A battery cabin air conditioner refrigerating capacity configuration method and system based on thermal runaway simulation

Abstract

This invention discloses a method and system for configuring the cooling capacity of a battery compartment air conditioning system based on thermal runaway simulation, specifically relating to the field of battery compartment thermal management safety technology. It addresses the problem that existing air conditioning cooling capacity configuration methods only consider a single heat load while neglecting the coordination of multiple safety objectives. By establishing a battery thermal runaway simulation model to obtain distribution data of heat load, combustible gas concentration, and cabin pressure, multi-objective conflict analysis is used to identify logical conflicts between cooling, explosion protection, and smoke extraction objectives. The impact of these conflicts on system performance is assessed, and the interference relationship between the air conditioning and smoke extraction systems is quantified. Finally, the air conditioning cooling capacity configuration value that coordinates multiple safety objectives is determined. This achieves coordinated operation of the air conditioning system with the gas control and smoke extraction systems, effectively improving the overall safety protection capability of the battery compartment.

Description

Technical Field

[0001] This invention relates to the field of battery compartment thermal management safety technology, and more specifically, to a method and system for configuring the cooling capacity of battery compartment air conditioning based on thermal runaway simulation. Background Technology

[0002] In the safety design of electrochemical energy storage power stations, the thermal management system of the battery compartment is crucial. To prevent the extreme safety risks of battery thermal runaway, existing technologies typically use the following method to configure the air conditioning cooling capacity of the battery compartment: based on the simulation of the battery thermal runaway process, the maximum instantaneous heat load released at the moment of runaway is calculated, and the required maximum air conditioning cooling capacity is determined accordingly. The core of this method is to provide sufficient cooling capacity through the air conditioning system in order to suppress the spread of heat between battery clusters when a thermal runaway event occurs. This design approach based on a single thermal parameter for equipment selection is a common practice in the current engineering field for configuring safety-grade air conditioning systems.

[0003] However, the aforementioned existing technologies have shortcomings: they only focus on configuring the air conditioning cooling capacity based on the single safety objective of suppressing heat spread, while neglecting the fact that in actual thermal runaway scenarios, cabin environment control is a complex system that needs to simultaneously satisfy multiple and contradictory safety objectives. Specifically, thermal runaway not only generates enormous heat but also releases a large amount of combustible gas and smoke. This creates inherent logical conflicts and physical interference between key safety objectives such as maximizing cooling, preventing the mixing and accumulation of combustible gases, and effectively exhausting smoke and depressurizing. Existing design methods based on isolated objectives result in the configured air conditioning system potentially causing control-level conflicts and performance cancellations with other key safety subsystems such as gas detection and fire smoke exhaust in actual extreme events. This makes it impossible to form a coordinated and effective safety protection in a real multi-objective coupled environment, thus creating serious systemic safety hazards. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for configuring the cooling capacity of the battery compartment air conditioning based on thermal runaway simulation to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for configuring the cooling capacity of a battery compartment air conditioning system based on thermal runaway simulation includes the following steps:

[0007] S1. Based on the battery thermal runaway simulation model, obtain the heat load distribution data, combustible gas concentration distribution data, and cabin pressure distribution data of the battery compartment during the thermal runaway event;

[0008] S2. Using a multi-objective conflict analysis algorithm, analyze the mutual interference relationship between heat load distribution data, combustible gas concentration distribution data and cabin pressure distribution data, and identify the logical conflict between the goal of maximizing cooling, the goal of preventing the mixing and accumulation of combustible gases and the goal of effective smoke exhaust and pressure relief.

[0009] S3. By coupling the interaction between airflow organization and gas diffusion, assess the degree of impact of logical conflicts on the cooling efficiency of the air conditioning system and the operating performance of the smoke exhaust system.

[0010] S4. Analyze the operational interference relationship between the air conditioning system and the smoke exhaust system, and obtain the interference coefficient by quantifying the impact of the smoke exhaust system on the cooling efficiency of the air conditioning system.

[0011] S5. Based on the analysis results of the degree of influence and interference coefficient, determine the configuration value of air conditioning cooling capacity to coordinate multiple security objectives.

[0012] Furthermore, based on the battery thermal runaway simulation model, data on the heat load distribution, combustible gas concentration distribution, and cabin pressure distribution of the battery compartment during a thermal runaway event are obtained, including:

[0013] Construct a geometric model that includes the battery layout and cabin structure;

[0014] Define the thermal runaway trigger location and propagation path in the geometric model;

[0015] Simulate the coupling effect of heat release, gas diffusion and pressure fluctuation during thermal runaway;

[0016] The temperature distribution within the battery compartment at different times is recorded as heat load distribution data, the concentration of combustible gas in the space is recorded as combustible gas concentration distribution data, and the pressure changes are recorded as pressure distribution data within the compartment.

[0017] Furthermore, constructing a geometric model that includes the battery layout and cabin structure includes: establishing a three-dimensional geometric contour based on the actual dimensions of the battery compartment, and accurately marking the arrangement position of the battery cells in the three-dimensional geometric contour; setting the thermal runaway trigger temperature threshold and thermal runaway propagation rate parameters according to the battery type; and defining the thermal runaway trigger condition as activating the heat source release when the temperature of the battery cell reaches the thermal runaway trigger temperature threshold.

[0018] Furthermore, using a multi-objective conflict analysis algorithm, the interference relationships between heat load distribution data, combustible gas concentration distribution data, and cabin pressure distribution data are analyzed to identify logical conflicts between the objectives of maximizing cooling, preventing combustible gas mixing and accumulation, and effective smoke exhaust and pressure relief, including:

[0019] Establish the primary correlation between heat load distribution data and the maximum cooling target;

[0020] Establish a second correlation between combustible gas concentration distribution data and the goal of preventing combustible gas mixing and accumulation;

[0021] Establish a third correlation between cabin pressure distribution data and effective smoke exhaust and pressure relief targets;

[0022] Based on the first, second, and third correlation relationships, identify the first conflict type between the airflow organization pattern required for the maximum cooling target and the airflow organization pattern required for the target of preventing the mixing and accumulation of combustible gases;

[0023] Identify a second type of conflict between the pressure environment required for maximizing cooling and the pressure environment required for effective smoke exhaust and pressure relief.

[0024] Furthermore, establishing the first correlation between heat load distribution data and the maximum cooling target includes: analyzing the degree of matching between the distribution of high-temperature areas in the heat load distribution data and the coverage of air conditioning supply; determining the airflow organization pattern required for the maximum cooling target based on the degree of matching; and identifying the difference between the corresponding airflow organization pattern and the airflow organization pattern required for the target of preventing the mixing and accumulation of combustible gases as the first conflict type.

[0025] Furthermore, by coupling the interaction between airflow organization and gas diffusion, the impact of logical conflicts on the cooling efficiency of the air conditioning system and the operational performance of the smoke exhaust system is evaluated, including:

[0026] Analyze the impact of airflow organization on gas diffusion paths and assess the impact of the conflict between the goal of preventing the mixing and accumulation of combustible gases and the goal of maximizing cooling on the cooling efficiency of the air conditioning system.

