Temperature adjusting method and system based on battery simulator simulation battery

Abstract

The invention relates to the technical field of battery simulation, in particular to a temperature adjustment method and system for simulating a battery based on a battery simulator. The method comprises the following steps: collecting the material thermal conductivity, thickness and layer number information of the battery cell lamination structure, and recording the information as battery cell lamination structure data; performing thermal resistance calculation on each layer of the battery cell according to the battery cell lamination structure data to obtain thermal resistance distribution data of each layer of the battery cell; performing space-time inversion simulation on the heat accumulation condition in the battery cell in the rapid charging process to obtain battery cell heat accumulation simulation data; and determining current density distribution data in the battery cell in the rapid charging process according to the thermal resistance distribution data of each layer of the battery cell and the battery cell heat accumulation simulation data. Through data monitoring technology data and data simulation technology, thermal resistance distribution calculation of heat accumulation in the battery cell is realized, accurate simulation of a heat accumulation path is realized, cooling efficiency is improved, and battery safety is enhanced.

Description

Technical Field

[0001] This invention relates to the field of battery simulation technology, and in particular to a method and system for simulating battery temperature adjustment based on a battery simulator. Background Technology

[0002] Batteries (such as lithium-ion batteries) typically employ a stacked structure, with the cell consisting of multiple layers of alternating positive electrodes, negative electrodes, and separators. This structure has the following characteristics: the stacked structure can effectively improve the volumetric energy density of the battery; the heat conduction path within the cell is complex, and there are differences in heat conduction efficiency between different layers; during rapid charging and discharging, the current density distribution within the cell is uneven, easily generating localized hot spots in certain areas; due to the stacked structure of the cell, the heat conduction path within the cell is complex, and heat accumulates in certain areas and is difficult to dissipate quickly; on the one hand, existing battery simulators are insufficient in simulating this heat accumulation and dissipation process; on the other hand, existing battery simulators struggle to effectively handle the complex heat conduction problems caused by the stacked cell structure when simulating thermal management systems; furthermore, existing battery simulators have many technical deficiencies in simulating battery temperature adjustment, especially in simulating the uneven heat conduction under the stacked cell structure, the heat accumulation problem during rapid charging, the compatibility of the cooling system with the stacked structure, and the risk of thermal runaway. Summary of the Invention

[0003] Therefore, it is necessary to provide a method and system for simulating battery temperature adjustment based on a battery simulator to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for simulating battery temperature adjustment based on a battery simulator is provided, the method comprising the following steps: Step S1: Collect information on the thermal conductivity, thickness, and number of layers of the battery cell stacked structure and record it as battery cell stacked structure data; calculate the thermal resistance of each layer of the battery cell based on the battery cell stacked structure data to obtain the thermal resistance distribution data of each layer of the battery cell; Step S2: Perform a spatiotemporal inversion simulation of the heat accumulation inside the cell during fast charging to obtain simulated heat accumulation data of the cell; determine the current density distribution data inside the cell during fast charging based on the thermal resistance distribution data of each layer of the cell and the simulated heat accumulation data of the cell; simulate the spatiotemporal accumulation path in the cell stacked structure based on the current density distribution data to generate simulated heat accumulation path data. Step S3: Based on the heat accumulation path simulation data, perform heat flux density matching on the battery cell cooling device to obtain heat flux cooling density data; adjust the flow rate and direction of the cooling medium in the cell cooling device according to the heat flux cooling density data to obtain the cell heat flux adjustment strategy; Step S4: Monitor the temperature adjustment function of the battery simulator in real time according to the cell heat flow adjustment strategy, simulate the temperature change trend of the battery cell under the operation of the cell cooling device, and generate cell temperature change trend data; conduct thermal runaway risk assessment on the cell temperature change trend data, and generate thermal runaway risk assessment report.

[0005] Preferably, in step S1, the information on the thermal conductivity, thickness, and number of layers of the battery cell stacked structure is collected and recorded as battery cell stacked structure data, including: On the surface of each layer of the battery cell stacked structure, multiple miniature thermistors are arranged sequentially along a preset trajectory, and the trajectory covers multiple key areas of the battery cell stacked structure. The thermal conductivity of each layer of material at different locations is measured using a miniature thermistor. The measurement process involves reading the resistance change of the thermistor under different temperature gradients, thereby calculating the thermal conductivity at each location. The thickness of each layer of the battery cell stacked structure is measured using a miniature laser rangefinder. The laser beam emitted by the laser rangefinder is perpendicular to the surface of the battery cell stacked structure, and the thickness data of each layer is calculated by the reflected signal. Interlayer markers are set at the edge of the battery cell stacked structure, and the interlayer markers are identified by an optical recognition device to determine the number of layers in the battery cell stacked structure. The collected thermal conductivity data, thickness data, and layer number data are synchronously classified and recorded to form cell stack-up structure data.

[0006] Preferably, step S1, which involves calculating the thermal resistance of each layer of the battery cell based on the cell's stacked structure data, includes: The thickness and thermal conductivity data of each layer in the battery cell stack-up structure data are processed to calculate the reference thermal resistance, and the reference thermal resistance value of each layer is obtained. The reference thermal resistance value of each layer is refined by region. Each layer is divided into multiple small regions of equal area, and the thickness and thermal conductivity data of each small region are processed to calculate the local thermal resistance value of each small region. The local thermal resistance value of the edge region is corrected by edge effect processing. The correction coefficient is introduced according to the geometry and material properties of the edge region to obtain the corrected thermal resistance value of the edge region. The local thermal resistance value of the interlayer contact area is corrected by contact thermal resistance correction. The corrected thermal resistance value of the interlayer contact area is obtained by introducing the contact thermal resistance correction value based on the contact quality and surface roughness of the interlayer material. The corrected thermal resistance values ​​of the edge region and the interlayer contact region are verified as a whole. The thermal resistance values ​​of adjacent layers are compared to check for any abnormal thermal resistance jumps. If any abnormality is found, the relevant parameters are adjusted and recalculated to obtain thermal resistance distribution data.

[0007] Preferably, step S2, which involves performing a spatiotemporal inversion simulation of heat accumulation inside the battery cell during fast charging, includes: Collect data on heat accumulation in the battery cells during fast charging; The heat accumulation data of the battery cell during fast charging is collected and processed in segments. The charging process is divided into multiple time intervals according to a preset time interval, and the heat accumulation in each time interval is recorded to obtain segmented heat accumulation data. The segmented heat accumulation data is traced backward, starting from the end of charging, and the heat accumulation path in each time interval is calculated step by step to reconstruct the heat propagation process inside the cell and obtain preliminary heat accumulation path data. Interlayer thermal resistance correction is performed on the preliminary heat accumulation path data. Combined with the thermal resistance distribution of the cell stack structure, the loss of heat during interlayer transfer is compensated to obtain the corrected heat accumulation path data. Local heat sources are identified by analyzing the corrected heat accumulation path data. Combined with the hardware structure of the electrodes and separators inside the cell, the source areas of heat accumulation are identified, and the local heat source distribution data are recorded to obtain simulated heat accumulation data of the cell.

[0008] Preferably, in step S2, determining the current density distribution data inside the battery cell during fast charging based on the thermal resistance distribution data of each layer of the battery cell and the simulated data of heat accumulation in the battery cell includes: Thermal resistance gradient analysis was performed on the thermal resistance distribution data of each layer of the battery cell to calculate the gradient change of thermal resistance in each layer and obtain the thermal resistance gradient distribution. Heat flux density is inverted from the simulated data of heat accumulation in the battery cell, and the heat flux density at each time point and spatial location is calculated based on the spatiotemporal distribution of heat accumulation, thus obtaining the heat flux density distribution. By jointly analyzing the thermal resistance gradient distribution and the heat flux density distribution, and combining the cell's stacked structure, the relationship between current density and thermal resistance gradient and heat flux density is analyzed to obtain preliminary current density distribution data. The preliminary current density distribution data is fitted in different regions. The cell is divided into multiple small regions, and the current density distribution of each small region is fitted to obtain the current density distribution curve of each small region. The stability of the current density distribution curve was verified by simulating the current density changes at different charging stages, and the verified current density distribution data was obtained.

[0009] Preferably, step S2, which involves simulating the spatiotemporal accumulation path in the cell stacked structure based on current density distribution data, includes: The current density distribution data is subjected to a nonlinear transformation, and the current density value is adjusted by a nonlinear function to obtain the adjusted current density distribution data. The adjusted current density distribution data is spatiotemporally coupled, and combined with the geometric features of the cell stack structure, the current density distribution is coupled with the time dimension to obtain the spatiotemporally coupled current density distribution. The current density distribution after spatiotemporal coupling is processed by thermal effect mapping. Based on the nonlinear relationship between current density and thermal effect, the current density distribution is mapped to the thermal effect distribution to obtain thermal effect distribution data. The thermal effect distribution data is traced in reverse. Starting from the heat output end of the cell, the propagation path of heat in the cell stacked structure is traced in reverse to obtain the reverse tracing path data. The reverse tracking path data is dynamically corrected, and the reverse tracking path is corrected in real time according to the dynamic changes of the battery cell during the charging process to obtain the dynamically corrected path data. Multi-layer fusion is performed on the dynamically corrected path data to generate complete heat accumulation path simulation data.

[0010] Preferably, step S3, which involves matching the heat flux density of the battery cell cooling device based on the heat accumulation path simulation data, includes: Heat flux density is analyzed from the simulation data of heat accumulation path to extract heat flux density information and obtain heat flux density distribution map; Cooling demand analysis is performed on the heat flux density distribution map. Based on the heat flux density distribution, the cooling demand of the battery cell cooling device in different areas is analyzed to obtain the cooling demand distribution map. The cooling demand distribution map is processed for data verification. By simulating cooling parameters under different operating conditions, the heat flux cooling density distribution characteristics are matched to obtain heat flux cooling density data.

