Digital modeling method and device for power battery immersion cooling

Abstract

This application relates to the field of power battery technology, specifically to a digital modeling method and apparatus for power battery immersion cooling. The method includes: determining a target operating condition parameter window for the target power battery through simulation analysis based on a pre-generated numerical model corresponding to a target power battery using a multi-level parameter system of the battery cell, combined with the current actual operating condition data of the target power battery; and outputting an experimental verification scheme including immersion process parameters and flow channel structure parameters based on the target operating condition parameter window. The multi-level parameter system of the battery cell is constructed through geometric parameter standardization design, thermal safety element database development, and iterative optimization of a multi-physics model, targeting the physical processes of phase change heat transfer in the immersion medium and short-circuit heat generation within the lithium-ion battery. The purpose of this application is to provide a digital modeling method and apparatus for power battery immersion cooling to address at least one of the technical problems mentioned in the background art.

Description

Technical Field

[0001] This application relates to the field of power battery technology, and more specifically, to a digital modeling method and device for power battery immersion cooling. Background Technology

[0002] As the "heart" of new energy vehicles, the performance and safety of power batteries directly constrain the electrification process of the entire vehicle. With the continuous improvement of battery energy density and the accelerating charging rate, the thermal management problem of battery systems is becoming increasingly prominent. Among them, lithium-ion batteries are prone to lithium dendrite growth under conditions such as overcharging, fast charging, or internal defects, leading to internal short circuits and inducing thermal runaway, which is the main cause of battery safety accidents.

[0003] To address the heat dissipation challenges under high heat flux, immersion cooling technology has emerged. This technology achieves direct and efficient heat exchange between the battery and the cooling medium by directly immersing the battery cell in a highly insulating and thermally conductive dielectric fluid, and is considered a disruptive approach to addressing the risk of thermal runaway in high-energy-density batteries. Currently, research on immersion cooling systems mainly relies on physical experimental verification, namely, evaluating their heat dissipation performance and safety by fabricating prototypes and building test platforms.

[0004] However, this traditional R&D model has significant technical bottlenecks: the impregnation cooling process involves the complex coupling of multiple physical fields, including electrochemical heat generation, dielectric fluid flow, phase change heat transfer, and structural heat conduction. Relying solely on physical experiments makes it difficult to accurately analyze the transient temperature field distribution, fluid flow patterns, and thermal runaway propagation paths within the battery pack under extreme conditions, resulting in a lack of precise theoretical guidance for design optimization. Therefore, how to construct an analytical method capable of simulating and predicting the thermal behavior of impregnation cooling systems under the coupling of multiple physical fields, to replace costly and repetitive physical experiments, and to provide quantitative basis for the design of impregnation cooling systems, has become an urgent technical problem to be solved. Summary of the Invention

[0005] The purpose of this application is to provide a digital modeling method and device for immersion cooling of power batteries, addressing at least one of the technical problems mentioned in the background art.

[0006] To achieve the above objectives, this application adopts the following technical solution: One aspect of this application provides a digital modeling method for immersion cooling of a power battery, comprising: Based on the numerical model corresponding to the target power battery generated in advance based on the multi-level parameter system of the battery cell, and combined with the current actual operating condition data of the target power battery, the target operating condition parameter window of the target power battery is determined through simulation analysis. An experimental verification scheme including impregnation process parameters and flow channel structure parameters is then output based on the target operating condition parameter window. The multi-level parameter system of the battery cell is constructed through geometric parameter standardization design, thermal safety element database development, and iterative optimization of a multi-physics model, targeting the physical processes of phase change heat transfer in the impregnation medium and short-circuit heat generation within the lithium-ion battery. The target operating condition parameter window includes: medium thermal conductivity, forced convection heat transfer coefficient, and pressure drop loss. The experimental verification scheme is output, and an immersion cooling verification system for the target power battery is prepared based on the experimental verification scheme and its performance is tested to obtain test data. The numerical model is corrected based on the test data, and the impregnation process parameters and flow channel structure parameters are optimized in a closed loop. The process is iterated until the preset convergence condition is met, and an impregnation cooling system design scheme for the target power battery is output.

[0007] Optionally, before determining the target operating condition parameter window of the target power battery through simulation analysis, the method further includes: To address the physical processes of phase change heat transfer in impregnating media and short-circuit heat generation in lithium-ion batteries, a multi-level parameter system for the battery cell was constructed through standardized geometric parameter design, development of a thermal safety element database, and iterative optimization of a multi-physics model. Based on the multi-level parameter system of the battery cell, the contact thermal resistance of the electrode-electrolyte interface is calibrated by iterative optimization of the multiphysics model and the thermal runaway trigger threshold model is corrected to obtain the numerical model of the target power battery that meets the preset accuracy requirements. The numerical model is a multiphysics coupled model established based on the computational fluid dynamics platform, which includes an electrochemical heat source model, a fluid flow and convection heat transfer model and a solid thermal conductivity equation.

[0008] Optionally, the multi-level parameter system of the battery cell includes: geometric parameters, thermal safety parameters, and optimization parameters; Correspondingly, the physical processes of phase change heat transfer in the impregnating medium and short-circuit heat generation in lithium-ion batteries are addressed through geometric parameter standardization design, thermal safety element database development, and iterative optimization of multiphysics model to construct a multi-level parameter system for the battery cell, including: The electrode double-sided coating thickness, electrode tab welding area geometry, and impregnation channel gap of the target power battery are used as geometric parameters. The thermal conductivity of the impregnation solution, the thermal shrinkage rate of the composite separator, and the concentration threshold of the thermal runaway gas components of the target power battery are used as thermal safety parameters. The electrode-electrolyte interface contact thermal resistance calibrated through orthogonal experiments and the thermal runaway trigger threshold model corrected based on high-temperature thermal abuse test data were used as optimization parameters to construct a multi-level parameter system for the battery cell.

[0009] Optionally, the step of iteratively optimizing and calibrating the electrode-electrolyte interface contact thermal resistance and correcting the thermal runaway trigger threshold model based on the multi-level parameter system of the battery cell to obtain the numerical model corresponding to the target power battery that meets the preset accuracy requirements includes: Orthogonal experiments were used to calibrate the contact thermal resistance at the electrode-electrolyte interface, and the calibrated contact thermal resistance was obtained. The thermal runaway triggering threshold model was modified based on high-temperature thermal abuse test data to obtain the modified thermal runaway triggering threshold model. Substituting the calibrated contact thermal resistance and the corrected thermal runaway trigger threshold model into the cell's multi-level parameter system, a numerical model with a temperature field prediction error of ≤±1.5℃ under 5C discharge conditions corresponding to the target power battery is obtained through iterative optimization.