[0027] The impact of airflow disturbances caused by the operation of the smoke exhaust system on the cooling effect of the air conditioning system was analyzed, and the degree of impact of the conflict between the effective smoke exhaust pressure relief target and the maximum cooling target on the cooling efficiency of the air conditioning system was evaluated.

[0028] By considering the interaction between airflow organization and gas diffusion, the impact of logical conflicts on the operational efficiency of the smoke exhaust system is assessed.

[0029] Furthermore, the operational interference relationship between the air conditioning system and the smoke exhaust system is analyzed. The interference coefficient is obtained by quantifying the impact of the smoke exhaust system's operation on the cooling efficiency of the air conditioning system, including:

[0030] Establish a baseline value for the cooling efficiency of the air conditioning system when the smoke exhaust system is closed;

[0031] Obtain the actual value of the air conditioning system's cooling efficiency when the smoke exhaust system is on;

[0032] Calculate the relative change between the actual value of the air conditioning system's cooling efficiency when the smoke exhaust system is on and the benchmark value of the air conditioning system's cooling efficiency when the smoke exhaust system is off;

[0033] The interference coefficient, which characterizes the impact of the smoke exhaust system's operation on the cooling efficiency of the air conditioning system, is determined based on the relative change.

[0034] Furthermore, based on the analysis results of the degree of impact and interference coefficient, the air conditioning cooling capacity configuration values ​​for coordinating multiple security objectives are determined, including:

[0035] The basic cooling capacity requirement is determined based on the degree of impact of logical conflicts on the cooling efficiency of the air conditioning system.

[0036] The basic cooling capacity requirement is corrected based on the interference coefficient to obtain the median value of cooling capacity after compensation for exhaust interference.

[0037] Based on the requirements of airflow organization to prevent the mixing and accumulation of combustible gases, adjust the air supply parameters corresponding to the intermediate value of cooling capacity.

[0038] The final air conditioning cooling capacity configuration value is determined based on the adjusted air supply parameters to coordinate multiple safety objectives.

[0039] Furthermore, determining the basic cooling capacity requirement based on the degree of impact of logical conflicts on the cooling efficiency of the air conditioning system includes: classifying the cooling capacity requirement levels according to the magnitude of the impact; weighting and correcting the basic cooling capacity requirement by combining the interference coefficient; adjusting the weighted and corrected cooling capacity requirement according to the restriction on the air supply parameters based on the goal of preventing the mixing and accumulation of combustible gases, and generating the final air conditioning cooling capacity configuration value.

[0040] On the other hand, the present invention provides a battery compartment air conditioning cooling capacity configuration system based on thermal runaway simulation, comprising the following modules:

[0041] The data acquisition module is used to acquire data on the heat load distribution, combustible gas concentration distribution, and internal pressure distribution of the battery compartment during a thermal runaway event, based on the battery thermal runaway simulation model.

[0042] The logic conflict module is used to analyze the mutual interference relationship between heat load distribution data, combustible gas concentration distribution data and cabin pressure distribution data using a multi-objective conflict analysis algorithm, and to identify the logical conflict between the goal of maximizing cooling, the goal of preventing the mixing and accumulation of combustible gases and the goal of effective smoke exhaust and pressure relief.

[0043] The impact level module is used to assess the impact of logical conflicts on the cooling efficiency of the air conditioning system and the operating performance of the smoke exhaust system by coupling the interaction between airflow organization and gas diffusion.

[0044] The coefficient acquisition module is used to analyze the operational interference relationship between the air conditioning system and the smoke exhaust system. The interference coefficient is obtained by quantifying the degree of influence of the smoke exhaust system on the cooling efficiency of the air conditioning system.

[0045] The results analysis module is used to determine the air conditioning cooling capacity configuration value that coordinates multiple security objectives based on the analysis results of the degree of influence and interference coefficient.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. By establishing a multi-dimensional coupled analysis framework of heat load distribution data, combustible gas concentration distribution data, and cabin pressure distribution data, the limitations of traditional single thermal parameter design are overcome. Based on battery thermal runaway simulation, the cooling capacity configuration of the air conditioning system, combustible gas control, and smoke exhaust and pressure relief requirements are incorporated into a unified analysis system, realizing the coordinated consideration of multiple safety objectives. Through multi-objective conflict analysis algorithm, the inherent contradictions between different safety objectives are accurately identified, providing a clear direction for subsequent system optimization. This effectively avoids the problem of the separation of various safety subsystems in traditional design, enabling the air conditioning system to maintain coordinated operation with other safety facilities even under extreme conditions.

[0048] 2. By incorporating the interaction between airflow organization and gas diffusion into the evaluation system, and by quantitatively analyzing the impact of logical conflicts on system performance, a scientific basis for cooling capacity configuration is provided. By establishing an operational interference model between the air conditioning system and the smoke exhaust system, and using interference coefficients to accurately characterize the mutual influence between the systems, the final cooling capacity configuration value fully considers the balance requirements of multiple safety objectives. This not only ensures the cooling efficiency of the air conditioning system in thermal runaway events, but also takes into account the requirements for combustible gas diffusion control and smoke exhaust pressure relief, significantly improving the reliability and coordination of the overall safety protection system of the battery compartment. Attached Figure Description

[0049] Figure 1 This is a flowchart of a battery compartment air conditioning cooling capacity configuration method based on thermal runaway simulation according to the present invention.

[0050] Figure 2 This is a schematic diagram of the structure of a battery compartment air conditioning cooling capacity configuration system based on thermal runaway simulation according to the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1: Figure 1 This invention presents a method for configuring the cooling capacity of a battery compartment air conditioning system based on thermal runaway simulation, which includes the following steps:

[0053] S1. Based on the battery thermal runaway simulation model, obtain the heat load distribution data, combustible gas concentration distribution data, and cabin pressure distribution data of the battery compartment during the thermal runaway event;

[0054] S2. Using a multi-objective conflict analysis algorithm, analyze the mutual interference relationship between heat load distribution data, combustible gas concentration distribution data and cabin pressure distribution data, and identify the logical conflict between the goal of maximizing cooling, the goal of preventing the mixing and accumulation of combustible gases and the goal of effective smoke exhaust and pressure relief.

[0055] S3. By coupling the interaction between airflow organization and gas diffusion, assess the degree of impact of logical conflicts on the cooling efficiency of the air conditioning system and the operating performance of the smoke exhaust system.

[0056] S4. Analyze the operational interference relationship between the air conditioning system and the smoke exhaust system, and obtain the interference coefficient by quantifying the impact of the smoke exhaust system on the cooling efficiency of the air conditioning system.

[0057] S5. Based on the analysis results of the degree of influence and interference coefficient, determine the configuration value of air conditioning cooling capacity to coordinate multiple security objectives.