[0011] Preferably, step S3, adjusting the flow rate and direction of the cooling medium in the cell cooling device based on the heat flux cooling density data, includes: Regional heat flux density analysis was performed on the heat flux cooling density data. Based on the heat flux density values ​​of different regions inside the cell, high heat flux density regions and low heat flux density regions were determined. Based on the regional heat flux density analysis results, the cooling channel of the battery cell cooling device is divided into zones with adjustable flow rates. The flow rate of the cooling medium is increased in the high heat flux density area and decreased in the low heat flux density area to meet the cooling needs of different areas. The flow direction of the cooling channel is optimized by guiding the heat flux density. Based on the heat flux density data, the guide vanes in the cooling device are adjusted to guide the cooling medium to flow preferentially to the high heat flux density area. The heat flux density matching of the adjusted cooling channel was verified by simulating the cooling effect of the cooling medium at the adjusted flow rate and flow direction to verify whether the heat flux density of the cooling channel matches the heat accumulation path of the battery cell. The parameters of the verified cooling channel were adjusted and confirmed. Based on the verification results, the adjusted flow rate and flow direction parameters were confirmed and recorded as the cell heat flow adjustment strategy.

[0012] Preferably, step S4 includes the following steps: Step S41: Perform real-time data acquisition and processing on the temperature adjustment function of the battery simulator, and collect the temperature data of the cell cooling device every 10 milliseconds to obtain real-time temperature monitoring data; Step S42: Perform temperature change trend analysis on the real-time temperature monitoring data, calculate the temperature difference between every two consecutive acquisition points, analyze the temperature change trend, and obtain the temperature change trend curve. Step S43: Simulate and verify the temperature change trend curve. Combine the cell heat flow adjustment strategy to simulate the temperature change trend of the cell under different cooling conditions and verify the actual cooling effect of the cooling device. Step S44: Perform thermal runaway risk identification processing on the temperature change trend data after simulation verification. Based on the temperature change trend curve, identify the temperature anomaly points that lead to thermal runaway and obtain thermal runaway risk identification data. Step S45: Perform risk assessment processing on the thermal runaway risk identification data, quantify the identified thermal runaway risks, and generate a thermal runaway risk assessment report.

[0013] The beneficial effects of this invention are as follows: By collecting information on the thermal conductivity, thickness, and number of layers of the battery cell stacked structure (battery cell stacked structure data), the thermal resistance distribution of each layer of the battery cell can be accurately calculated (thermal resistance distribution data of each layer of the battery cell); combined with the simulation data of heat accumulation inside the battery cell during fast charging, the current density distribution inside the battery cell can be accurately determined (current density distribution data); this process provides accurate basic data for subsequent heat accumulation path simulation, ensuring the reliability of the entire temperature adjustment method; based on the current density distribution data, the spatiotemporal accumulation path in the battery cell stacked structure is simulated to generate heat accumulation path simulation data; through this data, the heat flux density of the battery cell cooling device can be accurately matched to obtain heat flux cooling density data; then, the flow rate and direction of the cooling medium in the battery cell cooling device are adjusted according to the heat flux cooling density data to form a battery cell heat flux adjustment strategy; this strategy can effectively optimize the cooling process and ensure The flow of the cooling medium is highly matched with the heat accumulation path, thereby improving cooling efficiency and avoiding local overheating. The battery simulator's temperature adjustment function is monitored in real time according to the cell heat flow adjustment strategy, and the temperature change trend of the battery cell under the operation of the cell cooling device is simulated, generating cell temperature change trend data. A thermal runaway risk assessment report is generated by evaluating the cell temperature change trend data. This process enables real-time monitoring and accurate assessment of battery temperature changes during fast charging, allowing for early detection of potential thermal runaway risks and providing strong protection for battery safety. This invention forms a complete battery temperature adjustment method through precise thermal resistance calculation, heat accumulation path simulation, cooling strategy optimization, and real-time temperature monitoring and risk assessment. This method can effectively reduce the risk of thermal runaway during fast charging, extend battery life, and improve battery safety and reliability.

[0014] This specification also provides a battery temperature adjustment system based on a battery simulator, used to perform the battery temperature adjustment method based on a battery simulator as described above. This multi-channel battery charge / discharge testing system includes: The battery cell stack thermal resistance detection module is used to collect information on the thermal conductivity, thickness and number of layers of the battery cell stack structure, and record it as battery cell stack structure data; based on the battery cell stack structure data, the thermal resistance of each layer of the battery cell is calculated to obtain the thermal resistance distribution data of each layer of the battery cell. The heat accumulation path simulation module is used to perform spatiotemporal inversion simulation of heat accumulation inside the battery cell during fast charging to obtain heat accumulation simulation data of the battery cell; it determines the current density distribution data inside the battery cell during fast charging based on the thermal resistance distribution data of each layer of the battery cell and the heat accumulation simulation data of the battery cell; and it simulates the spatiotemporal accumulation path in the battery cell stacked structure based on the current density distribution data to generate heat accumulation path simulation data. The cell cooling adjustment module is used to match the heat flux density of the battery cell cooling device based on the heat accumulation path simulation data to obtain heat flux cooling density data; and to adjust the flow rate and direction of the cooling medium in the cell cooling device according to the heat flux cooling density data to obtain the cell heat flux adjustment strategy. The thermal runaway risk assessment module is used to monitor the temperature adjustment function of the battery simulator in real time according to the cell heat flow adjustment strategy, simulate the temperature change trend of the battery cell under the operation of the cell cooling device, generate cell temperature change trend data, and perform thermal runaway risk assessment on the cell temperature change trend data to generate a thermal runaway risk assessment report.

[0015] The temperature adjustment system of this invention collects information on the thermal conductivity, thickness, and number of layers of the battery cell material through a cell stacked thermal resistance detection module, and calculates the thermal resistance distribution of each layer; a heat accumulation path simulation module simulates the heat accumulation and current density distribution during fast charging, generating heat accumulation path data; a cell cooling adjustment module adjusts the flow rate and direction of the cooling device based on this data, forming a heat flow adjustment strategy; and a thermal runaway risk assessment module monitors temperature changes in real time, assesses the risk of thermal runaway, and generates a report. The entire system achieves full automation of the process of cell thermal resistance detection, heat accumulation simulation, cooling strategy optimization, and risk assessment, effectively reducing the risk of thermal runaway, improving battery safety and lifespan, and ensuring the safety and reliability of the fast charging process. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of a method for simulating battery temperature adjustment using a battery simulator. Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S4. 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

[0017] The technical method of the present invention 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 the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0018] 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.

[0019] 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.

[0020] To achieve the above objectives, please refer to Figures 1 to 2 A method for temperature adjustment based on a battery simulator, the method comprising the following steps: Step S1: Collect information on the thermal conductivity, thickness, and number of layers of the battery cell stacked structure and record it as battery cell stacked structure data; calculate the thermal resistance of each layer of the battery cell based on the battery cell stacked structure data to obtain the thermal resistance distribution data of each layer of the battery cell; In this embodiment of the invention, during step S1, the battery cell stacked structure is first disassembled, each layer of material is separated, and each layer is numbered sequentially from the outermost layer to the innermost layer, for example, numbered 1, 2, 3...n, where n is the total number of layers in the battery cell stacked structure. A high-precision thermal performance tester is used to measure the thermal conductivity of each layer. Specifically, the material to be tested is placed on the sample stage of the tester, ensuring the material surface is flat and in good contact with the tester. The tester is started, and either the steady-state method or the transient method is selected for measurement. During the measurement process, the ambient temperature is kept constant at 25 degrees Celsius, and the relative humidity is controlled at approximately 50% to avoid the influence of environmental factors on the measurement results. After the measurement is completed, the thermal conductivity value of each layer is recorded, with an accuracy requirement of 0.01 W / (m·K). Next, a high-precision laser thickness gauge is used to measure the thickness of each layer. The material is placed on the measurement platform of the thickness gauge, and the laser head of the thickness gauge is adjusted to be perpendicular to the material surface. Start the thickness gauge, ensuring the material surface is free of stains and scratches during measurement to guarantee accuracy. After measurement, record the thickness of each layer, with an accuracy of 0.001 mm. After completing the above measurements, integrate the thermal conductivity, thickness, and total number of layers of each material into battery cell stack-up structure data and store it in the data management system. The data format is [material number, thermal conductivity, thickness, total number of layers]. Then, calculate the thermal resistance of each layer of the battery cell. Retrieve the thermal conductivity and thickness data of each layer from the data management system. Set the unit area of ​​the battery cell stack-up structure to 1 square meter. Calculate the thermal resistance value of each layer according to the thermal resistance calculation formula: thermal resistance equals material thickness divided by material thermal conductivity multiplied by unit area. The specific calculation process is: divide the thickness of each layer by its thermal conductivity value, then multiply by the unit area of ​​1 square meter. The calculation result is retained to three decimal places, i.e., an accuracy of 0.001 K·m² / W. After the calculation is completed, the thermal resistance value of each layer of material is associated with its material number to form thermal resistance distribution data of each layer of the cell, and stored in the data management system. The data format is [material number, thermal resistance].

[0021] Step S2: Perform a spatiotemporal inversion simulation of the heat accumulation inside the cell during fast charging to obtain simulated heat accumulation data of the cell; determine the current density distribution data inside the cell during fast charging based on the thermal resistance distribution data of each layer of the cell and the simulated heat accumulation data of the cell; simulate the spatiotemporal accumulation path in the cell stacked structure based on the current density distribution data to generate simulated heat accumulation path data. In this embodiment of the invention, during step S2, the spatiotemporal inversion simulation of heat accumulation inside the battery cell during fast charging is first performed using finite element analysis software. The geometric model of the battery cell is imported into the software, and the thermophysical parameters of the battery cell material, including thermal conductivity, specific heat capacity, and density, are set. These parameters are extracted from the battery cell stacked structure data obtained in step S1. Boundary conditions for the battery cell are defined, including charging current input and heat dissipation boundary conditions. The charging current input is set to the current value in fast charging mode, and the heat dissipation boundary conditions are set according to the actual heat dissipation environment of the battery cell. The simulation is started with a time step of 0.1 seconds and a spatial mesh division accuracy of 1 millimeter. The simulation time covers the entire fast charging process. After the simulation is completed, the simulated data of battery cell heat accumulation is obtained, with the data format being [time point, location coordinates, heat accumulation value]. Next, based on the thermal resistance distribution data of each layer of the battery cell and the simulated data of battery cell heat accumulation, the current density distribution data inside the battery cell during fast charging is determined using the thermal resistance network analysis method. The thermal resistance distribution data of each layer of the battery cell is imported into the analysis system. Combined with the heat accumulation values ​​and time point information in the heat accumulation simulation data, the current density distribution of each layer inside the battery cell at different time points is calculated through thermal resistance network analysis. During the analysis, the iteration accuracy of the current density calculation is set to 0.001 A / cm². After the calculation, the current density distribution data is obtained, and the data format is [time point, location coordinates, current density value]. Finally, the spatiotemporal accumulation path in the battery cell stack structure is simulated based on the current density distribution data. Using a path tracing algorithm, based on the current density distribution data, the heat accumulation is tracked along the current path starting from the charging current input end of the battery cell. During the simulation, the time step of the path tracing is set to 0.05 seconds and the spatial step to 0.5 millimeters. The heat accumulation path inside the battery cell is dynamically tracked, generating heat accumulation path simulation data, and the data format is [time point, path coordinates, heat accumulation change].

