Liquid-cooled battery pack thermal management method and system

Abstract

The present application relates to the technical field of battery management, and particularly relates to a liquid-cooled battery pack thermal management method and system. The method comprises the following steps: obtaining battery pack information data; performing unit area thermal load analysis on the battery pack information data to generate unit area thermal load data; performing cooling liquid multi-stage shunt pipeline layout according to the battery unit area thermal load data to generate 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. The present application solves the problems of uneven cooling, low efficiency and inaccurate control in the traditional liquid-cooled battery pack thermal management through accurate thermal load analysis, cooling liquid pipeline layout optimization, flow control and reflux control, and realizes a more efficient and intelligent thermal management system.

Description

TECHNICAL FIELD

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

[0002] With the rapid development of electric vehicle technology, the improvement of battery energy density brings challenges to thermal management. The traditional air cooling method is not effective, especially in high-power and high-density battery applications, and it is difficult to meet the growing cooling demand. In order to solve this problem, liquid cooling systems have gradually become a more efficient way of thermal management. Liquid cooling technology originated from early industrial applications and gradually developed into the field of battery cooling. Initially, liquid cooling technology mainly removes battery heat through direct contact with liquid, and the liquid transfers heat through a heat exchanger to maintain the stability of the battery operating temperature. With the advancement of battery technology, liquid cooling systems have also been continuously improved, with the selection of cooling liquid, fluid mechanics design, and the integration of thermal management modules being optimized. Currently, the application of liquid cooling systems in battery thermal management has reached a relatively mature stage. By using high-thermal-conductivity liquid and innovative cooling plate design, liquid cooling systems can effectively control battery temperature fluctuations, avoid overheating and overcooling, and thus improve the charging and discharging efficiency and cycle life of the battery. However, the current traditional cooling liquid pipe layout often relies on empirical design, and there are problems such as poor cooling liquid flow and low efficiency. At the same time, cooling liquid flow control is usually difficult to accurately adjust, and local overheating or cooling liquid waste may occur. SUMMARY

[0003] Therefore, 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 purpose, a liquid-cooled battery pack thermal management method, the method comprising the following steps:

[0005] Step S1: obtaining battery pack information data; performing unit region thermal load analysis on the battery pack information data to generate unit region thermal load data; and performing cooling liquid multi-stage shunt pipe layout according to the battery unit region thermal load data to generate initial cooling liquid pipe layout data;

[0006] Step S2: calculating the local region heat aggregation index based on the initial cooling liquid pipe layout data to obtain the local region heat aggregation index; performing cooling liquid pipe layout optimization on the initial cooling liquid pipe layout data based on the local region heat aggregation index to generate annular flow cooling liquid layout optimization data or directional flow cooling liquid layout optimization data; and performing multi-stage shunt pipe layout linkage on the initial cooling liquid pipe layout data based on the annular flow cooling liquid layout optimization data or the directional flow cooling liquid layout optimization data to generate battery pack cooling pipe layout optimization data;

[0007] Step S3: Map cooling pipe sections to the battery pack cooling pipe layout optimization data to generate cooling pipe mapping data; predict the residual coolant volume in the cooling pipe mapping data to generate residual coolant data; control the normal coolant flow based on the unit area heat load data to generate battery pack coolant flow control data; and control the coolant return flow based on the battery pack coolant flow control data using the residual coolant data to generate coolant return control data.

[0008] Step S4: Use battery pack coolant flow control data and coolant return control data to evaluate the cooling effect of the cell area heat load data, generate a battery thermal management report, and perform liquid-cooled battery pack thermal management operations.

[0009] This invention, by acquiring detailed battery pack information and performing cell area heat load analysis, can accurately identify the heat requirements of different areas. Based on this data, a more rational coolant piping layout can be formulated, ensuring that the initial design of the thermal management system meets the battery pack's heat dissipation needs. By calculating the local heat accumulation index and optimizing the coolant piping layout (annular or directional flow), the flow efficiency of coolant within the battery pack can be effectively improved, hot spots reduced, cooling efficiency increased, and heat evenly distributed, thereby enhancing the battery pack's thermal management performance. By mapping the cooling pipes and predicting the residual coolant volume, the flow and recirculation of coolant in the pipes can be precisely controlled, ensuring that the coolant flow rate in each area is reasonably adjusted. This not only improves coolant utilization efficiency but also optimizes the recirculation system, avoids coolant waste, and enhances the overall stability of the thermal management system. By evaluating the coolant flow and recirculation control data, a comprehensive understanding of the battery pack's thermal management performance can be achieved, potential problems identified, and control strategies optimized. The generated thermal management report provides data support for further improvements to the battery pack's heat dissipation design and operation, thereby ensuring the battery pack's safety, performance, and lifespan. Therefore, this invention solves the problems of uneven cooling, low efficiency, and inaccurate control in the thermal management of traditional liquid-cooled battery packs through precise thermal load analysis, coolant pipeline layout optimization, flow control, and reflux control, and realizes a more efficient and intelligent thermal management system.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain battery pack information data;

[0012] Step S12: Perform structural topology analysis on the battery pack information data to generate battery pack structural topology data; divide the battery pack information data into battery cell regions based on the battery pack structural topology data to generate battery cell region division data.

[0013] Step S13: Use temperature sensors to analyze the thermal load of the battery cell area division data and generate thermal load data for the cell area.

[0014] Step S14: Layout the multi-stage coolant distribution pipeline based on the battery pack structure topology data using the battery cell area thermal load data, and generate initial coolant pipeline layout data.

[0015] This invention, through structural topology analysis of the battery pack, reveals the overall layout and relationships between cells, providing a solid foundation for subsequent region division and thermal load analysis. Dividing the battery cells into regions based on structural topology data allows for independent analysis of the thermal load in different areas, offering a flexible solution for thermal management. Utilizing temperature sensors to analyze the thermal load of each cell region enables real-time monitoring of temperature distribution, timely detection of potential overheating issues, and provides decision-making support for the battery management system. Optimizing the coolant piping layout based on the thermal load data of each cell region allows for a multi-stage distribution pipeline design, ensuring uniform coolant distribution to areas with high thermal loads, enhancing cooling efficiency, and preventing localized overheating. Precise thermal load analysis and coolant piping optimization effectively prevent battery overheating and avoid safety accidents caused by thermal runaway. Accurate region division and coolant distribution design enable more efficient thermal management, extending battery life and improving overall performance.

[0016] Preferably, step S13 includes the following steps:

[0017] Step S131: Use a temperature sensor to divide the battery cell area into regions and collect temperature data of the battery cell area to obtain battery cell temperature collection data; perform data preprocessing on the battery cell temperature collection data to generate standard battery cell temperature collection data, wherein data preprocessing includes data cleaning, data denoising, missing value imputation and data standardization.

[0018] Step S132: Extract the geometry, thermal conductivity characteristics and battery material properties of the battery pack information data to construct a thermal conductivity model of the battery cell area, and import the standard battery cell temperature acquisition data into the thermal conductivity model of the battery cell area to convert the thermal load information and generate battery thermal load information data.

[0019] Step S133: Calculate the regional heat load of the battery heat load information data using the heat conduction equation to obtain the regional heat load of the battery cell; perform heat load mapping on the battery cell regional division data based on the regional heat load of the battery cell to generate battery cell heat load mapping data.

[0020] Step S134: Perform regional temperature time series feature analysis on the battery cell heat load mapping data to generate regional temperature time series feature data; identify hot spot areas in the battery cell heat load mapping data using the regional temperature time series feature data to generate cell regional heat load data.

[0021] This invention collects temperature data from battery cell regions using temperature sensors, providing fundamental data for subsequent thermal load analysis. Data cleaning, noise reduction, missing value imputation, 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. Standard battery cell temperature data is input into the model for thermal load conversion. This process considers the battery's physical characteristics and material properties, making the thermal load calculation more accurate. Regional thermal load is calculated using the thermal conduction equation, and battery cell thermal load mapping data is generated through thermal load mapping. This step, through model calculation and mapping, provides quantitative data support for the thermal load of different regions, helping to identify high-temperature areas. By performing regional temperature time-series feature analysis on the thermal load mapping data, time-series feature data helpful in identifying hotspot areas is extracted. This analysis helps to discover areas with thermal anomalies in the battery pack, allowing for timely adjustments to cooling strategies. Through temperature acquisition and data preprocessing, high-precision battery cell temperature data can be obtained, ensuring accurate capture of temperature changes. The thermal conduction model and thermal load calculation enable accurate thermal load assessment, thereby optimizing the battery pack's thermal management scheme. By analyzing time-series characteristics and identifying hotspot areas, potential hotspot areas can be monitored and accurately located in real time, providing data support for optimizing the cooling system and improving battery safety.

[0022] Preferably, step S2 includes the following steps:

[0023] Step S21: Perform coolant flow path analysis on the initial coolant piping layout data to generate coolant flow path data; perform coolant velocity distribution analysis on the coolant flow path data to generate coolant velocity distribution data.

[0024] Step S22: Use coolant flow rate distribution data to identify uneven cooling regions in the heat load data of the unit area, and generate uneven cooling region identification data; calculate the local heat accumulation index from the uneven cooling region identification data to obtain the local heat accumulation index.

[0025] Step S23: Compare the local area heat accumulation index with the preset standard area heat accumulation threshold. When the local area heat accumulation index is less than or equal to the preset standard area heat accumulation threshold, optimize the layout of the annular flow coolant pipeline for the corresponding uneven cooling area identification data and generate annular flow coolant layout optimization data.

