Concrete cabin thermal analogue simulation optimization system of coupling environment

Abstract

The invention relates to a thermal analogue simulation optimization system for a concrete cabin in a coupling environment. The system comprises an environment modeling module used for constructing an externally coupled environment model according to collected historical meteorological data, topographic data and real-time weather data of a target deployment place; the structure modeling module is used for establishing a concrete cabin three-dimensional model according to the received concrete cabin configuration file; the thermal simulation analysis module is used for performing computational fluid dynamics and finite element analysis according to the three-dimensional model of the concrete cabin in combination with the environment model to obtain simulation data in the concrete cabin; and the optimization scheduling module is used for generating an optimal solution set based on a multi-objective algorithm according to the simulation data in the concrete cabin and the charging efficiency response. By the adoption of the system, balance between short-term precision and long-term stability of a simulation model can be achieved through fine structure modeling, heat source active control and coupling optimization, and optimization parameters are fed back in a cross-level mode from a heat management target to charging efficiency improvement.

Description

Technical Field

[0001] The present invention belongs to the field of engineering simulation systems, and in particular relates to a coupled environment concrete cabin thermal simulation optimization system. Background Art

[0002] With the development of new energy vehicle charging infrastructure technology, the number of electric vehicles (EVs) continues to rise in regions with extreme climates, such as high cold, high wind speeds, and frequent ice and snow. To improve the reliability, safety, and maintenance efficiency of charging piles in adverse conditions like low temperatures, high humidity, and snow, auxiliary insulation methods such as heated protective housings and enclosed cabin heating systems have emerged.

[0003] In traditional technologies, temperature control measures for new energy vehicle charging stations generally use external electric heaters to locally heat key components, set up simple insulation covers to block external cold currents, adjust the charging strategy to reduce power through software, and use fans or heat pump systems to keep electronic components within a safe temperature range.

[0004] However, current traditional methods have poor structural rigidity and insufficient thermal inertia. The lightweight cover is easily deformed by the pressure of wind and snow, the thermal insulation effect is short-lived, and the heat capacity is small. The energy consumption is high but the control is extensive. The heating system usually uses constant power drive without feedback adjustment, which wastes energy. There is a lack of system simulation support, and it is impossible to model and predict the thermal behavior of the cabin based on the environment, geothermal energy, material properties, etc. Summary of the Invention

[0005] Based on this, it is necessary to provide a concrete cabin thermal simulation and optimization system in a coupled environment that can be used for systematic solutions of cabin thermal performance simulation, heating system configuration optimization and energy consumption scheduling to address the above technical problems.

[0006] In a first aspect, the present application provides a coupled environment concrete cabin thermal simulation and optimization system, comprising:

[0007] The environmental modeling module is used to build an externally coupled environmental model based on the collected historical meteorological data, terrain data, and real-time weather data of the target deployment site; the historical meteorological data includes wind speed data; the terrain data includes soil thermal conductivity;

[0008] A structural modeling module is used to establish a three-dimensional model of the concrete tank according to a received concrete tank configuration file; the concrete tank configuration file includes concrete tank configuration data and heat source configuration data;

[0009] The thermal simulation analysis module is used to perform computational fluid dynamics and finite element analysis based on the concrete cabin 3D model combined with the environmental model to obtain simulation data inside the concrete cabin. The simulation data inside the concrete cabin includes the cabin temperature distribution and energy consumption.

[0010] The optimization scheduling module is used to generate an optimal solution set based on the multi-objective algorithm, according to the simulation data in the concrete cabin and the charging efficiency response; the optimal solution set includes the concrete cabin configuration optimization data.

[0011] In one embodiment, an externally coupled environment model is constructed based on the collected historical meteorological data, terrain data, and real-time weather data of the target deployment site, including:

[0012] The thermal boundary conditions are obtained by using the historical meteorological data using a regression model; the thermal boundary conditions include the outdoor temperature variation curve;

[0013] The Reynolds-averaged Navier-Stokes equations are used to derive the boundary layer wind speed vector field for the terrain data and wind speed data;

[0014] The boundary conditions of geothermal conduction are constructed based on the soil thermal conductivity;

[0015] An external coupled environment model is generated based on thermal boundary conditions, boundary layer wind velocity vector field, and ground heat conduction boundary conditions.

[0016] In one embodiment, the structural modeling module includes a model unit and an embedding unit;

[0017] The model unit is used to analyze the concrete cabin configuration data using a parametric modeling language and establish a 3D geometric model of the concrete cabin. The concrete cabin configuration data includes wall thickness, cavity ratio, and insulation layer material parameters. The 3D geometric model of the concrete cabin includes the materials of each layer and the corresponding material property parameters. The material property parameters include thermal conductivity, specific heat capacity, and density.

