereby ensuring battery pack safety, performance, and service life. Therefore, the present invention solves the problems of uneven cooling, low efficiency and inaccurate control in the thermal management of traditional liquid-cooled battery packs through precise heat load analysis, coolant pipeline layout optimization, flow control and reflux control, and realizes a more efficient and intelligent thermal management system.

[0006] Preferably, step S1 includes the following steps: Step S11: Obtaining battery pack information data; Step S12: performing a structural topology analysis on the battery pack information data to generate battery pack structural topology data; performing battery cell area division on the battery pack information data based on the battery pack structural topology data to generate battery cell area division data; Step S13: using a temperature sensor to perform unit area heat load analysis on the battery unit area division data to generate unit area heat load data; Step S14: Layout the multi-stage coolant pipelines for the battery pack structure topology data using the battery unit area heat load data to generate initial coolant pipeline layout data.

[0007] By analyzing the structural topology of the battery pack, the present invention can understand the overall layout of the battery pack and the relationship between the cells, which provides a solid foundation for subsequent regional division and heat load analysis. The battery cell area division based on the structural topology data enables the heat load of different areas to be analyzed independently, providing a flexible solution for thermal management. By using a temperature sensor to perform heat load analysis on each unit area, the temperature distribution of the battery cell can be monitored in real time, potential overheating problems can be discovered in a timely manner, and a decision-making basis can be provided for the battery management system. According to the heat load data of the battery cell area, the layout of the coolant pipeline is optimized, and a multi-level shunt pipeline design can be implemented to ensure that the coolant can be evenly distributed to each area with a higher heat load, enhance cooling efficiency, and avoid local overheating. Through accurate heat load analysis and coolant pipeline optimization, battery overheating problems can be effectively prevented and safety accidents caused by thermal runaway can be avoided. Accurate regional division and coolant shunt design can achieve more efficient thermal management, extend battery life and improve overall performance.

[0008] Preferably, step S13 includes the following steps: Step S131: using a temperature sensor to collect battery cell area temperature data on the battery cell area division data to obtain battery cell temperature collection data; performing data preprocessing on the battery cell temperature collection data to generate standard battery cell temperature collection data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling, and data standardization; Step S132: extracting the geometric shape, thermal conductivity characteristics, and battery material properties of the battery pack information data to construct a thermal conduction model of the battery cell area, and importing the standard battery cell temperature acquisition data into the thermal conduction model of the battery cell area to perform heat load information conversion to generate battery thermal load information data; Step S133: Calculating the regional heat load of the battery heat load information data using a heat conduction equation to obtain the regional heat load of the battery unit; performing heat load mapping on the battery unit regional division data based on the regional heat load of the battery unit to generate battery unit heat load mapping data; Step S134: performing regional temperature time series characteristic analysis on the battery unit thermal load mapping data to generate regional temperature time series characteristic data; identifying hot spots on the battery unit thermal load mapping data using the regional temperature time series characteristic data to generate unit regional thermal load data.

[0009] This invention uses temperature sensors to collect temperature data from battery cell regions, providing foundational data for subsequent thermal load analysis. Data cleaning, denoising, missing value filling, and data standardization during preprocessing effectively remove unnecessary interference, ensuring the accuracy and reliability of the collected data. A thermal conduction model is constructed by extracting the battery pack's geometry, thermal conductivity characteristics, and battery material properties. Standardized battery cell temperature data is then input into the model for thermal load conversion. This process considers the battery's physical and material properties, resulting in more accurate thermal load calculations. Regional thermal loads are calculated using heat conduction equations, and thermal load mapping is used to generate battery cell thermal load mapping data. This step, through model calculation and mapping, provides quantitative data support for the thermal loads of different regions, facilitating the identification of high-temperature areas. Regional temperature time-series analysis of the thermal load mapping data extracts time-series feature data that helps identify hotspots. This analysis helps identify areas of thermal anomalies within the battery pack and allows for timely adjustment of cooling strategies. Temperature acquisition and data preprocessing yield highly accurate battery cell temperature data, ensuring accurate capture of temperature variations. The thermal conduction model and thermal load calculation enable precise thermal load assessment, thereby optimizing the battery pack's thermal management strategy. Through time series feature analysis and hotspot area identification, potential hotspot areas can be monitored and accurately located in real time, providing data basis for optimizing the cooling system and improving battery safety.

[0010] Preferably, step S2 includes the following steps: Step S21: performing a coolant flow path analysis on the initial coolant pipeline layout data to generate coolant flow path data; performing a coolant flow velocity distribution analysis on the coolant flow path data to generate coolant flow velocity distribution data; Step S22: using the coolant flow rate distribution data to identify the cooling uneven area of ​​the unit area heat load data, and generating the cooling uneven area identification data; calculating the local heat concentration index of the cooling uneven area identification data, and obtaining the local area heat concentration index; Step S23: Comparing the local area heat concentration index with a preset standard area heat concentration threshold. When the local area heat concentration index is less than or equal to the preset standard area heat concentration threshold, optimizing the annular flow coolant pipeline layout for the corresponding uneven cooling area identification data to generate annular flow coolant layout optimization data. Step S24: When the local area heat concentration index is greater than a preset standard area heat concentration threshold, the directional heat conduction pipeline layout optimization is performed on the corresponding cooling uneven area identification data to generate directional flow coolant layout optimization data; Step S25: Based on the annular flow coolant layout optimization data and the directional flow coolant layout optimization data, the initial coolant pipeline layout data is designed for coolant diversion points, and coolant hierarchical diversion valve setting data is generated; the initial coolant pipeline layout data is linked to a multi-level diversion pipeline layout through the coolant hierarchical diversion valve setting data to generate battery pack cooling pipeline layout optimization data.

[0011] This invention analyzes the coolant piping flow path to ensure efficient coolant flow through each battery cell area. Coolant flow velocity distribution analysis clearly identifies differences in coolant flow velocity across different areas, providing a basis for subsequent optimization design. Flow velocity distribution data identifies areas of uneven cooling, which can lead to localized overheating. Furthermore, calculation of the local heat accumulation index quantifies the heat load in different areas, providing detailed information for optimizing the coolant flow path. By comparing the local heat accumulation index with a preset threshold, a decision is made regarding whether to optimize the coolant piping design with a circular flow pattern. When the local heat accumulation index is low, a circular layout ensures coolant flow in multiple directions, distributing heat more evenly. When the local heat accumulation index is high, a directional flow piping layout is optimized. This layout directs coolant to areas with higher heat loads, providing targeted cooling and effectively reducing the risk of overheating. Coolant flow paths are further optimized by designing coolant diversion points based on the optimized results of different coolant layouts. By configuring hierarchical coolant diversion valves, coolant flow can be adjusted for different areas with different cooling requirements, improving system flexibility and efficiency. Precise analysis of coolant flow rates and flow paths allows for effective optimization of coolant distribution, avoiding localized overheating and heat accumulation, and ensuring uniform temperature across the battery cells. Optimization of circular and directional coolant piping ensures effective cooling in areas with high heat loads, preventing battery damage or system failures caused by overheating. A multi-stage diversion piping layout and the coordinated design of hierarchical diversion valves allow for adjustment of coolant flow to meet the needs of different areas, improving the system's adaptability and responsiveness.

[0012] Preferably, step S23 includes the following steps: Step S231: Comparing the local area heat concentration index with a preset standard area heat concentration threshold. When the local area heat concentration index is less than or equal to the preset standard area heat concentration threshold, locating the highest heat concentration area of ​​the battery pack based on the corresponding uneven cooling area identification data to generate highest heat concentration area location data. Step S232: performing a heat accumulation gradient analysis on the local area heat accumulation index based on the highest heat accumulation area location data to generate local area heat accumulation gradient data; using the highest heat accumulation area location data as the inlet of the annular flow pipeline, and using the local area heat accumulation gradient data to perform a pipeline closed loop design on the inlet of the annular flow pipeline to generate a flow path of the annular flow pipeline; Step S233: Calculate the number of coolant circulation and heat dissipation turns of the annular flow pipeline flow path to obtain the number of coolant circulation and heat dissipation turns; optimize the annular flow coolant pipeline layout of the annular flow pipeline flow path using the number of coolant circulation and heat dissipation turns to generate annular flow coolant layout optimization data.

[0013] By comparing the heat accumulation index of a local area with a preset threshold, the present invention can determine which areas experience significant heat loads. When the heat accumulation index of a local area falls below the threshold, the area with the highest heat accumulation in the battery pack is located, providing a clear target area for subsequent optimization of the cooling circuit layout. Based on the location data of the highest heat accumulation area, the local heat accumulation index is subjected to a heat accumulation gradient analysis. This step identifies the changing trend of heat load in the battery pack and ensures that coolant effectively flows to the area with the highest heat load. Furthermore, the area with the highest heat accumulation serves as the inlet of the annular flow pipe, and a closed loop design is implemented at the pipe inlet based on the heat accumulation gradient, allowing the coolant to form an efficient circulation path within the area. By calculating the number of coolant circulation and heat dissipation turns within the annular flow pipe, effective circulation and heat dissipation of the coolant within the flow path are ensured. This optimization process effectively improves cooling efficiency and prevents localized overheating. By optimizing the pipe layout, the flow uniformity and overall heat dissipation capacity of the cooling system are improved. By comparing the heat accumulation index with the threshold, the hottest areas in the battery pack that require the most cooling are accurately identified, ensuring that cooling resources are prioritized to the most critical areas, thereby improving cooling efficiency. The ring-shaped piping design, combined with thermal gradient analysis, creates a closed, circulating flow path for the coolant in areas with high heat loads, effectively improving heat exchange efficiency and ensuring even coolant distribution. By calculating and optimizing the number of coolant circulation cycles, the coolant is ensured to fully circulate within the piping and remove heat, avoiding equipment failures caused by localized overheating. The optimized layout of the ring-shaped coolant piping results in greater efficiency and stability within the coolant flow path, effectively addressing the thermal management needs of batteries under high loads.