[0026] Step S24: When the local area heat accumulation index is greater than the preset standard area heat accumulation threshold, the corresponding cooling uneven area identification data is optimized for directional heat conduction pipe layout to generate directional flow coolant layout optimization data.

[0027] Step S25: Based on the optimization data of the annular flow coolant layout and the optimization data of the directional flow coolant layout, design the coolant diversion points for the initial coolant pipeline layout data and generate coolant tiered diversion valve setting data; use the coolant tiered diversion valve setting data to link the initial coolant pipeline layout data with multi-level diversion pipeline layout to generate battery pack cooling pipeline layout optimization data.

[0028] This invention analyzes the flow path of the coolant pipeline to ensure effective coolant flow through each battery cell area. Coolant velocity distribution analysis clearly reveals velocity differences in different regions, providing a basis for subsequent optimization design. Velocity distribution data identifies areas of uneven cooling, which can lead to localized overheating. Further 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, it is determined whether to optimize the design of a ring-shaped coolant pipeline. When the local heat accumulation index is low, a ring layout ensures coolant flow in multiple directions, thus distributing heat more evenly. When the local heat accumulation index is high, a directional flow pipeline layout is optimized. This layout guides the coolant to areas with higher heat loads, providing targeted cooling and effectively reducing the risk of overheating. Coolant diversion points are designed based on the optimization results of different coolant layouts to further optimize the coolant flow path. By setting coolant tiered diversion valves, coolant flow can be adjusted separately for areas with different cooling needs, improving system flexibility and efficiency. Precise analysis of coolant flow rate and path allows for effective optimization of coolant distribution, preventing localized overheating and heat accumulation, and ensuring uniform battery cell temperature. Optimization of annular and directional coolant piping ensures effective cooling of areas with high heat loads, preventing battery damage or system failure due to overheating. The multi-stage branch piping layout and the coordinated design of tiered branch valves allow for adjustment of coolant flow according to the needs of different areas, improving system adaptability and responsiveness.

[0029] Preferably, step S23 includes the following steps:

[0030] Step S231: Compare the local area heat accumulation index with the preset standard area heat accumulation threshold. When the local area heat accumulation index is less than or equal to the preset standard area heat accumulation threshold, locate the highest heat accumulation area of ​​the battery pack based on the corresponding uneven cooling area identification data and generate the highest heat accumulation area location data.

[0031] Step S232: Based on the location data of the highest heat accumulation area, perform heat accumulation gradient analysis on the local heat accumulation index to generate local heat accumulation gradient data; use the location data of the highest heat accumulation area as the inlet of the annular flow pipeline, and use the local heat accumulation gradient data to design a closed loop for the inlet of the annular flow pipeline to generate the flow path of the annular flow pipeline.

[0032] Step S233: Calculate the number of coolant circulation loops for the flow path of the annular flow pipeline to obtain the number of coolant circulation loops; use the number of coolant circulation loops to optimize the layout of the annular flow coolant pipeline and generate annular flow coolant layout optimization data.

[0033] This invention identifies areas with high heat loads by comparing the heat accumulation index of local areas with a preset standard threshold. When the heat accumulation index of a local area is 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 pipe layout. Based on the location data of the highest heat accumulation area, heat accumulation gradient analysis is performed on the local heat accumulation index. This step identifies the heat load change trend in the battery pack, ensuring that the coolant can effectively flow to the area with the highest heat load. Furthermore, the highest heat accumulation area is used 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, enabling the coolant to form an efficient circulating flow path within the area. By calculating the number of coolant circulation loops in the annular flow pipe path, effective circulation and heat dissipation of the coolant in the flow path are ensured. This optimization process effectively improves cooling efficiency and avoids excessively high local temperatures. 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 high-temperature areas in the battery pack that require the most cooling can be accurately identified, ensuring that cooling resources are prioritized for the most critical areas and improving cooling efficiency. The combination of annular piping design and heat accumulation gradient analysis creates a closed-loop flow path for the coolant in areas of high heat load, effectively improving heat exchange efficiency and ensuring uniform coolant distribution. Calculation and optimization of the number of coolant circulation loops ensure sufficient coolant circulation within the piping, carrying away heat and preventing equipment failures caused by localized overheating. The optimized layout of the annular flow coolant piping results in higher efficiency and greater stability of the coolant flow path, effectively addressing the thermal management requirements of the battery under high load conditions.

[0034] Preferably, step S24 includes the following steps:

[0035] Step S241: When the local area heat accumulation index is greater than the preset standard area heat accumulation threshold, the corresponding uneven cooling area identification data is used to locate the regional heat source and generate regional heat source location data.

[0036] Step S242: Based on the regional heat source location data, perform initial directional pipeline design to obtain initial directional pipeline design data; calculate the directional pipeline heat transfer efficiency based on the initial directional pipeline design data to obtain the directional pipeline heat transfer efficiency, wherein the formula for calculating the directional pipeline heat transfer efficiency is as follows: ;

[0037] In the formula, It is the amount of heat conduction. It refers to the thermal conductivity of the pipe material. It is the cross-sectional area of ​​the pipe. It is the temperature at which the coolant enters the pipe. It is the temperature of the coolant flowing out of the pipe. It is the length of the pipe;

[0038] Step S243: Optimize the layout of the directional heat conduction pipeline based on the heat conduction efficiency of the directional pipeline design data to generate optimized layout data for the directional flow coolant.

[0039] This invention identifies the specific location of heat sources in a battery pack when the local heat accumulation index exceeds a preset standard threshold. This helps to target high-temperature areas requiring special cooling, ensuring rational allocation of cooling resources and avoiding uneven cooling. An initial directional pipe design is performed based on the regional heat source location data to ensure the coolant flows accurately to the heat source area. Next, the heat transfer efficiency of the directional pipes is calculated to ensure effective heat transfer. The initial directional pipe design is optimized based on the calculated heat transfer efficiency. The optimized design ensures that the coolant flows directionally in areas with high heat load, improving cooling efficiency and reducing localized overheating. The optimized directional pipe layout improves the efficiency and reliability of the cooling system. Heat source location accurately identifies areas with high heat load, providing a basis for subsequent directional cooling pipe design. This allows the coolant to prioritize flow to the areas most in need of cooling, reducing unnecessary waste of cooling resources. The heat transfer efficiency calculation of the directional pipes optimizes the selection of pipe materials and pipe layout, enabling the coolant to effectively remove heat. Precise heat transfer calculations ensure minimal temperature changes in the coolant, reducing heat accumulation. Optimized directional piping design ensures effective coolant coverage of high-temperature areas, improving heat dissipation and preventing localized overheating, thus enhancing battery pack stability and safety. The optimized directional piping layout also facilitates efficient coolant flow, reducing energy waste caused by overcooling and uneven cooling. This improves the overall energy efficiency of the cooling system and reduces system operating costs.

[0040] Preferably, step S3 includes the following steps:

[0041] Step S31: Confirm the connection points of the battery pack cooling pipe layout optimization data to obtain the connection points of the battery pack cooling pipes; deploy coolant flow monitoring nodes based on the connection points of the battery pack cooling pipes to generate coolant flow monitoring node deployment data;

[0042] Step S32: Perform time-series analysis on the coolant flow monitoring node deployment data to generate coolant flow time-series data; extract trend segments from the coolant flow time-series data to obtain coolant flow change trend segments;

[0043] Step S33: Map the cooling pipe section of the battery pack cooling pipe layout optimization data through the coolant flow rate change trend segment to generate cooling pipe mapping data; use the coolant residual amount calculation formula to predict the coolant residual amount in the pipe mapping data to generate coolant pipe residual data.

[0044] Step S34: 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; perform coolant reflux control on the battery pack coolant flow control data based on the coolant pipe residual data to generate coolant reflux control data.

[0045] This invention identifies and marks key connection points in the battery pack cooling system by confirming pipeline connection points. Based on these connection points, coolant flow monitoring nodes are deployed to monitor the flow status and flow rate changes of the coolant in the pipelines in real time. Coolant flow rate monitoring data is crucial for subsequent optimization and control, ensuring the uniformity and efficiency of coolant flow. Time-series analysis of coolant flow rate helps identify trends and patterns of flow rate changes. By extracting trend segments, the characteristics of coolant flow rate changes in different time periods can be further identified. This process allows for special attention to time periods or areas with high heat loads, ensuring that the coolant flows more efficiently during these periods. The cooling pipeline layout is optimized and mapped using coolant flow rate change trend segments. This step helps understand the flow status of the coolant in the pipelines, thereby achieving more precise optimization. Furthermore, the coolant residual amount in each pipeline segment is predicted using the coolant residual amount calculation formula. This helps identify which pipelines have insufficient coolant or poor flow, allowing for timely system adjustments. Based on the heat load data of the unit area, the flow in the cooling pipelines is controlled to ensure that the coolant flows along the optimized path, providing the best cooling effect. By further combining the residual data of the coolant pipeline, the backflow control ensures that the coolant in the pipeline can be returned to the system in a timely manner, avoiding insufficient or excessive coolant loss.

[0046] Preferably, predicting the residual coolant level in the cooling pipe mapping data includes:

[0047] Coolant resistance characteristics are extracted from the cooling pipe mapping data to obtain coolant flow resistance characteristic data; computational fluid dynamics is used to simulate pipe flow in the cooling pipe mapping data to generate simulated coolant residence time data and simulated coolant flow velocity data.