[0018] The embedding unit is used to map the heat source configuration data based on the embedded nodes of the concrete cabin's three-dimensional geometric model to obtain a three-dimensional model of the concrete cabin; the heat source configuration data includes the heating equipment type, installation coordinates, power characteristics, and startup threshold.

[0019] In one embodiment, computational fluid dynamics and finite element analysis are performed based on the three-dimensional model of the concrete cabin combined with the environmental model to obtain simulation data inside the concrete cabin, including:

[0020] Generate a multi-region non-structural network based on the three-dimensional model of the concrete cabin; the multi-region non-structural network includes each layer of structure and corresponding physical parameters;

[0021] Finite element analysis is used to simulate the heat conduction of the multi-region unstructured network and obtain the temperature distribution in the cabin;

[0022] Computational fluid dynamics (CFD) methods were used to simulate the air convection in a three-dimensional concrete cabin model under an environmental model, and the convection heat flux was obtained.

[0023] The view factor method is used to simulate the radiation heat exchange between the concrete cabin 3D model and the environmental model to obtain the thermal radiation heat flux.

[0024] The energy consumption of the concrete cabin is obtained based on the cabin temperature distribution, convection heat flux and thermal radiation heat flux;

[0025] The temperature distribution inside the cabin and the energy consumption of the concrete cabin are determined as the simulation data inside the concrete cabin.

[0026] In one embodiment, the temperature distribution in the cabin is obtained by the following heat conduction governing equation:

[0027]

[0028] Among them, ρ is the density of the material characteristic parameters; c p is the specific heat capacity among the material characteristic parameters; k is the thermal conductivity among the material characteristic parameters; T is the temperature distribution in the cabin; Q is the heat source in the cabin;

[0029] The convective heat flux is obtained by the following formula:

[0030] q conv =h(T surface -T air )

[0031] Among them, q conv is the convective heat flux; h is the convective heat transfer coefficient, which is determined by the boundary layer wind speed vector field; T surface is the concrete bulkhead surface temperature; T air is the outdoor temperature;

[0032] The thermal radiation heat flux is obtained by the following formula:

[0033]

[0034] Among them, q rad,i is the net thermal radiation heat flux per unit area of the i-th surface; ∈ i is the surface emissivity of the i-th surface; σ is the Stefan-Boltzmann constant; T i 、T j is the absolute temperature of the i-th surface and the j-th surface; F ij is the viewing angle factor from the i-th surface to the j-th surface.

[0035] In one embodiment, based on a multi-objective algorithm, an optimal solution set is generated according to the simulation data in the concrete cabin and the charging efficiency response, including:

[0036] Perform regional analysis based on the simulation data inside the concrete cabin to obtain regional analysis parameters; regional analysis parameters include heat loss locations, temperature uneven locations, and heat source redundancy locations;

[0037] A battery charging efficiency curve is obtained based on the temperature distribution in the cabin to obtain the charging efficiency response;

[0038] A non-dominated sorting genetic algorithm is used to generate a Pareto front solution set based on regional analysis parameters and battery charging efficiency curves. The non-dominated sorting genetic algorithm includes preset low energy consumption targets, temperature fluctuation targets, and temperature threshold targets.

[0039] The concrete cabin configuration data and heat source configuration data are optimized according to the Pareto front solution set.

[0040] In one embodiment, the battery charging efficiency curve is generated by the following formula:

[0041]

[0042] Where T is the temperature distribution in the cabin; η chg is the battery charging efficiency at the corresponding temperature; T min is the lowest critical temperature; k is the fitting parameter; η0 is the initial battery charging efficiency.

[0043] In a second aspect, the present application also provides a coupled environment concrete cabin thermal simulation optimization method, comprising:

[0044] An externally coupled environmental model is constructed based on the historical meteorological data, terrain data, and real-time weather data collected at the target deployment site; the historical meteorological data includes wind speed data; and the terrain data includes soil thermal conductivity;

[0045] Establishing a three-dimensional model of the concrete tank according to the received concrete tank configuration file; the concrete tank configuration file includes concrete tank configuration data and heat source configuration data;

[0046] Based on the concrete cabin 3D model and the environmental model, computational fluid dynamics and finite element analysis are performed to obtain simulation data inside the concrete cabin. The simulation data inside the concrete cabin includes the cabin temperature distribution and energy consumption.

[0047] Based on a multi-objective algorithm, an optimal solution set is generated according to the simulation data inside the concrete cabin and the charging efficiency response; the optimal solution set includes the concrete cabin configuration optimization data.

[0048] In a third aspect, the present application also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned method for optimizing the thermal simulation of a concrete cabin in any coupled environment are implemented.

[0049] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for optimizing thermal simulation of a concrete cabin in any coupled environment.