[0014] Preferably, step S24 includes the following steps: Step S241: When the local area heat concentration index is greater than a preset standard area heat concentration threshold, regional heat source positioning is performed on the corresponding uneven cooling area identification data to generate regional heat source positioning data; Step S242: Perform initial directional pipeline design based on the regional heat source location data to obtain initial directional pipeline design data; perform directional pipeline heat conduction efficiency calculation on the initial directional pipeline design data to obtain directional pipeline heat conduction efficiency, wherein the formula for calculating the directional pipeline heat conduction efficiency is as follows: ; Where, is the amount of heat conducted, is the thermal conductivity of the pipe material, is the cross-sectional area of ​​the pipe, is the temperature of the coolant entering the pipe, is the temperature of the coolant flowing out of the pipe, is the length of the pipe; Step S243: Optimizing the directional heat conduction pipeline layout of the initial directional pipeline design data based on the directional pipeline heat conduction efficiency to generate directional flow coolant layout optimization data.

[0015] The present invention identifies the specific location of heat sources within a battery pack when the local heat concentration index exceeds a preset threshold. This helps target high-temperature areas requiring specific cooling, ensuring optimal allocation of cooling resources and avoiding uneven cooling. Based on the regional heat source location data, an initial directional piping design is performed to ensure that coolant flows precisely to the heat source area. Next, the directional piping's heat conduction efficiency is calculated to ensure the pipes effectively conduct heat. Based on the calculated heat conduction efficiency, the initial directional piping design is optimized. The optimized design ensures that coolant flows in a targeted manner in areas with high heat loads, improving cooling effectiveness and reducing localized overheating. The optimized directional piping layout improves the efficiency and reliability of the cooling system. Heat source location allows precise identification of areas with high heat loads, providing a foundation for subsequent directional cooling piping design. This prioritizes coolant flow to areas most in need of cooling, reducing unnecessary waste of cooling resources. Calculating the directional piping's heat conduction efficiency optimizes piping material selection and layout, ensuring that the coolant effectively removes heat. Accurately calculating heat conduction minimizes coolant temperature fluctuations and reduces heat accumulation. The optimized directional pipe layout ensures that coolant effectively covers high-temperature areas, improving heat dissipation. This prevents localized overheating and enhances the stability and safety of the battery pack. The optimized directional pipe layout promotes efficient coolant flow, reducing energy waste caused by overcooling and uneven cooling. This improves the energy efficiency of the entire cooling system and reduces system operating costs.

[0016] Preferably, step S3 includes the following steps: Step S31: confirming the pipeline connection points of the battery pack cooling pipeline layout optimization data to obtain the battery pack cooling pipeline connection points; deploying coolant flow monitoring nodes based on the battery pack cooling pipeline connection points to generate coolant flow monitoring node deployment data; Step S32: performing a coolant flow time series analysis on the coolant flow monitoring node deployment data to generate coolant flow time series data; performing a trend segment extraction on the coolant flow time series data to obtain a coolant flow change trend segment; Step S33: mapping the battery pack cooling pipe layout optimization data to cooling pipe segments using the coolant flow rate change trend segments to generate cooling pipe mapping data; predicting the remaining coolant amount in the cooling pipe mapping data using the coolant residual amount calculation formula to generate coolant pipe residual data; Step S34: performing normal coolant flow control on the cooling pipe mapping data according to the unit area heat load data to generate battery pack coolant flow control data; performing coolant reflux control on the battery pack coolant flow control data according to the coolant pipe residual data to generate coolant reflux control data.

[0017] This invention identifies key interconnected locations within the battery pack cooling system by identifying pipeline connectivity points. Based on these connectivity points, coolant flow monitoring nodes are deployed to monitor the coolant flow status and flow rate changes in real time. Coolant flow monitoring data is crucial for subsequent optimization and control, ensuring the uniformity and efficiency of coolant flow. Time-series analysis of coolant flow helps identify trends and patterns in flow rate changes. Trend segment extraction further identifies the characteristics of coolant flow changes over different time periods. This process allows for specific focus on time periods or areas with high heat loads, ensuring that coolant flows more efficiently during these periods. Coolant flow trend segments are used to optimize the cooling pipeline layout. This step helps understand the coolant flow status within the pipeline, enabling more precise optimization. Furthermore, a coolant residual volume calculation formula is used to predict the coolant residual volume in each pipeline segment. This helps identify pipelines with insufficient coolant or poor flow, allowing for timely system adjustments. Based on the heat load data of the unit area, the cooling pipeline flow is controlled to ensure that the coolant flows along the optimized path, providing optimal cooling results. Further combined with the residual data of the coolant pipeline, reflux control is used to ensure that the coolant in the pipeline can flow back to the system in time, avoiding insufficient or excessive loss of coolant.

[0018] Preferably, predicting the remaining amount of coolant in the cooling pipeline based on the cooling pipeline mapping data includes: Extract the coolant resistance characteristics from the cooling pipeline mapping data to obtain coolant flow resistance characteristic data; use computational fluid dynamics to simulate the pipeline flow on the cooling pipeline mapping data to generate coolant residence time simulation data and coolant flow rate simulation data; The coolant residual amount calculation formula is used to calculate the coolant residual amount based on the coolant flow resistance characteristic data, coolant residence time simulation data, and coolant flow rate simulation data to obtain the coolant pipeline residual data. The coolant residual amount calculation formula is as follows: ; in, is the residual amount in the coolant pipeline, is the outlet fluid pressure at a certain point in the pipeline, is the inlet fluid pressure at a certain point in the pipeline, is the flow resistance of a certain section of the pipeline, is the length of the pipe.

[0019] The present invention extracts the resistance characteristic data of the coolant flow from the cooling pipeline mapping data. Flow resistance is the main source of resistance to liquid flow in the pipeline, which is affected by factors such as the size, material, and curvature of the pipeline. Accurately extracting these resistance characteristic data helps to predict the flow condition of the coolant in the pipeline. Computational fluid dynamics (CFD) is used to simulate the flow of the pipeline to generate coolant residence time and flow velocity simulation data. CFD simulation can take into account the complex flow behavior of the fluid in the pipeline, identify phenomena such as uneven flow velocity, dead zones, and vortices that appear in the pipeline, and thus help analyze the flow state and distribution of the coolant. According to the coolant residual amount calculation formula, the coolant resistance characteristic data, residence time simulation data, and flow velocity simulation data are comprehensively calculated to obtain the coolant pipeline residual data. In the formula, For Represent the pressure at the inlet and outlet of the pipeline respectively, is the flow resistance in a certain section of the pipeline, is the length of the pipe. By integrating the formula, we can get the residual amount of coolant in the pipe , which helps analyze whether the coolant can cover the entire system or whether there is insufficient coolant flow. By extracting the resistance characteristics of the coolant flow and performing flow simulation, the flow of the coolant in each part of the pipeline can be accurately understood, which can help identify "dead zones" or low flow rate areas in the pipeline, thereby optimizing the distribution of the coolant and ensuring that all parts of the battery pack are cooled evenly. By calculating the residual coolant volume, it is possible to predict which pipeline sections have insufficient coolant. By timely adjusting the coolant flow and distribution strategy, it is ensured that each area is adequately cooled, the overall thermal management effect is optimized, local overheating is prevented, and the performance and life of the battery are improved.

[0020] Preferably, step S4 includes the following steps: Step S41: using the battery pack coolant flow control data and the coolant reflux control data to evaluate the battery thermal management cooling effect on the unit area heat load data, and generating battery thermal management cooling effect evaluation data; Step S42: Upload the battery thermal management cooling effect evaluation data to the cloud platform for data storage to generate battery thermal management storage data; visualize the battery thermal management storage data to generate a battery thermal management report to perform liquid-cooled battery pack thermal management operations.