[0048] The residual coolant volume is calculated using the formula for calculating residual coolant volume. This calculation is based on coolant flow resistance characteristics, simulated coolant residence time, and simulated coolant flow velocity data. The resulting residual coolant volume data is shown below: ;

[0049] in, This is the residual amount of coolant in the piping. The outlet fluid pressure at a certain point in the pipeline. The inlet fluid pressure at a certain point in the pipeline. The flow resistance of a certain section of the pipeline. This represents the length of the pipe.

[0050] This invention extracts coolant flow resistance characteristics data from cooling pipe mapping data. Flow resistance is the main source of resistance to liquid flow in pipes, and it is affected by factors such as pipe size, material, and curvature. Accurately extracting these resistance characteristics helps predict the flow conditions of coolant in pipes. Computational Fluid Dynamics (CFD) is used to simulate the flow in the pipes, generating simulated data on coolant residence time and velocity. CFD simulation can consider the complex flow behavior of fluids in pipes, identifying phenomena such as uneven velocity distribution, dead zones, and eddies, thereby helping to analyze the flow state and distribution of coolant. The coolant resistance characteristics data, residence time simulation data, and velocity simulation data are comprehensively calculated according to the coolant residual volume calculation formula to obtain the coolant pipe residual data. In the formula, To and These represent the pressures at the pipe inlet and outlet, respectively. It is the flow resistance of a certain section of the pipeline. This is the length of the pipe. The residual amount of coolant in the pipe can be obtained using an integral formula. This helps analyze whether the coolant can cover the entire system or whether there is insufficient coolant flow. By extracting the resistance characteristics of coolant flow and performing flow simulations, the flow of coolant in each part of the pipeline can be accurately understood. This helps identify "dead zones" or low-flow-rate areas in the pipeline, thereby optimizing coolant distribution 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 problems. By adjusting the coolant flow rate and distribution strategy in a timely manner, sufficient cooling can be ensured in each area, optimizing the overall thermal management effect, preventing local overheating, and improving battery performance and lifespan.

[0051] Preferably, step S4 includes the following steps:

[0052] Step S41: Use battery pack coolant flow control data and coolant return control data to evaluate the battery thermal management cooling effect based on the cell area heat load data, and generate battery thermal management cooling effect evaluation data;

[0053] 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 for performing liquid-cooled battery pack thermal management operations.

[0054] This invention evaluates the cooling effect of the entire battery pack by utilizing coolant flow control data and coolant return control data, combined with the thermal load data of the battery cells. This step analyzes temperature changes and coolant efficiency in battery cell areas by adjusting flow and return control, thereby determining the performance of the cooling system under different operating environments. This ensures that the cooling system can effectively regulate and distribute coolant under various loads, avoiding heat accumulation or overcooling, and maintaining the temperature of each area of ​​the battery pack within the ideal range. This evaluation allows for the timely identification of potential problems in the system, such as uneven cooling and poor return flow, enabling necessary optimization. The battery thermal management cooling effect evaluation data is uploaded to a cloud platform for efficient centralized storage and management. The purpose of data storage is to support subsequent long-term monitoring, performance tracking, and system optimization. Cloud platform storage and centralized management facilitate long-term tracking and analysis of battery pack thermal management data. The cloud platform allows for real-time access to the system's operating status, facilitating remote monitoring and operation, which is particularly crucial for the maintenance and scheduling of large-scale battery packs. Data visualization of battery thermal management storage data generates clear and intuitive battery thermal management reports. These reports include specific cooling performance, temperature trends in different areas, and changes in coolant flow and return flow, providing a clear picture of system performance. Through data visualization, managers can more easily understand and analyze the battery pack's operating status, providing a basis for decision-making. Visualized reports simplify complex thermal management data, and charts and trend lines can quickly identify potential risks, providing a reference for system optimization.

[0055] This specification provides a liquid-cooled battery pack thermal management system for performing the above-described liquid-cooled battery pack thermal management method. The liquid-cooled battery pack thermal management system includes:

[0056] The initial layout module is used to acquire battery pack information data; perform cell area heat load analysis on the battery pack information data to generate cell area heat load data; and perform multi-stage coolant distribution pipeline layout based on the battery cell area heat load data to generate initial coolant pipeline layout data.

[0057] The pipeline optimization module is used to calculate the local heat accumulation index of the initial coolant pipeline layout data to obtain the local area heat accumulation index; optimize the coolant pipeline layout of the initial coolant pipeline layout data based on the local area heat accumulation index to generate annular flow coolant layout optimization data or directional flow coolant layout optimization data; and perform multi-level branch pipeline layout linkage on the initial coolant pipeline layout data based on the annular flow coolant layout optimization data or directional flow coolant layout optimization data to generate battery pack cooling pipeline layout optimization data.

[0058] The flow control module is used to map cooling pipe sections to battery pack cooling pipe layout optimization data to generate cooling pipe mapping data; predict the residual coolant volume in the pipes based on the cooling pipe mapping data to generate residual coolant data; perform normal coolant flow control on the cooling pipe mapping data based on unit area heat load data to generate battery pack coolant flow control data; and perform coolant recirculation control on the battery pack coolant flow control data based on the residual coolant data to generate coolant recirculation control data.

[0059] The thermal management assessment module is used to evaluate the cooling effect of battery thermal management based on the heat load data of the cell area using battery pack coolant flow control data and coolant return control data, and generate a battery thermal management report to perform thermal management operations for liquid-cooled battery packs.

[0060] The beneficial effects of this invention lie in acquiring battery pack information data and performing cell area heat load analysis to ensure that the coolant piping layout design can accurately respond to the heat demands of different areas of the battery cells. This effectively prevents overheating caused by uneven heat load and improves the overall heat dissipation efficiency and operational stability of the battery pack. By calculating and optimizing the coolant piping layout through the local heat accumulation index, targeted coolant flow optimization can be performed for hot spots in the battery pack. Optimization of annular and directional flow layouts further improves coolant flow efficiency and reduces coolant distribution unevenness, thereby achieving comprehensive and efficient thermal management of the battery pack. Through precise coolant piping segment mapping and residual volume prediction, more precise coolant flow control and recirculation control can be achieved, ensuring that the coolant maintains stable flow in each piping segment and effectively recirculates back to the cooling system. This not only improves cooling efficiency but also avoids coolant waste and ensures the system's energy utilization rate. By comprehensively evaluating the thermal management effect using coolant flow control data and reflux control data, the performance of the cooling system can be accurately detected, potential problems identified, and a thermal management report generated. This provides data support for subsequent optimization, ensuring optimal temperature control of the battery pack, extending its lifespan, and improving system safety and reliability. Therefore, this invention solves the problems of uneven cooling, low efficiency, and inaccurate control in traditional liquid-cooled battery pack thermal management through precise heat load analysis, coolant piping layout optimization, and flow and reflux control, achieving a more efficient and intelligent thermal management system. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of the steps involved in a liquid-cooled battery pack thermal management method.

[0062] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2.

[0063] Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3.

[0064] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0065] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0066] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0067] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0068] To achieve the above objectives, please refer to Figures 1 to 3 A liquid-cooled battery pack thermal management method, the method comprising the following steps:

[0069] Step S1: Obtain battery pack information data; perform cell area heat load analysis on the battery pack information data to generate cell area heat load data; based on the cell area heat load data, perform multi-stage coolant distribution pipeline layout to generate initial coolant pipeline layout data.

[0070] Step S2: Calculate the local heat accumulation index on the initial coolant piping layout data to obtain the local area heat accumulation index; optimize the coolant piping layout based on the local area heat accumulation index to generate annular flow coolant layout optimization data or directional flow coolant layout optimization data; based on the annular flow coolant layout optimization data or directional flow coolant layout optimization data, perform multi-level branch piping layout linkage on the initial coolant piping layout data to generate battery pack cooling piping layout optimization data;

[0071] Step S3: Map cooling pipe sections to the battery pack cooling pipe layout optimization data to generate cooling pipe mapping data; predict the residual coolant volume in the cooling pipe mapping data to generate residual coolant data; control the normal coolant flow based on the unit area heat load data to generate battery pack coolant flow control data; and control the coolant return flow based on the battery pack coolant flow control data using the residual coolant data to generate coolant return control data.

[0072] Step S4: Use battery pack coolant flow control data and coolant return control data to evaluate the cooling effect of the cell area heat load data, generate a battery thermal management report, and perform liquid-cooled battery pack thermal management operations.

[0073] This invention, by acquiring detailed battery pack information and performing cell area heat load analysis, can accurately identify the heat requirements of different areas. Based on this data, a more rational coolant piping layout can be formulated, ensuring that the initial design of the thermal management system meets the battery pack's heat dissipation needs. By calculating the local heat accumulation index and optimizing the coolant piping layout (annular or directional flow), the flow efficiency of coolant within the battery pack can be effectively improved, hot spots reduced, cooling efficiency increased, and heat evenly distributed, thereby enhancing the battery pack's thermal management performance. By mapping the cooling pipes and predicting the residual coolant volume, the flow and recirculation of coolant in the pipes can be precisely controlled, ensuring that the coolant flow rate in each area is reasonably adjusted. This not only improves coolant utilization efficiency but also optimizes the recirculation system, avoids coolant waste, and enhances the overall stability of the thermal management system. By evaluating the coolant flow and recirculation control data, a comprehensive understanding of the battery pack's thermal management performance can be achieved, potential problems identified, and control strategies optimized. The generated thermal management report provides data support for further improvements to the battery pack's heat dissipation design and operation, thereby ensuring the battery pack's safety, performance, and lifespan. Therefore, this invention solves the problems of uneven cooling, low efficiency, and inaccurate control in the thermal management of traditional liquid-cooled battery packs through precise thermal load analysis, coolant pipeline layout optimization, flow control, and reflux control, and realizes a more efficient and intelligent thermal management system.