[0022] Step S3: Based on the heat accumulation path simulation data, perform heat flux density matching on the battery cell cooling device to obtain heat flux cooling density data; adjust the flow rate and direction of the cooling medium in the cell cooling device according to the heat flux cooling density data to obtain the cell heat flux adjustment strategy; In this embodiment of the invention, during step S3, the heat accumulation path simulation data is first processed using a heat flux density analysis system to achieve heat flux density matching of the battery cell cooling device. The heat accumulation path simulation data is imported into the system, with the data format being [time point, path coordinates, heat accumulation change]. The system calculates the heat flux density at the corresponding path coordinates based on the heat accumulation change value. The calculation formula is: heat flux density equals heat accumulation change divided by the time step and the path cross-sectional area. The path cross-sectional area is set to the cross-sectional area of ​​the cooling channel in the battery cell cooling device, for example, 1 square centimeter, and the time step is 0.05 seconds from the simulation data. After calculation, the heat flux cooling density data is obtained, with the data format being [path coordinates, heat flux density]. Next, the flow rate and direction of the cooling medium in the battery cell cooling device are adjusted based on the heat flux cooling density data to formulate a battery cell heat flux adjustment strategy. The heat flux cooling density data is input into the cooling system controller, and the controller adjusts the flow rate and direction of the cooling medium based on the heat flux density value. The specific operation is as follows: For path coordinates with high heat flux density, increase the flow rate of the cooling medium, with the flow rate adjustment range set to 1 to 5 m / s; for path coordinates with low heat flux density, decrease the flow rate of the cooling medium, with the flow rate adjustment range set to 0.5 to 2 m / s. Simultaneously, adjust the flow direction of the cooling medium according to the direction of the heat accumulation path to ensure that the cooling medium can flow along the heat accumulation path, thereby improving cooling efficiency. After adjustment, store the adjusted flow rate and flow direction parameters as the cell heat flux adjustment strategy, with the data format being [path coordinates, flow rate, flow direction].

[0023] Step S4: Monitor the temperature adjustment function of the battery simulator in real time according to the cell heat flow adjustment strategy, simulate the temperature change trend of the battery cell under the operation of the cell cooling device, and generate cell temperature change trend data; conduct thermal runaway risk assessment on the cell temperature change trend data, and generate thermal runaway risk assessment report.

[0024] In this embodiment of the invention, during step S4, the cell heat flow adjustment strategy is first imported into the temperature adjustment function module of the battery simulator. The battery simulator has a real-time monitoring function, which can adjust the internal temperature control unit of the simulator in real time according to the flow rate and flow direction parameters set in the cell heat flow adjustment strategy. The simulator collects temperature data of the cell under the operation of the cooling device through a thermocouple sensor array. The sensor array is distributed at key locations of the cell, including the center, edges, and surfaces in contact with the cooling medium. The sampling frequency of the sensors is set to 10 times per second to ensure that rapid temperature changes can be captured. Subsequently, the temperature simulation function of the battery simulator is used to simulate the temperature change trend of the battery cell under the operation of the cell cooling device based on the collected temperature data. The simulator uses a time step of 0.1 seconds to calculate the temperature change trend, comparing the temperature data in each time step with the data in the previous time step to calculate the temperature change rate. During the simulation, the simulator dynamically calculates the internal temperature distribution of the cell according to the thermophysical parameters of the cell (such as specific heat capacity and thermal conductivity) and the heat flux density adjustment strategy of the cooling device. The final result is the generation of cell temperature change trend data, formatted as [time point, location coordinates, temperature value, temperature change rate]. Next, a thermal runaway risk assessment is performed on this data. The assessment system analyzes the cell temperature change trend data based on preset thermal runaway risk thresholds, such as a temperature change rate exceeding 10 degrees Celsius / minute or a local temperature exceeding 60 degrees Celsius. For each time point and location coordinate, the system determines whether it exceeds the risk threshold. If it does, the system records the thermal runaway risk level for that location and generates a corresponding risk description. Finally, the assessment system integrates all risk assessment results to generate a thermal runaway risk assessment report. The report includes the risk level, risk location, risk occurrence time, and recommended countermeasures, formatted as [risk level, location coordinates, risk occurrence time, recommended measures].

[0025] Preferably, in step S1, the information on the thermal conductivity, thickness, and number of layers of the battery cell stacked structure is collected and recorded as battery cell stacked structure data, including: On the surface of each layer of the battery cell stacked structure, multiple miniature thermistors are arranged sequentially along a preset trajectory, and the trajectory covers multiple key areas of the battery cell stacked structure. The thermal conductivity of each layer of material at different locations is measured using a miniature thermistor. The measurement process involves reading the resistance change of the thermistor under different temperature gradients, thereby calculating the thermal conductivity at each location. The thickness of each layer of the battery cell stacked structure is measured using a miniature laser rangefinder. The laser beam emitted by the laser rangefinder is perpendicular to the surface of the battery cell stacked structure, and the thickness data of each layer is calculated by the reflected signal. Interlayer markers are set at the edge of the battery cell stacked structure, and the interlayer markers are identified by an optical recognition device to determine the number of layers in the battery cell stacked structure. The collected thermal conductivity data, thickness data, and layer number data are synchronously classified and recorded to form cell stack-up structure data.

[0026] In this embodiment of the invention, multiple miniature thermistors are sequentially arranged along a preset trajectory on the surface of each layer of the battery cell stacked structure. The preset trajectory covers multiple key areas of the battery cell stacked structure, including the central area, edge areas, and key nodes of the heat flow path of the battery cell. The spacing between the miniature thermistors is 10 mm to ensure comprehensive coverage of the key thermal characteristic areas of the battery cell. High-precision miniature thermistors are used, with a measurement accuracy of 0.01 degrees Celsius, enabling real-time detection of temperature changes on the surface of the battery cell. The thermal conductivity of each layer of material at different locations is measured using miniature thermistors. The measurement process includes reading the resistance changes of the thermistors under different temperature gradients. First, the battery cell stacked structure is placed in a constant temperature chamber, with temperature gradients set to 10 degrees Celsius / meter, 20 degrees Celsius / meter, and 30 degrees Celsius / meter. Under each temperature gradient, the resistance changes of the miniature thermistors are read. The resistance changes are measured using a high-precision multimeter with a measurement accuracy of 0.01 ohms. Based on the relationship between the resistance change of the thermistor and temperature, and combined with the known temperature gradient, the thermal conductivity at each location was calculated. The calculation accuracy of thermal conductivity reached 0.01 W / (m·K). A miniature laser rangefinder was used to measure the thickness of each layer of the battery cell stacked structure. The laser beam emitted by the miniature laser rangefinder was perpendicular to the surface of the battery cell stacked structure, and the thickness data of each layer was calculated through the reflected signal. The measurement accuracy of the laser rangefinder was 0.001 mm, enabling precise measurement of the thickness of each layer of the battery cell. During the measurement process, the laser rangefinder moved along a preset trajectory on the surface of the battery cell, collecting thickness data every 10 mm to ensure the comprehensiveness and accuracy of the data. Interlayer markers were set at the edges of the battery cell stacked structure. The interlayer markers used high-contrast optical marking materials to ensure clear identification by the optical recognition device. The interlayer markers were identified by the optical recognition device to determine the number of layers in the battery cell stacked structure. The optical recognition device used a high-resolution industrial camera with a resolution of 1920×1080 pixels, capable of accurately identifying the position and number of interlayer markers. During the identification process, an industrial camera scans the edge of the battery cell, covering the entire edge area. After scanning, image processing algorithms are used to count the number of interlayer markers, thus determining the number of layers in the battery cell. The collected thermal conductivity, thickness, and layer count data are simultaneously classified and recorded to form battery cell stacking structure data. The data recording system uses a high-precision data acquisition card with a sampling frequency of 100 times / second to ensure real-time data accuracy. Data is stored in the database in the format of [layer number, location coordinates, thermal conductivity, thickness], while also recording the total number of layers in the battery cell. The data storage format is [layer number, location coordinates, thermal conductivity (W / (m·K)), thickness (mm), total number of layers], ensuring data integrity and traceability.

[0027] Preferably, step S1, which involves calculating the thermal resistance of each layer of the battery cell based on the cell's stacked structure data, includes: The thickness and thermal conductivity data of each layer in the battery cell stacked structure data are processed to calculate the reference thermal resistance, and the reference thermal resistance value of each layer is obtained. The reference thermal resistance value of each layer is refined by region. Each layer is divided into multiple small regions of equal area, and the thickness and thermal conductivity data of each small region are processed to calculate the local thermal resistance value of each small region. The local thermal resistance value of the edge region is corrected by edge effect processing. The correction coefficient is introduced according to the geometry and material properties of the edge region to obtain the corrected thermal resistance value of the edge region. The local thermal resistance value of the interlayer contact area is corrected by contact thermal resistance correction. The corrected thermal resistance value of the interlayer contact area is obtained by introducing the contact thermal resistance correction value based on the contact quality and surface roughness of the interlayer material. The corrected thermal resistance values ​​of the edge region and the interlayer contact region are verified as a whole. The thermal resistance values ​​of adjacent layers are compared to check for any abnormal thermal resistance jumps. If any abnormality is found, the relevant parameters are adjusted and recalculated to obtain thermal resistance distribution data.