[0050] This coupled-environment concrete cabin thermal simulation and optimization system transcends the limitations of traditional insulation strategies, enabling intelligent modeling and optimization of cabin thermal management in extremely cold environments. Incorporating the concrete cabin as the primary protection and thermal control element for charging piles, this system, through detailed structural modeling, active heat source control, and coupled optimization, provides the cabin with adaptive thermal regulation capabilities. This system can be widely used in extremely cold and remote areas, pushing the boundaries of new energy vehicle charging infrastructure deployment. The external environment model incorporates a historical-real-time fusion mechanism to ensure a balance between short-term accuracy and long-term stability. This system provides full-year thermal performance assessment capabilities, making it particularly suitable for deployment assessments and preemptive thermal strategy generation in regions experiencing significant climate change, enhancing the realism and stability of simulation modeling. Its adaptive optimization capabilities enable rapid generation of adaptive structural solutions for different deployment locations, implementing a cross-level feedback mechanism from thermal management objectives to improved charging efficiency. The ultimate optimization objective extends beyond thermal parameter control to enhance battery system energy efficiency, particularly mitigating the degradation of lithium battery charging performance in low-temperature environments. This system holds significant engineering significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 This is a structural diagram of the coupled environment concrete cabin thermal simulation optimization system of the present invention;

[0053] Figure 2 This is a schematic diagram of the process flow corresponding to the environment modeling module of the present invention;

[0054] Figure 3 This is a schematic diagram of the process flow corresponding to the thermal simulation analysis module of the present invention;

[0055] Figure 4 It is a flow chart of the coupled environment concrete cabin thermal simulation optimization method of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0057] In one embodiment, Figure 1As shown, a coupled environment concrete tank thermal simulation and optimization system is provided. This embodiment uses the system applied to a terminal as an example for illustration. It is understandable that the system can also be applied to a server, or to a system including a terminal and a server, and implemented through the interaction between the terminal and the server. This embodiment includes:

[0058] The environmental modeling module 101 is used to construct an externally coupled environmental model based on the collected historical meteorological data, terrain data and real-time weather data of the target deployment site; the historical meteorological data includes wind speed data; and the terrain data includes soil thermal conductivity.

[0059] The environmental modeling module accurately simulates the actual thermal boundary of the concrete pod's deployment site, including air temperature variations, wind velocity field evolution, solar radiation intensity, and ground and underground heat conduction characteristics. This model provides a realistic physical context for simulation analysis, improving the reliability and adaptability of the simulation results.

[0060] Schematically, the environmental modeling module relies on three core data sources: historical meteorological data, topographic data, and real-time weather data. Historical meteorological data primarily includes multi-year time series of temperature, wind speed, relative humidity, and solar irradiance. This data can be obtained through the data platforms of national or regional meteorological bureaus, or by extracting long-term meteorological series for specific locations using publicly available climate models. By statistically modeling the time-domain frequency of historical wind speed data, a boundary layer wind speed vector field can be generated to describe the heat transfer effects caused by air convection.

[0061] Furthermore, topographic data is used to incorporate the deployment site's land cover type, soil composition, and geological structure, thereby deriving geothermal boundary parameters such as soil thermal conductivity, specific heat capacity, and thermal diffusivity. Topographic data can be obtained through remote sensing maps, geological databases, or direct field surveys. Topographic characteristics not only influence geothermal conduction but also determine wind flow paths, snow distribution, and thermal insulation.

[0062] Optionally, access to real-time weather data enables the system to respond to short-term climate disturbances, such as cold waves, blizzards, or sudden changes in wind direction, with real-time adjustment capabilities. Real-time data can come from local sensor networks connected to the Internet of Things, or it can be connected to the Internet meteorological service API (Application Programming Interface) for dynamic subscription and updates.

[0063] Schematically, the system adopts a numerical modeling method driven by geographic parameters, combined with local microclimate simulation tools such as the CFD (Computational Fluid Dynamics) tool preprocessing module to construct a three-dimensional wind temperature field and surface-atmosphere heat exchange boundary conditions, and finally form a time-space coupled environmental thermal boundary model, namely the environmental model.

[0064] The structural modeling module 102 is used to establish a three-dimensional model of the concrete tank according to the received concrete tank configuration file; the concrete tank configuration file includes concrete tank configuration data and heat source configuration data.

[0065] The structural modeling module is used to construct a three-dimensional structural model of the cabin based on the input configuration file to reproduce the spatial geometric outline of the concrete cabin, as well as the multi-layer material structure of the concrete cabin and its physical parameters, including thermal conductivity, density and specific heat capacity.

[0066] Schematically, the structural modeling module receives the user-configured concrete cabin configuration file from the design end or an existing database, including concrete cabin configuration data and heat source configuration data, wherein the concrete cabin configuration data includes the three-dimensional dimensions of the cabin, the wall thickness, and the arrangement order of each layer of materials, such as the inner insulation board, the middle concrete layer, and the outer protective coating. The heat source configuration data specifically records the type, installation location, output power, and working threshold of the heating equipment, such as the start and stop temperature. For example, the configuration data can be exported through a CAD modeling tool, or expressed in a custom JSON (javascript object notation, data exchange format) or XML (extensible markup language) file format.