[0074] In this embodiment of the invention, reference Figure 1 The above is a schematic flowchart of the steps of a liquid-cooled battery pack thermal management method according to the present invention. In this example, the liquid-cooled battery pack thermal management method includes the following steps:

[0075] Step S1: Obtain battery pack information data; perform cell area heat load analysis on the battery pack information data to generate cell area heat load data; based on the cell area heat load data, perform multi-stage coolant distribution pipeline layout to generate initial coolant pipeline layout data.

[0076] In this embodiment of the invention, basic parameter information of the battery pack is collected, such as the number, size, power density, and operating temperature range of battery cells. Data sources include battery pack design documents, sensor data, or testing equipment. Electrical information of the battery cells, such as current, voltage, and power output, is acquired to assess the thermal load of the battery pack under different operating conditions. Internal structural information of the battery pack is also acquired, including the layout of the battery cells and the design of the cooling system (such as coolant piping layout and coolant flow path). The collected battery pack information data is cleaned, erroneous or missing data is removed, and standardization is performed to ensure data consistency and accuracy. Based on the electrical parameters of the battery pack (such as power output, operating voltage, and current), combined with the battery's thermal model (such as the 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 area to generate thermal load data for each battery cell area. Three-dimensional thermal simulation using thermal analysis software further reveals 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 to enhance the cooling system design to balance the heat load in each area is assessed. Unit area heat load data is generated, providing fundamental data for subsequent cooling system design (such as coolant piping layout). Based on the heat load data for each unit area, the coolant flow requirements are determined. Considering the differences in heat load across different areas, a multi-stage branch piping layout is designed to ensure that each area receives an appropriate coolant flow rate according to actual needs. The design of the branch piping layout needs to consider the following factors: ensuring that the coolant can pass through each area quickly and evenly, avoiding localized overheating or insufficient cooling; and ensuring that the pressure in each branch pipe is balanced to ensure the flow efficiency of the coolant in each area. Based on the unit area heat load data, engineering simulation software (such as CFD analysis software) is used to simulate coolant flow, predicting the flow state, pressure, and temperature distribution of the coolant in different pipes. Using pipe layout optimization algorithms (such as shortest path algorithms and network flow optimization), initial coolant piping layout data is designed, including the layout of each coolant pipe, pipe dimensions, and branch point locations. The initial coolant piping layout data was validated using coolant flow simulation to confirm the rationality of the coolant flow path and whether the flow rate met requirements. The coolant velocity distribution and cooling effect were verified to ensure sufficient cooling in high-heat-load areas and to ensure a well-designed coolant return system to prevent liquid accumulation.

[0077] Step S2: Calculate the local heat accumulation index on the initial coolant piping layout data to obtain the local area heat accumulation index; optimize the coolant piping layout based on the local area heat accumulation index to generate annular flow coolant layout optimization data or directional flow coolant layout optimization data; based on the annular flow coolant layout optimization data or directional flow coolant layout optimization data, perform multi-level branch piping layout linkage on the initial coolant piping layout data to generate battery pack cooling piping layout optimization data;

[0078] In this embodiment of the invention, a local heat accumulation index is calculated based on the initial coolant piping layout design. The local heat accumulation index is an indicator that measures the degree of heat concentration in different areas within the battery pack, helping to identify areas of high temperature concentration. The calculation considers factors such as the power density of the battery cells, heat load, coolant flow path, and flow rate. The formula for the local heat accumulation index is: Heat Accumulation Index. ;in It is the heat load of the battery cell area. It is the volume of the battery cell region. This refers to the number of analysis regions. After obtaining the local heat accumulation index, the coolant piping layout is optimized to ensure sufficient cooling in areas with high heat accumulation, preventing excessive local temperatures that could lead to battery overheating. The optimization goal is to evenly distribute the coolant flow while reducing system energy consumption. For areas with severe heat accumulation, a ring-shaped coolant flow layout can be designed. The ring-shaped flow piping, with its circular arrangement of coolant pipes, makes the coolant flow path more uniform, avoiding excessive concentration in any one area. The design considers factors such as the velocity distribution and pipe size of the ring path to ensure balanced flow. Through simulation and optimization algorithms, the ring-shaped flow path is ensured to effectively improve heat exchange efficiency and reduce heat accumulation in local areas. For other regions, a directional flow coolant layout optimization can be used. This layout, through the arrangement of directional pipes, ensures that the coolant flows to specific areas, providing precise cooling. Directional flow optimization designs precise piping paths based on the local heat accumulation index and coolant requirements to improve heat dissipation in areas with high heat load. Based on the ring-shaped or directional flow coolant layout optimization data, further multi-stage branching piping layout optimization is performed on the coolant piping. The purpose of multi-stage branch pipe design is to achieve precise flow regulation, enabling each coolant line to dynamically adjust its flow rate according to the heat load, avoiding excessive coolant flow in some areas and insufficient flow in others. The multi-stage branch pipe layout considers the coordinated control between different branch points. The purpose of this coordination is to ensure that the flow rate and pressure are balanced when the coolant is distributed from the main pipe to each cooling zone. Especially in areas with high flow rates, the pipe's load-bearing capacity needs to be enhanced, while in areas with low cooling loads, energy consumption can be saved by reducing the flow rate or adjusting the pipe size. Computational fluid dynamics (CFD) simulations are used to further verify and optimize the design of each branch point, ensuring the efficiency and stability of coolant flow. By integrating optimization data from either annular or directional flow coolant layouts, the coordinated results of the multi-stage branch pipe layout are analyzed in conjunction with the overall cooling requirements of the battery pack, generating optimized battery pack cooling pipe layout data. This data reflects the final optimized coolant flow path, branch pipe layout, flow distribution, and pipe dimensions. The optimized piping layout was validated using thermal simulation tools to check the cooling effect and flow efficiency, ensuring that high-temperature areas were adequately cooled and avoiding localized heat accumulation. Based on the validation results, necessary adjustments were made to further improve the uniformity of coolant flow and heat dissipation efficiency.

[0079] Step S3: Map cooling pipe sections to the battery pack cooling pipe layout optimization data to generate cooling pipe mapping data; predict the residual coolant volume in the cooling pipe mapping data to generate residual coolant data; control the normal coolant flow based on the unit area heat load data to generate battery pack coolant flow control data; and control the coolant return flow based on the battery pack coolant flow control data using the residual coolant data to generate coolant return control data.

[0080] In this embodiment of the invention, geometric information of pipe segments, including pipe length, diameter, number of bends, and interface locations, is extracted from cooling pipe layout optimization data. Combined with heat load data from the battery pack unit area, the heat load of each pipe segment is determined. The distribution of heat load affects the flow distribution of coolant in the pipes. Using the above information, the heat load and flow characteristics of each cooling pipe are mapped to the specific location of the pipe segment, generating cooling pipe mapping data. This data includes the heat load, flow rate, flow velocity, and pipe parameters of each pipe segment. Flow resistance characteristics of each pipe segment are extracted from the cooling pipe mapping data, including pipe material, length, inner diameter, and flow velocity. Computational fluid dynamics (CFD) is used to simulate the flow of the cooling pipe segments, obtaining information such as the flow velocity distribution and residence time of the coolant in the pipes. Based on the coolant flow resistance characteristics, residence time, and flow velocity data, the coolant residual volume calculation formula is used to calculate the residual data of the coolant pipes. This data contains the residual coolant volume of each pipe segment, helping to evaluate the effective utilization of coolant in the pipes. Based on cooling pipe mapping data and combined with heat load data for each region, the flow requirements of coolant in each pipe section are analyzed. Differences in heat load across different regions affect coolant flow distribution. Based on the analysis results, coolant flow is adjusted to ensure the flow rate matches the heat load. Optimization of cooling effect is achieved through flow control of each pipe. Finally, coolant flow control data for the battery pack is generated, including coolant flow rate, velocity, and adjustment strategies for each pipe section. Based on residual coolant pipe data, the coolant return flow requirement in the pipes is analyzed to assess whether there is sufficient coolant for return flow to replenish the flow system. Based on the return flow requirement and residual data, a return flow control strategy is formulated, adjusting the coolant return path and flow rate to ensure efficient coolant flow throughout the entire pipe system. Finally, based on the residual coolant pipe data, return flow control is applied to the coolant flow control data to generate coolant return flow control data.

[0081] Step S4: Use battery pack coolant flow control data and coolant return control data to evaluate the cooling effect of the cell area heat load data, generate a battery thermal management report, and perform liquid-cooled battery pack thermal management operations.

[0082] In this embodiment of the invention, battery pack coolant flow control data and coolant return flow control data are collected and integrated. These data include coolant flow rate, velocity, temperature, return path, and flow control strategy for each pipe section. Simultaneously, heat load data for each cell area is collected, i.e., the heat dissipation demand data for each battery cell area. This data varies according to the battery's operating state and its heat dissipation requirements. Based on the principles of heat conduction and coolant flow characteristics, a cooling effect evaluation model is established. The model needs to consider the following factors: assessing whether the coolant flow rate matches the heat load demand of each battery cell, ensuring that the temperature control requirements of each area are met; evaluating whether the coolant effectively removes heat within the expected time by calculating the temperature distribution of different areas; evaluating the coolant reuse efficiency by analyzing the return path and return flow rate, ensuring that the system does not experience excessive coolant loss; analyzing the coolant's heat dissipation effect on the heat load of the battery cell area by calculating the coolant's residence time, velocity, and flow resistance in the pipes; calculating the temperature control error by comparing the actual cooling effect with the preset temperature standard, and further optimizing the cooling strategy based on the magnitude of the error; and generating battery pack thermal management cooling effect evaluation data based on the calculation results of the cooling effect evaluation model. The cooling effect evaluation data is compiled, including coolant flow and reflux control, temperature control effect of each battery cell, and coolant flow efficiency and heat load matching. Charts and heat maps are used to visualize the cooling effect, providing a clear and intuitive display of temperature distribution, coolant flow, and heat load matching. The generated report is uploaded to a cloud platform for storage and management, facilitating subsequent retrieval and analysis.