[0010] Optionally, the step of determining the target operating condition parameter window of the target power battery through simulation analysis based on the numerical model corresponding to the target power battery generated in advance based on the multi-level parameter system of the battery cell, combined with the current actual operating condition data of the target power battery, and outputting an experimental verification scheme including impregnation process parameters and flow channel structure parameters according to the target operating condition parameter window, includes: Based on the numerical model, simulations were performed using the target power battery under 5C fast charging conditions, an ambient temperature range of -30℃ to 60℃, and thermal abuse triggering conditions as actual operating conditions. The influence of the impregnation medium flow rate on temperature uniformity was analyzed through single-factor simulation. The interaction between the flow channel topology and the filling rate was optimized by combining orthogonal experiments to determine the target operating condition parameter window of the target power battery. The target operating condition parameter window includes: medium thermal conductivity > 0.25 W / (m•K), forced convection heat transfer coefficient 450~600 W / (m²•K), and pressure drop loss < 15 kPa. The experimental verification scheme, which includes vacuum pressure impregnation process parameters and cellular flow channel battery compartment structure parameters, is output based on the target operating condition parameter window. The experimental verification scheme is used to prepare an impregnation cooling verification system that integrates a distributed optical fiber temperature sensing network and a particle image velocimetry system.

[0011] Optionally, the step of correcting the numerical model based on the test data and performing closed-loop optimization on the impregnation process parameters and flow channel structure parameters, iterating until a preset convergence condition is met, and outputting an impregnation cooling system design scheme for the target power battery, includes: Acquire test data generated from performance testing of the immersion cooling verification system. The test data includes: thermal runaway propagation delay obtained from the needle penetration trigger test of GB / T 31467.3-2015 standard, the dielectric thermal conductivity decay rate obtained from the 3000 charge-discharge cycle aging test, and flow velocity non-uniformity data obtained from the flow field distribution test. Based on the test data, fault tree analysis was used to locate residual hotspots of air bubbles, and the vacuum level of the impregnation process was increased to 10. - ³Pa level; the flow channel curvature radius is adjusted to >5mm through topology optimization; Using a volumetric heat transfer coefficient ≥8.7kW / m³·K as a preset convergence condition, the numerical model, impregnation process parameters, and flow channel structure parameters are iteratively optimized until the preset convergence condition is met, resulting in an impregnation cooling system design scheme with a thermal runaway trigger threshold >245℃, pump power <120W, and 5C discharge temperature rise <8℃.

[0012] Optionally, the charge-discharge cycle aging test is conducted according to ISO 12405-4 standard, and the number of tests is 3000. The flow field distribution test was performed using a particle image velocimetry system, with a tracer particle concentration of 5 × 10⁻⁶. 6 / m³, optimized flow velocity non-uniformity ≤8%; The fault tree analysis for locating residual hotspots of bubbles includes locating microbubble aggregation areas using X-ray scanning.

[0013] Another aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the digital modeling method for immersion cooling of power batteries provided in this application.

[0014] A third aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the digital modeling method for immersion cooling of power batteries provided in this application.

[0015] A fourth aspect of this application provides a computer program product, including a computer program, characterized in that, when executed by a processor, the computer program implements the digital modeling method for immersion cooling of power batteries provided in this application.

[0016] The technical solution provided in this application can achieve at least one of the following beneficial effects: The digital modeling method and equipment for immersion cooling of power batteries provided in this application, through the numerical model pre-generated based on the multi-level parameter system of the battery cell, combined with the actual operating condition data of the target power battery for simulation analysis, can quantify the thermal behavior of the immersion cooling system under the coupling effect of multiple physical fields, determine the target operating condition parameter window including the thermal conductivity of the medium, the forced convection heat transfer coefficient, and the pressure drop loss, provide a quantitative basis for the design of the immersion cooling system, reduce the dependence on physical experiments, shorten the R&D cycle, and reduce development costs; by outputting experimental verification schemes and correcting the numerical model based on the test data obtained from performance tests, and simultaneously implementing closed-loop optimization of immersion process parameters and flow channel structure parameters, iterating until the preset convergence conditions are met, it can improve the prediction accuracy of the numerical model and the reliability of the immersion cooling system design scheme, and achieve effective prevention and control of the risk of thermal runaway of power batteries.

[0017] The additional technical features and advantages of this application will become more apparent from the following description or from practical application. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the specific embodiments of this application, the accompanying drawings used in the description of the specific embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating one implementation of the digital modeling method for immersion cooling of power batteries provided in this application. Figure 2 This is a schematic diagram of the digital modeling and analysis method for immersion cooling of power batteries provided in the embodiments of this application; Figure 3 This is a schematic diagram illustrating the process of constructing a multi-level parameter system for battery cells provided in an embodiment of this application. Figure 4 This is a simplified theoretical framework flowchart for the construction process provided in the embodiments of this application. Detailed Implementation

[0020] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0022] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0023] like Figure 1 As shown, one aspect of this application provides a digital modeling method for immersion cooling of a power battery, comprising: Step 100: Based on the numerical model corresponding to the target power battery generated in advance based on the multi-level parameter system of the battery cell, and combined with the current actual operating condition data of the target power battery, the target operating condition parameter window of the target power battery is determined through simulation analysis, and an experimental verification scheme including impregnation process parameters and flow channel structure parameters is output according to the target operating condition parameter window; wherein, the multi-level parameter system of the battery cell is constructed through geometric parameter standardization design, thermal safety element database development and multi-physics field model iterative optimization for the physical processes of phase change heat transfer of impregnation medium and short-circuit heat generation in lithium-ion batteries; the target operating condition parameter window includes: medium thermal conductivity, forced convection heat transfer coefficient and pressure drop loss; Step 200: Output the experimental verification scheme, and prepare an immersion cooling verification system for the target power battery based on the experimental verification scheme and conduct performance tests to obtain test data; Step 300: Based on the test data, the numerical model is corrected, and the impregnation process parameters and flow channel structure parameters are optimized in a closed loop. The process is iterated until the preset convergence condition is met, and the impregnation cooling system design scheme for the target power battery is output.