[0021] This invention utilizes coolant flow control data and coolant return control data, combined with battery cell thermal load data, to evaluate the cooling effectiveness of the entire battery pack. This step, by adjusting flow and return control, analyzes temperature variations in the battery cell area and the efficiency of the coolant, thereby determining the cooling system's performance under different operating conditions. This ensures that the cooling system effectively regulates and distributes coolant under various loads, avoiding heat accumulation or overcooling, and maintaining the ideal temperature range across all battery pack areas. This evaluation allows for timely identification of potential system issues, such as uneven cooling and poor return flow, allowing necessary optimization. The battery thermal management cooling effectiveness evaluation data is uploaded to a cloud platform for efficient centralized storage and management. The purpose of this data storage is to support subsequent long-term monitoring, performance tracing, and system optimization. The storage and centralized management of the cloud platform facilitates the long-term tracking and analysis of battery pack thermal management data. The cloud platform provides real-time access to the system's operating status, facilitating remote monitoring and operation, which is crucial for the maintenance and scheduling of large-scale battery packs. The battery thermal management data is visualized to generate clear and intuitive battery thermal management reports. These reports include detailed cooling performance, temperature trends in each area, and changes in coolant flow and return flow, providing a direct reflection of system performance. Through data visualization, managers can more easily understand and analyze the operating status of the battery pack, providing a basis for decision-making. Visualized reports make complex thermal management data concise and easy to understand, and through charts and trend lines, potential risk points can be quickly identified, providing a reference for system optimization.

[0022] In this specification, a liquid-cooled battery pack thermal management system is provided, which is used to implement the above-mentioned liquid-cooled battery pack thermal management method. The liquid-cooled battery pack thermal management system includes: An initial layout module is used to obtain battery pack information data; perform unit area heat load analysis on the battery pack information data to generate unit area heat load data; perform multi-stage coolant diversion pipeline layout based on the battery unit area heat load data to generate initial coolant pipeline layout data; A pipeline optimization module is used to calculate the local heat concentration index of the initial coolant pipeline layout data to obtain the local area heat concentration index; optimize the coolant pipeline layout of the initial coolant pipeline layout data using the local area heat concentration index to generate annular flow coolant layout optimization data or directional flow coolant layout optimization data; and perform multi-stage diversion pipeline layout linkage on the initial coolant pipeline layout data based on the annular flow coolant layout optimization data or the directional flow coolant layout optimization data to generate battery pack cooling pipeline layout optimization data; A flow control module is configured to map cooling pipe sections based on the battery pack cooling pipe layout optimization data to generate cooling pipe mapping data; predict the remaining coolant amount in the cooling pipe mapping data to generate coolant pipe residual data; perform normal coolant flow control on the cooling pipe mapping data based on the unit area heat load data to generate battery pack coolant flow control data; and perform coolant reflux control on the battery pack coolant flow control data using the coolant pipe residual data to generate coolant reflux control data; The thermal management evaluation module is used to evaluate the battery thermal management cooling effect by using the battery pack coolant flow control data and the coolant return control data on the unit area heat load data, generate a battery thermal management report, and perform liquid-cooled battery pack thermal management operations.

[0023] The beneficial effect of the present invention is that by obtaining battery pack information data and performing unit area heat load analysis, the coolant pipeline layout design can accurately respond to the thermal requirements of different areas of the battery cell, which can effectively prevent overheating due to uneven heat load and improve the overall heat dissipation efficiency and working stability of the battery pack. By calculating the local heat concentration index and optimizing the coolant pipeline layout, directional coolant flow optimization can be performed for the hot spot areas in the battery pack. Annular flow and directional flow layout optimization further improve the flow efficiency of the coolant and reduce the unevenness of the coolant distribution, thereby achieving all-round and efficient thermal management of the battery pack. Through precise coolant pipeline section mapping and residual amount prediction, more accurate coolant flow control and reflux control can be achieved, ensuring that the coolant always maintains a stable flow in each pipeline section and effectively returns to the cooling system, which not only improves the cooling efficiency, but also avoids the waste of coolant and ensures the energy utilization of the system. Using coolant flow control data and reflux control data to comprehensively evaluate thermal management effectiveness can accurately monitor cooling system performance, identify potential issues, and generate thermal management reports. This provides data support for subsequent optimization, ensuring optimal battery pack temperature control, extending the battery pack's service life, and improving system safety and reliability. Therefore, through precise heat load analysis, coolant line layout optimization, flow control, and reflux control, the present invention solves the problems of uneven cooling, inefficiency, and inaccurate control in traditional liquid-cooled battery pack thermal management, achieving a more efficient and intelligent thermal management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic flow chart of the steps of a thermal management method for a liquid-cooled battery pack; Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG. Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0025] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0027] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 , a liquid-cooled battery pack thermal management method, the method comprising the following steps: Step S1: Obtaining battery pack information data; performing unit area heat load analysis on the battery pack information data to generate unit area heat load data; performing multi-stage coolant diversion pipeline layout based on the battery unit area heat load data to generate initial coolant pipeline layout data; Step S2: Calculating a local heat concentration index on the initial coolant pipeline layout data to obtain a local area heat concentration index; optimizing the initial coolant pipeline layout data using the local area heat concentration index to generate annular flow coolant layout optimization data or directional flow coolant layout optimization data; and performing multi-stage flow splitting pipeline layout linkage on the initial coolant pipeline layout data based on the annular flow coolant layout optimization data or the directional flow coolant layout optimization data to generate battery pack cooling pipeline layout optimization data; Step S3: Mapping the battery pack cooling line layout optimization data to generate cooling line mapping data; predicting the remaining coolant amount in the cooling line mapping data to generate coolant line remaining data; performing normal coolant flow control on the cooling line mapping data based on the unit area heat load data to generate battery pack coolant flow control data; and performing coolant reflux control on the battery pack coolant flow control data using the coolant line remaining data to generate coolant reflux control data. Step S4: Using the battery pack coolant flow control data and the coolant reflux control data to evaluate the battery thermal management cooling effect on the unit area heat load data, generate a battery thermal management report, and perform liquid-cooled battery pack thermal management operations.

[0029] By acquiring detailed battery pack information and performing unit-area heat load analysis, this method accurately identifies the heat requirements of different regions. Based on this data, a more optimal coolant piping layout can be developed, ensuring that the initial design of the thermal management system meets the battery pack's heat dissipation requirements. By calculating the local heat concentration index and optimizing the coolant piping layout (circular or directional flow), the system effectively improves coolant flow efficiency within the battery pack, reduces hotspots, enhances cooling efficiency, and ensures uniform heat distribution, thereby enhancing the battery pack's thermal management effectiveness. By mapping the cooling piping and predicting the remaining coolant volume, the system precisely controls coolant flow and recirculation within the piping, ensuring optimal coolant flow in each region. This not only improves coolant utilization efficiency but also optimizes the recirculation system, avoiding coolant waste and enhancing the stability of the overall thermal management system. By evaluating coolant flow and recirculation control data, the system can comprehensively understand the battery pack's thermal management effectiveness, identify potential issues, and optimize control strategies. The resulting thermal management report provides data support for further improvements to the battery pack's heat dissipation design and operation, thereby ensuring battery pack safety, performance, and service life. Therefore, the present invention solves the problems of uneven cooling, low efficiency and inaccurate control in the thermal management of traditional liquid-cooled battery packs through precise heat load analysis, coolant pipeline layout optimization, flow control and reflux control, and realizes a more efficient and intelligent thermal management system.

[0030] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a liquid-cooled battery pack thermal management method of the present invention. In this example, the liquid-cooled battery pack thermal management method includes the following steps: Step S1: Obtaining battery pack information data; performing unit area heat load analysis on the battery pack information data to generate unit area heat load data; performing multi-stage coolant diversion pipeline layout based on the battery unit area heat load data to generate initial coolant pipeline layout data; In an embodiment of the present invention, basic battery pack parameter information, such as the number, size, power density, and operating temperature range of battery cells, is collected. Data sources include battery pack design documents, sensor data, or test equipment. Battery cell electrical information, such as current, voltage, and power output, is obtained to assess the thermal load of the battery pack under different operating conditions. Internal structural information is also obtained, including the battery cell layout and cooling system design (e.g., coolant piping layout and coolant flow path). The collected battery pack information is cleaned to remove erroneous or missing data and standardized to ensure data consistency and accuracy. Based on the battery pack's electrical parameters (e.g., power output, operating voltage, current), combined with the battery's thermal model (e.g., heat generated by the battery's internal resistance), the thermal load of each cell is calculated. The thermal load analysis of each cell is integrated into the battery pack's regional distribution to generate thermal load data for each cell region. Using thermal analysis software to perform three-dimensional thermal simulations further understands the temperature distribution of the battery pack under different operating conditions. Based on the analysis results, high heat load areas are identified and the need for enhanced cooling system design to balance the heat load across these areas is assessed. Unit area heat load data is generated, providing foundational data for subsequent cooling system design (such as coolant piping layout). Based on the heat load data for each unit area, coolant flow requirements are determined. Taking into account the varying heat loads across different areas, a multi-stage flow distribution layout is designed to ensure appropriate coolant flow to each area based on actual needs. The flow distribution layout design must consider the following factors: ensuring rapid and uniform coolant flow across all areas to avoid localized overheating or insufficient cooling. Pressure balance within each flow distribution channel is essential to ensure efficient coolant flow across all areas. Based on the unit area heat load data, engineering simulation software (such as CFD analysis software) is used to simulate the coolant flow to predict the flow state, pressure, and temperature distribution within each channel. Using pipeline layout optimization algorithms (such as the shortest path algorithm and network flow optimization), initial coolant pipeline layout data is designed, including the layout of each coolant channel, channel dimensions, and the location of flow distribution points. Use coolant flow simulation to verify the performance of the initial coolant piping layout data, confirming that the coolant flow path is reasonable and that the flow rate meets the requirements. Verify the coolant flow rate distribution and cooling effect to ensure that high heat load areas receive adequate cooling and that the coolant return system is well designed to avoid liquid accumulation.