[0083] Preferably, step S1 includes the following steps:

[0084] Step S11: Obtain battery pack information data;

[0085] Step S12: Perform structural topology analysis on the battery pack information data to generate battery pack structural topology data; divide the battery pack information data into battery cell regions based on the battery pack structural topology data to generate battery cell region division data.

[0086] Step S13: Use temperature sensors to analyze the thermal load of the battery cell area division data and generate thermal load data for the cell area.

[0087] Step S14: Layout the multi-stage coolant distribution pipeline based on the battery pack structure topology data using the battery cell area thermal load data, and generate initial coolant pipeline layout data.

[0088] In this embodiment of the invention, detailed information about the battery pack is extracted from the Battery Management System (BMS), including basic parameters such as voltage, temperature, and charging status of each battery cell. If the data comes from different sources, multi-source data fusion is required to ensure data consistency and integrity. Graph theory algorithms are used to perform structural topology analysis on the physical layout of the battery pack, identifying the connection relationships between each battery cell and generating a topology diagram of the battery pack. Based on this topology data, the battery cells are divided into regions. For example, the battery cells can be divided into several regions (such as high-load areas, low-load areas, etc.) based on factors such as temperature characteristics and power consumption. The output of the division data is a table of region numbers and corresponding battery cells, further facilitating subsequent analysis. In each divided battery cell region, the heat load of each region is calculated using data collected by temperature sensors. Heat load analysis can be based on factors such as the power output and charging / discharging status of the battery cells. A heat conduction model is applied to analyze the heat transfer methods in different battery cell regions, thereby assessing the regional heat load distribution. The analysis results will serve as an important basis for determining cooling requirements and coolant allocation. Based on thermal load analysis data and battery pack topology data, a multi-stage coolant distribution pipeline layout is designed using optimization algorithms (such as shortest path algorithms or genetic algorithms). The design takes into account the structural characteristics of the battery pack, ensuring that the coolant can effectively cover high-temperature areas and maintain uniform cooling. This layout design 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 liquid waste. Initial coolant piping layout data is output, including pipe direction, branch points, coolant flow rate, etc., providing a basis for further cooling system implementation.

[0089] Preferably, step S13 includes the following steps:

[0090] Step S131: Use a temperature sensor to divide the battery cell area into regions and collect temperature data of the battery cell area to obtain battery cell temperature collection data; perform data preprocessing on the battery cell temperature collection data to generate standard battery cell temperature collection data, wherein data preprocessing includes data cleaning, data denoising, missing value imputation and data standardization.

[0091] Step S132: Extract the geometry, thermal conductivity characteristics and battery material properties of the battery pack information data to construct a thermal conductivity model of the battery cell area, and import the standard battery cell temperature acquisition data into the thermal conductivity model of the battery cell area to convert the thermal load information and generate battery thermal load information data.

[0092] Step S133: Calculate the regional heat load of the battery heat load information data using the heat conduction equation to obtain the regional heat load of the battery cell; perform heat load mapping on the battery cell regional division data based on the regional heat load of the battery cell to generate battery cell heat load mapping data.

[0093] Step S134: Perform regional temperature time series feature analysis on the battery cell heat load mapping data to generate regional temperature time series feature data; identify hot spot areas in the battery cell heat load mapping data using the regional temperature time series feature data to generate cell regional heat load data.

[0094] In this embodiment of the invention, real-time temperature acquisition is performed in different battery cell regions using temperature sensors. The temperature data from each battery cell forms a time-series dataset, recording the battery cell temperature value at a specific time point. A multi-point sensor array ensures that the spatial distribution of temperature acquisition covers all regions of the battery pack. Abnormal data caused by sensor malfunctions or other issues is checked and removed. Initial cleaning is performed by setting thresholds (e.g., temperatures exceeding a reasonable range). Random noise in the temperature data is removed using filtering algorithms (e.g., low-pass filters or mean filters). If some sensor data is missing, interpolation methods (e.g., linear interpolation or spline interpolation) can be used to fill in the missing data. The temperature data is normalized to ensure comparability of temperature data from different regions or time periods, typically using Z-score normalization or minimum-maximum normalization. The preprocessed data is referred to as standard battery cell temperature acquisition data, laying the foundation for subsequent heat load calculation and modeling. Based on battery pack information (e.g., geometry, thermal conductivity characteristics, thermal properties of battery materials), a heat conduction model of the battery cell region is constructed. 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 impact heat conduction. For example, whether the cells are arranged in a planar array or a stacked configuration. The thermal conductivity of different materials (such as the battery casing, conductive materials, and coolant) needs to be considered in the model. The type of battery (such as lithium-ion batteries) affects its thermal characteristics, including specific heat capacity and thermal conductivity. Standard battery cell temperature data is imported into the heat conduction model, and the heat load for each region is calculated through a numerical solution process. The purpose of heat load information conversion is to combine the temperature data collected by sensors with the thermal conduction characteristics of the battery cell regions, forming a dataset containing heat load density (e.g., W / m²), which describes the heat load situation of each region. The battery heat load information data is calculated using heat conduction equations (such as Fourier's law or steady-state heat conduction equations). The heat load for each battery cell region is calculated based on the temperature field and thermal conduction characteristics of the battery cells. 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 the cooling system requirements. Based on the heat load of the battery cell regions, the heat load data is mapped back to the battery cell region partitioning data. The heat load value of each region is matched with its corresponding battery cell region to generate battery cell heat load mapping data. Using this battery cell region heat load mapping data, temperature time-series characteristics are extracted. 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 change patterns. These time-series characteristics reveal the temperature change trends of different battery cell regions, helping to identify which regions are overheating or experiencing large temperature fluctuations. Further analysis of the regional temperature time-series characteristic data uses methods such as cluster analysis and thresholding to identify hotspot regions with high heat loads and large temperature fluctuations.The results of hotspot area identification will help identify areas requiring focused attention and cooling, as the temperature in these areas impacts the safety and lifespan of the battery pack, necessitating specially designed cooling systems. Finally, through comprehensive analysis of hotspot area identification and thermal load data, cell region thermal load data is generated, including the thermal load, temperature time-series characteristics, and hotspot area information for each battery cell region.

[0095] As an example of the present invention, reference is made to Figure 2 As shown, in this example, step S2 includes:

[0096] Step S21: Perform coolant flow path analysis on the initial coolant piping layout data to generate coolant flow path data; perform coolant velocity distribution analysis on the coolant flow path data to generate coolant velocity distribution data.

[0097] Step S22: Use coolant flow rate distribution data to identify uneven cooling regions in the heat load data of the unit area, and generate uneven cooling region identification data; calculate the local heat accumulation index from the uneven cooling region identification data to obtain the local heat accumulation index.

[0098] Step S23: Compare the local area heat accumulation index with the preset standard area heat accumulation threshold. When the local area heat accumulation index is less than or equal to the preset standard area heat accumulation threshold, optimize the layout of the annular flow coolant pipeline for the corresponding uneven cooling area identification data and generate annular flow coolant layout optimization data.

[0099] Step S24: When the local area heat accumulation index is greater than the preset standard area heat accumulation threshold, the corresponding cooling uneven area identification data is optimized for directional heat conduction pipe layout to generate directional flow coolant layout optimization data.

[0100] Step S25: Based on the optimization data of the annular flow coolant layout and the optimization data of the directional flow coolant layout, design the coolant diversion points for the initial coolant pipeline layout data and generate coolant tiered diversion valve setting data; use the coolant tiered diversion valve setting data to link the initial coolant pipeline layout data with multi-level diversion pipeline layout to generate battery pack cooling pipeline layout optimization data.