[0058] S1. Based on the battery thermal runaway simulation model, obtain the heat load distribution data, combustible gas concentration distribution data, and cabin pressure distribution data of the battery compartment during a thermal runaway event. The specific implementation is as follows:

[0059] When constructing the geometric model including the battery layout and cabin structure, a three-dimensional geometric contour is established based on the actual dimensions of the battery cabin. This three-dimensional geometric contour is drawn using computer-aided design software. Specific parameters include, for example, a cuboid cabin structure with a length of 8 meters, a width of 2.5 meters, and a height of 2.8 meters. The arrangement positions of the battery cells are precisely marked in the three-dimensional geometric contour. The battery cells are arranged in a matrix manner, for example, 16 battery cells arranged horizontally and 12 battery cells arranged vertically, with a 15 mm spacing between adjacent battery cells. Thermal runaway trigger temperature thresholds and thermal runaway propagation rate parameters are set according to the battery type. For lithium iron phosphate batteries, the thermal runaway trigger temperature threshold is set to 210 degrees Celsius, and for ternary lithium batteries, it is set to 180 degrees Celsius. The setting of the thermal runaway trigger temperature threshold is based on experimental data of the thermal stability of the battery materials, determined by measuring the exothermic initiation point of the battery sample during the heating process using a thermal analyzer. The thermal runaway propagation rate parameter is determined based on the thermal conductivity coefficient and spacing between battery cells, and is set at a propagation speed of 0.8 battery cells per second. The thermal conductivity coefficient is obtained through material thermal conductivity testing; for example, the thermal conductivity of the battery casing material is measured to be 0.5 W / m / Kelvin using a thermal conductivity meter. The thermal runaway trigger condition is defined as the activation of the heat source release when the temperature of a battery cell reaches the thermal runaway trigger temperature threshold. The heat source release power is set at 12 times the rated capacity of the battery; for example, each battery cell releases 24 kW of heat power during thermal runaway. The heat power calculation is based on the battery energy density and experimental values ​​of the enthalpy change of the thermal runaway reaction.

[0060] When defining the thermal runaway trigger location and propagation path in the geometric model, the battery cell located at the geometric center of the battery array is selected as the initial thermal runaway trigger location. The thermal runaway propagation path is determined according to the battery cell arrangement direction, preferentially propagating along the lateral direction, and secondarily along the longitudinal direction. Each battery cell along the propagation path, after receiving heat transferred from adjacent battery cells, activates its own heat source release when its internal temperature reaches the thermal runaway trigger temperature threshold. During the thermal runaway propagation process, the temperature change curve of each battery cell is recorded, with a temperature sampling frequency set to 10 times per second. When the temperature of a battery cell rises by more than 100 degrees Celsius within 3 seconds, the battery cell is determined to have entered a thermal runaway state. The threshold for the temperature rise rate is set based on experimental data of battery thermal runaway characteristics.

[0061] To simulate the coupling effect of heat release, gas diffusion, and pressure fluctuations during thermal runaway, computational fluid dynamics (CFD) is used to solve the energy, mass, and momentum conservation equations. The energy conservation equations consider three heat transfer mechanisms: convection, conduction, and radiation. The convection heat transfer coefficient is determined using the Reynolds number; for example, based on an air velocity of 2 m / s, the convection heat transfer coefficient is set to 25 W / m² / Kelvin. The gas diffusion process is simulated using the species transport equation, with the diffusion coefficient determined based on the gas molecular weight and temperature; for example, the diffusion coefficient of hydrogen in air is set to 0.61 cm² / s. Pressure fluctuations are solved using the Navier-Stokes equations, with the viscosity coefficient set to 1.8 x 10⁻⁵ Pa·s based on aerodynamic characteristics. The simulation time step is set to 0.1 seconds, and the total simulation duration is set to 30 minutes. During the simulation, the temperature, gas concentration, and pressure values ​​of each grid cell are updated in real time. The specific implementation of the coupling effect is as follows: when the temperature of a certain grid cell reaches the ignition point of the combustible gas, the concentration of combustible gas in that grid cell immediately participates in the combustion reaction calculation, and at the same time, the pressure fluctuation generated by combustion is fed back to the flow field calculation. The ignition point temperature is set according to the experimental data of the lowest ignition energy of the combustible gas, for example, the ignition point of hydrogen is 560 degrees Celsius.

[0062] Temperature distribution within the battery compartment at different times is recorded as heat load distribution data. The temperature data is stored in a three-dimensional matrix with dimensions of 160×50×56, corresponding to dividing the battery compartment space into 160 grids along the length, 50 grids along the width, and 56 grids along the height. The temperature data recorded at each grid point includes instantaneous temperature and cumulative heat load value. The cumulative heat load value is calculated by integrating the instantaneous temperature over time, with the integration interval from the start of thermal runaway to the current moment. Combustible gas concentration is recorded as combustible gas concentration distribution data. The volume concentration values ​​of hydrogen, carbon monoxide, and methane are recorded at each grid point. The concentration data is collected at a frequency of 5 times per second, and the concentration values ​​are obtained through simulated output from gas sensors. Pressure changes are recorded as pressure distribution data within the compartment. Pressure monitoring points are arranged at three height levels: the top, middle, and bottom of the battery compartment. Nine pressure sensors are located at each level. Pressure data is recorded in absolute pressure values, with a sampling frequency of 20 times per second. The pressure sensor range is set to 0 to 10 kPa.

[0063] The processing of heat load distribution data includes smoothing and filtering the raw temperature data using a moving average method with a sliding window width of 5 data points. The filtered data is used to eliminate measurement noise. Combustible gas concentration distribution data is normalized by dividing the concentration value of each combustible gas by its lower explosive limit (LEL) to obtain the relative concentration distribution. The LLE is set based on safety standards; for example, the LLE for hydrogen is 4% by volume. The pressure distribution data within the chamber is calculated using differential methods to obtain the pressure change gradient. This gradient is used for subsequent analysis of the impact of pressure fluctuations on the operation of the exhaust system. The differential calculation uses the central difference method with a step size of 0.1 seconds. All distribution data is stored in time-series format, with timestamps synchronized with the simulation time. The data storage format is binary files to reduce storage space usage. The binary file structure includes header information recording data dimensions and sampling frequency.

[0064] During the data validation phase, the simulated temperature distribution data was compared with experimental measurements. The allowable error range for temperature difference was set to ±5 degrees Celsius. Experimental measurements were performed using a thermocouple array, with the thermocouple positions corresponding to the simulation grid points. Validation of combustible gas concentration data was achieved by comparing simulated concentration values ​​with gas chromatograph (GC) measurements, with the relative concentration error controlled within 10%. The GC sampling frequency was once per second. Validation of pressure distribution data used measurements from a high-precision pressure sensor array as a benchmark, with a pressure measurement error not exceeding 50 Pascals. Pressure sensor calibration was based on a standard pressure source. When the validation error exceeded the allowable range, the simulation was recalculated by adjusting the grid size and turbulence model parameters until the error requirements were met. The grid size adjustment range was from 0.01 meters to 0.1 meters, and turbulence model parameters, such as turbulence intensity, were set to 5% based on the Reynolds-averaged Navier-Stokes equations.