[0028] In this embodiment of the invention, firstly, the thickness and thermal conductivity data of each layer in the battery cell stacked structure data are processed to calculate the baseline thermal resistance. The thickness and thermal conductivity data of each layer are extracted from the database, and the calculation is performed using the formula "thermal resistance = thickness / thermal conductivity", where the thickness is in meters and the thermal conductivity is in W / (m·K). The baseline thermal resistance value of each layer is calculated and stored in the format [layer number, baseline thermal resistance value]. Next, the baseline thermal resistance value of each layer is refined by region. Each layer is divided into multiple small regions of equal area, for example, each small region has an area of ​​10 mm × 10 mm. For each small region, the corresponding thickness and thermal conductivity data are extracted from the battery cell stacked structure data, and the formula "thermal resistance = thickness / thermal conductivity" is applied again to calculate the local thermal resistance value of each small region. After the calculation is completed, the local thermal resistance value of each small region is stored in the format [layer number, small region location coordinates, local thermal resistance value]. Subsequently, edge effect correction processing is performed on the local thermal resistance value of the edge regions. A correction coefficient is introduced based on the geometry and material properties of the edge regions. For example, the correction factor can be set to 1.1 for rectangular edge regions and 1.2 for circular edge regions. The local thermal resistance value of each small edge region is multiplied by the corresponding correction factor to obtain the corrected thermal resistance value for the edge region. The corrected data is then stored in the format [layer number, edge region location coordinates, corrected thermal resistance value]. Contact thermal resistance correction is applied to the local thermal resistance values ​​of the interlayer contact regions. Contact thermal resistance correction values ​​are introduced based on the contact quality and surface roughness of the interlayer materials. For example, when the contact quality is good and the surface is smooth, the contact thermal resistance correction value is 0.01 K·m² / W; when the contact quality is poor or the surface is rough, the contact thermal resistance correction value is 0.05 K·m² / W. The local thermal resistance value of each small interlayer contact region is added to the corresponding contact thermal resistance correction value to obtain the corrected thermal resistance value for the interlayer contact region. The corrected data is then stored in the format [layer number, interlayer contact region location coordinates, corrected thermal resistance value]. Finally, the corrected thermal resistance values ​​are verified overall. The thermal resistance values ​​of adjacent layers are compared to check for any abnormal thermal resistance jumps. For example, if the difference in thermal resistance between adjacent small regions exceeds 10%, it is considered an anomaly. For regions with anomalies, relevant parameters such as thickness, thermal conductivity, correction factor, and contact thermal resistance correction value are re-examined. The abnormal parameters are adjusted according to the actual situation, such as adjusting the accuracy of the thickness data or the value of the correction factor, and then the thermal resistance value is recalculated. This verification process is repeated until the thermal resistance values ​​of all small regions meet the reasonableness requirements, ultimately obtaining complete thermal resistance distribution data, stored in the format of [layer number, small region location coordinates, final thermal resistance value].

[0029] Preferably, step S2, which involves performing a spatiotemporal inversion simulation of heat accumulation inside the battery cell during fast charging, includes: Collect data on heat accumulation in the battery cells during fast charging; The heat accumulation data of the battery cell during fast charging is collected and processed in segments. The charging process is divided into multiple time intervals according to a preset time interval, and the heat accumulation in each time interval is recorded to obtain segmented heat accumulation data. The segmented heat accumulation data is traced backward, starting from the end of charging, and the heat accumulation path in each time interval is calculated step by step to reconstruct the heat propagation process inside the cell and obtain preliminary heat accumulation path data. Interlayer thermal resistance correction is performed on the preliminary heat accumulation path data. Combined with the thermal resistance distribution of the cell stack structure, the loss of heat during interlayer transfer is compensated to obtain the corrected heat accumulation path data. Local heat sources are identified by analyzing the corrected heat accumulation path data. Combined with the hardware structure of the electrodes and separators inside the cell, the source areas of heat accumulation are identified, and the local heat source distribution data are recorded to obtain simulated heat accumulation data of the cell.

[0030] In this embodiment of the invention, firstly, high-precision heat flow sensors are used to collect heat accumulation data of the battery cell during fast charging. Multiple heat flow sensors are evenly arranged at key locations on and inside the battery cell, including the positive and negative electrodes, between the electrodes, and near the separator. The sensor sampling frequency is set to 10 times per second to ensure accurate capture of dynamic changes in heat accumulation. During the acquisition process, real-time heat flow data from each sensor during charging is recorded in the format [timestamp, sensor location, heat flow value]. Next, the collected heat accumulation data is processed in segments. Based on a preset time interval, for example, every 30 seconds, the entire fast charging process is divided into multiple time intervals. Within each time interval, the heat accumulation is recorded. Specifically, the average heat flow value of all heat flow sensors within each time interval is calculated as the representative value of heat accumulation for that time interval. After processing, segmented heat accumulation data is obtained in the format [time interval number, average heat flow value]. Subsequently, the segmented heat accumulation data is processed using reverse tracking. Starting from the end of charging, the heat accumulation path within each time interval is calculated backwards. Using a reverse tracing algorithm, combined with the cell's geometry and material properties, the direction and path of heat propagation within the cell are analyzed. By calculating the heat propagation speed and direction within each time interval, the heat propagation process within the cell is reconstructed. During the reverse tracing process, the calculation accuracy of the heat propagation speed is set to 0.1 mm / s, ultimately obtaining preliminary heat accumulation path data in the format of [time interval number, heat propagation path coordinates]. Interlayer thermal resistance correction is then applied to the preliminary heat accumulation path data. Combining the thermal resistance distribution data of the cell's stacked structure, heat loss during interlayer transfer is compensated. Specifically, based on the thermal resistance value of each layer, the heat loss when passing through each layer is calculated and added back to the preliminary heat accumulation path data. For example, if the thermal resistance of a certain layer is 0.1 K·m² / W, the heat loss when passing through that layer can be calculated by multiplying the thermal resistance by the heat flux. After calibration, the calibrated heat accumulation path data is obtained, in the format of [time interval number, calibrated heat propagation path coordinates]. Finally, local heat sources are identified from the calibrated heat accumulation path data. Combining the internal hardware structure of the cell, such as electrodes and separators, a heat source identification algorithm is used to analyze the source areas of heat accumulation. By comparing the positional relationship between the heat accumulation path and the electrode and separator structures, the main source areas of heat accumulation are identified. For example, if the heat accumulation path is concentrated near the electrode, this area is identified as a local heat source. The local heat source distribution data is recorded in the format of [local heat source number, location coordinates, heat source intensity], ultimately yielding simulated heat accumulation data for the cell.

[0031] Preferably, in step S2, determining the current density distribution data inside the battery cell during fast charging based on the thermal resistance distribution data of each layer of the battery cell and the simulated data of heat accumulation in the battery cell includes: Thermal resistance gradient analysis was performed on the thermal resistance distribution data of each layer of the battery cell to calculate the gradient change of thermal resistance in each layer and obtain the thermal resistance gradient distribution. Heat flux density is inverted from the simulated data of heat accumulation in the battery cell, and the heat flux density at each time point and spatial location is calculated based on the spatiotemporal distribution of heat accumulation, thus obtaining the heat flux density distribution. By jointly analyzing the thermal resistance gradient distribution and the heat flux density distribution, and combining the cell's stacked structure, the relationship between current density and thermal resistance gradient and heat flux density is analyzed to obtain preliminary current density distribution data. The preliminary current density distribution data is fitted in different regions. The cell is divided into multiple small regions, and the current density distribution of each small region is fitted to obtain the current density distribution curve of each small region. The stability of the current density distribution curve was verified by simulating the current density changes at different charging stages, and the verified current density distribution data was obtained.

[0032] In this embodiment of the invention, firstly, thermal resistance gradient analysis is performed on the thermal resistance distribution data of each layer of the battery cell. Thermal resistance distribution data for each layer of the battery cell is extracted from the database, with the data format being [layer number, location coordinates, thermal resistance value]. The gradient change of thermal resistance for each layer is calculated using numerical calculation methods. Specifically, within each layer, two adjacent location coordinates are selected, and the ratio of the difference in thermal resistance value to the difference in location coordinates is calculated as the thermal resistance gradient for that region. For example, for two adjacent location coordinates (x1, y1) and (x2, y2) of layer number 1, their thermal resistance values ​​are R1 and R2 respectively, then the thermal resistance gradient for that region is (R2-R1) / ((x2-x1)+(y2-y1)). After the calculation, the thermal resistance gradient distribution is obtained, with the data format being [layer number, location coordinates, thermal resistance gradient]. Next, heat flux density inversion is performed on the simulated data of battery cell heat accumulation. Simulated data of battery cell heat accumulation is extracted from the database, with the data format being [time point, location coordinates, heat accumulation value]. Based on the spatiotemporal distribution of heat accumulation, the heat flux density at each time point and spatial location is calculated using the finite difference method. Specifically, at each time point, two adjacent location coordinates are selected, and the ratio of the difference in heat accumulation value to the difference in location coordinates is calculated. This ratio is then divided by the time step to obtain the heat flux density for that region. For example, for time point t, the heat accumulation values ​​at location coordinates (x1, y1) and (x2, y2) are Q1 and Q2 respectively, and the time step is Δt. Therefore, the heat flux density for that region is (Q2-Q1) / ((x2-x1)+(y2-y1)) / Δt. After the inversion, the heat flux density distribution is obtained, with the data format being [time point, location coordinates, heat flux density]. Subsequently, the thermal resistance gradient distribution and the heat flux density distribution are jointly analyzed. Considering the stacked structure of the battery cell, the relationship between current density and the thermal resistance gradient and heat flux density is analyzed. The specific operation is as follows: At each time point and location coordinate, based on Ohm's law and the heat conduction equation of the battery cell, establish the mathematical relationship between current density, thermal resistance gradient, and heat flux density. For example, assuming the current density is I, the thermal resistance gradient is G, and the heat flux density is H, then I = k1 × G + k2 × H, where k1 and k2 are proportionality coefficients obtained through experimental calibration. Calculate the preliminary current density value at each time point and location coordinate to obtain preliminary current density distribution data, in the format of [time point, location coordinate, preliminary current density value]. Perform regional fitting on the preliminary current density distribution data. Divide the battery cell into multiple small regions, for example, each region has an area of ​​10mm × 10mm. Fit the preliminary current density distribution data for each small region using a polynomial fitting method. Specifically, within each small region, select the preliminary current density values ​​at multiple time points and location coordinates, and fit a polynomial curve as the current density distribution curve for that small region. For example, for small region 1, select the preliminary current density values ​​at 5 time points and location coordinates, and fit a quadratic polynomial curve.After fitting, the current density distribution curve for each small region is obtained, with the data format being [small region number, current density distribution curve]. Finally, the stability of the current density distribution curve is verified. The stability of the current density distribution is verified by simulating the current density changes at different charging stages. Specifically, within each small region, the current density changes of the battery cell at different charging stages (such as the initial, middle, and final stages of charging) are simulated, and the deviation between the current density distribution curve and the fitted curve for each stage is calculated. For example, for small region 1, the deviation values ​​between the current density distribution curve and the fitted curve are calculated at the initial, middle, and final stages of charging. If the deviation values ​​are all less than a set threshold (such as 0.01 A / cm²), the current density distribution curve for that small region is considered stable. After verification, the verified current density distribution data is obtained, with the data format being [small region number, verified current density distribution curve].