[0031] Step S2: Calculating a local heat concentration index on the initial coolant pipeline layout data to obtain a local area heat concentration index; optimizing the initial coolant pipeline layout data using the local area heat concentration index to generate annular flow coolant layout optimization data or directional flow coolant layout optimization data; and performing multi-stage flow splitting pipeline layout linkage on the initial coolant pipeline layout data based on the annular flow coolant layout optimization data or the directional flow coolant layout optimization data to generate battery pack cooling pipeline layout optimization data; In the embodiment of the present invention, the local heat concentration index is calculated based on the initial coolant pipeline layout design. The local heat concentration index is an indicator that measures the degree of heat concentration in different areas within the battery pack and can help identify areas with high temperature concentration. When calculating, factors such as the power density, heat load, coolant flow path, and flow rate of the battery cell are considered. Local heat concentration index formula: Heat concentration index ;in is the heat load of the battery cell area, is the volume of the battery cell area, is the number of analysis zones. After determining the local heat accumulation index, the coolant piping layout is optimized to ensure adequate cooling in high-heat accumulation areas, preventing localized overheating that could lead to battery overheating. The optimization goal is to evenly distribute coolant flow while reducing system energy consumption. For areas with severe heat accumulation, a circular flow coolant layout can be designed. This layout arranges coolant pipes in a ring shape, creating a more uniform coolant flow path and avoiding excessive concentration in a single area. The design considers factors such as the flow velocity distribution and pipe dimensions within the circular path to ensure balanced flow. Through simulation and optimization algorithms, the circular flow path is ensured to effectively improve heat exchange efficiency and reduce localized heat accumulation. For other areas, a directed flow coolant layout optimization can be employed. This layout uses directional pipes to ensure coolant flow to specific areas, providing precise cooling. Directed flow optimization designs precise pipe routing based on the local heat accumulation index and coolant demand to improve heat dissipation in heat-loaded areas. Based on the circular flow or directional flow coolant layout optimization data, the coolant piping layout is further optimized using a multi-stage flow diversion pipeline layout. The goal of multi-stage diversion piping design is to achieve refined flow regulation, enabling each coolant line to dynamically adjust flow based on the heat load, thus avoiding excessive coolant flow in some areas and insufficient coolant flow in others. In the multi-stage diversion piping layout, linkage control between different diversion points is considered. This linkage ensures balanced flow and pressure when coolant is diverted from the main pipeline to each cooling zone. In particular, in areas with high flow rates, the pipe's carrying capacity needs to be enhanced, while in areas with lower cooling loads, energy can be saved by reducing flow or adjusting pipe size. Computational fluid dynamics (CFD) simulations are used to further verify and optimize the design of each diversion point to ensure efficient and stable coolant flow. By integrating optimization data for annular flow coolant layouts or directional flow coolant layouts, the linkage results of the multi-stage diversion piping layout are analyzed with the overall cooling requirements of the battery pack to generate optimized battery pack cooling pipe layout data. This data reflects the final optimized coolant flow path, diversion pipe layout, flow distribution, and pipe sizing. Thermal simulation tools are used to verify the optimized piping layout, checking cooling effectiveness and flow efficiency to ensure adequate cooling in high-temperature areas and avoid localized heat accumulation. Necessary adjustments are made based on the verification results to further improve coolant flow uniformity and heat dissipation efficiency.

[0032] Step S3: Mapping the battery pack cooling line layout optimization data to generate cooling line mapping data; predicting the remaining coolant amount in the cooling line mapping data to generate coolant line remaining data; performing normal coolant flow control on the cooling line mapping data based on the unit area heat load data to generate battery pack coolant flow control data; and performing coolant reflux control on the battery pack coolant flow control data using the coolant line remaining data to generate coolant reflux control data. In an embodiment of the present invention, geometric information about pipe segments, including pipe length, diameter, number of elbows, and interface locations, is extracted from cooling pipeline layout optimization data. Combined with the battery pack unit area heat load data, the heat load of each pipe segment is determined. The distribution of heat load affects the coolant flow distribution within the pipeline. Using this information, the heat load and flow characteristics of each cooling pipeline are mapped to the specific location of the pipeline segment to generate cooling pipeline mapping data. This data includes the heat load, flow rate, flow velocity, and pipeline parameters for each pipeline segment. The flow resistance characteristics of each pipeline segment, including pipe material, length, inner diameter, and flow velocity, are extracted from the cooling pipeline mapping data. Computational fluid dynamics (CFD) is used to simulate the flow of the cooling pipeline segment to obtain information such as the coolant flow velocity distribution and residence time within the pipeline. Based on the coolant flow resistance characteristics, residence time, and flow velocity data, the remaining coolant amount is calculated using a formula to generate remaining coolant data. This data contains the remaining coolant amount for each pipeline segment and helps assess the effective utilization of coolant within the pipeline. Based on the cooling pipeline mapping data and combined with the heat load data of each area, the flow requirements of the coolant in each pipeline section are analyzed. The difference in heat load between different areas will affect the distribution of coolant flow. Based on the analysis results, the flow of the coolant is adjusted to ensure that the flow matches the heat load. By controlling the flow of each pipeline, the cooling effect is optimized. Finally, the coolant flow control data of the battery pack is generated, which includes the coolant flow, flow rate and adjustment strategy of each pipeline section. Based on the residual data of the coolant pipeline, the coolant return demand in the pipeline is analyzed to evaluate whether there is enough coolant to return to replenish the flow system. Based on the return demand and residual amount data, a return control strategy is formulated to adjust the return path and flow of the coolant to ensure efficient flow of coolant in the entire pipeline system. Finally, based on the residual data of the coolant pipeline, the coolant flow control data is returned and the coolant return control data is generated.

[0033] Step S4: Using the battery pack coolant flow control data and the coolant reflux control data to evaluate the battery thermal management cooling effect on the unit area heat load data, generate a battery thermal management report, and perform liquid-cooled battery pack thermal management operations.

[0034] In this embodiment of the present invention, battery pack coolant flow control data and coolant return control data are collected and integrated. This data includes coolant flow rate, flow velocity, temperature, return path, and flow control strategy for each pipeline segment. Furthermore, unit area heat load data—that is, the heat dissipation requirements of each battery cell area—is collected. This data varies depending on the battery's operating status and heat dissipation requirements. A cooling effectiveness evaluation model is established based on the principles of heat conduction and coolant flow characteristics. This model considers the following factors: It assesses whether the coolant flow rate matches the heat load requirements of each battery cell, ensuring that the temperature control requirements of each area are met. It calculates the temperature distribution of different areas to assess whether the coolant effectively removes heat within the expected timeframe. It analyzes the return path and return flow rate to assess the coolant reuse efficiency and ensure that the system does not experience excessive coolant loss. It analyzes the coolant's heat dissipation effect on the heat load of the battery cell area by calculating data such as the coolant's residence time in the pipeline, flow velocity, and flow resistance. The actual cooling effect is compared with a preset temperature standard to calculate the temperature control error, and the cooling strategy is further optimized based on the error. The results of the cooling effectiveness evaluation model generate battery pack thermal management cooling effectiveness evaluation data. Summarize cooling performance evaluation data, including coolant flow and return flow control, temperature control of each battery cell, and the matching of coolant flow efficiency and heat load. Use charts and heat maps to visualize cooling performance, conveniently and intuitively displaying temperature distribution and the matching of coolant flow and heat load. Upload the generated report to the cloud platform for storage and management, facilitating subsequent query and analysis.

[0035] Preferably, step S1 includes the following steps: Step S11: Obtaining battery pack information data; Step S12: performing a structural topology analysis on the battery pack information data to generate battery pack structural topology data; performing battery cell area division on the battery pack information data based on the battery pack structural topology data to generate battery cell area division data; Step S13: using a temperature sensor to perform unit area heat load analysis on the battery unit area division data to generate unit area heat load data; Step S14: Layout the multi-stage coolant pipelines for the battery pack structure topology data using the battery unit area heat load data to generate initial coolant pipeline layout data.