[0065] S2. Using a multi-objective conflict analysis algorithm, analyze the interference relationship between heat load distribution data, combustible gas concentration distribution data, and cabin pressure distribution data to identify the logical conflicts between the objectives of maximizing cooling, preventing combustible gas mixing and accumulation, and effective smoke exhaust and pressure relief. Specifically, the implementation is as follows:

[0066] When establishing the initial correlation between heat load distribution data and the maximum cooling target, the matching degree between the high-temperature area distribution in the heat load distribution data and the air conditioning supply coverage is analyzed. Specifically, the three-dimensional space of the battery compartment is divided into 0.1m × 0.1m × 0.1m grid cells. Grid cells with temperatures exceeding 150 degrees Celsius are extracted to form a high-temperature area set. The air conditioning supply coverage is calculated based on the geometric parameters of the air outlets and the air supply angle, which is adjustable from 0 to 90 degrees. The matching degree is calculated by statistically analyzing the spatial overlap between the high-temperature area grid cells and the air supply coverage area grid cells. For example, when the number of overlapping grid cells accounts for 80% of the total number of high-temperature area grid cells, the matching degree is considered good. This threshold is determined based on the balance between cooling efficiency and energy consumption. The airflow organization pattern required for maximizing cooling is determined based on the degree of matching. When the matching degree is good, an upward supply and downward return airflow organization pattern is adopted, with the supply air velocity set to 2 m / s and the return air velocity set to 1.5 m / s. When the matching degree is less than 60%, a side supply and side return airflow organization pattern is adopted, with the supply air velocity adjusted to 2.5 m / s and the return air velocity adjusted to 2 m / s. These parameters are optimized through airflow organization experimental data. The difference between the corresponding airflow organization pattern and the airflow organization pattern required to prevent the mixing and accumulation of combustible gases is identified as the first type of conflict. The target of preventing the mixing and accumulation of combustible gases requires the use of a downward supply and upward return airflow organization pattern, which forms a reverse conflict with the upward supply and downward return pattern in the airflow direction. The degree of conflict is quantified by calculating the angle between the airflow vectors of the two patterns. For example, when the vector angle is greater than 90 degrees, it is judged as a severe conflict.

[0067] When establishing the second correlation between combustible gas concentration distribution data and the goal of preventing the mixing and accumulation of combustible gases, areas where the concentration value exceeds 10% of the lower explosive limit (LEL) are first identified as high-risk accumulation areas. The LLE is set according to the characteristics of different gas types; for example, the LLE for hydrogen is 4% by volume, and for carbon monoxide it is 12.5% ​​by volume. The goal of preventing the mixing and accumulation of combustible gases requires controlling the concentration of combustible gases at any location to below 25% of the LLE. This safety factor is determined based on combustible gas explosion limit experimental data. The second correlation is established by analyzing the spatial distribution characteristics of high-risk accumulation areas. Specifically, the gas concentration gradient value of each grid cell is calculated. When the gas concentration gradient is greater than 0.5% per meter, the area is considered to have an accumulation trend. This threshold is verified by comparing gas diffusion simulations with measured data. Based on the distribution characteristics of the accumulation trend, the airflow organization pattern required to prevent the mixing and accumulation of combustible gases is determined. A vertical unidirectional flow pattern is required, and the lower limit of the airflow velocity is calculated using Stokes' law, for example, not less than 0.3 meters per second. This value ensures that the force of the airflow on the gas particles is greater than the Brownian motion diffusion force.

[0068] When establishing the third correlation between cabin pressure distribution data and the effective smoke exhaust and pressure relief target, the pressure gradient distribution characteristics in the cabin pressure distribution data are analyzed. The effective smoke exhaust and pressure relief target requires maintaining the cabin pressure within the range of -10 Pa to +10 Pa. This pressure range is determined through wind pressure experiments based on the building structure's pressure bearing capacity and flue gas control requirements. The third correlation is established by calculating the pressure uniformity index, defined as the ratio of the maximum pressure difference to the average pressure. When this ratio exceeds 0.5, the pressure distribution is considered uneven. This threshold is obtained through statistical analysis of multiple sets of pressure distribution data. Based on the pressure uniformity index, the airflow organization pattern required for the effective smoke exhaust and pressure relief target is determined. A uniform smoke exhaust mode is required, and the density of the smoke exhaust outlets is determined through flue gas flow simulation optimization. For example, one smoke exhaust outlet is set every 4 square meters, and the size of the smoke exhaust outlet is determined to be 0.3 meters × 0.3 meters based on the smoke exhaust volume.

[0069] Based on the first, second, and third correlations, the first type of conflict is identified between the airflow organization pattern required for maximizing cooling and the airflow organization pattern required for preventing the mixing and accumulation of combustible gases. This first type of conflict manifests as conflict in airflow direction and parameter configuration. The upward supply and downward return mode required for maximizing cooling and the downward supply and upward return mode required for preventing the mixing and accumulation of combustible gases exhibit directional contradictions in airflow organization. Furthermore, the airflow velocity parameters differ; the airflow velocity range required for maximizing cooling is 2–2.5 m / s, while the airflow velocity range required for preventing the mixing and accumulation of combustible gases is 0.3–0.8 m / s. The degree of conflict is quantified by calculating the proportion of the overlapping area between the two airflow organization modes. A proportion exceeding 40% of the total area is considered a severe conflict, between 20% and 40% is a moderate conflict, and below 20% is a minor conflict. These thresholds are derived from the analysis of multiple conflict case studies.

[0070] A second type of conflict was identified between the pressure environment required for maximizing cooling and the pressure environment required for effective flue gas depressurization. The maximizing cooling target requires maintaining a slightly positive pressure environment within the cabin, controlled within the range of 5 to 15 Pa. This range was determined through differential pressure experiments based on the need to prevent the infiltration of external hot air. The effective flue gas depressurization target requires maintaining a slightly negative pressure environment within the cabin, controlled within the range of -5 to -15 Pa. This range was determined through flue gas flow simulation based on flue gas efficiency requirements. This second type of conflict manifests as a conflict in pressure direction and pressure value, with a fundamental contradiction between the positive and negative pressure requirements in their pressure settings. The degree of conflict was quantified by calculating the absolute difference between the two pressure requirements. A pressure difference exceeding 10 Pa was considered a severe conflict, a difference between 5 and 10 Pa was a moderate conflict, and a difference below 5 Pa was a mild conflict. These conflict level thresholds were determined based on an analysis of the impact of pressure disturbances on system performance. The identification results of all conflict types were recorded in the conflict analysis report for subsequent impact assessment and coordinated optimization of multiple safety objectives.