[0033] Preferably, step S2, which involves simulating the spatiotemporal accumulation path in the cell stacked structure based on current density distribution data, includes: The current density distribution data is subjected to a nonlinear transformation, and the current density value is adjusted by a nonlinear function to obtain the adjusted current density distribution data. The adjusted current density distribution data is spatiotemporally coupled, and combined with the geometric features of the cell stack structure, the current density distribution is coupled with the time dimension to obtain the spatiotemporally coupled current density distribution. The current density distribution after spatiotemporal coupling is processed by thermal effect mapping. Based on the nonlinear relationship between current density and thermal effect, the current density distribution is mapped to the thermal effect distribution to obtain thermal effect distribution data. The thermal effect distribution data is traced in reverse. Starting from the heat output end of the cell, the propagation path of heat in the cell stacked structure is traced in reverse to obtain the reverse tracing path data. The reverse tracking path data is dynamically corrected, and the reverse tracking path is corrected in real time according to the dynamic changes of the battery cell during the charging process to obtain the dynamically corrected path data. Multi-layer fusion is performed on the dynamically corrected path data to generate complete heat accumulation path simulation data.

[0034] In this embodiment of the invention, firstly, the current density distribution data undergoes nonlinear transformation processing. Current density distribution data is extracted from the database, with the data format being [small region number, location coordinates, current density value]. A nonlinear adjustment method is used to adjust the current density value through pre-calibrated nonlinear parameters. Specifically, according to the experimentally calibrated nonlinear adjustment parameters, the current density value of each small region is adjusted. The adjusted current density value is equal to the original current density value multiplied by a nonlinear coefficient, which dynamically changes according to the magnitude of the current density. For example, when the original current density value is small, the nonlinear coefficient takes a smaller value; when the original current density value is large, the nonlinear coefficient takes a larger value. After adjustment, the adjusted current density distribution data is obtained, with the data format being [small region number, location coordinates, adjusted current density value]. Next, the adjusted current density distribution data undergoes spatiotemporal coupling processing. Combining the geometric characteristics of the battery cell stacked structure, the current density distribution is coupled with the time dimension. Specifically, within each small region, based on the battery cell charging time step (e.g., 0.1 seconds) and the current density change trend, the current density value at each time point is calculated. By analyzing the variation of current density over time, the current density values ​​are correlated with time points to form a spatiotemporally coupled current density distribution. After calculation, the spatiotemporally coupled current density distribution is obtained, with the data format being [small region number, time point, location coordinates, spatiotemporally coupled current density value]. Subsequently, thermal effect mapping processing is performed on the spatiotemporally coupled current density distribution. Based on the nonlinear relationship between current density and thermal effect, the current density distribution is mapped to a thermal effect distribution. Specifically, using pre-calibrated nonlinear mapping parameters, the spatiotemporally coupled current density values ​​at each time point and location coordinate are converted into thermal effect values. The calculation of the thermal effect value considers the weighted sum of the square term, the linear term, and the constant term of the current density, with the weighting coefficients determined based on experimental data. For example, when the current density is large, the thermal effect value is mainly determined by the square term; when the current density is small, the thermal effect value is mainly determined by the linear term and the constant term. After calculation, the thermal effect distribution data is obtained, with the data format being [small region number, time point, location coordinates, thermal effect value]. Reverse tracing processing is then performed on the thermal effect distribution data. Starting from the heat output end of the battery cell, the heat propagation path within the cell's laminated structure is traced backwards. Specifically, starting from the coordinates of the heat output end, the heat propagation path is calculated progressively forward based on the changing trend and gradient direction of the thermal effect value. At each time point, the direction of heat propagation is determined based on the gradient direction of the thermal effect value, and the distance of heat propagation is calculated. During the reverse tracing process, the geometric features and material properties of the cell's laminated structure are considered to ensure the rationality of the tracing path. After calculation, the reverse tracing path data is obtained, in the format of [time point, reverse tracing path coordinates]. Dynamic correction processing is then performed on the reverse tracing path data.Based on the dynamic changes of the battery cell during charging, the reverse tracking path is corrected in real time. Specifically, at each time point, the direction and length of the reverse tracking path are adjusted according to the rate of change of the charging current and the rate of change of the temperature. For example, when the charging current increases, the length of the reverse tracking path is appropriately shortened; when the temperature rises, the direction of the reverse tracking path is adjusted to bring it closer to the area of ​​heat accumulation. During the dynamic correction process, the dynamic parameters of the battery cell are monitored in real time to ensure that the corrected path data accurately reflects the heat propagation process. After correction, the dynamically corrected path data is obtained, with the data format being [time point, dynamic correction path coordinates]. Finally, multi-layer fusion processing is performed on the dynamically corrected path data. Path data from different layers are fused to generate complete heat accumulation path simulation data. Specifically, based on the geometric characteristics of the battery cell's stacked structure, the dynamic correction path data of adjacent layers are spliced ​​and fused. In the interlayer contact area, a smooth transition process is used to ensure the continuity and consistency of the path. For example, for two adjacent layers of path data, interpolation processing is performed in the interlayer contact area to make the path transition naturally between layers. After fusion, complete heat accumulation path simulation data is obtained, with the data format being [time point, coordinates of complete heat accumulation path].

[0035] Preferably, step S3, which involves matching the heat flux density of the battery cell cooling device based on the heat accumulation path simulation data, includes: Heat flux density is analyzed from the simulation data of heat accumulation path to extract heat flux density information and obtain heat flux density distribution map; Cooling demand analysis is performed on the heat flux density distribution map. Based on the heat flux density distribution, the cooling demand of the battery cell cooling device in different areas is analyzed to obtain the cooling demand distribution map. The cooling demand distribution map is processed for data verification. By simulating cooling parameters under different operating conditions, the heat flux cooling density distribution characteristics are matched to obtain heat flux cooling density data.

[0036] In this embodiment of the invention, firstly, heat flux density analysis is performed on the heat accumulation path simulation data. The heat accumulation path simulation data is extracted from the database, with the data format being [time point, complete heat accumulation path coordinates, thermal effect value]. A heat flux density analysis algorithm is used to extract the heat flux density information from the simulation data. Specifically, at each time point and path coordinate, the heat flux density is calculated based on the thermal effect value and the geometric characteristics of the path. The calculation of heat flux density considers the cross-sectional area of ​​the path and the distribution of the thermal effect value, determining the heat flux density through the thermal effect value per unit area. For example, if the path cross-sectional area is 1 square centimeter and the thermal effect value is 10 joules / second, then the heat flux density is 10 watts / square centimeter. After the calculation, a heat flux density distribution map is obtained, with the data format being [time point, path coordinates, heat flux density]. Next, a cooling demand analysis is performed on the heat flux density distribution map. Based on the heat flux density distribution, the cooling demand of the battery cell cooling device in different regions is analyzed. Specifically, the battery cell is divided into multiple cooling regions, for example, according to the battery cell's geometric structure and heat flux density distribution characteristics, it is divided into a central region, an edge region, and a transition region. Within each cooling zone, cooling requirements are determined based on the magnitude and distribution of heat flux density. For example, the cooling requirement is set to high for the central area with high heat flux density and low for the edge areas with low heat flux density. Quantification of cooling requirements is achieved by setting cooling parameters, such as the flow rate, volume, and temperature of the cooling medium. After calculation, a cooling requirement distribution map is obtained, with the data format being [cooling zone number, path coordinates, cooling requirement parameters]. Finally, the cooling requirement distribution map undergoes data verification. The heat flux density distribution characteristics are matched by simulating cooling parameters under different operating conditions. Specifically, different operating conditions are set in the simulation environment, such as different charging currents, ambient temperatures, and cooling medium types. For each operating condition, cooling parameters, including the flow rate, volume, and temperature of the cooling medium, are adjusted to match the heat flux density distribution characteristics. For example, if the heat flux density is high, the flow rate and volume of the cooling medium are increased; if the heat flux density is low, the flow rate and volume of the cooling medium are appropriately decreased. Through multiple simulations and adjustments, the rationality of the cooling demand distribution map was verified, and the heat flux cooling density data was finally obtained. The data format is [operating condition number, path coordinates, heat flux cooling density].

[0037] Preferably, step S3, adjusting the flow rate and direction of the cooling medium in the cell cooling device based on the heat flux cooling density data, includes: Regional heat flux density analysis was performed on the heat flux cooling density data. Based on the heat flux density values ​​of different regions inside the cell, high heat flux density regions and low heat flux density regions were determined. Based on the regional heat flux density analysis results, the cooling channel of the battery cell cooling device is divided into zones with adjustable flow rates. The flow rate of the cooling medium is increased in the high heat flux density area and decreased in the low heat flux density area to meet the cooling needs of different areas. The flow direction of the cooling channel is optimized by guiding the heat flux density. Based on the heat flux density data, the guide vanes in the cooling device are adjusted to guide the cooling medium to flow preferentially to the high heat flux density area. The heat flux density matching of the adjusted cooling channel was verified by simulating the cooling effect of the cooling medium at the adjusted flow rate and flow direction to verify whether the heat flux density of the cooling channel matches the heat accumulation path of the battery cell. The parameters of the verified cooling channel were adjusted and confirmed. Based on the verification results, the adjusted flow rate and flow direction parameters were confirmed and recorded as the cell heat flow adjustment strategy.