[0067] Furthermore, during the construction process, the system uses a preprocessing module called OpenFOAM (Open Source Field Operation and Manipulation, a physical phenomenon simulation library) or a structural modeling engine like ANSYS SpaceClaim to generate a geometric model, within which a virtual heat source model is embedded. Each material layer is assigned corresponding physical properties, forming a multi-layered heterogeneous material structure.

[0068] The thermal simulation analysis module 103 is used to perform computational fluid dynamics and finite element analysis based on the concrete cabin three-dimensional model combined with the environmental model to obtain simulation data inside the concrete cabin; the simulation data inside the concrete cabin includes the cabin temperature distribution and the concrete cabin energy consumption.

[0069] Based on the constructed environmental model and structural model, the thermal simulation analysis module uses computational fluid dynamics (CFD) and finite element analysis (FEA) technology to perform multi-physics field simulation of the thermal field and energy consumption behavior inside the concrete cabin, revealing the thermal behavior pattern of the concrete cabin under different boundary conditions, including indicators such as temperature distribution, heat loss path, heating efficiency and energy consumption per unit time, thereby providing data support for optimized scheduling.

[0070] Schematically, based on the thermal boundary conditions output by the environmental model, including the external wind speed vector field, air temperature field, and underground heat flow boundary, the material properties and heat source configuration within the structural model are introduced to simulate three types of heat transfer mechanisms: heat conduction, heat convection, and heat radiation. After integrating the three heat transfer mechanisms, the system constructs a set of cabin thermal balance equations, and uses iterative numerical methods such as the finite volume method and the Galerkin method to solve the steady-state and dynamic temperature distribution within the cabin. At the same time, the heat source power input per unit time and the minimum power required to maintain heat are tracked to derive the system entropy increase trend, which is used to evaluate the energy efficiency potential of the thermal management system.

[0071] The simulation output results include temperature distribution diagrams of key nodes in the cabin, heat loss paths of various parts of the structure, power consumption per unit time, and thermal response capabilities under different heating configurations, providing objective function references for thermal safety assessment and multi-objective optimization.

[0072] The optimization scheduling module 104 is used to generate an optimal solution set based on the multi-objective algorithm according to the simulation data in the concrete cabin and the charging efficiency response; the optimal solution set includes the concrete cabin configuration optimization data.

[0073] The optimization scheduling module uses a multi-objective evolutionary algorithm to achieve the joint optimization of structural thermal performance and energy efficiency utilization. That is, based on the output data of the simulation module, a multi-objective function is constructed to generate the optimal solution set of concrete cabin design scheme and heating strategy.

[0074] Schematically, the optimization objectives mainly include reducing energy consumption per unit time, improving cabin temperature stability, compressing the total power of heating equipment, delaying the system entropy growth rate, etc. Optimization variables include cabin wall thickness, insulation material selection and layer sequence, heat source type, installation coordinates and startup logic, etc. The optimization scheduling module calls genetic algorithms, particle swarm optimization and other algorithms to generate parameter combinations, fitness evaluation and non-dominated sorting for the above variables, and finally form a Pareto optimal solution set. Specifically, users can set specific optimization preferences such as energy consumption priority or insulation priority, and the system will automatically select the optimal design configuration according to the preference, and output it as an updated concrete cabin configuration file to achieve closed-loop iterative design. Optionally, this module also supports association with construction parameters to achieve a feedback loop from thermal analysis to structural selection.

[0075] In the aforementioned coupled-environment concrete tank thermal simulation and optimization system, the environmental modeling module incorporates historical meteorological data, topographic data, and real-time weather data to construct an external coupled environmental model. This allows for accurate simulation of complex climates such as extreme cold, windy conditions, and large diurnal temperature swings. This ensures that subsequent simulations are closer to real-world operating conditions and enhances modeling reliability. The structural modeling module constructs a three-dimensional model based on the concrete tank configuration and heat source configuration data. This model accurately reproduces the layered structure of different materials and represents the thermal behavior of the insulation layer and the actual spatial layout of the heating equipment. This ensures accurate structural foundations and heat source input boundaries during thermal simulation, thereby enhancing the validity of the simulation results. The thermal simulation analysis module combines finite element analysis with computational fluid dynamics methods for coupled simulation, simultaneously accounting for conduction, convection, and radiation heat transfer mechanisms, enabling full thermal energy flow analysis in the simulation data. The optimization scheduling module uses a non-dominated sorting genetic algorithm to perform multi-objective optimization of the concrete tank configuration and heat source configuration, achieving low-energy operation while also balancing temperature stability and positive response to battery charging efficiency. Through the coordinated feedback of simulation data and charging performance, the optimal solution set of the three-dimensional coupling of structure, heat source and performance can be automatically generated, the adaptive adjustment of structural parameters can be achieved, and the intelligent adaptation capability of the system under different deployment conditions can be improved.