[0071] S3. By coupling the interaction between airflow organization and gas diffusion, assess the impact of logical conflicts on the cooling efficiency of the air conditioning system and the operating performance of the smoke exhaust system. The specific implementation is as follows:

[0072] When analyzing the impact of airflow organization on gas diffusion paths, a coupled model of the airflow velocity field and gas concentration field is established to assess the degree of conflict between the goal of preventing combustible gas mixing and accumulation and the goal of maximizing cooling efficiency on the air conditioning system. In specific implementation, the airflow velocity field is generated based on the airflow organization pattern parameters determined in the previous step. These parameters include supply air velocity, return air velocity, and airflow direction. For example, the supply air velocity is set to 2 m / s, the return air velocity to 1.5 m / s, and the airflow direction is determined based on the positional relationship between the supply and return air inlets. The gas concentration field is constructed based on the combustible gas concentration distribution data obtained in the previous step, mapping the gas concentration value of each grid cell to three-dimensional space. The coupled model is established using computational fluid dynamics methods, simulating the interaction between airflow and gas diffusion by solving the mass conservation equation and the momentum conservation equation. The mass conservation equation considers airflow convection and gas diffusion terms, while the momentum conservation equation considers pressure gradient and viscous force terms. The assessment of the impact is achieved by calculating the change in the cooling efficiency of the air conditioning system. The cooling efficiency is defined as the ratio of actual heat exchange to the theoretical maximum heat exchange. Actual heat exchange is obtained by measuring the temperature difference between the supply and return air temperatures and multiplying it by the airflow rate. The theoretical maximum heat exchange is calculated based on ideal heat exchange conditions. When a conflict exists, the cooling efficiency decreases due to a mismatch between the airflow organization and the gas diffusion path. The magnitude of the decrease is calculated by comparing the cooling efficiency values ​​before and after the conflict. For example, when there is a conflict between the airflow organization pattern required to prevent the mixing and accumulation of combustible gases and the airflow organization pattern required to maximize cooling, the cooling efficiency may decrease by 15% to 30%. This range is based on statistical analysis of multiple sets of simulation data. The quantification of the impact level is based on the magnitude of the cooling efficiency decrease. For example, a decrease exceeding 20% ​​is considered a high impact, a decrease between 10% and 20% is a moderate impact, and a decrease less than 10% is a low impact. These thresholds are determined by analyzing the air conditioning system performance curves and safety requirements. The performance curves are plotted based on technical parameters provided by the air conditioning equipment manufacturer, and the safety requirements are based on industry standards regarding the stability of the refrigeration system.

[0073] When analyzing the impact of airflow disturbances caused by the operation of the smoke exhaust system on the cooling effect of the air conditioning system, the impact of the conflict between the effective smoke exhaust pressure relief target and the maximum cooling target on the cooling efficiency of the air conditioning system is evaluated by simulating the changes in the airflow field during the start-up and shutdown of the smoke exhaust system. In the specific implementation, the operating parameters of the smoke exhaust system include the smoke exhaust volume, the location of the smoke exhaust outlet, and the smoke exhaust velocity. These parameters are set based on the design requirements of the smoke exhaust system in the previous step. For example, the smoke exhaust volume is calculated based on the cabin volume and the number of air changes, with a rate of 6 times per hour. The location of the smoke exhaust outlet is uniformly distributed according to the cabin structure, and the smoke exhaust velocity is calculated by the ratio of the smoke exhaust volume to the area of ​​the smoke exhaust outlet. The simulation of airflow disturbances uses transient fluid dynamics methods to calculate the degree of interference to the air conditioning supply airflow after the smoke exhaust system is turned on. The degree of interference is characterized by the airflow deflection angle and the velocity attenuation. The airflow deflection angle is calculated by comparing the airflow direction vectors before and after the smoke exhaust system is turned on, and the velocity attenuation is calculated by comparing the airflow velocity values ​​before and after the smoke exhaust system is turned on. The impact level is assessed by measuring changes in the cooling effect of the air conditioning system. The cooling effect is comprehensively evaluated based on the temperature difference between the supply and return air temperatures and the airflow uniformity index. The temperature difference between the supply and return air temperatures is obtained from temperature sensor data, and the airflow uniformity index is calculated by statistically analyzing the standard deviation of airflow velocity in each grid cell. Under disturbances caused by the exhaust system, the temperature difference may decrease by 2 to 5 degrees Celsius, and the airflow uniformity may decrease by 10% to 25%. These data are derived from dynamic simulation results. The level of impact is classified according to the degree of deterioration in cooling effect. For example, a temperature difference reduction exceeding 3 degrees Celsius and an airflow uniformity decrease exceeding 15% are considered high impacts. These thresholds are determined through thermal performance testing and airflow organization optimization experiments. Thermal performance testing uses a standard heat source to simulate battery heat load, and airflow organization optimization experiments observe changes in airflow distribution by adjusting supply air parameters.

[0074] When assessing the impact of logical conflicts on the operational efficiency of the smoke exhaust system by integrating the interaction results of airflow organization and gas diffusion, the output data from the first two analysis steps are first integrated, including data on the impact of airflow organization on the gas diffusion path and data on the impact of airflow disturbance in the smoke exhaust system on the cooling effect. The integration method employs a weighted fusion algorithm to map the interaction parameters of airflow organization and gas diffusion onto the operational efficiency indicators of the smoke exhaust system. These indicators include smoke exhaust efficiency, pressure control accuracy, and energy consumption ratio. Smoke exhaust efficiency is defined as the ratio of actual smoke exhaust volume to the theoretical maximum smoke exhaust volume; pressure control accuracy is defined as the absolute value of the deviation between the actual pressure and the target pressure; and energy consumption ratio is defined as the ratio of the power of the smoke exhaust system to the smoke exhaust volume. The degree of impact of logical conflicts on the operational efficiency of the smoke exhaust system is determined by calculating the rate of change of these indicators. For example, a logical conflict is considered significant if it causes a decrease in smoke exhaust efficiency exceeding 10% or a deviation in pressure control accuracy exceeding 5 Pa. The quantification of the impact level employs a multi-level scoring system, based on a comprehensive score according to the rate of change of indicators. For example, a score above 80 indicates a high impact, a score between 60 and 80 indicates a moderate impact, and a score below 60 indicates a low impact. The scoring thresholds are derived from the design specifications of the smoke exhaust system and statistical analysis of operational data. The design specifications for the smoke exhaust system refer to the performance requirements of smoke exhaust equipment in industry standards, and the operational data is collected through historical operation records and experimental tests. The final evaluation results are used to guide the formulation of coordinated control strategies for the air conditioning system and the smoke exhaust system, ensuring the achievement of multiple safety objectives.