[0038] In this embodiment of the invention, firstly, regional heat flux density analysis is performed on the heat flux cooling density data. Heat flux cooling density data is extracted from the database, with the data format being [operating condition number, path coordinates, heat flux cooling density]. Based on the heat flux density values ​​of different regions within the battery cell, the battery cell is divided into high heat flux density regions and low heat flux density regions. Specifically, a heat flux density threshold is set, for example, 50 W / cm². Regions with heat flux densities higher than this threshold are marked as high heat flux density regions; regions with heat flux densities lower than this threshold are marked as low heat flux density regions. After the analysis is completed, the regional heat flux density analysis results are obtained, with the data format being [region number, path coordinates, heat flux density level (high / low)]. Next, based on the regional heat flux density analysis results, the cooling channels of the battery cell cooling device are adjusted by zone flow rate regulation. Specifically, in high heat flux density regions, the flow rate of the cooling medium is increased by adjusting the flow controller in the cooling device. For example, the flow rate is adjusted from 2 m / s to 3 m / s. In low heat flux density areas, reduce the flow rate of the cooling medium, for example, adjusting it from 2 m / s to 1 m / s. During adjustment, ensure smooth changes in the cooling medium flow rate to avoid additional thermal stress on the battery cells. After adjustment, record the flow rate adjustment parameters for each area in the format [area number, path coordinates, adjusted flow rate]. Subsequently, optimize the flow direction of the cooling channels by guiding heat flux density. Based on the heat flux density data, adjust the guide vanes or distributors in the cooling device. Specifically, in high heat flux density areas, adjust the angle of the guide vanes or the position of the distributors to guide the cooling medium to flow preferentially to that area. For example, adjust the angle of the guide vanes from 30 degrees to 45 degrees so that the cooling medium can more effectively cover the high heat flux density area. After adjustment, record the adjustment parameters of the guide vanes or distributors in the format [area number, path coordinates, guide vane angle / distributor position]. Verify the heat flux density matching of the adjusted cooling channels. The cooling effect of the cooling medium under adjusted flow rates and directions is simulated to verify whether the heat flux density of the cooling channel matches the heat accumulation path of the battery cell. Specifically, computational fluid dynamics (CFD) simulation software is used, with the adjusted flow rate and direction parameters input, to simulate the flow of the cooling medium and its cooling effect inside the battery cell. The simulation results are compared with the heat accumulation path simulation data to check whether the cooling effect meets the cooling requirements of the battery cell. For example, if the simulation results show insufficient cooling in high heat flux density areas, further adjustments to the flow rate or direction are needed. After verification, the heat flux density matching verification results are obtained, with the data format being [region number, path coordinates, matching verification result (match / mismatch)]. Finally, the parameters of the verified cooling channel are adjusted and confirmed. Based on the verification results, the adjusted flow rate and direction parameters are confirmed and recorded as the battery cell heat flux adjustment strategy.The specific steps are as follows: for regions where the verification result is "matched", confirm their flow rate and flow direction parameters; for regions where the verification result is "not matched", adjust the flow rate or flow direction according to the verification result until the verification result is "matched".

[0039] As an example of the present invention, reference is made to... Figure 2 As shown, step S4 in this example includes: Step S41: Perform real-time data acquisition and processing on the temperature adjustment function of the battery simulator, and collect the temperature data of the cell cooling device every 10 milliseconds to obtain real-time temperature monitoring data; Step S42: Perform temperature change trend analysis on the real-time temperature monitoring data, calculate the temperature difference between every two consecutive acquisition points, analyze the temperature change trend, and obtain the temperature change trend curve. Step S43: Simulate and verify the temperature change trend curve. Combine the cell heat flow adjustment strategy to simulate the temperature change trend of the cell under different cooling conditions and verify the actual cooling effect of the cooling device. Step S44: Perform thermal runaway risk identification processing on the temperature change trend data after simulation verification. Based on the temperature change trend curve, identify the temperature anomaly points that lead to thermal runaway and obtain thermal runaway risk identification data. Step S45: Perform risk assessment processing on the thermal runaway risk identification data, quantify the identified thermal runaway risks, and generate a thermal runaway risk assessment report.

[0040] In this embodiment of the invention, real-time data acquisition and processing are performed on the temperature adjustment function of the battery simulator. A high-precision temperature sensor array is installed at key locations in the cell cooling device, including the cooling channel inlet, outlet, and multiple monitoring points on the cell surface. The data acquisition system is set to a sampling frequency of once every 10 milliseconds to collect temperature data during the operation of the cell cooling device. The collected data includes a timestamp and the corresponding temperature value, in the format [timestamp (milliseconds), monitoring point number, temperature value (degrees Celsius)]. The acquisition system transmits this data to the data processing unit in real time to obtain real-time temperature monitoring data. Next, temperature change trend analysis is performed on the real-time temperature monitoring data. The real-time temperature monitoring data is extracted from the data processing unit, and the temperature difference between every two consecutive acquisition points is calculated. Specifically, for each monitoring point, the temperature values ​​corresponding to two adjacent timestamps are taken, and their difference is calculated. For example, for monitoring point 1, the temperature values ​​at timestamps of 10 milliseconds and 20 milliseconds are 30 degrees Celsius and 30.5 degrees Celsius, respectively, so the temperature difference is 0.5 degrees Celsius. Based on the variation pattern of the temperature difference, the temperature change trend is analyzed, and a temperature change trend curve is generated. The curve's horizontal axis represents time (milliseconds), and the vertical axis represents temperature difference (degrees Celsius). The data format is [monitoring point number, timestamp (milliseconds), temperature difference (degrees Celsius)]. Subsequently, the temperature change trend curve is simulated and verified. Combining the cell heat flux adjustment strategy, heat flux simulation software is used to simulate the cell's temperature change trend under different cooling conditions. Specifically, the flow rate and direction parameters from the cell heat flux adjustment strategy are input into the simulation software, and initial temperature and boundary conditions are set according to the temperature change trend curve. The simulation software calculates the cell's temperature distribution at different time points based on the heat flux density distribution and the flow rate and direction of the cooling medium. By comparing the simulation results with the actual collected temperature change trend curve, the actual cooling effect of the cooling device is verified. After verification, the simulated temperature change trend data is obtained, with the data format [monitoring point number, timestamp (milliseconds), simulated temperature value (degrees Celsius), actual temperature value (degrees Celsius)]. Thermal runaway risk identification processing is then performed on the simulated temperature change trend data. Based on the temperature change trend curve, temperature anomalies leading to thermal runaway are identified. The specific operation is as follows: A temperature anomaly threshold is set, for example, a temperature difference exceeding 2 degrees Celsius per second. For each monitoring point, its temperature change trend curve is analyzed to identify any temperature anomalies exceeding the threshold. For example, if the temperature difference at monitoring point 2 reaches 2.5 degrees Celsius per second at a certain time point, this point is identified as a temperature anomaly. The timestamps and temperature values ​​of these anomalies are recorded to obtain thermal runaway risk identification data, in the format of [monitoring point number, anomaly timestamp (milliseconds), anomaly temperature value (degrees Celsius)]. Finally, the thermal runaway risk identification data undergoes risk assessment processing. A quantitative assessment is performed based on the identified thermal runaway risk points.The specific operation is as follows: Assess the thermal runaway risk level based on the abnormal temperature value and the number of abnormal points. For example, if the abnormal temperature value exceeds 60 degrees Celsius and the number of abnormal points exceeds 3, it is assessed as high risk; if the abnormal temperature value is between 50 and 60 degrees Celsius and the number of abnormal points is between 1 and 3, it is assessed as medium risk; if the abnormal temperature value is below 50 degrees Celsius and the number of abnormal points is less than 1, it is assessed as low risk. Generate a thermal runaway risk assessment report, which includes the risk level, abnormal point location, abnormal timestamp, and recommended countermeasures. The data format is [Risk Level (High / Medium / Low), Monitoring Point Number, Abnormal Timestamp (Milliseconds), Abnormal Temperature Value (Degrees Celsius), Recommended Measures].

[0041] Of particular importance, step S42 includes the following steps: Step S421: Perform layered extraction processing on the real-time temperature monitoring data. Based on the cell stacking structure, extract the temperature data for each layer of the cell to obtain the temperature data sequence of each layer. Step S422: Perform local difference calculation on the temperature data sequence of each layer, calculate the temperature difference between adjacent acquisition points in each layer, and obtain the temperature difference data of each layer; Step S423: Perform regional feature analysis on the temperature difference data of each layer, and identify the high-sensitivity and low-sensitivity regions of temperature change by combining the physical location and heat flux density distribution of each layer of the cell. Step S424: Weight the temperature difference data of the highly sensitive area and obtain the weighted temperature difference data based on the heat flux density and temperature change rate of the highly sensitive area. Step S425: Perform trend fitting processing on the weighted temperature difference data, and use polynomial fitting technology to fit the temperature change trend curve of each layer.