[0076] In one embodiment, Figure 2 As shown in the figure, an external coupled environment model is constructed based on the collected historical meteorological data, terrain data and real-time weather data of the target deployment site, including:

[0077] S201. Use a regression model to obtain thermal boundary conditions using historical meteorological data; the thermal boundary conditions include an outdoor temperature change curve.

[0078] The construction of the environmental model relies on the integration and processing of three types of raw data at the target deployment site: historical meteorological data, terrain data, and real-time weather data. Among them, historical meteorological data mainly includes outdoor temperature, wind speed, humidity, and irradiance information obtained from long-term observations or reanalysis. Its time scale covers at least five years and has strong climate representativeness. In order to extract temperature boundary conditions that can be used for simulation models from historical meteorological sequences, the system introduces polynomial regression, Fourier series fitting, or time series-based machine learning models for predictive modeling, and then obtains a function curve of outdoor temperature changes over time, which serves as the thermal boundary condition in the external wall heat transfer simulation, providing a basis for realistically reproducing day and night temperature fluctuations in the simulation.

[0079] S202. Derivate the boundary layer wind speed vector field using the Reynolds-averaged Navier-Stokes equations for the terrain data and wind speed data.

[0080] By fusing wind speed observations with surface topography data, the Reynolds-averaged Navier-Stokes equations (RANS) are used to numerically decouple boundary layer flow characteristics, generating a boundary layer wind velocity vector field around the deployment site. This process considers the effects of terrain undulation on wind direction deflection and velocity attenuation, using ground roughness, local elevation differences, and thermal inhomogeneity as initial disturbance sources. Turbulence models such as k-ε are used to simulate the variation of wind velocity vectors with height and position. The resulting wind velocity vector field forms an irregular convection field outside the cabin.

[0081] S203. Constructing geothermal conduction boundary conditions based on soil thermal conductivity.

[0082] The deployment of underground thermophysical parameters, particularly soil thermal conductivity and thermal diffusivity, is inverted by analyzing soil type and moisture distribution. Using Fourier's law of heat conduction and a one-dimensional geothermal conduction model, the surface-subsurface temperature gradient is constructed, and geothermal conduction boundary conditions are derived. These are used to simulate heat exchange between the concrete slab or foundation and the ground, which is particularly critical in environments such as snow, frozen soil, or sand.

[0083] S204. Generate an external coupling environment model based on the thermal boundary conditions, the boundary layer wind speed vector field, and the ground heat conduction boundary conditions.

[0084] Three types of boundary information, namely thermal boundary conditions (outdoor temperature change curve), boundary layer wind speed vector field, and geothermal conduction boundary conditions, are input into the pre-processing system of the thermal simulation module in a unified format. Through grid overlap and multi-physics field coupling strategies, the system generates an external coupling environment model that integrates temperature, wind flow, and geothermal factors, realizing the abstract expression of physical real boundaries and serving as a basic component for dynamic adaptation of concrete cabin thermal performance and energy efficiency regulation under multiple scenarios.

[0085] In one embodiment, the structural modeling module includes a model unit and an embedding unit;

[0086] The model unit is used to parse the concrete cabin configuration data using a parametric modeling language and establish a three-dimensional geometric model of the concrete cabin; the concrete cabin configuration data includes wall thickness, cavity ratio and insulation layer material parameters; the three-dimensional geometric model of the concrete cabin includes the materials of each layer and the corresponding material property parameters; the material property parameters include thermal conductivity, specific heat capacity and density.

[0087] The structural modeling module primarily generates a 3D model of the concrete tank with physical properties and completes the spatial mapping of the heat source layout. This module is composed of model elements and embedding elements. The former focuses on constructing the geometric structure and thermophysical parameters, while the latter focuses on model integration of active heating elements, providing a complete input space for subsequent simulation analysis.

[0088] Schematically, the concrete pod's structural components are coded and modeled using parametric modeling languages such as OpenSCAD, the FreeCAD API, or a custom DSL. Compared to traditional graphical interface-based drawing methods, parametric modeling languages can automatically generate concrete pods of any size, proportion, and material combination by reading key parameters from a structural configuration file. This improves the reusability and flexibility of the system model, making it particularly suitable for mass deployment and simulating structural changes in diverse scenarios.