[0036] In this embodiment of the present invention, detailed battery pack information, including basic parameters such as each battery cell's voltage, temperature, and state of charge, is extracted from a battery management system (BMS). If the data comes from different sources, multi-source data fusion is required to ensure data consistency and integrity. A graph theory algorithm is used to perform a topological structural analysis of the battery pack's physical layout, identifying the connections between the battery cells and generating a topological diagram of the battery pack. Based on this topological data, the battery cells are divided into zones (e.g., high-load zone, low-load zone, etc.) based on factors such as temperature characteristics and power consumption. The output data is a table that maps zone numbers to battery cells, further facilitating subsequent analysis. Within each zone, data collected by temperature sensors is used to calculate the heat load of each zone. Heat load analysis can be based on factors such as the battery cell's power output and charge / discharge state. A heat conduction model is applied to analyze how heat is transferred between different battery cell zones to assess the regional heat load distribution. This analysis results serve as an important basis for determining cooling requirements and coolant allocation. Based on heat load analysis data and the battery pack's structural topology, an optimization algorithm (such as a shortest path algorithm or genetic algorithm) is used to design a multi-stage coolant distribution pipeline layout. The design takes into account the structural characteristics of the battery pack, ensuring that the coolant effectively covers high-temperature areas and maintains uniform cooling. This layout not only ensures that the heat dissipation requirements of each area are met, but also considers the rational arrangement of pipes to avoid excessive energy loss or fluid waste. Initial coolant pipeline layout data is output, including information such as pipeline routing, diversion points, and coolant flow rate, providing a basis for further cooling system implementation.

[0037] Preferably, step S13 includes the following steps: Step S131: using a temperature sensor to collect battery cell area temperature data on the battery cell area division data to obtain battery cell temperature collection data; performing data preprocessing on the battery cell temperature collection data to generate standard battery cell temperature collection data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling, and data standardization; Step S132: extracting the geometric shape, thermal conductivity characteristics, and battery material properties of the battery pack information data to construct a thermal conduction model of the battery cell area, and importing the standard battery cell temperature acquisition data into the thermal conduction model of the battery cell area to perform heat load information conversion to generate battery thermal load information data; Step S133: Calculating the regional heat load of the battery heat load information data using a heat conduction equation to obtain the regional heat load of the battery unit; performing heat load mapping on the battery unit regional division data based on the regional heat load of the battery unit to generate battery unit heat load mapping data; Step S134: performing regional temperature time series characteristic analysis on the battery unit thermal load mapping data to generate regional temperature time series characteristic data; identifying hot spots on the battery unit thermal load mapping data using the regional temperature time series characteristic data to generate unit regional thermal load data.

[0038] In this embodiment of the present invention, temperature sensors are used to collect real-time temperature data within different battery cell regions. The temperature data for each battery cell forms a time-series dataset, recording the battery cell temperature value at a specific point in time. A multi-point sensor array ensures that the spatial distribution of temperature data covers all areas of the battery pack. Abnormal data caused by sensor failure or other issues is checked and removed. Preliminary cleaning is performed by setting thresholds (e.g., when the temperature exceeds a reasonable range). A filtering algorithm (such as a low-pass filter or mean filter) is used to remove random noise from the temperature data. If some sensor data is missing, interpolation methods (such as linear interpolation or spline interpolation) can be used to fill the missing data. Temperature data is normalized to ensure comparability across different regions or time periods, typically using Z-score normalization or minimum-maximum normalization. This preprocessed data is referred to as standard battery cell temperature data and lays the foundation for subsequent thermal load calculation and modeling. A thermal conduction model for the battery cell region is constructed based on battery pack information (such as geometry, thermal conductivity characteristics, and thermal properties of battery materials). Numerical methods such as finite element analysis (FEA) can be used to simulate the heat conduction process within the battery cell. The shape and arrangement of battery cells significantly influence heat conduction. For example, whether the cells are arranged in a planar array or stacked. The thermal conductivity of different materials (such as the battery housing, conductive materials, and coolant) needs to be factored into the model. The battery type (e.g., lithium-ion) affects its thermal properties, including specific heat capacity and thermal conductivity. Standard battery cell temperature data is imported into the thermal conduction model, and the heat load for each region is calculated through a numerical solution process. The goal of heat load information conversion is to combine the temperature data collected by the sensor with the thermal conductivity characteristics of the battery cell region to form a data set containing heat load density (e.g., W / m²), which describes the heat load for each region. Heat load information is calculated using heat conduction equations (such as Fourier's law or the steady-state heat conduction equation). Based on the battery cell temperature field and thermal conductivity characteristics, the heat load for each battery cell region is calculated. The heat load calculation typically includes factors such as internal heat generation, external heat dissipation, and coolant flow. The heat load value can serve as an important indicator of cooling system requirements. Based on the heat load of the battery cell region, the heat load data is mapped back to the battery cell region segmentation data. The heat load value of each region is matched to its corresponding battery cell region to generate battery cell heat load mapping data. This battery cell region heat load mapping data is used to extract temperature time series features. Time domain analysis (such as Fourier transform and power spectral density analysis) and statistical analysis (such as mean and variance) can be used to identify regional temperature variation patterns. These time series features reveal temperature trends across different battery cell regions, helping to identify areas experiencing overheating or significant temperature fluctuations. Further analysis of the regional temperature time series feature data allows for the identification of hotspots with high heat loads and large temperature fluctuations using methods such as cluster analysis and thresholding.Hotspot identification helps identify areas requiring special attention and cooling. The temperatures in these areas impact the safety and lifespan of the battery pack, necessitating specialized cooling system design. Ultimately, through comprehensive analysis of hotspot identification and heat load data, unit area heat load data is generated, including the heat load, temperature time series characteristics, and hotspot information for each battery cell area.

[0039] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: performing a coolant flow path analysis on the initial coolant pipeline layout data to generate coolant flow path data; performing a coolant flow velocity distribution analysis on the coolant flow path data to generate coolant flow velocity distribution data; Step S22: using the coolant flow rate distribution data to identify the cooling uneven area of ​​the unit area heat load data, and generating the cooling uneven area identification data; calculating the local heat concentration index of the cooling uneven area identification data, and obtaining the local area heat concentration index; Step S23: Comparing the local area heat concentration index with a preset standard area heat concentration threshold. When the local area heat concentration index is less than or equal to the preset standard area heat concentration threshold, optimizing the annular flow coolant pipeline layout for the corresponding uneven cooling area identification data to generate annular flow coolant layout optimization data. Step S24: When the local area heat concentration index is greater than a preset standard area heat concentration threshold, the directional heat conduction pipeline layout optimization is performed on the corresponding cooling uneven area identification data to generate directional flow coolant layout optimization data; Step S25: Based on the annular flow coolant layout optimization data and the directional flow coolant layout optimization data, the initial coolant pipeline layout data is designed for coolant diversion points, and coolant hierarchical diversion valve setting data is generated; the initial coolant pipeline layout data is linked to a multi-level diversion pipeline layout through the coolant hierarchical diversion valve setting data to generate battery pack cooling pipeline layout optimization data.

[0040] In an embodiment of the present invention, a computational fluid dynamics (CFD) simulation tool is used to perform flow path analysis on the initial coolant pipeline layout data. This analysis analyzes the coolant's flow path and distribution within the pipeline system, as well as any dead ends or bottlenecks. The simulation considers the coolant's physical properties (such as viscosity and density) and the pipeline's geometric characteristics (such as diameter, elbows, and branch points). CFD software (such as ANSYS Fluent) is used to calculate the fluid flow path and generate coolant flow path data. Based on this flow path data, the coolant flow velocity distribution in different areas is analyzed. This flow velocity distribution helps determine the coolant's heat transfer and cooling efficiency within the battery pack. CFD calculates the flow velocity field and generates coolant flow velocity distribution data, facilitating subsequent analysis of areas with insufficient or excessive flow velocity. Based on this coolant flow velocity distribution data, areas with low coolant flow velocity are identified, which can lead to uneven temperature distribution and overheating. A coolant flow velocity threshold is set, and areas below this threshold are marked as areas of uneven cooling. Further analysis of the battery pack's thermal load determines areas of uneven cooling by comparing coolant flow rate with the regional heat load of the battery cells. A local heat concentration index (LTI) is calculated for identified areas of uneven cooling. This index is a weighted measure of heat concentration in a region, based on temperature fluctuations, heat load density, and coolant flow rate. A high LTI indicates severe heat accumulation, leading to battery overheating or performance degradation. For areas of uneven cooling where the LTI is less than or equal to a preset threshold for regional heat concentration, a circular flow coolant piping layout optimization approach is employed. By optimizing the coolant piping layout, coolant is distributed to hotspots, forming a circular flow path that ensures uniform coolant flow across the area, effectively reducing the LTI. In practice, this optimization approach involves adding circular branch pipes within the cooling system, redirecting coolant flow, or optimizing flow efficiency by changing the piping layout. For areas of uneven cooling where the LTI exceeds the preset threshold for regional heat concentration, a directional heat conduction piping layout optimization approach is employed. In this case, coolant flows directly to the hotspots through directional piping, achieving more efficient cooling. Directional pipeline layout includes adding straight pipelines, reducing turns or adding dedicated cooling channels to accurately deliver coolant to hot spots. Directional heat conduction pipeline optimization effectively reduces the temperature of hot spots by reducing heat accumulation. Based on the annular flow coolant layout optimization data and the directional flow coolant layout optimization data, the coolant diversion points are designed. These diversion points will ensure that the coolant can be properly distributed according to the needs of the battery cell area. When designing the diversion points, it is necessary to consider the coolant flow rate, temperature distribution and pressure loss of the pipeline to ensure that the coolant flow and temperature are reasonably distributed. Through the coolant hierarchical diversion valve setting data, the initial coolant pipeline layout is optimized through multi-stage diversion pipeline layout linkage.Diverter valves control the flow of coolant in various areas, ensuring that hot spots receive more coolant flow. In a multi-stage diversion system, coolant flow can be adjusted as needed to ensure balanced cooling and reduce cooling system energy consumption. This optimization ultimately generates optimized data for the battery pack cooling line layout. This data serves as the basis for cooling system design, ensuring the optimal layout of coolant flow paths, flow rate distribution, and diversion points, thereby effectively improving cooling efficiency, preventing overheating, and extending battery life.