[0042] In this embodiment of the invention, real-time temperature monitoring data is extracted in layers. Real-time temperature monitoring data is extracted from the database, with the data format being [timestamp (milliseconds), monitoring point number, temperature value (degrees Celsius)]. Based on the cell stacking structure, temperature data is extracted for each layer of the cell. Specifically, monitoring points are assigned to corresponding cell layers according to the geometric features of the cell stacking structure and the physical location of the monitoring points. For example, if a cell has 5 layers, monitoring points 1 to 10 belong to layer 1, monitoring points 11 to 20 belong to layer 2, and so on. After extraction, a temperature data sequence for each layer is obtained, with the data format being [layer number, timestamp (milliseconds), temperature value (degrees Celsius)]. Next, local difference calculation is performed on the temperature data sequence for each layer. Specifically, for each layer, the temperature difference between adjacent sampling points is calculated. For example, in layer 1, if the temperature values ​​at timestamps of 10 milliseconds and 20 milliseconds are 30 degrees Celsius and 30.5 degrees Celsius respectively, the temperature difference is 0.5 degrees Celsius. After calculation, temperature difference data for each layer is obtained, in the format [layer number, timestamp (milliseconds), temperature difference (degrees Celsius)]. Subsequently, regional feature analysis is performed on the temperature difference data for each layer. Combining the physical location and heat flux density distribution of each layer of the battery cell, high-sensitivity and low-sensitivity regions for temperature changes are identified. Specifically, a heat flux density threshold, such as 50 W / cm², is set based on the heat flux density distribution of each layer of the battery cell. Regions with heat flux densities higher than this threshold are marked as high-sensitivity regions; regions with heat flux densities lower than this threshold are marked as low-sensitivity regions. Simultaneously, the temperature change rate is analyzed based on the temperature difference data to further confirm sensitive regions. For example, if the temperature difference change rate of a region exceeds 0.1 degrees Celsius / second, it is confirmed as a high-sensitivity region. After analysis, the regional feature analysis results are obtained, in the format [layer number, region number, sensitive region type (high / low)]. The temperature difference data for high-sensitivity regions is then weighted. The temperature difference data is weighted according to the heat flux density and temperature change rate of the high-sensitivity regions. The specific operation is as follows: For highly sensitive areas, weighting coefficients are calculated based on heat flux density and temperature change rate. For example, the higher the heat flux density or the faster the temperature change rate, the larger the weighting coefficient. The weighting coefficient calculation formula is assumed to be: Weighting coefficient = Heat flux density (W / cm²) × Temperature change rate (°C / s). The temperature difference data for each highly sensitive area is multiplied by the corresponding weighting coefficient to obtain weighted temperature difference data, in the format of [layer number, region number, timestamp (milliseconds), weighted temperature difference (°C)]. Finally, trend fitting processing is performed on the weighted temperature difference data. Polynomial fitting technology is used to fit the temperature change trend curve for each layer. Specifically, for each layer, an appropriate polynomial order (e.g., a quadratic polynomial) is selected for fitting.Using timestamps as independent variables and weighted temperature differences as dependent variables, a polynomial fit was performed using the least squares method. After fitting, the temperature change trend curve for each layer was obtained, with the data format being [layer number, polynomial coefficients (a0, a1, a2...)], where the polynomial coefficients represent the parameters of the fitted curve.

[0043] Of particular importance, step S43 includes the following steps: Step S431: Perform segmented fitting on the temperature change trend curve. Based on the cell's stacked structure and heat flux density distribution, divide the curve into multiple segments, each segment corresponding to a specific region of the cell. Step S432: Perform local simulation processing on the temperature change trend of each segment, and combine the cell heat flow adjustment strategy in this area to simulate the temperature change of the cell under different cooling conditions and generate a local simulated temperature curve; Step S433: Perform heat flux density correction on the local simulated temperature curve. Correct the simulation results according to the physical structure and material properties of the battery cell. Step S434: Perform overall fusion processing on the corrected local simulated temperature curves, integrate the simulation results of all segments, and generate a complete simulated temperature change trend curve; Step S435: Compare the actual cooling effect of the complete simulated temperature change trend curve with the actual monitored temperature change trend curve to analyze the actual cooling effect of the cooling device under different cooling conditions. Step S436: Compare the processed data to adjust the cooling strategy. Based on the comparison results, adjust the operating parameters of the cooling device and optimize the cooling strategy to improve the cooling effect. Step S437: Perform a second simulation verification process on the adjusted cooling strategy, re-simulate the temperature change trend of the cell under the adjusted cooling conditions, and verify whether the adjusted cooling strategy is effective.

[0044] In this embodiment of the invention, the temperature change trend curve is segmented and fitted. Based on the cell's stacked structure and heat flux density distribution, the curve is divided into multiple segments, each corresponding to a specific region of the cell. Specifically, the temperature change trend curve is divided according to different layers and key regions of the cell based on the geometric characteristics of the cell's stacked structure and heat flux density distribution. For example, if the cell has 5 layers, each layer is further divided into a central region, an edge region, and a transition region based on the heat flux density distribution. For each region, an appropriate fitting function (such as a linear or quadratic function) is selected to ensure that the fitted curve accurately reflects the temperature change trend of that region. After fitting, the temperature change trend fitted curve for each segment is obtained, with the data format being [region number, fitting function parameters]. Next, local simulation processing is performed on the temperature change trend of each segment. Combined with the cell's heat flux adjustment strategy for that region, the temperature changes of the cell under different cooling conditions are simulated. Specifically, based on the heat flux adjustment strategy for each region (such as the flow rate and direction of the cooling medium), local simulation is performed on each segment using heat flux simulation software. Input the initial temperature, boundary conditions, and cooling parameters for the region to simulate the temperature change of the battery cell under different cooling conditions. After the simulation is completed, a local simulated temperature curve is generated, with the data format being [region number, timestamp (milliseconds), simulated temperature value (degrees Celsius)]. Heat flux density correction is performed on the local simulated temperature curve. The simulation results are corrected based on the physical structure and material properties of the battery cell. Specifically, the local simulated temperature curve is corrected according to the thermal conductivity, specific heat capacity, and geometric characteristics of the battery cell material's stacked structure. For example, for regions with high heat flux density, the amplitude of temperature change is appropriately increased; for regions with low heat flux density, the amplitude of temperature change is appropriately decreased. After correction, the corrected local simulated temperature curve is obtained, with the data format being [region number, timestamp (milliseconds), corrected simulated temperature value (degrees Celsius)]. The corrected local simulated temperature curve is then fused as a whole. The simulation results of all segments are integrated to generate a complete simulated temperature change trend curve. Specifically, the corrected simulated temperature curves of each segment are spliced ​​and merged according to the battery cell's stacked structure and region division. In the transition areas between paragraphs, a smoothing transition function (such as linear interpolation or spline interpolation) is used to ensure the continuity and smoothness of the overall curve. After fusion, a complete simulated temperature change trend curve is obtained, with the data format being [timestamp (milliseconds), overall simulated temperature value (degrees Celsius)]. The complete simulated temperature change trend curve is then compared with the actual cooling effect. The simulation results are compared with the actual monitored temperature change trend curve to analyze the actual cooling effect of the cooling device under different cooling conditions. Specifically, the complete simulated temperature change trend curve is compared with the actual monitored temperature change trend curve, and the deviation between the two is calculated.For example, calculate the difference between the simulated temperature value and the actual temperature value at each time point, and analyze the magnitude and trend of the deviation. After comparison, obtain the actual cooling effect comparison data, with the data format as [timestamp (milliseconds), simulated temperature value (degrees Celsius), actual temperature value (degrees Celsius), deviation value (degrees Celsius)]. Adjust the cooling strategy based on the compared data. According to the comparison results, adjust the operating parameters of the cooling device and optimize the cooling strategy to improve the cooling effect. Specifically, based on the actual cooling effect comparison data, analyze the areas and time points with large deviations and adjust the operating parameters of the cooling device. For example, if the deviation in a certain area is large, increase the flow rate of the cooling medium in that area or adjust the flow direction of the cooling medium. After adjustment, record the adjusted cooling strategy parameters, with the data format as [area number, adjusted flow rate (m / s), adjusted flow direction angle (degrees)]. Finally, perform a second simulation verification process on the adjusted cooling strategy. Re-simulate the temperature change trend of the battery cell under the adjusted cooling conditions to verify the effectiveness of the adjusted cooling strategy. Specifically, input the adjusted cooling strategy parameters into the heat flow simulation software and re-simulate. After the simulation is completed, a new simulated temperature change trend curve is generated and compared with the actual monitored temperature change trend curve to analyze whether the adjusted cooling strategy is effective. After the verification is completed, the secondary simulation verification results are obtained, and the data format is [timestamp (milliseconds), secondary simulated temperature value (degrees Celsius), actual temperature value (degrees Celsius), secondary deviation value (degrees Celsius)].

[0045] This specification also provides a battery temperature adjustment system based on a battery simulator, used to perform the battery temperature adjustment method based on a battery simulator as described above. This multi-channel battery charge / discharge testing system includes: The battery cell stack thermal resistance detection module is used to collect information on the thermal conductivity, thickness and number of layers of the battery cell stack structure, and record it as battery cell stack structure data; based on the battery cell stack structure data, the thermal resistance of each layer of the battery cell is calculated to obtain the thermal resistance distribution data of each layer of the battery cell. The heat accumulation path simulation module is used to perform spatiotemporal inversion simulation of heat accumulation inside the battery cell during fast charging to obtain heat accumulation simulation data of the battery cell; it determines the current density distribution data inside the battery cell during fast charging based on the thermal resistance distribution data of each layer of the battery cell and the heat accumulation simulation data of the battery cell; and it simulates the spatiotemporal accumulation path in the battery cell stacked structure based on the current density distribution data to generate heat accumulation path simulation data. The cell cooling adjustment module is used to match the heat flux density of the battery cell cooling device based on the heat accumulation path simulation data to obtain heat flux cooling density data; and to adjust the flow rate and direction of the cooling medium in the cell cooling device according to the heat flux cooling density data to obtain the cell heat flux adjustment strategy. The thermal runaway risk assessment module is used to monitor the temperature adjustment function of the battery simulator in real time according to the cell heat flow adjustment strategy, simulate the temperature change trend of the battery cell under the operation of the cell cooling device, generate cell temperature change trend data, and perform thermal runaway risk assessment on the cell temperature change trend data to generate a thermal runaway risk assessment report.

[0046] 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.

[0047] 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 method for temperature adjustment based on a battery simulator, characterized in that, Includes the following steps: Step S1: Collect information on the thermal conductivity, thickness, and number of layers of the battery cell stacked structure and record it as battery cell stacked structure data; calculate the thermal resistance of each layer of the battery cell based on the battery cell stacked structure data to obtain the thermal resistance distribution data of each layer of the battery cell; Step S2: Perform a spatiotemporal inversion simulation of the heat accumulation inside the cell during fast charging to obtain simulated data of cell heat accumulation; determine the current density distribution data inside the cell during fast charging based on the thermal resistance distribution data of each layer of the cell and the simulated data of cell heat accumulation. Simulation of the spatiotemporal accumulation path in the cell stacked structure is performed based on current density distribution data to generate heat accumulation path simulation data. Step S3: Based on the heat accumulation path simulation data, perform heat flux density matching on the battery cell cooling device to obtain heat flux cooling density data; adjust the flow rate and direction of the cooling medium in the cell cooling device according to the heat flux cooling density data to obtain the cell heat flux adjustment strategy; Step S4: Monitor the temperature adjustment function of the battery simulator in real time according to the cell heat flow adjustment strategy, simulate the temperature change trend of the battery cell under the operation of the cell cooling device, and generate cell temperature change trend data; conduct thermal runaway risk assessment on the cell temperature change trend data, and generate thermal runaway risk assessment report.