[0024] The numerical model described in this application embodiment is referred to as a high-fidelity numerical model or model by Haike; the target power battery includes a cylindrical power battery, but can also be a power battery of other shapes; the target operating condition parameter window can also be referred to as the optimal operating condition parameter window; the impregnation process includes a vacuum pressure impregnation process; the flow channel structure parameters include honeycomb flow channels and flow channel curvature radius; the geometric parameter standardization design includes defining the key dimensions of the cell: electrode double-sided coating thickness (80-120μm), electrode tab welding area geometry topology and cooling flow channel gap (0.5-2.0mm); the thermal safety element database development includes establishing a domain-specific dataset containing the impregnation liquid thermal conductivity (0.12-0.63 W / (m•K)) and composite separator thermal shrinkage rate (≤5%@150℃), and may also include ten other core parameters; the multiphysics model iterative optimization includes calibrating the electrode-electrolyte interface contact thermal resistance (10) through orthogonal experiments. -4 -10 - The performance test includes a thermal runaway trigger threshold model based on high-temperature thermal abuse test data (on the order of 3 m²•K / W) and a temperature field prediction error of ≤±1.5℃ (5C discharge condition). The performance test includes verifying the blocking capability through the thermal runaway propagation test of GB / T 31467.3-2015 (needle penetration trigger + 200℃ / s temperature rise impact), quantifying the thermal runaway propagation delay >620s, evaluating the medium stability (thermal conductivity decay rate <3%) by combining 3000 charge-discharge cycle aging test, and simultaneously collecting flow field distribution data (flow velocity non-uniformity ≤8%).

[0025] The digital modeling method for immersion cooling of power batteries provided in this application, through a numerical model pre-generated based on the multi-level parameter system of the battery cell, combined with simulation analysis of the actual operating conditions data of the target power battery, can quantify the thermal behavior of the immersion cooling system under the coupling effect of multiple physical fields, determine the target operating condition parameter window including the thermal conductivity of the medium, the forced convection heat transfer coefficient, and the pressure drop loss, provide a quantitative basis for the design of the immersion cooling system, reduce the dependence on physical experiments, shorten the R&D cycle, and reduce development costs; by outputting experimental verification schemes and correcting the numerical model based on the test data obtained from performance tests, and simultaneously implementing closed-loop optimization of immersion process parameters and flow channel structure parameters, iterating until the preset convergence conditions are met, it can improve the prediction accuracy of the numerical model and the reliability of the immersion cooling system design scheme, and achieve effective prevention and control of the risk of thermal runaway of power batteries.

[0026] Optionally, before determining the target operating condition parameter window of the target power battery through simulation analysis, the method further includes: To address the physical processes of phase change heat transfer in impregnating media and short-circuit heat generation in lithium-ion batteries, a multi-level parameter system for the battery cell was constructed through standardized geometric parameter design, development of a thermal safety element database, and iterative optimization of a multi-physics model. Based on the multi-level parameter system of the battery cell, the contact thermal resistance of the electrode-electrolyte interface is calibrated by iterative optimization of the multiphysics model and the thermal runaway trigger threshold model is corrected to obtain the numerical model of the target power battery that meets the preset accuracy requirements. The numerical model is a multiphysics coupled model established based on the computational fluid dynamics platform, which includes an electrochemical heat source model, a fluid flow and convection heat transfer model and a solid thermal conductivity equation.

[0027] In this embodiment, the preset accuracy requirement includes a temperature field prediction error ≤ ±1.5℃; the computational fluid dynamics platform is preferably a CFD platform such as ANSYS Fluent; the electrochemical heat source model can also be called a battery heat generation model; the geometric parameters are obtained by geometric parameter standardization design, the thermal safety parameters are developed by thermal safety element database, and the optimization parameters are obtained by multiphysics model iterative optimization.

[0028] Optionally, the multi-level parameter system of the battery cell includes: geometric parameters, thermal safety parameters, and optimization parameters; Correspondingly, the physical processes of phase change heat transfer in the impregnating medium and short-circuit heat generation in lithium-ion batteries are addressed through geometric parameter standardization design, thermal safety element database development, and iterative optimization of multiphysics model to construct a multi-level parameter system for the battery cell, including: The electrode double-sided coating thickness, electrode tab welding area geometry, and impregnation channel gap of the target power battery are used as geometric parameters. The thermal conductivity of the impregnation solution, the thermal shrinkage rate of the composite separator, and the concentration threshold of the thermal runaway gas components of the target power battery are used as thermal safety parameters. The electrode-electrolyte interface contact thermal resistance calibrated through orthogonal experiments and the thermal runaway trigger threshold model corrected based on high-temperature thermal abuse test data were used as optimization parameters to construct a multi-level parameter system for the battery cell.

[0029] Optionally, the step of iteratively optimizing and calibrating the electrode-electrolyte interface contact thermal resistance and correcting the thermal runaway trigger threshold model based on the multi-level parameter system of the battery cell to obtain the numerical model corresponding to the target power battery that meets the preset accuracy requirements includes: Orthogonal experiments were used to calibrate the contact thermal resistance at the electrode-electrolyte interface, and the calibrated contact thermal resistance was obtained. The thermal runaway triggering threshold model was modified based on high-temperature thermal abuse test data to obtain the modified thermal runaway triggering threshold model. Substituting the calibrated contact thermal resistance and the corrected thermal runaway trigger threshold model into the cell's multi-level parameter system, a numerical model with a temperature field prediction error of ≤±1.5℃ under 5C discharge conditions corresponding to the target power battery is obtained through iterative optimization.

[0030] Optionally, the step of determining the target operating condition parameter window of the target power battery through simulation analysis based on the numerical model corresponding to the target power battery generated in advance based on the multi-level parameter system of the battery cell, combined with the current actual operating condition data of the target power battery, and outputting an experimental verification scheme including impregnation process parameters and flow channel structure parameters according to the target operating condition parameter window, includes: Based on the numerical model, simulations were performed using the target power battery under 5C fast charging conditions, an ambient temperature range of -30℃ to 60℃, and thermal abuse triggering conditions as actual operating conditions. The influence of the impregnation medium flow rate on temperature uniformity was analyzed through single-factor simulation. The interaction between the flow channel topology and the filling rate was optimized by combining orthogonal experiments to determine the target operating condition parameter window of the target power battery. The target operating condition parameter window includes: medium thermal conductivity > 0.25 W / (m•K), forced convection heat transfer coefficient 450~600 W / (m²•K), and pressure drop loss < 15 kPa. The experimental verification scheme, which includes vacuum pressure impregnation process parameters and cellular flow channel battery compartment structure parameters, is output based on the target operating condition parameter window. The experimental verification scheme is used to prepare an impregnation cooling verification system that integrates a distributed optical fiber temperature sensing network and a particle image velocimetry system.