[0101] In this embodiment of the invention, computational fluid dynamics (CFD) simulation tools are used to analyze the flow path of the initial coolant piping layout data. The analysis examines the flow path, distribution, and any dead zones or flow bottlenecks of the coolant within the piping system. The simulation considers the physical properties of the coolant (e.g., viscosity, density) and the geometric properties of the piping (e.g., diameter, bends, branch points). The fluid flow path is calculated using CFD software (e.g., ANSYS Fluent), generating coolant flow path data. Based on this data, the velocity distribution of the coolant in different regions is analyzed. This velocity distribution helps determine the heat transfer effect and cooling efficiency of the coolant within the battery pack. The velocity field is calculated using CFD, generating coolant velocity distribution data to facilitate subsequent analysis of areas with insufficient or excessive velocity. Based on the coolant velocity distribution data, regions with low coolant velocities are identified, which can lead to uneven temperature distribution and overheating problems. By setting a coolant velocity threshold, regions below this threshold are marked as areas with uneven cooling. Further analysis of the battery pack's thermal load revealed areas of cooling unevenness by comparing coolant flow rate and battery cell area thermal load. For identified areas of uneven cooling, a local heat accumulation index was calculated. This index, weighted by temperature fluctuations, heat load density, and coolant flow rate, measures the degree of heat accumulation in the area. A high local heat accumulation index indicates severe heat buildup, leading to battery overheating or performance degradation. For uneven cooling areas with a local heat accumulation index less than or equal to a preset standard area heat accumulation threshold, a ring-shaped flow coolant piping layout optimization method was adopted. By optimizing the coolant piping layout, coolant is distributed to hot spots, forming a ring-shaped flow path to ensure uniform coolant flow in the area, effectively reducing the heat accumulation index. In practice, this optimization involves adding ring-shaped branch pipes within the cooling system to improve coolant flow direction or optimizing flow efficiency by changing the pipe layout. For uneven cooling areas with a local heat accumulation index greater than the preset standard area heat accumulation threshold, a directional heat conduction piping layout optimization was adopted. In this case, coolant flows directly to hot spots through directional pipes for more efficient cooling. Directional piping layout involves adding straight pipes, reducing bends, or adding dedicated cooling channels to precisely deliver coolant to hot spots. Optimization of directional heat conduction piping effectively reduces the temperature of hot spots by minimizing heat buildup. Based on optimization data for both annular and directional coolant flow layouts, coolant distribution points are designed to ensure appropriate coolant distribution according to the needs of the battery cell areas. The design of distribution points must consider coolant flow rate, temperature distribution, and pipe pressure loss to ensure reasonable distribution of coolant flow and temperature. Multi-stage, interconnected optimization of the initial coolant piping layout is performed using coolant tiered distribution valve setting data.The diversion valve controls the flow of coolant in each zone, ensuring that hot spots receive more coolant. In a multi-stage diversion system, the coolant flow rate can be adjusted as needed to ensure balanced cooling and reduce energy consumption. Through these optimizations, optimized data for the battery pack cooling pipe layout is generated. This data serves as the basis for cooling system design, ensuring a rational layout of coolant flow paths, velocity distribution, and diversion points, thereby effectively improving cooling efficiency, preventing overheating, and extending battery life.

[0102] Preferably, step S23 includes the following steps:

[0103] Step S231: Compare the local area heat accumulation index with the preset standard area heat accumulation threshold. When the local area heat accumulation index is less than or equal to the preset standard area heat accumulation threshold, locate the highest heat accumulation area of ​​the battery pack based on the corresponding uneven cooling area identification data and generate the highest heat accumulation area location data.

[0104] Step S232: Based on the location data of the highest heat accumulation area, perform heat accumulation gradient analysis on the local heat accumulation index to generate local heat accumulation gradient data; use the location data of the highest heat accumulation area as the inlet of the annular flow pipeline, and use the local heat accumulation gradient data to design a closed loop for the inlet of the annular flow pipeline to generate the flow path of the annular flow pipeline.

[0105] Step S233: Calculate the number of coolant circulation loops for the flow path of the annular flow pipeline to obtain the number of coolant circulation loops; use the number of coolant circulation loops to optimize the layout of the annular flow coolant pipeline and generate annular flow coolant layout optimization data.

[0106] In this embodiment of the invention, the calculated local area heat accumulation index is compared with a preset standard area heat accumulation threshold. If the local area heat accumulation index is less than or equal to the standard threshold, it indicates that the heat accumulation in that area is within an acceptable range, and the coolant flow does not require further optimization; otherwise, the optimization stage begins. For areas exceeding the standard threshold, subsequent identification of cooling non-uniform areas is performed; these areas are hotspots requiring special attention. Based on the identification results of cooling non-uniform areas, the highest heat accumulation area is located. This step determines the area in the battery pack with the highest temperature, the most concentrated heat load, and the greatest need for cooling optimization. The highest heat accumulation area location data includes the area's coordinates, heat load density, heat accumulation index, and other information, providing a basis for subsequent pipeline layout design. Based on the highest heat accumulation area location data, heat accumulation gradient analysis is performed on this area. By calculating the temperature and heat load changes between this area and the surrounding areas, heat accumulation gradient data is obtained, which reflects the heat distribution trend in this area. The information provided by the heat accumulation gradient data will be used to determine the most effective coolant flow path and areas requiring special attention. By using the location data of the highest heat accumulation area, the inlet position of the annular flow pipeline is determined. This inlet will become the starting point of the annular pipeline, ensuring that the coolant can directly enter the area with high heat accumulation. Based on the heat accumulation gradient data, the closed-loop design of the pipeline is optimized. The flow path is designed according to the heat distribution, allowing the coolant to efficiently cover the entire heat accumulation area and effectively remove heat. Based on the above analysis, the flow path of the annular flow pipeline is generated. This path will bypass the highest heat accumulation area and operate in a closed loop. In the actual design, the path of the annular pipeline will pass through areas with high coolant demand, ensuring uniform coolant flow to cover hot spots. The number of coolant circulation loops for the annular flow pipeline is calculated. This calculation is based on the coolant flow path length, the design parameters of the annular pipeline (such as pipe diameter, flow velocity, etc.), and the heat load distribution of the battery cells. Through calculation, the required number of circulation loops can be obtained to ensure that the coolant can fully flow through all hot spot areas, remove excess heat, and ultimately control the battery pack temperature within a safe range. Based on the calculated number of coolant circulation loops, the flow path of the annular flow piping is further optimized. This optimization includes adjusting the pipe diameter, adding branch pipes, and appropriately configuring flow control valves to ensure more uniform and efficient coolant flow while reducing system pressure loss and energy consumption. On this basis, optimized annular flow coolant layout data is generated, providing detailed layout diagrams and parameters for actual coolant piping design. The optimized layout data ensures uniform and efficient coolant flow while reducing overheating areas, effectively improving the overall performance of the cooling system.

[0107] Preferably, step S24 includes the following steps:

[0108] Step S241: When the local area heat accumulation index is greater than the preset standard area heat accumulation threshold, the corresponding uneven cooling area identification data is used to locate the regional heat source and generate regional heat source location data.

[0109] Step S242: Based on the regional heat source location data, perform initial directional pipeline design to obtain initial directional pipeline design data; calculate the directional pipeline heat transfer efficiency based on the initial directional pipeline design data to obtain the directional pipeline heat transfer efficiency, wherein the formula for calculating the directional pipeline heat transfer efficiency is as follows: ;

[0110] In the formula, It is the amount of heat conduction. It refers to the thermal conductivity of the pipe material. It is the cross-sectional area of ​​the pipe. It is the temperature at which the coolant enters the pipe. It is the temperature of the coolant flowing out of the pipe. It is the length of the pipe;

[0111] Step S243: Optimize the layout of the directional heat conduction pipeline based on the heat conduction efficiency of the directional pipeline design data to generate optimized layout data for the directional flow coolant.

[0112] In this embodiment of the invention, when the local area heat accumulation index exceeds a preset standard area heat accumulation threshold, uneven cooling areas are first identified. These areas, due to excessive heat accumulation, require further optimization design. Using the uneven cooling area identification data, precise thermal imaging analysis or other temperature measurement methods are used to locate the regional heat sources. The goal of this step is 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 pipeline design, ensuring that the pipeline layout accurately covers these heat source areas to improve cooling efficiency. Based on the regional heat source location data, an initial directional pipeline design is performed. This design ensures that the coolant can flow to the heat source areas and effectively remove heat from those areas. Factors considered in the preliminary design include: pipeline size, pipeline path, and coolant flow rate. The generated initial directional pipeline design data provides a foundation for subsequent pipeline heat transfer efficiency calculations. After designing the directional pipeline, the pipeline's heat transfer efficiency needs to be calculated to ensure that it can effectively transfer heat from the heat source areas to the coolant. The calculation formula is as follows: ;

[0113] In the formula, It is the amount of heat conduction. It refers to the thermal conductivity of the pipe material. It is the cross-sectional area of ​​the pipe. It is the temperature at which the coolant enters the pipe. It is the temperature of the coolant flowing out of the pipe. The calculation of the directional piping heat transfer efficiency helps determine the efficiency of the coolant piping in the heat source region and whether adjustments to the piping material, cross-section, or length are needed to improve heat transfer capacity. Based on the calculated directional piping heat transfer efficiency, the initial directional piping design data is optimized. The optimization goal is to maximize heat transfer efficiency, reduce coolant flow resistance, and ensure that the coolant efficiently removes heat from the heat source region. During optimization, the piping layout, piping material, piping cross-section, and coolant flow rate are adjusted to ensure the system maintains optimal heat transfer performance under different heat load conditions. The optimized data will include adjusted piping paths, dimensions, and material selections to ensure the coolant flow path maximizes heat transfer efficiency and addresses uneven cooling issues. Optimized piping layout ensures sufficient heat removal in the heat source region, thereby reducing localized temperatures and ensuring the safe operation of the battery pack.

[0114] As an example of the present invention, reference is made to Figure 3 As shown, step S3 in this example includes:

[0115] Step S31: Confirm the connection points of the battery pack cooling pipe layout optimization data to obtain the connection points of the battery pack cooling pipes; deploy coolant flow monitoring nodes based on the connection points of the battery pack cooling pipes to generate coolant flow monitoring node deployment data;

[0116] Step S32: Perform time-series analysis on the coolant flow monitoring node deployment data to generate coolant flow time-series data; extract trend segments from the coolant flow time-series data to obtain coolant flow change trend segments;

[0117] Step S33: Map the cooling pipe section of the battery pack cooling pipe layout optimization data through the coolant flow rate change trend segment to generate cooling pipe mapping data; use the coolant residual amount calculation formula to predict the coolant residual amount in the pipe mapping data to generate coolant pipe residual data.

[0118] Step S34: 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; perform coolant reflux control on the battery pack coolant flow control data based on the coolant pipe residual data to generate coolant reflux control data.