[0041] Preferably, step S23 includes the following steps: Step S231: Comparing the local area heat concentration index with a preset standard area heat concentration threshold. When the local area heat concentration index is less than or equal to the preset standard area heat concentration threshold, locating the highest heat concentration area of ​​the battery pack based on the corresponding uneven cooling area identification data to generate highest heat concentration area location data. Step S232: performing a heat accumulation gradient analysis on the local area heat accumulation index based on the highest heat accumulation area location data to generate local area heat accumulation gradient data; using the highest heat accumulation area location data as the inlet of the annular flow pipeline, and using the local area heat accumulation gradient data to perform a pipeline closed loop design on the inlet of the annular flow pipeline to generate a flow path of the annular flow pipeline; Step S233: Calculate the number of coolant circulation and heat dissipation turns of the annular flow pipeline flow path to obtain the number of coolant circulation and heat dissipation turns; optimize the annular flow coolant pipeline layout of the annular flow pipeline flow path using the number of coolant circulation and heat dissipation turns to generate annular flow coolant layout optimization data.

[0042] In this embodiment of the present invention, the calculated local heat accumulation index is compared with a preset standard regional heat accumulation threshold. If the local heat accumulation index is less than or equal to the standard threshold, the heat accumulation in that area is within an acceptable range, and further coolant flow optimization is not required. Otherwise, the optimization phase begins. For areas exceeding the standard threshold, subsequent identification of uneven cooling regions is performed; these areas are hotspots requiring special attention. Based on the identification results of uneven cooling regions, the highest heat accumulation area is located. This step identifies the area in the battery pack with the highest temperature, the most concentrated heat load, and the area most in need of cooling optimization. The location data for the highest heat accumulation area includes information such as the coordinates, heat load density, and heat accumulation index of the area, providing a basis for subsequent piping layout design. Based on the location data for the highest heat accumulation area, a heat accumulation gradient analysis is performed on that area. By calculating the temperature and heat load changes between that area and the surrounding areas, heat accumulation gradient data is generated, which reflects the heat distribution trend within the area. The information provided by the heat accumulation gradient data is used to determine the most effective coolant flow path and areas requiring special attention. Using the data on the locations of the highest heat accumulation areas, the inlet of the annular flow pipe is determined. This inlet serves as the starting point of the annular pipe and ensures that the coolant directly enters the areas of high heat concentration. Based on the heat accumulation gradient data, the closed-loop design of the pipe is optimized. The flow path is designed based on the heat distribution, ensuring that the coolant efficiently covers the entire heat accumulation area and effectively removes heat. Based on this analysis, the annular flow pipe flow path is generated, bypassing the areas of highest heat accumulation and operating in a closed loop. In the actual design, the annular pipe path will pass through areas with high coolant demand and ensure uniform coolant flow to cover hot spots. The number of coolant circulation turns required for the annular flow pipe flow path is calculated based on the coolant flow path length, the annular pipe design parameters (such as pipe diameter and flow rate), and the thermal load distribution of the battery cells. This calculation determines the required number of circulation turns to ensure that the coolant fully flows through all hot spots, removes excess heat, and ultimately controls the battery pack temperature within a safe range. Based on the calculated number of coolant circulation and heat dissipation circles, the flow path of the annular flow pipeline is further optimized. This optimization step includes adjusting the diameter of the pipeline, adding branch pipelines, and rationally configuring flow control valves to ensure a more uniform and efficient coolant flow while reducing the system's pressure loss and energy consumption. On this basis, the annular flow coolant layout optimization data is generated. This data will provide detailed layout diagrams and parameters for the actual coolant pipeline design. The optimized layout data will ensure the uniformity and efficiency of the coolant flow, while reducing the occurrence of overheating areas and effectively improving the overall performance of the cooling system.

[0043] Preferably, step S24 includes the following steps: Step S241: When the local area heat concentration index is greater than a preset standard area heat concentration threshold, regional heat source positioning is performed on the corresponding uneven cooling area identification data to generate regional heat source positioning data; Step S242: Perform initial directional pipeline design based on the regional heat source location data to obtain initial directional pipeline design data; perform directional pipeline heat conduction efficiency calculation on the initial directional pipeline design data to obtain directional pipeline heat conduction efficiency, wherein the formula for calculating the directional pipeline heat conduction efficiency is as follows: ; Where, is the amount of heat conducted, is the thermal conductivity of the pipe material, is the cross-sectional area of ​​the pipe, is the temperature of the coolant entering the pipe, is the temperature of the coolant flowing out of the pipe, is the length of the pipe; Step S243: Optimizing the directional heat conduction pipeline layout of the initial directional pipeline design data based on the directional pipeline heat conduction efficiency to generate directional flow coolant layout optimization data.

[0044] In this embodiment of the present invention, when the local heat accumulation index exceeds a preset standard regional heat accumulation threshold, uneven cooling areas are first identified. These areas, due to excessive heat accumulation, require further optimization. Using this uneven cooling area identification data, regional heat sources are located through precise thermal imaging analysis or other temperature measurement methods. This step aims to identify areas with excessively high temperatures (i.e., heat source areas) and generate regional heat source location data, including the location, intensity, and distribution of the heat sources. This regional heat source location data provides a basis for subsequent directional piping design, ensuring that the piping layout accurately covers these heat source areas and improves cooling effectiveness. Based on this regional heat source location data, an initial directional piping design is performed. This design ensures that coolant flows to the heat source areas and effectively removes heat from them. Factors considered during the preliminary design process include pipe size, pipe routing, and coolant flow rate. The generated initial directional piping design data provides the basis for subsequent calculation of the pipe heat conduction efficiency. After designing the directional piping, the pipe heat conduction efficiency needs to be calculated to ensure that it effectively transfers heat from the heat source areas to the coolant. The calculation formula is: ; Where, is the amount of heat conducted, is the thermal conductivity of the pipe material, is the cross-sectional area of ​​the pipe, is the temperature of the coolant entering the pipe, is the temperature of the coolant flowing out of the pipe, is the length of the pipe. Calculating the directional pipe heat transfer efficiency helps determine the coolant pipe's efficiency in the heat source area and whether adjustments to the pipe material, cross-section, or length are needed to improve heat transfer capacity. Based on the calculated directional pipe heat transfer efficiency, the initial directional pipe design data is optimized. The optimization goal is to maximize heat transfer efficiency, reduce coolant flow resistance, and ensure that the coolant can efficiently remove heat from the heat source area. During the optimization process, adjustments are made to the pipe layout, pipe material, pipe cross-section, and coolant flow rate to ensure that the system can maintain optimal heat transfer performance under varying heat load conditions. The optimized data will include adjusted pipe paths, dimensions, material selection, and other factors to ensure that the coolant flow path maximizes heat transfer efficiency and addresses uneven cooling. The optimized pipe layout ensures that heat is adequately removed from the heat source area, thereby reducing local temperatures and ensuring safe operation of the battery pack.

[0045] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: confirming the pipeline connection points of the battery pack cooling pipeline layout optimization data to obtain the battery pack cooling pipeline connection points; deploying coolant flow monitoring nodes based on the battery pack cooling pipeline connection points to generate coolant flow monitoring node deployment data; Step S32: performing a coolant flow time series analysis on the coolant flow monitoring node deployment data to generate coolant flow time series data; performing a trend segment extraction on the coolant flow time series data to obtain a coolant flow change trend segment; Step S33: mapping the battery pack cooling pipe layout optimization data to cooling pipe segments using the coolant flow rate change trend segments to generate cooling pipe mapping data; predicting the remaining coolant amount in the cooling pipe mapping data using the coolant residual amount calculation formula to generate coolant pipe residual data; Step S34: performing normal coolant flow control on the cooling pipe mapping data according to the unit area heat load data to generate battery pack coolant flow control data; performing coolant reflux control on the battery pack coolant flow control data according to the coolant pipe residual data to generate coolant reflux control data.