[0031] Optionally, the step of correcting the numerical model based on the test data and performing closed-loop optimization on the impregnation process parameters and flow channel structure parameters, iterating until a preset convergence condition is met, and outputting an impregnation cooling system design scheme for the target power battery, includes: Acquire test data generated from performance testing of the immersion cooling verification system. The test data includes: thermal runaway propagation delay obtained from the needle penetration trigger test of GB / T 31467.3-2015 standard, the dielectric thermal conductivity decay rate obtained from the 3000 charge-discharge cycle aging test, and flow velocity non-uniformity data obtained from the flow field distribution test. Based on the test data, fault tree analysis was used to locate residual hotspots of air bubbles, and the vacuum level of the impregnation process was increased to 10. - ³Pa level; the flow channel curvature radius is adjusted to >5mm through topology optimization; Using a volumetric heat transfer coefficient ≥8.7kW / m³·K as a preset convergence condition, the numerical model, impregnation process parameters, and flow channel structure parameters are iteratively optimized until the preset convergence condition is met, resulting in an impregnation cooling system design scheme with a thermal runaway trigger threshold >245℃, pump power <120W, and 5C discharge temperature rise <8℃.

[0032] Optionally, the charge-discharge cycle aging test is conducted according to ISO 12405-4 standard, and the number of tests is 3000. The flow field distribution test was performed using a particle image velocimetry system, with a tracer particle concentration of 5 × 10⁻⁶. 6 / m³, optimized flow velocity non-uniformity ≤8%; The fault tree analysis for locating residual hotspots of bubbles includes locating microbubble aggregation areas using X-ray scanning.

[0033] Another aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the digital modeling method for immersion cooling of power batteries provided in this application.

[0034] A third aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the digital modeling method for immersion cooling of power batteries provided in this application.

[0035] A fourth aspect of this application provides a computer program product, including a computer program, characterized in that, when executed by a processor, the computer program implements the digital modeling method for immersion cooling of power batteries provided in this application.

[0036] To better illustrate the digital modeling method and equipment for immersion cooling of power batteries provided in this application, this application also provides an application example of the digital modeling method and equipment for immersion cooling of power batteries, which is as follows: This application aims to construct a complete multiphysics coupled modeling and analysis method for impregnation cooling of power batteries, focusing on solving the precise thermal management challenge of high-energy-density batteries (energy density ≥300Wh / kg) in thermal runaway protection. This method takes electric vehicle power battery systems as the research object, integrating mesoscopic property characterization (phase change enthalpy and viscosity-temperature characteristics of the impregnation medium) with macroscopic heat transfer modeling (heat conduction paths in heterogeneous structures) to establish a quantitative analysis system for impregnation cooling covering the material-component-system levels. Its core technological value lies in transforming traditional experience-driven design into a "digital twin-driven" development paradigm, significantly improving the prediction accuracy of heat exchange efficiency of the impregnation cooling system under extreme conditions (within 30s after thermal abuse triggering) (error ≤±1.5℃), providing a theoretical foundation and engineering practice guide for the development of high-safety power batteries. Specifically, it includes: Step 1: For physical processes supported by theoretical models (such as phase change heat transfer in the impregnation medium and short-circuit heat generation in lithium-ion batteries), systematically construct a multi-level parameter system for the battery cell: 1) Standardized design of geometric parameters: Define key dimensions of the battery cell: electrode double-sided coating thickness (80-120μm), electrode tab welding area geometry topology, cooling channel gap (0.5-2.0mm); 2) Development of thermal safety element database: Establish a domain-specific dataset containing 12 core parameters such as impregnation liquid thermal conductivity (0.12-0.63 W / (m•K)) and composite separator thermal shrinkage rate (≤5%@150℃); 3) Iterative optimization of multiphysics model: Calibrate the electrode-electrolyte interface contact thermal resistance (10) through orthogonal experiments. -4 -10 - (On the order of ³ m²•K / W); Based on high-temperature thermal abuse test data, the thermal runaway trigger threshold model is corrected to achieve a temperature field prediction error of ≤±1.5℃ (5C discharge condition).

[0037] Step Two: Based on the actual operating conditions of the power battery (5C fast charging, ambient temperature range of -30~60℃, thermal abuse triggering conditions, etc.), a simplified theoretical assumption system is constructed: the influence of the impregnation medium flow rate (0.1~1.5m / s) on temperature uniformity is analyzed through single-factor simulation, and the interaction between the flow channel topology and the filling rate (≥99.2%) is optimized by orthogonal experiments. The simulation results show that the optimal operating condition parameter window is: medium thermal conductivity > 0.25W / (m•K), forced convection heat transfer coefficient 450~600W / (m²•K), and pressure drop loss < 15kPa. Based on this, an experimental verification scheme is designed—a prototype of the honeycomb flow channel battery compartment is prepared using a vacuum pressure impregnation process, integrating a distributed fiber optic temperature sensing network (accuracy ±0.1℃) and a particle image velocimetry system, and conducting thermal runaway prevention tests (needle penetration trigger + 200℃ / s temperature rise impact) to achieve closed-loop verification of manufacturing parameters and thermal management performance.

[0038] Step 3: Based on the prepared impregnation cooling verification system (including vacuum pressure impregnation of the honeycomb chamber, distributed fiber optic temperature sensing network, and high-speed particle velocimetry unit), multi-dimensional performance tests were performed: the blocking capability was verified through the GB / T 31467.3-2015 thermal runaway propagation test (needle penetration trigger + 200℃ / s temperature rise impact), and the thermal runaway propagation delay was quantified to be >620s; the medium stability was evaluated by combining 3000 charge-discharge cycle aging tests (thermal conductivity decay rate <3%); and flow field distribution data was collected simultaneously (flow velocity non-uniformity ≤8%). Closed-loop optimization was implemented based on the test results: fault tree analysis was used to locate residual hot spots of bubbles, and the vacuum degree of the impregnation process was improved to 10. -The pressure drop was reduced by 18% by adjusting the flow channel curvature radius (>5mm) through topology optimization. The iterative process aimed to achieve a volumetric heat transfer coefficient ≥8.7kW / m³•K until the optimal solution for overall thermal-fluid-electric performance was reached.