[0089] Furthermore, the concrete cabin configuration data is used as the basic input for modeling, including wall thickness, cavity ratio, and insulation material parameters. Among them, the wall thickness determines the length of the heat transfer path and is a key variable in the construction of thermal resistance; the cavity ratio reflects the effective air volume and convection stability in the cabin; and the insulation material directly affects the thermal buffering behavior through its thermal conductivity and heat capacity coefficient. A three-dimensional geometric model containing components such as walls, insulation layers, and air cavities is automatically generated based on the parameters, and boundary contact surfaces are applied between the components to form an overall structure with a thermal coupling relationship. The three-dimensional model not only defines the spatial shape, but also embeds the thermophysical properties of each layer of material, including thermal conductivity (λ), specific heat capacity (c), and density (ρ), which are used to construct local constitutive coefficients of the heat transfer differential equation during the thermal simulation analysis phase.

[0090] The embedding unit is used to map the heat source configuration data based on the embedded nodes of the three-dimensional geometric model of the concrete cabin to obtain a three-dimensional model of the concrete cabin; the heat source configuration data includes the type of heating equipment, installation coordinates, power characteristics and startup threshold. The embedding unit integrates the information of the heating equipment into the three-dimensional structural model to realize the spatial distribution simulation of the active heat source. In order to ensure the spatial response accuracy of the heat source layout, the system pre-marks the embedded nodes in the three-dimensional structural model. These nodes are set according to the building structure and the evolution path of the heat flux density field and have heat flux regulation sensitivity. The received heat source configuration data includes the type of heating equipment, installation coordinates, power characteristics and startup threshold. The embedding unit binds these heat source information to the corresponding nodes in the three-dimensional structural model through a mapping relationship to generate a complete cabin model containing the active heat source field.

[0091] Optionally, to ensure that energy exchange between the heat source and structural elements conforms to the laws of heat conduction, convection, and radiation, the system defines local source term expressions, such as nodal heat flux and local radiation source terms, at each heat source embedding location. These are converted into heat source boundary conditions or volume source terms during the numerical solution phase and participate in the energy balance calculation. The heat source response can also be configured with a control function, enabling the simulation to dynamically respond to external temperature changes, triggering the heat source to activate according to a set strategy, thereby simulating the coupled behavior of the heat source and structure under actual operating conditions.

[0092] In one embodiment, Figure 3As shown in the figure, computational fluid dynamics and finite element analysis are performed based on the three-dimensional model of the concrete cabin combined with the environmental model to obtain the simulation data inside the concrete cabin, including:

[0093] S301. Generate a multi-region non-structural network based on the three-dimensional model of the concrete cabin; the multi-region non-structural network includes each layer of structural bodies and corresponding physical parameters.

[0094] The thermal simulation analysis module evaluates the concrete cabin's thermal response and heat retention performance under a variety of extreme external environmental conditions. Specifically, it physically couples the outputs of the environmental modeling module with the structural modeling module, employing a multi-physics collaborative simulation approach to accurately calculate the cabin's internal temperature distribution, heat loss paths, and energy consumption per unit time. This allows for the evaluation of the heating strategy and the thermal efficiency and control potential of the structural design.

[0095] Schematically, based on the three-dimensional model of the concrete cabin output by the structural modeling module, a multi-region unstructured network is generated to express the geometric shape and thermal physical properties of each structural layer unit inside the cabin in a discretized form. A multi-region unstructured network means that the three-dimensional model is divided into multiple grid regions with independent physical properties, such as concrete walls, insulation layers, air cavities, etc. The division of regions is not limited to regular grids, but uses unstructured grids that adapt to complex geometric shapes to improve the accuracy and flexibility of the simulation. Corresponding material parameters, such as thermal conductivity, density, specific heat capacity, etc., are embedded in each regional unit to ensure the true restoration of the heat conduction process.

[0096] S302. Use finite element analysis to simulate the heat conduction of the multi-region non-structural network to obtain the temperature distribution in the cabin.

[0097] Finite element analysis (FEA) is used to apply thermal boundary conditions to the multi-region structural grid and solve the steady-state or transient heat conduction governing equations to simulate the thermal energy diffusion process from high temperature to low temperature in the solid structure. The simulation results provide the steady-state or time-evolving temperature distribution of each region of the cabin, reflecting the cabin's thermal buffering capacity and thermal gradient characteristics.

[0098] S303. Using a computational fluid dynamics method, simulate the air thermal convection in the three-dimensional concrete cabin model under the environmental model to obtain the convection heat flux.

[0099] To further describe the thermal convection behavior of the air medium within the cabin, the module integrates an air flow simulation unit based on computational fluid dynamics (CFD). This module uses the wind velocity boundary layer vector field output by the environmental modeling module as the inlet boundary condition, coupled with the temperature difference generated by the distribution of heat sources within the cabin, and uses the NS equations for incompressible fluids to describe the coupling between the cabin air flow field and the temperature field. The simulation automatically extracts flow field characteristics in key areas such as buoyancy drive, boundary layer surface flow, and recirculation, and solves for the convective heat flux distribution on the cabin's inner surface, thereby reflecting the energy exchange efficiency caused by natural or forced convection.