[0046] In an embodiment of the present invention, the battery pack cooling system first requires identifying the cooling line interconnection points—that is, the connection and intersection points between the coolant lines. This process ensures coolant flow throughout the system. Using the battery pack cooling line layout optimization data, graph theory, network analysis methods, or fluid dynamics simulations can be used to determine the connection points of each cooling line. The resulting cooling line interconnection point data provides an accurate basis for subsequent flow monitoring. Based on the cooling line interconnection points, coolant flow monitoring nodes are deployed at appropriate pipeline nodes. These nodes monitor coolant flow in real time, including flow rate, flow rate, and other relevant parameters. The coolant flow monitoring node deployment data includes information such as the location of each monitoring point, the monitoring method (e.g., flow sensor or flow meter), and the monitoring range. Flow data obtained from each coolant flow monitoring node is collected and subjected to coolant flow time series analysis. Time series analysis of flow data can identify patterns, anomalies, and trends in flow rate variations, generate coolant flow time series data, and model the data using time series analysis methods (such as autoregressive models and stationarity tests). After analyzing the coolant flow time series data, coolant flow trend segments are extracted—time periods where flow changes exhibit a certain regularity. These trend segments include trends such as increases, decreases, and stabilization, providing important insights for subsequent pipeline optimization and control. Based on the coolant flow trend segments, cooling pipeline segment mapping is performed on the battery pack cooling pipeline layout optimization data. This mapping process associates trend segments with specific pipeline segments, helping to identify which pipeline segments have experienced significant coolant flow changes. The resulting cooling pipeline mapping data displays flow rate changes for each pipeline segment, providing information for subsequent flow prediction and coolant distribution control. A coolant residual volume calculation formula is used to predict the cooling pipeline mapping data. This formula considers pipeline flow, coolant consumption, and specific coolant flow characteristics to predict the coolant residual volume for each pipeline segment. This coolant pipeline residual volume data provides critical information on coolant sufficiency and the risk of flow reduction, helping to adjust the coolant supply strategy. Based on the unit area heat load data, normal coolant flow control is implemented for the cooling pipelines. This process adjusts the coolant flow based on the heat load requirements to ensure that each area receives sufficient coolant flow. The generated battery pack coolant flow control data will include flow control strategies, pipeline allocation, valve settings and other operating parameters to ensure that the coolant can be reasonably distributed to each unit area. Using the coolant pipeline residual data, the coolant flow control is further reflux controlled. The reflux control aims to adjust the coolant reflux strategy according to the amount of residual coolant in the pipeline to ensure that the coolant can circulate effectively and avoid waste. The generated coolant reflux control data will include information such as the adjustment parameters of the reflux valve and the optimization of the reflux path, thereby maximizing the utilization efficiency of the coolant.

[0047] Preferably, predicting the remaining amount of coolant in the cooling pipeline based on the cooling pipeline mapping data includes: Extract the coolant resistance characteristics from the cooling pipeline mapping data to obtain coolant flow resistance characteristic data; use computational fluid dynamics to simulate the pipeline flow on the cooling pipeline mapping data to generate coolant residence time simulation data and coolant flow rate simulation data; The coolant residual amount calculation formula is used to calculate the coolant residual amount based on the coolant flow resistance characteristic data, coolant residence time simulation data, and coolant flow rate simulation data to obtain the coolant pipeline residual data. The coolant residual amount calculation formula is as follows: ; in, is the residual amount in the coolant pipeline, is the outlet fluid pressure at a certain point in the pipeline, is the inlet fluid pressure at a certain point in the pipeline, is the flow resistance of a certain section of the pipeline, is the length of the pipe.

[0048] In an embodiment of the present invention, the coolant flow resistance characteristics of the cooling pipeline mapping data are extracted based on information such as the geometric structure, material, and surface roughness of the cooling pipeline. Relevant fluid mechanics theories, such as the Darcy-Weisbach equation, are used to calculate the flow resistance of each section in the pipeline. The resistance characteristic data can be derived from variables such as the pressure and flow rate of the flow in the pipeline. The generated coolant flow resistance characteristic data includes the flow resistance of each section in the pipeline (such as friction resistance, local resistance, etc.). These data will play an important role in the prediction of coolant residual amount. Computational fluid dynamics (CFD) technology is used to perform flow simulation on the cooling pipeline mapping data. CFD can accurately simulate the flow behavior of the coolant in the pipeline. In the pipeline, the coolant residence time refers to the time the coolant flows through the pipeline, which affects the cooling efficiency. Through simulation, the coolant residence time of each section of the pipeline is obtained. The coolant flow rate of each section of the pipeline is simulated, especially the change in flow rate under different pipeline conditions. The output of the CFD results will provide the necessary input data for the subsequent coolant residual amount prediction. The coolant residual amount is calculated according to the given formula: ; in, is the residual amount in the coolant pipeline, is the outlet fluid pressure at a certain point in the pipeline, is the inlet fluid pressure at a certain point in the pipeline, is the flow resistance of a certain section of the pipeline, is the length of the pipeline. The coolant flow rate simulation data and coolant residence time simulation data obtained by CFD simulation are combined with the flow resistance characteristic data of the cooling pipeline to calculate the residual amount of coolant according to the above integral formula. This calculation process is obtained by combining the analysis of the fluid pressure difference and flow resistance at different positions in the pipeline, so as to evaluate the coolant retention and effective cooling capacity in the pipeline. After the residual amount is calculated, the final coolant pipeline residual data is generated according to the distribution characteristics of the coolant and the needs of each cooling pipe section. This data includes the remaining amount of coolant in each section of the pipeline, the flow state, and the coolant distribution strategy that needs to be adjusted. The coolant pipeline residual data will be used to optimize coolant flow control, determine whether the coolant needs to be replenished or refluxed, and ensure the effectiveness and stability of the entire cooling system.

[0049] Preferably, step S4 includes the following steps: Step S41: using the battery pack coolant flow control data and the coolant reflux control data to evaluate the battery thermal management cooling effect on the unit area heat load data, and generating battery thermal management cooling effect evaluation data; Step S42: Upload the battery thermal management cooling effect evaluation data to the cloud platform for data storage to generate battery thermal management storage data; visualize the battery thermal management storage data to generate a battery thermal management report to perform liquid-cooled battery pack thermal management operations.

[0050] In this embodiment of the present invention, the battery pack coolant flow control data and coolant return control data generated in step S34 are used as basic input data, combined with unit area heat load data to perform a cooling effectiveness evaluation. Based on the heat load conditions (e.g., temperature distribution, power density, etc.) of the battery unit area, the cooling capacity of the coolant flow in different areas is analyzed. Parameters such as flow velocity, flow rate, and temperature difference are primarily considered to assess the heat exchange efficiency of the coolant. Based on thermal principles such as heat conduction and convection, and combined with experimental data or simulation models, a cooling effectiveness evaluation model is established. This model evaluates the temperature control of each unit area in the battery pack and determines whether the cooling effect meets expectations. Key cooling effectiveness data, such as the temperature drop, thermal equilibrium state, and coolant utilization efficiency of each unit area, are calculated. This data generates battery thermal management cooling effectiveness evaluation data, which serves as a basis for subsequent optimization. The battery thermal management cooling effectiveness evaluation data is uploaded to a cloud platform for long-term storage, management, and analysis. The data storage provided by the cloud platform ensures data reliability and security, facilitating access and updates at any time. When uploading data, a secure transmission protocol is used to ensure the integrity and confidentiality of the data. On the cloud platform, data visualization technologies (such as charts, heat maps, and 3D visualization interfaces) provide a visual display of the battery pack's thermal management performance. Visualization tools are used to conduct detailed analysis of cooling evaluation data, enabling personnel to clearly understand temperature changes and coolant flow in each area. Based on the visualization analysis results, a detailed battery thermal management report is generated. This report includes information on coolant flow control, recirculation control efficiency, temperature distribution, and cooling performance by area. The report also provides optimization recommendations and warnings (for example, identifying areas with excessively high temperatures or insufficiently cooled cells). Based on the battery thermal management report, appropriate thermal management measures are implemented. For example, if the report indicates unsatisfactory cooling performance in certain areas, optimization measures can be implemented by adjusting coolant flow rates, adding recirculation systems, or changing the cooling line layout. This process ensures optimal thermal management throughout the battery pack, preventing overheating and uneven cooling, and improving battery life and safety.

[0051] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0052] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A liquid-cooled battery pack thermal management method, characterized in that: The following steps are involved: Step S1: Obtaining battery pack information data; performing unit area heat load analysis on the battery pack information data to generate unit area heat load data; performing multi-stage coolant diversion pipeline layout based on the battery unit area heat load data to generate initial coolant pipeline layout data; Step S2: Calculating a local heat concentration index on the initial coolant pipeline layout data to obtain a local area heat concentration index; optimizing the initial coolant pipeline layout data using the local area heat concentration index to generate annular flow coolant layout optimization data or directional flow coolant layout optimization data; and performing multi-stage flow splitting pipeline layout linkage on the initial coolant pipeline layout data based on the annular flow coolant layout optimization data or the directional flow coolant layout optimization data to generate battery pack cooling pipeline layout optimization data; Step S3: Mapping the battery pack cooling line layout optimization data to generate cooling line mapping data; predicting the remaining coolant amount in the cooling line mapping data to generate coolant line remaining data; performing normal coolant flow control on the cooling line mapping data based on the unit area heat load data to generate battery pack coolant flow control data; and performing coolant reflux control on the battery pack coolant flow control data using the coolant line remaining data to generate coolant reflux control data. Step S4: Using the battery pack coolant flow control data and the coolant reflux control data to evaluate the battery thermal management cooling effect on the unit area heat load data, generate a battery thermal management report, and perform liquid-cooled battery pack thermal management operations.