[0039] Advantages of Space Integration and Production Optimization: The immersion cooling theoretical modeling and analysis method achieves a high degree of integration of the thermal management system through multi-physics field coupled simulation (such as full-domain thermal flux coupling of cell-dielectric-shell). This increases the volumetric energy density of the battery pack by more than 18%, freeing up more than 25% of design space, perfectly adapting to the compact layout requirements of new high-energy-density systems such as solid-state batteries (≥300Wh / kg). This method significantly improves the consistency of thermal management—the temperature difference between modules is controlled within ±1.5℃ (GB / T 34015-2017 standard), directly reducing thermal imaging inspection processes by 30% in production and simplifying manufacturing processes such as vacuum liquid injection and pipeline welding.

[0040] Significant improvements in thermal safety and product reliability: Immersion cooling technology relies on direct contact heat exchange with a highly thermally conductive medium (fluorinated liquid has a thermal conductivity of 0.25 W / (m•K), 10 times higher than air), achieving millisecond-level response under extreme thermal shock of 200℃ / s, with thermal runaway propagation delay exceeding 600 seconds (needle penetration trigger test). Its energy efficiency advantages are significant: pumping power consumption is only 1 / 3 of traditional liquid cooling systems, and the system energy efficiency ratio (COP) reaches 4.8. More importantly, based on modeling and simulation-guided optimization of the medium flow channel topology, the cell surface temperature uniformity is improved to over 95%, fundamentally solving the problem of localized overheating in lithium dendrites.

[0041] Development cycle compression and cross-domain spillover effects: This modeling and analysis method constructs a closed-loop development system of "digital twin - virtual verification - physical iteration," shortening the new product development cycle by 40%. It automatically generates over 200 design schemes through parametric models, replacing 80% of physical trial-and-error costs; the Fault Tree Analysis (FTA) tool accurately locates over 90% of thermal runaway protection defects. Its originality lies in forming transferable technical assets—an experience database covering 12 types of material failure models and 8 thermal propagation scenarios.

[0042] This application constructs a multiphysics coupled modeling and analysis method for impregnation cooling, focusing on solving the problem of precise protection against thermal runaway in solid-state batteries with energy densities ≥300Wh / kg. By integrating mesoscale property characterization (enthalpy change of impregnation medium phase transition ≥180kJ / kg, viscosity-temperature coefficient -0.002 / (m•K) / ℃) with macroscopic system heat transfer modeling (topology optimization of heat flow paths in heterogeneous structures), a quantitative analysis system covering the material, monomer, and module levels is established. Its core breakthrough lies in achieving a paradigm shift towards "digital twin-driven" development: based on the VOF two-phase flow model of ANSYS Fluent, coupled with an electrochemical heat source, the transient heat transfer process within 30 seconds after thermal abuse triggering is accurately reproduced (temperature field prediction error ≤±1.5℃@200℃ / s temperature rise); combined with a virtual verification platform, the vacuum pressure impregnation process parameters (vacuum degree ≤10) are optimized. - With a pressure of ≥3Pa and a filling rate ≥99.2%, the thermal runaway propagation delay exceeds 600 seconds (GB / T 31467.3 needle penetration test). Specifically, it includes: Step 1: Based on theoretical models supporting physical processes such as phase change heat transfer in the impregnation medium and Joule heat generation in internal short circuits, a multi-level parameterized engineering system for the battery cell is systematically constructed. In terms of geometric parameter standardization, the thickness of the double-sided electrode coating (80-120μm tolerance ±3μm), the fish-scale lap welding topology of the tab welding area (penetration depth ≥90%), and the impregnation channel gap (0.5-2.0mm gradient design) are precisely defined. A thermal safety element database integrating 12 core parameters is developed, covering the dynamic thermal conductivity of the impregnation liquid (0.12-0.63 W / (m•K) @ -30~80℃), the thermal shrinkage rate of the composite diaphragm (≤5% @ 150℃ / 10min), and the thermal runaway gas component concentration threshold (CO ≥ 200ppm). Through iterative optimization using a multi-physics model, orthogonal experiments are employed to calibrate the electrode-electrolyte interface contact thermal resistance (10... -4 -10 - The model (electrolyte vapor pressure-temperature correlation equation) is on the order of ³ m²•K / W (DOE variance analysis R²>0.95). Combined with the thermal abuse test data of GB / T 31467.3, the thermal runaway triggering model is corrected to achieve a temperature field prediction error of ≤±1.5℃ (confidence interval 95%) under 5C discharge conditions, providing a quantitative basis for the immersion cooling design of high-energy-density batteries.

[0043] For physical quantities or processes with theoretical models, design and formulate the size parameters of power battery cells, design metadata parameters in the field of power batteries, and continuously adjust and optimize them to establish mathematical and physical models.

[0044] This includes designing the battery cell size specifications and structural design by incorporating specific internal parameters of the battery through theoretical modeling.

[0045] Heat transfer can be classified into three types according to its physical nature: heat conduction, heat convection, and heat radiation. In a static coolant, heat transfer is mainly conducted, while in a flowing coolant, it is mainly convection or heat radiation. Spontaneous flow caused by density differences due to uneven temperature fields within the fluid, under the influence of gravity, is called natural convection. The velocity distribution of the boundary layer in natural convection is characterized by being low at both ends and high in the middle. At the wall, the velocity is zero due to viscosity; outside the thin layer edge, there is no temperature or pressure, and the velocity reaches a peak in the middle of the thin layer.

[0046] Mathematical model analysis follows these steps: 1) Thermal analysis of the inside of the rapid cooling water tank The coolant in the cooling water tank flows violently due to the influence of the rotating turbine, creating forced convection heat transfer with the battery casing. The electrolyte inside the battery (for non-solid-state batteries) is stationary. The temperature gradient between the fluid near the battery casing and the fluid inside the battery creates heat conduction; the temperature gradient leads to a density gradient, resulting in natural convection heat transfer.

[0047] 2) Basic Equations of Flow The coolant in the cooling water tank is considered an incompressible fluid, and the flow continuity equation is as shown in equation (1): (1), For an incompressible fluid ρ = const (a constant, and ▽v = 0), if the flow is considered isothermal and the viscosity is treated as constant, then the Navier-Stokes equation is equation (2): (2), in, (3), Flow problems are usually treated as incompressible and of constant viscosity. The expansion of the Navier-Stokes equation for incompressible fluids of constant viscosity is given by equation (4): (4), The vector form of this equation is equation (5): (5), In the formula, ρ—fluid density, kg / m³; μ—fluid kinematic viscosity, kg / (m•s); p—pressure, Pa; vx, vy, vz—fluid velocity and its components in the x, y, and z directions, m / s; x, y, z—distributed resistance, representing the flow resistance of the fluid in the cooling water tank. The distributed resistance term is related to the following factors: local head loss, friction coefficient, and permeability.