[0119] In this embodiment of the invention, for the battery pack cooling system, the connection points of the cooling pipes, i.e., the connection and intersection points between each coolant pipe, are first identified. This process ensures that the coolant can flow throughout the system. Using the battery pack's cooling pipe layout optimization data, the connection points of each cooling pipe can be determined through graph theory, network analysis methods, or fluid dynamics simulation. The generated cooling pipe connection point data provides an accurate basis for subsequent flow monitoring. Based on the cooling pipe connection points, coolant flow monitoring nodes are deployed at appropriate pipe nodes. These nodes are used to monitor the coolant flow in real time, including flow velocity, flow rate, and other relevant parameters. The coolant flow monitoring node deployment data will include information such as the location of each monitoring point, the monitoring method (e.g., flow velocity sensor or flow meter), and the monitoring range. Flow data obtained from each coolant flow monitoring node is collected, and coolant flow time series analysis is performed. Through time series analysis of the flow data, the patterns, anomalies, and trends of flow changes can be identified, coolant flow time series data can be generated, and time series analysis methods (such as autoregressive models, stationarity tests, etc.) can be used to model the data. After analyzing the time-series data of coolant flow rate, trend segments of coolant flow rate variation are extracted, i.e., time periods in which flow rate changes have a certain regularity. These trend segments include different trends such as increase, decrease, and stabilization of coolant flow rate, providing an important basis for subsequent pipeline optimization and control. Based on the coolant flow rate variation 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 analyze which pipeline segments have experienced significant changes in coolant flow rate. The generated cooling pipeline mapping data will display the flow rate changes of each pipeline segment, providing information for subsequent flow rate prediction and coolant distribution control. The coolant residual volume calculation formula is used to predict the cooling pipeline mapping data. This formula predicts the coolant residual volume of each pipeline segment by considering the pipeline flow rate, coolant consumption, and specific characteristics of coolant flow. The coolant pipeline residual volume data will provide key information about whether the coolant is sufficient and whether there is a risk of flow rate reduction, helping to adjust the coolant supply strategy. Based on the heat load data of the unit area, normal coolant flow control is performed on the cooling pipeline flow. This process adjusts the coolant flow rate based on heat load requirements to ensure sufficient coolant flow to each area. The generated battery pack coolant flow control data includes operational parameters such as flow control strategies, pipe allocation, and valve settings to ensure that coolant is rationally distributed to each unit area. Further backflow control is performed using residual coolant data from the pipes. Backflow control aims to adjust the coolant backflow strategy based on the amount of residual coolant in the pipes, ensuring effective coolant circulation and avoiding waste. The generated coolant backflow control data includes adjustment parameters for backflow valves and optimization of backflow paths, thereby maximizing coolant utilization efficiency.

[0120] Preferably, predicting the residual coolant level in the cooling pipe mapping data includes:

[0121] Coolant resistance characteristics are extracted from the cooling pipe mapping data to obtain coolant flow resistance characteristic data; computational fluid dynamics is used to simulate pipe flow in the cooling pipe mapping data to generate simulated coolant residence time data and simulated coolant flow velocity data.

[0122] The residual coolant volume is calculated using the formula for calculating residual coolant volume. This calculation is based on coolant flow resistance characteristics, simulated coolant residence time, and simulated coolant flow velocity data. The resulting residual coolant volume data is shown below: ;

[0123] in, This is the residual amount of coolant in the piping. The outlet fluid pressure at a certain point in the pipeline. The inlet fluid pressure at a certain point in the pipeline. The flow resistance of a certain section of the pipeline. This represents the length of the pipe.

[0124] In this embodiment of the invention, coolant flow resistance characteristics are extracted from the cooling pipe mapping data based on information such as the geometry, material, and surface roughness of the cooling pipe. Relevant fluid mechanics theories, such as the Darcy-Weisbach equation, are used to calculate the flow resistance of each section of the pipe. The resistance characteristic data can be derived from variables such as pressure and flow rate within the pipe. The generated coolant flow resistance characteristic data includes the flow resistance of each section of the pipe (such as frictional resistance and local resistance), which plays an important role in predicting coolant residual volume. Computational fluid dynamics (CFD) technology is used to simulate the flow of the cooling pipe mapping data. CFD can accurately simulate the flow behavior of coolant in the pipe. In the pipe, the coolant residence time refers to the time the coolant spends flowing through the pipe, affecting cooling efficiency. The residence time of the coolant in each pipe section is obtained through simulation. The coolant velocity in each pipe section is simulated, especially the velocity variation under different pipe conditions. The output of the CFD results will provide necessary input data for subsequent coolant residual volume prediction. The coolant residual volume is calculated according to the given formula: ;

[0125] in, This is the residual amount of coolant in the piping. The outlet fluid pressure at a certain point in the pipeline. The inlet fluid pressure at a certain point in the pipeline. The flow resistance of a certain section of the pipeline. The length of the pipe is used. Using CFD simulation data of coolant flow velocity and residence time, combined with flow resistance characteristics of the cooling pipe, the residual coolant volume is calculated according to the integral formula described above. This calculation is obtained by analyzing the fluid pressure differences and flow resistance at different locations in the pipe, thereby assessing the amount of coolant remaining in the pipe and the effective cooling capacity. After calculating the residual volume, based on the coolant distribution characteristics and the requirements of each cooling pipe section, the final coolant pipe residual data is generated. This data includes the remaining coolant volume in each pipe section, the flow state, and the coolant distribution strategy that needs adjustment. The coolant pipe residual data will be used to optimize coolant flow control, determine whether coolant needs to be added or recirculated, and ensure the effectiveness and stability of the entire cooling system.

[0126] Preferably, step S4 includes the following steps:

[0127] Step S41: Use battery pack coolant flow control data and coolant return control data to evaluate the battery thermal management cooling effect based on the cell area heat load data, and generate battery thermal management cooling effect evaluation data;

[0128] 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 for performing liquid-cooled battery pack thermal management operations.

[0129] In this embodiment of the invention, the cooling effect is evaluated by using the battery pack coolant flow control data and coolant return control data generated in step S34 as basic input data, combined with the heat load data of the cell area. Based on the heat load conditions of the battery cell area (such as temperature distribution, power density, etc.), the cooling capacity of the coolant flow in different areas is analyzed. Parameters such as flow rate, flow volume, and temperature difference are mainly considered to evaluate 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 effect evaluation model is established. This model evaluates the temperature control of each cell area of ​​the battery pack and determines whether the cooling effect meets expectations. Key data on the cooling effect are calculated, such as the temperature drop, thermal equilibrium state, and coolant utilization efficiency of each cell area. These data generate battery thermal management cooling effect evaluation data, which serves as the basis for subsequent optimization. The battery thermal management cooling effect evaluation data is uploaded to a cloud platform for long-term storage, management, and analysis. The data storage provided by the cloud platform ensures the reliability and security of the data, facilitating access and updates at any time. During data upload, a secure transmission protocol is used to ensure the integrity and confidentiality of the data transmission process. On the cloud platform, data visualization technologies (such as charts, heatmaps, and 3D visualization interfaces) provide an intuitive display of the battery pack's thermal management performance. Visualization tools are used to analyze cooling effect evaluation data in detail, enabling relevant personnel to clearly understand temperature changes and coolant flow in each area. Based on the visualization analysis results, a comprehensive battery thermal management report is generated. The report includes coolant flow control, recirculation control efficiency, temperature distribution, and cooling effect in each area. The report can also provide optimization suggestions and warnings (e.g., areas with excessively high temperatures or insufficiently cooled cells). Based on the battery thermal management report, corresponding thermal management measures are taken. For example, when the report shows that the cooling effect in certain areas is unsatisfactory, optimization can be achieved by adjusting the coolant flow rate, adding a recirculation system, or changing the cooling pipe layout. This process ensures that the entire battery pack's thermal management process is always in optimal condition, avoiding overheating or uneven cooling, and improving battery life and safety.

[0130] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0131] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the 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 invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A liquid-cooled battery pack thermal management method, characterized by, The method comprises the following steps: Step S1: obtaining battery pack information data; performing unit area thermal load analysis on the battery pack information data to generate unit area thermal load data; and performing cooling liquid multi-stage shunt pipeline layout according to the battery unit area thermal load data to generate initial cooling liquid pipeline layout data; Step S2: performing local thermal aggregation index calculation on the initial cooling liquid pipeline layout data to obtain a local area thermal aggregation index; performing cooling liquid pipeline layout optimization on the initial cooling liquid pipeline layout data through the local area thermal aggregation index to generate annular flow cooling liquid layout optimization data or directional flow cooling liquid layout optimization data; performing multi-stage shunt pipeline layout linkage on the initial cooling liquid pipeline layout data according to the annular flow cooling liquid layout optimization data or the directional flow cooling liquid layout optimization data to generate battery pack cooling pipeline layout optimization data; wherein, step S2 comprises the following steps: Step S21: performing cooling liquid flow path analysis on the initial cooling liquid pipeline layout data to generate cooling liquid flow path data; and performing cooling liquid flow rate distribution analysis on the cooling liquid flow path data to generate cooling liquid flow rate distribution data; Step S22: performing cooling non-uniform area identification on the unit area thermal load data by using the cooling liquid flow rate distribution data to generate cooling non-uniform area identification data; and performing local thermal aggregation index calculation on the cooling non-uniform area identification data to obtain a local area thermal aggregation index; Step S23: comparing the local area thermal aggregation index with a preset standard area thermal aggregation threshold value; when the local area thermal aggregation index is less than or equal to the preset standard area thermal aggregation threshold value, annular flow cooling liquid pipeline layout optimization is performed on the corresponding cooling non-uniform area identification data to generate annular flow cooling liquid layout optimization data; Step S24: when the local area thermal aggregation index is greater than the preset standard area thermal aggregation threshold value, directional heat conduction pipeline layout optimization is performed on the corresponding cooling non-uniform area identification data to generate directional flow cooling liquid layout optimization data; Step S25: based on the annular flow cooling liquid layout optimization data and the directional flow cooling liquid layout optimization data, cooling liquid shunt point design is performed on the initial cooling liquid pipeline layout data to generate cooling liquid hierarchical shunt valve setting data; and multi-stage shunt pipeline layout linkage is performed on the initial cooling liquid pipeline layout data through the cooling liquid hierarchical shunt valve setting data to generate battery pack cooling pipeline layout optimization data; Step S3: performing cooling pipeline segment mapping on the battery pack cooling pipeline layout optimization data to generate cooling pipeline mapping data; performing pipeline cooling liquid residual amount prediction on the cooling pipeline mapping data to generate cooling liquid pipeline residual data; performing normal cooling liquid flow control on the cooling pipeline mapping data according to the unit area thermal load data to generate battery pack cooling liquid flow control data; and performing cooling liquid backflow control on the battery pack cooling liquid flow control data through the cooling liquid pipeline residual data to generate cooling liquid backflow control data; Step S4: Perform battery thermal management cooling effect evaluation on the unit area thermal load data using the battery pack cooling liquid flow control data and the cooling liquid return control data, generate a battery thermal management report to perform liquid-cooled battery pack thermal management operation.