[0075] S4. Analyze the operational interference relationship between the air conditioning system and the exhaust system. Obtain the interference coefficient by quantifying the impact of the exhaust system's operation on the air conditioning system's cooling efficiency. The specific implementation is as follows:

[0076] To establish a baseline value for the cooling efficiency of the air conditioning system when the exhaust system is off, the system is operated independently under standard conditions, and its thermal parameters are measured. The standard operating conditions are set based on typical operating environmental conditions of the battery compartment, including an ambient temperature of 25 degrees Celsius, relative humidity of 50%, and atmospheric pressure of 101.325 kPa. These parameters are precisely controlled through an environmental simulation chamber. The operating parameters of the air conditioning system include supply air temperature, return air temperature, supply air volume, and return air volume. The supply air temperature is measured using a platinum resistance temperature sensor installed in the supply air duct, with a measurement accuracy of 0.1 degrees Celsius. The return air temperature is measured using a thermocouple temperature sensor installed at the return air inlet. The supply and return air volumes are measured using a vortex flow meter, with a flow rate range of 0-2 cubic meters per second. The baseline value for the cooling efficiency of the air conditioning system is calculated using the enthalpy difference method. The actual cooling capacity is obtained by multiplying the difference in enthalpy between the supply and return air by the air flow rate, and then dividing this by the rated cooling capacity of the air conditioning system to obtain the cooling efficiency ratio. In the actual cooling capacity calculation, air density is calculated based on actual temperature and pressure. For example, under standard operating conditions, the air density is 1.2 kg / m³, and the specific heat capacity of air is taken as 1.005 kJ / kg / degree Celsius. The temperature difference between the supply air temperature and the return air temperature is obtained by averaging 10 consecutive measurements. When establishing the baseline value, it is necessary to ensure that the exhaust system is completely shut off. Shutting off the exhaust system is achieved by controlling the exhaust valve to the fully closed position. The status of the exhaust valve is verified by a limit switch, and the exhaust fan current is monitored to be zero to confirm that the exhaust system has stopped operating. During the baseline value measurement, the air conditioning system is kept running stably. Stable operation is defined as a supply air temperature fluctuation of no more than 0.5 degrees Celsius and a supply air volume fluctuation of no more than 3% within 10 consecutive minutes. These stability thresholds are determined based on the control accuracy of the air conditioning system and measurement error analysis.

[0077] To obtain the actual cooling efficiency of the air conditioning system when the exhaust system is active, the air conditioning system and the exhaust system are operated synchronously, and changes in thermal parameters are measured. The exhaust system's operating parameters include exhaust air volume, exhaust velocity, and exhaust outlet opening. The exhaust air volume is controlled by adjusting the exhaust fan frequency, which ranges from 0-50 Hz. For example, a 40 Hz exhaust fan frequency corresponds to an exhaust air volume of 2.5 cubic meters per second. The exhaust velocity is calculated as the ratio of the exhaust air volume to the total area of ​​the exhaust outlet. The exhaust outlet opening is controlled to be fully open by an electric actuator. During the actual value measurement, the air conditioning system operating parameters are kept consistent with those measured against the baseline values. This includes maintaining constant supply air temperature setpoint, supply air volume setpoint, and return air temperature setpoint through the controller. The actual cooling efficiency of the air conditioning system is calculated using the same method as the baseline value. The actual cooling capacity is calculated by real-time measurement of supply air temperature, return air temperature, supply air volume, and return air volume, and then divided by the rated cooling capacity to obtain the actual cooling efficiency. When obtaining actual values, it is necessary to ensure that the measurement time point corresponds to the stable operating period of the smoke exhaust system. Stable operation of the smoke exhaust system is defined as a fluctuation of no more than 5% in the smoke exhaust air volume and a fluctuation of no more than 10 Pa in the static pressure at the smoke exhaust outlet. These stability thresholds are determined based on the dynamic characteristic test data of the smoke exhaust system. During the measurement process, changes in environmental parameters should be recorded simultaneously. If the change in ambient temperature exceeds 2 degrees Celsius or the change in relative humidity exceeds 10%, the measurement system needs to be recalibrated.

[0078] When calculating the relative change between the actual cooling efficiency of the air conditioning system when the exhaust system is on and the baseline cooling efficiency when the exhaust system is off, a percentage change rate method is used. The relative change rate calculation process includes three stages: data preprocessing, core calculation, and data post-processing. In the data preprocessing stage, the baseline and actual cooling efficiency values ​​are validated. Validity conditions include a baseline cooling efficiency value greater than 0.5 and less than 1.0, and an actual cooling efficiency value greater than 0.4 and less than 1.0. These ranges are determined based on the normal operating range of the air conditioning system and the equipment performance curve. In the core calculation stage, the relative change rate formula is used: the difference between the actual cooling efficiency value and the baseline cooling efficiency value, divided by the baseline cooling efficiency value, multiplied by 100%, yields the percentage change rate. In the data post-processing stage, the calculation results are smoothed using a moving average method to eliminate random fluctuations. The moving average window width is set to 5 data points, corresponding to a time window of 25 seconds. The time window length is determined based on the system's thermal inertia time constant. The final result of the relative change needs to be outlier removed. The outlier criterion is that it exceeds three times the standard deviation of the mean. This threshold is determined based on the statistical process control principle.

[0079] When determining the interference coefficient, which characterizes the impact of the exhaust system's operation on the cooling efficiency of the air conditioning system, based on the relative change, a mapping relationship between the relative change and the interference coefficient is established. The interference coefficient is defined as a dimensionless value, ranging from 0 to 1, where 0 represents no effect and 1 represents maximum effect. The mapping relationship is established using a piecewise linear function, with the segment threshold determined based on statistical analysis of a large amount of experimental data. For example, multiple sets of relative change data are obtained by changing the operating parameters of the exhaust system, and the segment positions are determined according to the critical point of cooling efficiency decline. Specifically, the mapping rules are as follows: when the absolute value of the relative change is between 0% and 10%, the interference coefficient is the absolute value of the relative change divided by 10; when the absolute value of the relative change is between 10% and 30%, the interference coefficient is 0.5 plus the absolute value of the relative change minus 10, then divided by 40; when the absolute value of the relative change exceeds 30%, the interference coefficient is 1.0. The determination of the interference coefficient also needs to consider environmental factor compensation, including ambient temperature deviation and air density change. Ambient temperature deviation compensation is achieved by multiplying the difference between the measured ambient temperature and the standard operating temperature by a temperature influence coefficient, which is set at 0.02 degrees Celsius and determined based on thermodynamic analysis. Air density change compensation is achieved by multiplying the difference between the measured atmospheric pressure and the standard pressure by a pressure influence coefficient, which is set at 0.001 kPa and derived based on the gas law. The final output of the interference coefficient needs to be verified for accuracy by comparing the deviation between the calculated and measured values. The allowable deviation range is set to ±0.05, and this threshold is determined based on the accuracy requirements of the control system and data from multiple repeated experiments.

[0080] S5. Based on the analysis results of the degree of influence and interference coefficient, determine the air conditioning cooling capacity configuration value to coordinate multiple security objectives, specifically as follows:

[0081] When determining the basic cooling capacity requirement based on the impact of logical conflicts on the cooling efficiency of an air conditioning system, the first step is to classify the cooling capacity requirement into levels according to the magnitude of the impact. The impact level data comes from the assessment results of the previous step, categorizing the impact into three levels: high impact, moderate impact, and low impact. The classification criteria are based on the quantitative value of the impact level; for example, an impact level greater than 80 points is considered high impact, between 60 and 80 points is moderate impact, and less than 60 points is low impact. The basic cooling capacity requirement is determined according to the cooling capacity requirement level. For example, a low impact corresponds to a basic cooling capacity requirement of 70% of the rated cooling capacity of the air conditioning system, a moderate impact corresponds to 85% of the rated cooling capacity, and a high impact corresponds to 95% of the rated cooling capacity. These percentage thresholds are determined through experimental testing based on the operating characteristics of the air conditioning system under different loads. The specific calculation of the basic cooling capacity requirement is achieved by multiplying the rated cooling capacity of the air conditioning system by the corresponding percentage coefficient.