[0048] 3) Basic heat transfer equations (1) Battery casing surface: In a cooling environment, the surface temperature of the battery casing is close to the temperature of the electrolyte inside the battery, and higher than the ambient temperature provided by the coolant. There is a temperature difference heat transfer between the two, so the heat transfer equation of the battery casing surface is Equation (6): (6), In the formula, α is the convective heat transfer coefficient, W / (m²·K); λ is the thermal conductivity in the x, y, z directions, W / (m·K); nx, y, z are the direction factors; x, y, z are the directions of the rectangular coordinate axes. Based on the assumption that the battery cell is isotropic, we can obtain formula (7): (7), In the formula, T—the thermodynamic temperature of the battery casing surface, K; T —Cooling medium temperature, K; (2) Battery casing interior: Cooling the inside of the battery cell is necessary. To accurately determine the cooling rate and uniformity, a detailed analysis of the internal heat transfer is required. Fourier's Law is fundamental to heat conduction, and equation (8) can be derived: (8); In the formula, q — heat transfer per unit area, W / m2.

[0049] The internal structure of the battery cell is subject to unsteady heat conduction, and the differential equation is given by equation (9): (9), In the formula, ρ is the cell density (kg / m³), λ is the thermal conductivity in the cell direction (W / (m•K), c is the specific heat of the rigid cell (J / (kg•K), and τ is the cooling time (s).

[0050] (3) Unified modeling: Due to the wide variety of cell casings (including square and cylindrical cells), and the different materials, shapes, and sizes of cells, model building requires specific analysis for specific problems. This application mainly focuses on 18650 batteries, but is not limited to this type of cylindrical battery. It is assumed that the characteristic dimensions of 18650 cells are cylinders with a base diameter of R, and their central axis is used as the coordinate axis.

[0051] The heat transfer equation for the battery cell casing is shown in equation (10): (10) The thermal conductivity equation inside the battery cell is given by equation (11), where 0 < r < R for the cylinder: (11), In the formula, r is the spatial position inside the battery cell; m is the shape factor outside the battery cell, which is 1 for an infinitely large flat plate, 2 for an infinitely long cylinder, and 3 for a sphere.

[0052] Step Two: Based on the stringent operating conditions of the power battery (5C fast charging peak current, -30~60℃ extreme temperature range, GB / T31467.3 thermal abuse triggering conditions), a simplified engineering theoretical framework is constructed: The influence of the impregnation medium flow rate (0.1~1.5m / s) on the module temperature uniformity is quantified through single-factor simulation (target δT≤3℃). The interaction mechanism between the flow channel branch angle (45°-90°) and the vacuum filling rate (≥99.2%) is analyzed using orthogonal experiments. The simulation determines the optimal parameter window as: medium dynamic thermal conductivity >0.25W / (m•K)@80℃, forced convection heat transfer coefficient 450~600W / (m²•K), and flow channel pressure drop <15kPa (corresponding to a 40% reduction in pump power). Based on this, an experimental verification platform is developed—using a 10 - The titanium alloy honeycomb chamber is manufactured using a ³Pa-level vacuum pressure impregnation process. It integrates a 128-channel distributed fiber optic temperature measurement network (±0.1℃ accuracy / 1mm spatial resolution) and a high-speed PIV flow field diagnostic system (2000fps frame rate). It performs ISO 12405-4 standard thermal runaway prevention test: simulates internal short circuits at a temperature rise rate of 200℃ / s, monitors the thermal propagation path after needle penetration triggering, and finally achieves closed-loop verification of manufacturing parameters (such as flow channel roughness Ra≤0.8μm) and cooling performance (thermal runaway suppression>620s), providing a data foundation for mass production solutions.

[0053] Step 3: Based on the independently developed impregnation cooling verification platform (including a titanium alloy vacuum pressure impregnation honeycomb chamber, a 256-channel distributed fiber optic temperature sensing network (±0.1℃ accuracy / 1mm spatial resolution) and a laser Doppler velocimetry system (2000fps frame rate)), three-dimensional engineering verification was performed: A needle-triggered thermal runaway propagation test (200℃ / s temperature rise impact) was conducted according to GB / T 31467.3-2015, quantifying the thermal propagation blocking delay as >620 seconds (24% improvement over the national standard requirement); A 3000-cycle charge-discharge aging test was conducted according to ISO12405-4 standard, confirming that the thermal conductivity decay rate of the impregnation medium was <3% (verification of the molecular chain stability of the fluorinated liquid); Simultaneously, flow field distribution cloud maps were acquired (PIV tracer particle concentration 5×10⁻⁶). 6 / m³), flow rate non-uniformity ≤8% (SAE J2901 Class A). Fault tree closed-loop optimization was implemented based on test data: microbubble aggregation areas (residual rate >0.05%) were located using X-ray scanning, and the vacuum level of the impregnation process was increased to 10. -The pressure drop was reduced by 18% to the 3Pa level (99.9% bubble elimination rate). The flow channel curvature radius was adjusted to >5mm using topology optimization (turbulence intensity decreased by 40%). The iterative process used a volumetric heat transfer coefficient ≥8.7kW / m³•K as the convergence threshold (corresponding to the thermal management requirements of a 300Wh / kg battery pack), ultimately achieving a synergistic optimal solution for thermal runaway protection (>245℃ trigger threshold), flow resistance characteristics (pump power <120W), and electrical performance (5C discharge temperature rise <8℃).

[0054] Specific examples: Example 1 With a constant coolant flow rate, the coolant temperature was varied for simulation, and the settable temperature range was determined by comparing the temperatures at monitoring points 1 and 2. Taking a flow rate of 0.01 m / s as an example, the cooling curves at the monitoring points were plotted based on the simulation results at different temperatures. The temperatures were 252 K, 255 K, 258 K, 261 K, 264 K, 267 K, 270 K, and 273 K. The cooling simulation effect is shown to be good.

[0055] Example 2 Using the above simulation conditions, taking a flow velocity of 0.01 m / s as an example, temperature and velocity change curves at monitoring points under different temperatures were plotted based on the simulation results. It can be seen that the temperature and velocity change curves meet expectations.

[0056] Example 3 Using the above simulation conditions, taking a flow rate of 0.01 m / s as an example, we can see the effect of temperature on the cooling rate. It is evident that, at a constant flow rate, the higher the coolant temperature, the slower the cooling rate of the battery cell. As the temperature decreases, the cooling rate tends to level off because the decrease in temperature difference weakens convective heat transfer outside the battery cell, thus reducing heat conduction within the cell. At 252 K, the maximum cooling rate can reach 1.79 K / min.