2. The liquid-cooled battery pack thermal management method of claim 1, wherein, Step S1 includes the following steps: Step S11: Obtain battery pack information data; Step S12: Perform structural topology analysis on the battery pack information data to generate battery pack structural topology data; based on the battery pack structural topology data, perform battery unit area division on the battery pack information data to generate battery unit area division data; Step S13: Perform unit area thermal load analysis on the battery unit area division data using temperature sensors to generate unit area thermal load data; Step S14: Perform cooling liquid multi-stage shunt pipeline layout on the battery pack structural topology data based on the battery unit area thermal load data to generate initial cooling liquid pipeline layout data.

3. The liquid-cooled battery pack thermal management method of claim 2, wherein, Step S13 includes the following steps: Step S131: Collect battery unit temperature acquisition data by collecting battery unit area temperature data of the battery unit area division data using temperature sensors; perform data preprocessing on the battery unit temperature acquisition data to generate standard battery unit temperature acquisition data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling, and data standardization; Step S132: Extract 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 unit area, and import the standard battery unit temperature acquisition data into the thermal conduction model of the battery unit area for thermal load information conversion to generate battery thermal load information data; Step S133: Perform regional thermal load calculation on the battery thermal load information data using the thermal conduction equation to obtain battery unit area thermal load; perform thermal load mapping on the battery unit area division data according to the battery unit area thermal load to generate battery unit thermal load mapping data; Step S134: Perform regional temperature time series feature analysis on the battery unit thermal load mapping data to generate regional temperature time series feature data; perform hot spot area identification on the battery unit thermal load mapping data through the regional temperature time series feature data to generate unit area thermal load data.

4. The liquid-cooled battery pack thermal management method of claim 1, wherein, Step S23 includes the following steps: Step S231: Compare the local area heat aggregation index with the preset standard area heat aggregation threshold value, when the local area heat aggregation index is less than or equal to the preset standard area heat aggregation threshold value, then perform battery pack highest heat aggregation area positioning on the corresponding cooling uneven area identification data to generate highest heat aggregation area positioning data; Step S232: Perform heat aggregation gradient analysis on the local area heat aggregation index according to the highest heat aggregation area positioning data to generate local area heat aggregation gradient data; take the highest heat aggregation area positioning data as the inlet of the ring-shaped flow pipeline, and perform pipeline closed loop design on the inlet of the ring-shaped flow pipeline using the local area heat aggregation gradient data to generate the ring-shaped flow pipeline flow path; Step S233: Calculate the cooling liquid circulation heat dissipation circle number for the annular flow pipeline flow path, and obtain the cooling liquid circulation heat dissipation circle number; use the cooling liquid circulation heat dissipation circle number to optimize the annular flow cooling liquid pipeline layout for the annular flow pipeline flow path, and generate annular flow cooling liquid layout optimization data.

5. The liquid-cooled battery pack thermal management method of claim 1, wherein, Step S24 includes the following steps: Step S241: When the local area heat accumulation index is greater than the preset standard area heat accumulation threshold value, the corresponding cooling uneven area recognition data is subjected to area heat source positioning to generate area heat source positioning data; Step S242: performing initial directional pipe design according to the regional heat source positioning data to obtain initial directional pipe design data; performing directional pipe heat conduction efficiency calculation on the initial directional pipe design data to obtain directional pipe heat conduction efficiency, wherein the formula of the directional pipe heat conduction efficiency calculation is as follows: ; wherein is the amount of heat conduction, 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 exiting the pipe, is the length of the pipe; Step S243: Based on the directional pipe heat conduction efficiency, the initial directional pipe design data is subjected to directional heat conduction pipe layout optimization to generate directional flow cooling liquid layout optimization data.

6. The liquid-cooled battery pack thermal management method of claim 1, wherein, Step S3 includes the following steps: Step S31: Confirm the pipeline connection point of the battery pack cooling pipeline layout optimization data to obtain the battery pack cooling pipeline connection point; and based on the battery pack cooling pipeline connection point, deploy the cooling liquid flow monitoring node to generate cooling liquid flow monitoring node deployment data; Step S32: Perform cooling liquid flow time sequence analysis on the cooling liquid flow monitoring node deployment data to generate cooling liquid flow time sequence data; and extract the trend segment of the cooling liquid flow time sequence data to obtain the cooling liquid flow change trend segment; Step S33: Map the cooling pipeline segment of the battery pack cooling pipeline layout optimization data through the cooling liquid flow change trend segment to generate cooling pipeline mapping data; and use the cooling liquid residual amount calculation formula to predict the pipeline cooling liquid residual amount of the cooling pipeline mapping data to generate cooling liquid pipeline residual data; Step S34: Perform normal cooling liquid flow control on the cooling pipeline mapping data according to the unit area heat load data to generate battery pack cooling liquid flow control data; and perform cooling liquid backflow control on the battery pack cooling liquid flow control data through the cooling liquid pipeline residual data to generate cooling liquid backflow control data.

7. The liquid-cooled battery pack thermal management method of claim 6, wherein, The pipeline cooling liquid residual amount prediction on the cooling pipeline mapping data includes: Extract the cooling liquid resistance characteristic data from the cooling pipeline mapping data; use computational fluid dynamics to simulate the pipeline flow of the cooling pipeline mapping data to generate cooling liquid residence time simulation data and cooling liquid flow rate simulation data; Use the cooling liquid residual amount calculation formula to calculate the cooling liquid residual amount of the cooling liquid flow resistance characteristic data, the cooling liquid residence time simulation data, and the cooling liquid flow rate simulation data to obtain the cooling liquid pipeline residual data, wherein the cooling liquid residual amount calculation formula is as follows: ； wherein, is the residual volume of the cooling liquid line, is the outlet fluid pressure at a point in the pipe, is the inlet fluid pressure at a point in the pipe, is the flow resistance of a section of the pipe, is the length of the pipe.

8. The liquid-cooled battery pack thermal management method of claim 1, wherein, Step S4 includes the following steps: Step S41: Use the battery pack cooling liquid flow control data and the cooling liquid backflow control data to evaluate the battery thermal management cooling effect of the unit area heat load data to generate 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; and visualize the battery thermal management storage data to generate a battery thermal management report to perform liquid-cooled battery pack thermal management work.

9. A liquid-cooled battery pack thermal management system, comprising: A liquid-cooled battery pack thermal management system for performing the method of claim 1, the liquid-cooled battery pack thermal management system comprising: An initial layout module configured to obtain battery pack information data, perform cell region thermal load analysis on the battery pack information data to generate cell region thermal load data, and perform cooling liquid multi-stage shunt pipeline layout according to the cell region thermal load data to generate initial cooling liquid pipeline layout data; A pipeline optimization module configured to perform local thermal aggregation index calculation on the initial cooling liquid pipeline layout data to obtain local region thermal aggregation index, perform cooling liquid pipeline layout optimization on the initial cooling liquid pipeline layout data through the local region thermal aggregation index to generate annular flow cooling liquid layout optimization data or directional flow cooling liquid layout optimization data, and perform multi-stage shunt pipeline layout linkage on the initial cooling liquid pipeline layout data according to the annular flow cooling liquid layout optimization data or the directional flow cooling liquid layout optimization data to generate battery pack cooling pipeline layout optimization data; A flow control module configured to perform cooling pipeline segment mapping on the battery pack cooling pipeline layout optimization data to generate cooling pipeline mapping data, perform pipeline cooling liquid residual amount prediction on the cooling pipeline mapping data to generate cooling liquid pipeline residual data, perform normal cooling liquid flow control on the cooling pipeline mapping data according to the cell region thermal load data to generate battery pack cooling liquid flow control data, and perform cooling liquid backflow control on the battery pack cooling liquid flow control data through the cooling liquid pipeline residual data to generate cooling liquid backflow control data; A thermal management evaluation module configured to perform battery thermal management cooling effect evaluation on the cell region thermal load data using the battery pack cooling liquid flow control data and the cooling liquid backflow control data to generate a battery thermal management report to perform liquid-cooled battery pack thermal management operation.

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