[0082] When correcting the basic cooling capacity demand based on the interference coefficient to obtain the intermediate value of cooling capacity after compensation for exhaust interference, a weighted correction method is used. The interference coefficient is derived from the calculation results of the previous step and ranges from 0 to 1. The correction coefficient is obtained through interference coefficient mapping; for example, when the interference coefficient is less than 0.3, the correction coefficient is 1.0; when the interference coefficient is between 0.3 and 0.7, the correction coefficient is 1.2; and when the interference coefficient is greater than 0.7, the correction coefficient is 1.5. These mapping relationships are determined based on experimental data analysis of the cooling capacity loss caused by exhaust interference. The intermediate value of cooling capacity after compensation for exhaust interference is calculated by multiplying the basic cooling capacity demand by the correction coefficient. During the correction process, the applicable range of the correction coefficient needs to be considered. When the calculated intermediate value of cooling capacity exceeds the maximum allowable cooling capacity of the air conditioning system, the maximum allowable cooling capacity of the air conditioning system is taken as the intermediate value of cooling capacity. The maximum allowable cooling capacity of the air conditioning system is determined according to the equipment technical specifications.

[0083] When adjusting the air supply parameters corresponding to the intermediate value of the cooling capacity in conjunction with the requirements of airflow organization to prevent the mixing and accumulation of combustible gases, the airflow organization parameters required to achieve this goal must first be determined. These parameters include air supply velocity, air supply temperature, and air supply angle. The air supply velocity is controlled between 0.3 m / s and 0.8 m / s, a range determined based on experiments on the diffusion characteristics of combustible gases. The air supply temperature is controlled between 15°C and 20°C, a range determined based on gas condensation temperature and a safe temperature margin. The air supply angle is controlled between 0°C and 45°C, a range determined based on the requirement for uniform airflow coverage. Adjustments to the air supply parameters are achieved by establishing a correlation between the cooling capacity and the air supply parameters. For example, with an intermediate cooling capacity of 24.48 kWh, the corresponding air supply velocity is 0.6 m / s, the air supply temperature is 17°C, and the air supply angle is 30°C. These correspondences are determined based on the energy conservation equation and experimental data on airflow organization. The adjusted air supply parameters need to be verified for feasibility. The verification criteria include whether the air supply speed is within the allowable range, whether the air supply temperature is within the set range, and whether the air supply angle meets the coverage requirements.

[0084] When determining the final air conditioning cooling capacity configuration value to coordinate multiple safety objectives based on the adjusted supply air parameters, the cooling capacity is calculated by back-calculating the supply air parameters. The back-calculation process considers the difference between the actual supply air parameters and the standard supply air parameters, which correspond to the parameter settings under the rated operating conditions of the air conditioning system. The cooling capacity is calculated using the enthalpy difference method, taking into account the supply air temperature, return air temperature, and supply air velocity. For example, with a supply air temperature of 17 degrees Celsius, a return air temperature of 25 degrees Celsius, and a supply air velocity of 0.6 meters per second, the actual cooling capacity is calculated as air density multiplied by supply air velocity, supply air area, specific heat capacity, and the temperature difference between the supply air temperature and the return air temperature. The final air conditioning cooling capacity configuration value to coordinate multiple safety objectives is determined by multiplying the actual cooling capacity by a safety factor. The safety factor ranges from 1.0 to 1.2, with the specific value determined based on the importance level of the multiple safety objectives. For example, when preventing the accumulation of combustible gas mixtures is the highest priority objective, the safety factor is 1.2. The final air conditioning cooling capacity configuration value needs to be verified within a certain range. The verification standard is that the configuration value should not exceed the maximum cooling capacity of the air conditioning system and should not be lower than the minimum cooling capacity required to maintain the thermal environment inside the cabin. These range values ​​are determined based on the equipment performance parameters and heat load calculation results.

[0085] Example 2: Figure 2 A schematic diagram of a battery compartment air conditioning cooling capacity configuration system based on thermal runaway simulation is provided. This system includes the following modules:

[0086] The data acquisition module is used to acquire data on the heat load distribution, combustible gas concentration distribution, and internal pressure distribution of the battery compartment during a thermal runaway event, based on the battery thermal runaway simulation model.

[0087] The logic conflict module is used to analyze the mutual interference relationship between heat load distribution data, combustible gas concentration distribution data and cabin pressure distribution data using a multi-objective conflict analysis algorithm, and to identify the logical conflict between the goal of maximizing cooling, the goal of preventing the mixing and accumulation of combustible gases and the goal of effective smoke exhaust and pressure relief.

[0088] The impact level module is used to assess the impact of logical conflicts on the cooling efficiency of the air conditioning system and the operating performance of the smoke exhaust system by coupling the interaction between airflow organization and gas diffusion.

[0089] The coefficient acquisition module is used to analyze the operational interference relationship between the air conditioning system and the smoke exhaust system. The interference coefficient is obtained by quantifying the degree of influence of the smoke exhaust system on the cooling efficiency of the air conditioning system.

[0090] The results analysis module is used to determine the air conditioning cooling capacity configuration value that coordinates multiple security objectives based on the analysis results of the degree of influence and interference coefficient.

[0091] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0092] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0093] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0094] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0096] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0097] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for configuring the cooling capacity of a battery compartment air conditioning system based on thermal runaway simulation, characterized in that, Includes the following steps: S1. Based on the battery thermal runaway simulation model, obtain the heat load distribution data, combustible gas concentration distribution data, and cabin pressure distribution data of the battery compartment during the thermal runaway event; S2. Using a multi-objective conflict analysis algorithm, analyze the mutual interference relationship between heat load distribution data, combustible gas concentration distribution data and cabin pressure distribution data, and identify the logical conflict between the goal of maximizing cooling, the goal of preventing the mixing and accumulation of combustible gases and the goal of effective smoke exhaust and pressure relief. S3. By coupling the interaction between airflow organization and gas diffusion, assess the degree of impact of logical conflicts on the cooling efficiency of the air conditioning system and the operating performance of the smoke exhaust system. S4. Analyze the operational interference relationship between the air conditioning system and the smoke exhaust system, and obtain the interference coefficient by quantifying the impact of the smoke exhaust system on the cooling efficiency of the air conditioning system. S5. Based on the analysis results of the degree of influence and interference coefficient, determine the configuration value of air conditioning cooling capacity to coordinate multiple security objectives.

2. The method for configuring the cooling capacity of a battery compartment air conditioning system based on thermal runaway simulation according to claim 1, characterized in that, Based on a battery thermal runaway simulation model, data on the heat load distribution, combustible gas concentration distribution, and pressure distribution within the battery compartment during a thermal runaway event were obtained, including: Construct a geometric model that includes the battery layout and cabin structure; Define the thermal runaway trigger location and propagation path in the geometric model; Simulate the coupling effect of heat release, gas diffusion and pressure fluctuation during thermal runaway; The temperature distribution within the battery compartment at different times is recorded as heat load distribution data, the concentration of combustible gas in the space is recorded as combustible gas concentration distribution data, and the pressure changes are recorded as pressure distribution data within the compartment.