[0057] Innovation Point 1: For physical processes supported by theoretical models (such as phase change heat transfer in impregnating media, internal short-circuit heat generation in lithium-ion batteries, etc.), a multi-level parameter system for the battery cell is systematically constructed: 1) Standardized design of geometric parameters: defining key dimensions of the battery cell: electrode double-sided coating thickness (80-120μm), electrode tab welding area geometry topology, cooling channel gap (0.5-2.0mm); 2) Development of thermal safety element database: establishing a domain-specific dataset containing 12 core parameters such as impregnating liquid thermal conductivity (0.12-0.63 W / (m•K)) and composite separator thermal shrinkage rate (≤5%@150℃); 3) Iterative optimization of multiphysics model: calibrating the electrode-electrolyte interface contact thermal resistance (10) through orthogonal experiments. -4 -10 -(On the order of ³ m²•K / W); Based on high-temperature thermal abuse test data, the thermal runaway trigger threshold model is corrected to achieve a temperature field prediction error of ≤±1.5℃ (5C discharge condition).

[0058] Beneficial Effects: Spatial Integration and Production Optimization Advantages: The immersion cooling theoretical modeling and analysis method achieves a high degree of integration of the thermal management system through multi-physics field coupled simulation (such as full-domain thermal flux coupling of cell-dielectric-shell). This increases the volumetric energy density of the battery pack by more than 18%, freeing up more than 25% of design space, perfectly adapting to the compact layout requirements of new high-energy-density systems such as solid-state batteries (≥300Wh / kg). This method significantly improves thermal management consistency—the temperature difference between modules is controlled within ±1.5℃ (GB / T 34015-2017 standard), directly reducing thermal imaging inspection processes by 30% in production and simplifying manufacturing processes such as vacuum liquid injection and pipeline welding.

[0059] Innovation Point 2 of this application: Based on the actual operating conditions of power batteries (5C fast charging, ambient temperature range of -30~60℃, thermal abuse triggering conditions, etc.), a simplified theoretical assumption system is constructed: the influence of the impregnation medium flow rate (0.1~1.5m / s) on temperature uniformity is analyzed through single-factor simulation, and the interaction between the flow channel topology and the filling rate (≥99.2%) is optimized by orthogonal experiments. The simulation results show that the optimal operating condition parameter window is: medium thermal conductivity > 0.25W / (m•K), forced convection heat transfer coefficient 450~600W / (m²•K), and pressure drop loss < 15kPa. Based on this, an experimental verification scheme is designed—a prototype of the honeycomb flow channel battery compartment is prepared using a vacuum pressure impregnation process, integrating a distributed fiber optic temperature sensing network (accuracy ±0.1℃) and a particle image velocimetry system, and conducting thermal runaway prevention tests (needle penetration trigger + 200℃ / s temperature rise impact) to achieve closed-loop verification of manufacturing parameters and thermal management performance.

[0060] Beneficial Effects: A Leap in Thermal Safety and Product Reliability: Immersion cooling technology relies on direct contact heat exchange with a highly thermally conductive medium (fluorinated liquid has a thermal conductivity of 0.25 W / (m•K), 10 times higher than air), achieving millisecond-level response under extreme thermal shock at 200℃ / s, with thermal runaway propagation delay exceeding 600 seconds (needle penetration trigger test). Its energy efficiency advantages are significant: pumping power consumption is only 1 / 3 of traditional liquid cooling systems, and the system energy efficiency ratio (COP) reaches 4.8. More importantly, based on modeling and simulation-guided optimization of the medium flow channel topology, the uniformity of cell surface temperature is improved to over 95%, fundamentally solving the problem of localized overheating in lithium dendrites. BYD's actual tests show that this technology extends battery cycle life to 4000 cycles (EOL 80% capacity) and increases the thermal runaway trigger threshold to 245℃. Its value lies not only in improving the single-batch manufacturing yield (>99.92%), but also in driving the innovation of battery design paradigms from the mechanism level—for example, guiding the development of "electrode-cooling integrated" structure, removing technical obstacles for the commercialization of high-safety and long-life batteries.

[0061] Innovation Point 3 of this application: Based on the prepared impregnation cooling verification system (including a vacuum pressure impregnation honeycomb chamber, a distributed optical fiber temperature sensing network, and a high-speed particle velocimetry unit), multi-dimensional performance tests were performed: the blocking capability was verified through the GB / T 31467.3-2015 thermal runaway propagation test (needle penetration trigger + 200℃ / s temperature rise impact), and the thermal runaway propagation delay was quantified to be >620s; the stability of the medium was evaluated by combining a 3000-cycle charge-discharge aging test (thermal conductivity decay rate <3%); and flow field distribution data was collected simultaneously (flow velocity non-uniformity ≤8%). Closed-loop optimization was implemented based on the test results: fault tree analysis was used to locate residual hot spots of bubbles, and the vacuum degree of the impregnation process was improved to 10. - The pressure drop was reduced by 18% by adjusting the flow channel curvature radius (>5mm) through topology optimization. The iterative process aimed to achieve a volumetric heat transfer coefficient ≥8.7kW / m³•K until the optimal solution for overall thermal-fluid-electric performance was reached.

[0062] Beneficial Effects: Development Cycle Compression and Cross-Domain Radiation Effect: This modeling and analysis method constructs a closed-loop development system of "digital twin - virtual verification - physical iteration," shortening the new product development cycle by 40%. It automatically generates over 200 design schemes through parametric models, replacing 80% of physical trial-and-error costs; the Fault Tree Analysis (FTA) tool accurately locates over 90% of thermal runaway protection defects. Its originality lies in forming transferable technical assets—an experience database covering 12 types of material failure models and 8 thermal propagation scenarios.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A digital modeling method for immersion cooling of power batteries, characterized in that, include: Based on the numerical model corresponding to the target power battery generated in advance based on the multi-level parameter system of the battery cell, and combined with the current actual operating condition data of the target power battery, the target operating condition parameter window of the target power battery is determined through simulation analysis. An experimental verification scheme including impregnation process parameters and flow channel structure parameters is then output based on the target operating condition parameter window. The multi-level parameter system of the battery cell is constructed through geometric parameter standardization design, thermal safety element database development, and iterative optimization of a multi-physics model, targeting the physical processes of phase change heat transfer in the impregnation medium and short-circuit heat generation within the lithium-ion battery. The target operating condition parameter window includes: medium thermal conductivity, forced convection heat transfer coefficient, and pressure drop loss. The experimental verification scheme is output, and an immersion cooling verification system for the target power battery is prepared based on the experimental verification scheme and its performance is tested to obtain test data. The numerical model is corrected based on the test data, and the impregnation process parameters and flow channel structure parameters are optimized in a closed loop. The process is iterated until the preset convergence condition is met, and an impregnation cooling system design scheme for the target power battery is output.