[0100] S304. Use the view factor method to simulate the radiation heat exchange between the three-dimensional concrete cabin model and the environmental model to obtain the thermal radiation heat flux.

[0101] Considering the radiative heat transfer between the cabin and the external environment due to temperature differences and visibility differences, the view factor method is used to calculate the viewing angle between the cabin walls and the external heat source. Combined with the emissivity parameters of the material surface, the Stefan-Boltzmann law is used to establish a solution model for the radiative heat flux. The view factor method is a geometric method used to quantify the ratio of radiative energy exchange between any two surfaces. It accounts for factors such as surface orientation, distance, and obstruction, and is an indispensable component of radiative heat transfer calculations.

[0102] S305. Obtain the energy consumption of the concrete cabin according to the cabin temperature distribution, convective heat flux, and thermal radiation heat flux.

[0103] In schematic form, after completing the simulation calculations of the three heat transfer mechanisms of conduction, convection, and radiation, the system further integrates the overall thermal energy consumption data of the concrete cabin under the current operating conditions. This energy consumption not only includes the total heat flow and heat flux density distribution, but also covers the minimum power input level required for the cabin to maintain internal thermal stability. In addition, the system also performs entropy increase analysis on the energy consumption evolution trend to assist in determining the thermal stability and energy efficiency boundaries of the current design in long-term operation.

[0104] S306: Determine the temperature distribution in the cabin and the energy consumption of the concrete cabin as simulation data in the concrete cabin.

[0105] The simulation module outputs results including, but not limited to, a 3D temperature distribution map within the cabin, flux data for active and passive heat exchange paths, thermal energy consumption per unit time and its temporal variation, and an assessment of the structural thermal resistance and thermal stability under various operating conditions. This simulation data, collectively referred to as concrete cabin simulation data, is transmitted to the optimization and scheduling module, serving as the basis for subsequent structural design and scheduling, and for reconstructing the control strategy.

[0106] Through the above method, the thermal simulation analysis module not only establishes the multi-physics field mapping relationship between the structure and the environment, but also provides high-resolution, physically based thermodynamic evaluation support for intelligent optimization scheduling, effectively improving the energy efficiency, stability and environmental adaptability of the system design.

[0107] In one embodiment, the temperature distribution in the cabin is obtained by the following heat conduction governing equation:

[0108]

[0109] Among them, ρ is the density of the material characteristic parameters; c p is the specific heat capacity among the material characteristic parameters; k is the thermal conductivity among the material characteristic parameters; T is the temperature distribution in the cabin; Q is the heat source in the cabin;

[0110] The convective heat flux is obtained by the following formula:

[0111] q conv =h(T surface -T air )

[0112] Among them, q conv is the convective heat flux; h is the convective heat transfer coefficient, which is determined by the boundary layer wind speed vector field; T surface is the concrete bulkhead surface temperature; T air is the outdoor temperature;

[0113] The thermal radiation heat flux is obtained by the following formula:

[0114]

[0115] Among them, q rad,i is the net thermal radiation heat flux per unit area of the i-th surface; ∈ i is the surface emissivity of the i-th surface; σ is the Stefan-Boltzmann constant; T i 、T j is the absolute temperature of the i-th surface and the j-th surface; F ij is the viewing angle factor from the i-th surface to the j-th surface.

[0116] In one embodiment, based on a multi-objective algorithm, an optimal solution set is generated according to the simulation data in the concrete cabin and the charging efficiency response, including:

[0117] S41. Perform regional analysis based on the simulation data in the concrete cabin to obtain regional analysis parameters; the regional analysis parameters include heat loss locations, temperature uneven locations, and heat source redundant locations.

[0118] Based on the multi-dimensional thermodynamic data obtained from simulation analysis and the response evaluation of the charging equipment performance, the optimization scheduling module uses a multi-objective optimization algorithm to intelligently adjust the structural configuration and heat source configuration of the concrete cabin, and ultimately outputs a set of optimal structural strategies to guide engineering deployment and control parameter setting, achieving the goal of maintaining thermal stability inside the charging cabin with minimum energy consumption.

[0119] Schematically, the system receives simulation data from the concrete cabin output by the thermal simulation analysis module. This data includes thermodynamic indicators such as temperature distribution in each cabin region, heat loss paths, and energy consumption per unit time. This data provides a basis for evaluating the cabin's thermal performance and serves as a prerequisite for determining the rationality of the structural design and the effectiveness of the heat source layout. In a preliminary analysis, the system uses a regional partitioning algorithm to locally cluster the three-dimensional temperature field, identifying areas with the most significant heat loss, areas with the most uneven temperature distribution, and the locations of heating equipment with redundant energy efficiency due to excessive heat accumulation. These identification results are abstracted into regional analysis parameters, including but not limited to areas of heat loss, areas of uneven temperature, and areas of redundant heat sources, enabling targeted local strengthening or weakening adjustments for structural optimization.