3. The method for configuring the cooling capacity of a battery compartment air conditioning system based on thermal runaway simulation according to claim 2, characterized in that, The construction of a geometric model that includes battery layout and cabin structure includes: establishing a three-dimensional geometric outline based on the actual dimensions of the battery compartment, and accurately marking the arrangement position of the battery cells in the three-dimensional geometric outline; setting the thermal runaway trigger temperature threshold and thermal runaway propagation rate parameters according to the battery type; and defining the thermal runaway trigger condition as activating the heat source release when the temperature of the battery cell reaches the thermal runaway trigger temperature threshold.

4. The method for configuring the cooling capacity of a battery compartment air conditioning system based on thermal runaway simulation according to claim 1, characterized in that, Using a multi-objective conflict analysis algorithm, the interference relationships between heat load distribution data, combustible gas concentration distribution data, and cabin pressure distribution data are analyzed to identify logical conflicts between the objectives of maximizing cooling, preventing combustible gas mixing and accumulation, and effective smoke exhaust and pressure relief, including: Establish the primary correlation between heat load distribution data and the maximum cooling target; Establish a second correlation between combustible gas concentration distribution data and the goal of preventing combustible gas mixing and accumulation; Establish a third correlation between cabin pressure distribution data and effective smoke exhaust and pressure relief targets; Based on the first, second, and third correlation relationships, identify the first conflict type between the airflow organization pattern required for the maximum cooling target and the airflow organization pattern required for the target of preventing the mixing and accumulation of combustible gases; Identify a second type of conflict between the pressure environment required for maximizing cooling and the pressure environment required for effective smoke exhaust and pressure relief.

5. A method for configuring the cooling capacity of a battery compartment air conditioning system based on thermal runaway simulation according to claim 4, characterized in that, Establishing the first correlation between heat load distribution data and the maximum cooling target includes: analyzing the degree of matching between the distribution of high-temperature areas in the heat load distribution data and the coverage of air conditioning supply; determining the airflow organization pattern required for the maximum cooling target based on the degree of matching; and identifying the difference between the corresponding airflow organization pattern and the airflow organization pattern required for the target of preventing the mixing and accumulation of combustible gases as the first type of conflict.

6. The method for configuring the cooling capacity of a battery compartment air conditioning system based on thermal runaway simulation according to claim 1, characterized in that, By coupling the interaction between airflow organization and gas diffusion, the impact of logical conflicts on the cooling efficiency of the air conditioning system and the operational performance of the smoke exhaust system is evaluated, including: Analyze the impact of airflow organization on gas diffusion paths and assess the impact of the conflict between the goal of preventing the mixing and accumulation of combustible gases and the goal of maximizing cooling on the cooling efficiency of the air conditioning system. The impact of airflow disturbances caused by the operation of the smoke exhaust system on the cooling effect of the air conditioning system was analyzed, and the degree of impact of the conflict between the effective smoke exhaust pressure relief target and the maximum cooling target on the cooling efficiency of the air conditioning system was evaluated. By considering the interaction between airflow organization and gas diffusion, the impact of logical conflicts on the operational efficiency of the smoke exhaust system is assessed.

7. The method for configuring the cooling capacity of a battery compartment air conditioning system based on thermal runaway simulation according to claim 1, characterized in that, The interference relationship between the air conditioning system and the exhaust system is analyzed. The interference coefficient is obtained by quantifying the impact of the exhaust system's operation on the cooling efficiency of the air conditioning system. This includes: Establish a baseline value for the cooling efficiency of the air conditioning system when the smoke exhaust system is closed; Obtain the actual value of the air conditioning system's cooling efficiency when the smoke exhaust system is on; Calculate the relative change between the actual value of the air conditioning system's cooling efficiency when the smoke exhaust system is on and the benchmark value of the air conditioning system's cooling efficiency when the smoke exhaust system is off; The interference coefficient, which characterizes the impact of the smoke exhaust system's operation on the cooling efficiency of the air conditioning system, is determined based on the relative change.

8. The method for configuring the cooling capacity of a battery compartment air conditioning system based on thermal runaway simulation according to claim 1, characterized in that, Based on the analysis results of the degree of impact and interference coefficient, the air conditioning cooling capacity configuration values ​​for coordinating multiple security objectives are determined, including: The basic cooling capacity requirement is determined based on the degree of impact of logical conflicts on the cooling efficiency of the air conditioning system. The basic cooling capacity requirement is corrected based on the interference coefficient to obtain the median value of cooling capacity after compensation for exhaust interference. Based on the requirements of airflow organization to prevent the mixing and accumulation of combustible gases, adjust the air supply parameters corresponding to the intermediate value of cooling capacity. The final air conditioning cooling capacity configuration value is determined based on the adjusted air supply parameters to coordinate multiple safety objectives.

9. A method for configuring the cooling capacity of a battery compartment air conditioning system based on thermal runaway simulation according to claim 8, characterized in that, Determining the basic cooling capacity requirement based on the degree of impact of logical conflicts on the cooling efficiency of the air conditioning system includes: classifying the cooling capacity requirement level according to the degree of impact; weighting and correcting the basic cooling capacity requirement by combining the interference coefficient; adjusting the weighted and corrected cooling capacity requirement according to the restriction on the air supply parameters to prevent the mixing and accumulation of combustible gases, and generating the final air conditioning cooling capacity configuration value.

10. A battery compartment air conditioning cooling capacity configuration system based on thermal runaway simulation, used to implement the battery compartment air conditioning cooling capacity configuration method based on thermal runaway simulation as described in any one of claims 1-9, characterized in that, Includes the following modules: The data acquisition module is used to acquire data on the heat load distribution, combustible gas concentration distribution, and internal pressure distribution of the battery compartment during a thermal runaway event, based on the battery thermal runaway simulation model. The logic conflict module is used to analyze the mutual interference relationship between heat load distribution data, combustible gas concentration distribution data and cabin pressure distribution data using a multi-objective conflict analysis algorithm, and to identify the logical conflict between the goal of maximizing cooling, the goal of preventing the mixing and accumulation of combustible gases and the goal of effective smoke exhaust and pressure relief. The impact level module is used to assess the impact of logical conflicts on the cooling efficiency of the air conditioning system and the operating performance of the smoke exhaust system by coupling the interaction between airflow organization and gas diffusion. The coefficient acquisition module is used to analyze the operational interference relationship between the air conditioning system and the smoke exhaust system. The interference coefficient is obtained by quantifying the degree of influence of the smoke exhaust system on the cooling efficiency of the air conditioning system. The results analysis module is used to determine the air conditioning cooling capacity configuration value that coordinates multiple security objectives based on the analysis results of the degree of influence and interference coefficient.

Images