2. The digital modeling method for immersion cooling of power batteries according to claim 1, characterized in that, Before determining the target operating condition parameter window of the target power battery through simulation analysis, the method further includes: To address the physical processes of phase change heat transfer in impregnating media and short-circuit heat generation in lithium-ion batteries, a multi-level parameter system for the battery cell was constructed through standardized geometric parameter design, development of a thermal safety element database, and iterative optimization of a multi-physics model. Based on the multi-level parameter system of the battery cell, the contact thermal resistance of the electrode-electrolyte interface is calibrated by iterative optimization of the multiphysics model and the thermal runaway trigger threshold model is corrected to obtain the numerical model of the target power battery that meets the preset accuracy requirements. The numerical model is a multiphysics coupled model established based on the computational fluid dynamics platform, which includes an electrochemical heat source model, a fluid flow and convection heat transfer model and a solid thermal conductivity equation.

3. The digital modeling method for immersion cooling of power batteries according to claim 2, characterized in that, The multi-level parameter system of the battery cell includes: geometric parameters, thermal safety parameters, and optimization parameters; Correspondingly, the physical processes of phase change heat transfer in the impregnating medium and short-circuit heat generation in lithium-ion batteries are addressed through geometric parameter standardization design, thermal safety element database development, and iterative optimization of multiphysics model to construct a multi-level parameter system for the battery cell, including: The electrode double-sided coating thickness, electrode tab welding area geometry, and impregnation channel gap of the target power battery are used as geometric parameters. The thermal conductivity of the impregnation solution, the thermal shrinkage rate of the composite separator, and the concentration threshold of the thermal runaway gas components of the target power battery are used as thermal safety parameters. The electrode-electrolyte interface contact thermal resistance calibrated through orthogonal experiments and the thermal runaway trigger threshold model corrected based on high-temperature thermal abuse test data were used as optimization parameters to construct a multi-level parameter system for the battery cell.

4. The digital modeling method for immersion cooling of power batteries according to claim 2, characterized in that, Based on the multi-level parameter system of the battery cell, the numerical model corresponding to the target power battery that meets the preset accuracy requirements is obtained by iteratively optimizing and calibrating the electrode-electrolyte interface contact thermal resistance and correcting the thermal runaway trigger threshold model through a multi-physics field model, including: Orthogonal experiments were used to calibrate the contact thermal resistance at the electrode-electrolyte interface, and the calibrated contact thermal resistance was obtained. The thermal runaway trigger threshold model was modified based on high-temperature thermal abuse test data to obtain the modified thermal runaway trigger threshold model. Substituting the calibrated contact thermal resistance and the corrected thermal runaway trigger threshold model into the cell's multi-level parameter system, a numerical model with a temperature field prediction error of ≤±1.5℃ under 5C discharge conditions corresponding to the target power battery is obtained through iterative optimization.

5. The digital modeling method for immersion cooling of power batteries according to claim 1, characterized in that, The process involves using a numerical model of the target power battery, pre-generated based on a multi-level parameter system of the battery cell, combined with the current actual operating condition data of the target power battery, to determine the target operating condition parameter window of the target power battery through simulation analysis. Based on the target operating condition parameter window, an experimental verification scheme including impregnation process parameters and flow channel structure parameters is output, including: Based on the numerical model, simulations were conducted using the target power battery under 5C fast charging conditions, an ambient temperature range of -30℃ to 60℃, and thermal abuse triggering conditions as actual operating conditions. The influence of the impregnation medium flow rate on temperature uniformity was analyzed through single-factor simulation. The interaction between the flow channel topology and the filling rate was optimized by combining orthogonal experiments to determine the target operating condition parameter window of the target power battery. The target operating condition parameter window includes: medium thermal conductivity > 0.25 W / (m•K), forced convection heat transfer coefficient 450~600 W / (m²•K), and pressure drop loss < 15 kPa. The experimental verification scheme, which includes vacuum pressure impregnation process parameters and cellular flow channel battery compartment structure parameters, is output based on the target operating condition parameter window. The experimental verification scheme is used to prepare an impregnation cooling verification system that integrates a distributed optical fiber temperature sensing network and a particle image velocimetry system.

6. The digital modeling method for immersion cooling of power batteries according to claim 1, characterized in that, The numerical model is corrected based on the test data, and closed-loop optimization is performed on the impregnation process parameters and flow channel structure parameters. This process is iterated until a preset convergence condition is met, and an impregnation cooling system design scheme for the target power battery is output, including: Acquire test data generated from performance testing of the immersion cooling verification system. The test data includes: thermal runaway propagation delay obtained from the needle penetration trigger test of GB / T 31467.3-2015 standard, the dielectric thermal conductivity decay rate obtained from the 3000 charge-discharge cycle aging test, and flow velocity non-uniformity data obtained from the flow field distribution test. Based on the test data, fault tree analysis was used to locate residual hot spots of air bubbles, and the vacuum level of the impregnation process was increased to 10. - ³Pa level; the flow channel curvature radius is adjusted to >5mm through topology optimization; Using a volumetric heat transfer coefficient ≥8.7kW / m³·K as a preset convergence condition, the numerical model, impregnation process parameters, and flow channel structure parameters are iteratively optimized until the preset convergence condition is met, resulting in an impregnation cooling system design scheme with a thermal runaway trigger threshold >245℃, pump power <120W, and 5C discharge temperature rise <8℃.

7. The digital modeling method for immersion cooling of power batteries according to claim 6, characterized in that, The charge-discharge cycle aging test was conducted in accordance with ISO 12405-4 standard, and the number of tests was 3000. The flow field distribution test was performed using a particle image velocimetry system, with a tracer particle concentration of 5 × 10⁻⁶. 6 / m³, optimized flow velocity non-uniformity ≤8%; The fault tree analysis for locating residual hotspots of bubbles includes locating microbubble aggregation areas using X-ray scanning.

8. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the digital modeling method for immersion cooling of power batteries as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the digital modeling method for immersion cooling of power batteries as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the digital modeling method for immersion cooling of power batteries as described in any one of claims 1 to 7.

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