2. The liquid-cooled battery pack thermal management method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtaining battery pack information data; Step S12: performing a structural topology analysis on the battery pack information data to generate battery pack structural topology data; performing battery cell area division on the battery pack information data based on the battery pack structural topology data to generate battery cell area division data; Step S13: using a temperature sensor to perform unit area heat load analysis on the battery unit area division data to generate unit area heat load data; Step S14: Layout the multi-stage coolant pipelines for the battery pack structure topology data using the battery unit area heat load data to generate initial coolant pipeline layout data.

3. The liquid-cooled battery pack thermal management method according to claim 2, characterized in that: Step S13 includes the following steps: Step S131: using a temperature sensor to collect battery cell area temperature data on the battery cell area division data to obtain battery cell temperature collection data; performing data preprocessing on the battery cell temperature collection data to generate standard battery cell temperature collection data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling, and data standardization; Step S132: extracting the geometric shape, thermal conductivity characteristics, and battery material properties of the battery pack information data to construct a thermal conduction model of the battery cell area, and importing the standard battery cell temperature acquisition data into the thermal conduction model of the battery cell area to perform heat load information conversion to generate battery thermal load information data; Step S133: Calculating the regional heat load of the battery heat load information data using a heat conduction equation to obtain the regional heat load of the battery unit; performing heat load mapping on the battery unit regional division data based on the regional heat load of the battery unit to generate battery unit heat load mapping data; Step S134: performing regional temperature time series characteristic analysis on the battery unit thermal load mapping data to generate regional temperature time series characteristic data; identifying hot spots on the battery unit thermal load mapping data using the regional temperature time series characteristic data to generate unit regional thermal load data.

4. The liquid-cooled battery pack thermal management method according to claim 1, wherein: Step S2 includes the following steps: Step S21: performing a coolant flow path analysis on the initial coolant pipeline layout data to generate coolant flow path data; performing a coolant flow velocity distribution analysis on the coolant flow path data to generate coolant flow velocity distribution data; Step S22: using the coolant flow rate distribution data to identify the cooling uneven area of ​​the unit area heat load data, and generating the cooling uneven area identification data; calculating the local heat concentration index of the cooling uneven area identification data, and obtaining the local area heat concentration index; Step S23: Comparing the local area heat concentration index with a preset standard area heat concentration threshold. When the local area heat concentration index is less than or equal to the preset standard area heat concentration threshold, optimizing the annular flow coolant pipeline layout for the corresponding uneven cooling area identification data to generate annular flow coolant layout optimization data. Step S24: When the local area heat concentration index is greater than a preset standard area heat concentration threshold, the directional heat conduction pipeline layout optimization is performed on the corresponding cooling uneven area identification data to generate directional flow coolant layout optimization data; Step S25: Based on the annular flow coolant layout optimization data and the directional flow coolant layout optimization data, the initial coolant pipeline layout data is designed for coolant diversion points, and coolant hierarchical diversion valve setting data is generated; the initial coolant pipeline layout data is linked to a multi-level diversion pipeline layout through the coolant hierarchical diversion valve setting data to generate battery pack cooling pipeline layout optimization data.

5. The liquid-cooled battery pack thermal management method according to claim 4, characterized in that: Step S23 includes the following steps: Step S231: Comparing the local area heat concentration index with a preset standard area heat concentration threshold. When the local area heat concentration index is less than or equal to the preset standard area heat concentration threshold, locating the highest heat concentration area of ​​the battery pack based on the corresponding uneven cooling area identification data to generate highest heat concentration area location data. Step S232: performing a heat accumulation gradient analysis on the local area heat accumulation index based on the highest heat accumulation area location data to generate local area heat accumulation gradient data; using the highest heat accumulation area location data as the inlet of the annular flow pipeline, and using the local area heat accumulation gradient data to perform a pipeline closed loop design on the inlet of the annular flow pipeline to generate a flow path of the annular flow pipeline; Step S233: Calculate the number of coolant circulation and heat dissipation turns of the annular flow pipeline flow path to obtain the number of coolant circulation and heat dissipation turns; optimize the annular flow coolant pipeline layout of the annular flow pipeline flow path using the number of coolant circulation and heat dissipation turns to generate annular flow coolant layout optimization data.

6. The liquid-cooled battery pack thermal management method according to claim 4, characterized in that: Step S24 includes the following steps: Step S241: When the local area heat concentration index is greater than a preset standard area heat concentration threshold, regional heat source positioning is performed on the corresponding uneven cooling area identification data to generate regional heat source positioning data; Step S242: Perform initial directional pipeline design based on the regional heat source location data to obtain initial directional pipeline design data; perform directional pipeline heat conduction efficiency calculation on the initial directional pipeline design data to obtain directional pipeline heat conduction efficiency, wherein the formula for calculating the directional pipeline heat conduction efficiency is as follows: ； Where, is the amount of heat conducted, is the thermal conductivity of the pipe material, is the cross-sectional area of ​​the pipe, is the temperature of the coolant entering the pipe, is the temperature of the coolant flowing out of the pipe, is the length of the pipe; Step S243: Optimizing the directional heat conduction pipeline layout of the initial directional pipeline design data based on the directional pipeline heat conduction efficiency to generate directional flow coolant layout optimization data.

7. The liquid-cooled battery pack thermal management method according to claim 1, wherein: Step S3 includes the following steps: Step S31: confirming the pipeline connection points of the battery pack cooling pipeline layout optimization data to obtain the battery pack cooling pipeline connection points; deploying coolant flow monitoring nodes based on the battery pack cooling pipeline connection points to generate coolant flow monitoring node deployment data; Step S32: performing a coolant flow time series analysis on the coolant flow monitoring node deployment data to generate coolant flow time series data; performing a trend segment extraction on the coolant flow time series data to obtain a coolant flow change trend segment; Step S33: mapping the battery pack cooling pipe layout optimization data to cooling pipe segments using the coolant flow rate change trend segments to generate cooling pipe mapping data; predicting the remaining coolant amount in the cooling pipe mapping data using the coolant residual amount calculation formula to generate coolant pipe residual data; Step S34: performing normal coolant flow control on the cooling pipe mapping data according to the unit area heat load data to generate battery pack coolant flow control data; performing coolant reflux control on the battery pack coolant flow control data according to the coolant pipe residual data to generate coolant reflux control data.

8. The liquid-cooled battery pack thermal management method according to claim 7, characterized in that: Predicting the remaining coolant in cooling line mapping data includes: Extract the coolant resistance characteristics from the cooling pipeline mapping data to obtain coolant flow resistance characteristic data; use computational fluid dynamics to simulate the pipeline flow on the cooling pipeline mapping data to generate coolant residence time simulation data and coolant flow rate simulation data; The coolant residual amount calculation formula is used to calculate the coolant residual amount based on the coolant flow resistance characteristic data, coolant residence time simulation data, and coolant flow rate simulation data to obtain the coolant pipeline residual data. The coolant residual amount calculation formula is as follows: ； in, is the residual amount in the coolant pipeline, is the outlet fluid pressure at a certain point in the pipeline, is the inlet fluid pressure at a certain point in the pipeline, is the flow resistance of a certain section of the pipeline, is the length of the pipe.

9. The liquid-cooled battery pack thermal management method according to claim 1, wherein: Step S4 includes the following steps: Step S41: using the battery pack coolant flow control data and the coolant reflux control data to evaluate the battery thermal management cooling effect on the unit area heat load data, and generating battery thermal management cooling effect evaluation data; Step S42: Upload the battery thermal management cooling effect evaluation data to the cloud platform for data storage to generate battery thermal management storage data; visualize the battery thermal management storage data to generate a battery thermal management report to perform liquid-cooled battery pack thermal management operations.

10. A liquid-cooled battery pack thermal management system, characterized in that: For executing the liquid-cooled battery pack thermal management method according to claim 1, the liquid-cooled battery pack thermal management system comprises: An initial layout module is used to obtain battery pack information data; perform unit area heat load analysis on the battery pack information data to generate unit area heat load data; perform multi-stage coolant diversion pipeline layout based on the battery unit area heat load data to generate initial coolant pipeline layout data; A pipeline optimization module is used to calculate the local heat concentration index of the initial coolant pipeline layout data to obtain the local area heat concentration index; optimize the coolant pipeline layout of the initial coolant pipeline layout data using the local area heat concentration index to generate annular flow coolant layout optimization data or directional flow coolant layout optimization data; and perform multi-stage diversion pipeline layout linkage on the initial coolant pipeline layout data based on the annular flow coolant layout optimization data or the directional flow coolant layout optimization data to generate battery pack cooling pipeline layout optimization data; A flow control module is configured to map cooling pipe sections based on the battery pack cooling pipe layout optimization data to generate cooling pipe mapping data; predict the remaining coolant amount in the cooling pipe mapping data to generate coolant pipe residual data; perform normal coolant flow control on the cooling pipe mapping data based on the unit area heat load data to generate battery pack coolant flow control data; and perform coolant reflux control on the battery pack coolant flow control data using the coolant pipe residual data to generate coolant reflux control data; The thermal management evaluation module is used to evaluate the battery thermal management cooling effect by using the battery pack coolant flow control data and the coolant return control data on the unit area heat load data, generate a battery thermal management report, and perform liquid-cooled battery pack thermal management operations.

Images