2. The method for temperature adjustment based on a battery simulator according to claim 1, characterized in that, In step S1, the thermal conductivity, thickness, and number of layers of the battery cell stacked structure are collected and recorded as battery cell stacked structure data, including: On the surface of each layer of the battery cell stacked structure, multiple miniature thermistors are arranged sequentially along a preset trajectory, and the trajectory covers multiple key areas of the battery cell stacked structure. The thermal conductivity of each layer of material at different locations is measured using a miniature thermistor. The measurement process involves reading the resistance change of the thermistor under different temperature gradients, thereby calculating the thermal conductivity at each location. The thickness of each layer of the battery cell stacked structure is measured using a miniature laser rangefinder. The laser beam emitted by the laser rangefinder is perpendicular to the surface of the battery cell stacked structure, and the thickness data of each layer is calculated by the reflected signal. Interlayer markers are set at the edge of the battery cell stacked structure, and the interlayer markers are identified by an optical recognition device to determine the number of layers in the battery cell stacked structure. The collected thermal conductivity data, thickness data, and layer number data are synchronously classified and recorded to form cell stack-up structure data.

3. The method for temperature adjustment based on a battery simulator according to claim 1, characterized in that, Step S1 involves calculating the thermal resistance of each layer of the battery cell based on the cell's stacked structure data, including: The thickness and thermal conductivity data of each layer in the battery cell stack-up structure data are processed to calculate the reference thermal resistance, and the reference thermal resistance value of each layer is obtained. The reference thermal resistance value of each layer is refined by region. Each layer is divided into multiple small regions of equal area, and the thickness and thermal conductivity data of each small region are processed to calculate the local thermal resistance value of each small region. The local thermal resistance value of the edge region is corrected by edge effect processing. The correction coefficient is introduced according to the geometry and material properties of the edge region to obtain the corrected thermal resistance value of the edge region. The local thermal resistance value of the interlayer contact area is corrected by contact thermal resistance correction. The corrected thermal resistance value of the interlayer contact area is obtained by introducing the contact thermal resistance correction value based on the contact quality and surface roughness of the interlayer material. The corrected thermal resistance values ​​of the edge region and the interlayer contact region are verified as a whole. The thermal resistance values ​​of adjacent layers are compared to check for any abnormal thermal resistance jumps. If any abnormality is found, the relevant parameters are adjusted and recalculated to obtain thermal resistance distribution data.

4. The method for temperature adjustment based on a battery simulator according to claim 1, characterized in that, Step S2 involves performing a spatiotemporal inversion simulation of heat accumulation inside the battery cell during fast charging, including: Collect data on heat accumulation in the battery cells during fast charging; The heat accumulation data of the battery cell during fast charging is collected and processed in segments. The charging process is divided into multiple time intervals according to a preset time interval, and the heat accumulation in each time interval is recorded to obtain segmented heat accumulation data. The segmented heat accumulation data is traced backward, starting from the end of charging, and the heat accumulation path in each time interval is calculated step by step to reconstruct the heat propagation process inside the cell and obtain preliminary heat accumulation path data. Interlayer thermal resistance correction is performed on the preliminary heat accumulation path data. Combined with the thermal resistance distribution of the cell stack structure, the loss of heat during interlayer transfer is compensated to obtain the corrected heat accumulation path data. Local heat sources are identified by analyzing the corrected heat accumulation path data. Combined with the hardware structure of the electrodes and separators inside the cell, the source areas of heat accumulation are identified, and the local heat source distribution data are recorded to obtain simulated heat accumulation data of the cell.

5. The method for temperature adjustment based on a battery simulator according to claim 1, characterized in that, In step S2, the current density distribution data inside the battery cell during fast charging is determined based on the thermal resistance distribution data of each layer of the battery cell and the simulated data of heat accumulation in the battery cell. Thermal resistance gradient analysis was performed on the thermal resistance distribution data of each layer of the battery cell to calculate the gradient change of thermal resistance in each layer and obtain the thermal resistance gradient distribution. Heat flux density is inverted from the simulated data of heat accumulation in the battery cell, and the heat flux density at each time point and spatial location is calculated based on the spatiotemporal distribution of heat accumulation, thus obtaining the heat flux density distribution. By jointly analyzing the thermal resistance gradient distribution and the heat flux density distribution, and combining the cell's stacked structure, the relationship between current density and thermal resistance gradient and heat flux density is analyzed to obtain preliminary current density distribution data. The preliminary current density distribution data is fitted in different regions. The cell is divided into multiple small regions, and the current density distribution of each small region is fitted to obtain the current density distribution curve of each small region. The stability of the current density distribution curve was verified by simulating the current density changes at different charging stages, and the verified current density distribution data was obtained.

6. The method for temperature adjustment based on a battery simulator according to claim 1, characterized in that, Step S2, which involves simulating the spatiotemporal accumulation path in the battery cell stacked structure based on current density distribution data, includes: The current density distribution data is subjected to a nonlinear transformation, and the current density value is adjusted by a nonlinear function to obtain the adjusted current density distribution data. The adjusted current density distribution data is spatiotemporally coupled, and combined with the geometric features of the cell stack structure, the current density distribution is coupled with the time dimension to obtain the spatiotemporally coupled current density distribution. The current density distribution after spatiotemporal coupling is processed by thermal effect mapping. Based on the nonlinear relationship between current density and thermal effect, the current density distribution is mapped to the thermal effect distribution to obtain thermal effect distribution data. The thermal effect distribution data is traced in reverse. Starting from the heat output end of the cell, the propagation path of heat in the cell stacked structure is traced in reverse to obtain the reverse tracing path data. The reverse tracking path data is dynamically corrected, and the reverse tracking path is corrected in real time according to the dynamic changes of the battery cell during the charging process to obtain the dynamically corrected path data. Multi-layer fusion is performed on the dynamically corrected path data to generate complete heat accumulation path simulation data.

7. The method for temperature adjustment based on a battery simulator according to claim 1, characterized in that, Step S3, which involves matching the heat flux density of the battery cell cooling device based on the heat accumulation path simulation data, includes: Heat flux density is analyzed from the simulation data of heat accumulation path to extract heat flux density information and obtain heat flux density distribution map; Cooling demand analysis is performed on the heat flux density distribution map. Based on the heat flux density distribution, the cooling demand of the battery cell cooling device in different areas is analyzed to obtain the cooling demand distribution map. The cooling demand distribution map is processed for data verification. By simulating cooling parameters under different operating conditions, the heat flux cooling density distribution characteristics are matched to obtain heat flux cooling density data.

8. The method for temperature adjustment based on a battery simulator according to claim 1, characterized in that, Step S3, adjusting the flow rate and direction of the cooling medium in the cell cooling device based on the heat flux cooling density data, includes: Regional heat flux density analysis was performed on the heat flux cooling density data. Based on the heat flux density values ​​of different regions inside the cell, high heat flux density regions and low heat flux density regions were determined. Based on the regional heat flux density analysis results, the cooling channel of the battery cell cooling device is divided into zones with adjustable flow rates. The flow rate of the cooling medium is increased in the high heat flux density area and decreased in the low heat flux density area to meet the cooling needs of different areas. The flow direction of the cooling channel is optimized by guiding the heat flux density. Based on the heat flux density data, the guide vanes in the cooling device are adjusted to guide the cooling medium to flow preferentially to the high heat flux density area. The heat flux density matching of the adjusted cooling channel was verified by simulating the cooling effect of the cooling medium at the adjusted flow rate and flow direction to verify whether the heat flux density of the cooling channel matches the heat accumulation path of the battery cell. The parameters of the verified cooling channel were adjusted and confirmed. Based on the verification results, the adjusted flow rate and flow direction parameters were confirmed and recorded as the cell heat flow adjustment strategy.

9. The method for temperature adjustment based on a battery simulator according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Perform real-time data acquisition and processing on the temperature adjustment function of the battery simulator, and collect the temperature data of the cell cooling device every 10 milliseconds to obtain real-time temperature monitoring data; Step S42: Perform temperature change trend analysis on the real-time temperature monitoring data, calculate the temperature difference between every two consecutive acquisition points, analyze the temperature change trend, and obtain the temperature change trend curve. Step S43: Simulate and verify the temperature change trend curve. Combine the cell heat flow adjustment strategy to simulate the temperature change trend of the cell under different cooling conditions and verify the actual cooling effect of the cooling device. Step S44: Perform thermal runaway risk identification processing on the temperature change trend data after simulation verification. Based on the temperature change trend curve, identify the temperature anomaly points that lead to thermal runaway and obtain thermal runaway risk identification data. Step S45: Perform risk assessment processing on the thermal runaway risk identification data, quantify the identified thermal runaway risks, and generate a thermal runaway risk assessment report.

10. A temperature regulation system based on a battery simulator, characterized in that, For performing the battery simulator-based temperature adjustment method as described in claim 1, the battery simulator-based temperature adjustment system comprises: The battery cell stack thermal resistance detection module is used to collect information on the thermal conductivity, thickness and number of layers of the battery cell stack structure, and record it as battery cell stack structure data; based on the battery cell stack structure data, the thermal resistance of each layer of the battery cell is calculated to obtain the thermal resistance distribution data of each layer of the battery cell. The heat accumulation path simulation module is used to perform spatiotemporal inversion simulation of heat accumulation inside the battery cell during fast charging to obtain heat accumulation simulation data of the battery cell; it determines the current density distribution data inside the battery cell during fast charging based on the thermal resistance distribution data of each layer of the battery cell and the heat accumulation simulation data of the battery cell; and it simulates the spatiotemporal accumulation path in the battery cell stacked structure based on the current density distribution data to generate heat accumulation path simulation data. The cell cooling adjustment module is used to match the heat flux density of the battery cell cooling device based on the heat accumulation path simulation data to obtain heat flux cooling density data; and to adjust the flow rate and direction of the cooling medium in the cell cooling device according to the heat flux cooling density data to obtain the cell heat flux adjustment strategy. The thermal runaway risk assessment module is used to monitor the temperature adjustment function of the battery simulator in real time according to the cell heat flow adjustment strategy, simulate the temperature change trend of the battery cell under the operation of the cell cooling device, generate cell temperature change trend data, and perform thermal runaway risk assessment on the cell temperature change trend data to generate a thermal runaway risk assessment report.

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