[0120] S42. Obtain a battery charging efficiency curve corresponding to the charging efficiency according to the temperature distribution in the cabin.

[0121] The optimization scheduling module further incorporates energy efficiency feedback during the charging process, establishing a functional mapping between the thermal environment and charging efficiency. The system calculates the battery charging efficiency response curves for different temperature ranges using the temperature control curve of the battery model in a simulated environment. This reflects the energy efficiency degradation trend of the battery under high-temperature overheating or low-temperature insufficient cooling. This ensures that the optimization algorithm not only maintains cabin thermal stability but also ensures battery charging efficiency as a scheduling guide.

[0122] S43. A non-dominated sorting genetic algorithm is used to generate a Pareto front solution set based on the regional analysis parameters and the battery charging efficiency curve; the non-dominated sorting genetic algorithm includes a preset low energy consumption target, a temperature fluctuation target, and a temperature threshold target.

[0123] Schematically, the non-dominated sorting genetic algorithm (NSGA-II) was introduced as the core optimization solution for multi-objective optimization. This algorithm is capable of handling high-dimensional, multi-constrained, and multi-objective optimization problems, and is particularly well-suited for balancing the conflicting objectives of minimizing energy consumption, uniformizing temperature distribution, and controlling critical thresholds. In this system, the objective function of the NSGA is predefined as three dimensions: the low energy consumption objective is to minimize the cabin's thermal energy consumption per unit time; the temperature fluctuation objective is to minimize the temperature fluctuation range, i.e., the difference between the maximum and minimum temperatures; and the temperature threshold objective is to prevent any local temperature from falling below or exceeding a safe threshold. Individual encodings include structural configuration parameters such as wall thickness, cavity ratio, and insulation material type, as well as heat source configuration parameters such as heater layout coordinates, power level, and activation threshold. Through genetic encoding, fitness evaluation, non-dominated sorting, crowding distance calculation, selection, and crossover mutation, the algorithm continuously iteratively generates a Pareto frontier solution set in the solution space. Each solution on the Pareto front represents a suboptimal solution in which one objective is optimal while other objectives remain acceptable, which enables users to obtain a dynamic trade-off choice space between low energy consumption and efficient charging.

[0124] S44. Optimize the concrete cabin configuration data and heat source configuration data based on the Pareto front solution set.

[0125] Based on the Pareto solutions generated by the algorithm, the system uses sorting and clustering to select the configuration that best meets the current project deployment requirements. This results in a set of optimized concrete cabin configuration data, including updated wall structural parameters, insulation material selection recommendations, optimized heat source equipment layout paths, and start-up and shutdown strategies. This data set is fed back into the structural modeling module as a basis for updates, enabling system structural remodeling. It is also used in the back-end deployment automation control platform to provide personalized structural thermal control designs for different climate zones and charging station models.

[0126] In one embodiment, the battery charging efficiency curve is generated by the following formula:

[0127]

[0128] Where T is the temperature distribution in the cabin; η chg is the battery charging efficiency at the corresponding temperature; T min is the lowest critical temperature; k is the fitting parameter; η0 is the initial battery charging efficiency.

[0129] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0130] Based on the same inventive concept, embodiments of the present application also provide a coupled-environment concrete tank thermal simulation and optimization method for implementing the aforementioned coupled-environment concrete tank thermal simulation and optimization system. The solution provided by this method is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more of the following coupled-environment concrete tank thermal simulation and optimization method embodiments can be found in the aforementioned limitations of the coupled-environment concrete tank thermal simulation and optimization system, and will not be further elaborated here.

[0131] In an exemplary embodiment, Figure 4 As shown, a coupled environment concrete cabin thermal simulation optimization method is provided, including:

[0132] S401. Construct an externally coupled environment model based on the collected historical meteorological data, terrain data, and real-time weather data of the target deployment site; the historical meteorological data includes wind speed data; and the terrain data includes soil thermal conductivity.

[0133] S402. Establish a three-dimensional model of the concrete cabin according to the received concrete cabin configuration file; the concrete cabin configuration file includes concrete cabin configuration data and heat source configuration data.

[0134] S403. Perform computational fluid dynamics and finite element analysis based on the three-dimensional model of the concrete cabin in combination with the environmental model to obtain simulation data inside the concrete cabin; the simulation data inside the concrete cabin includes temperature distribution inside the cabin and energy consumption of the concrete cabin.

[0135] S404. Based on a multi-objective algorithm, an optimal solution set is generated according to the simulation data in the concrete cabin and the charging efficiency response; the optimal solution set includes the concrete cabin configuration optimization data.

[0136] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0137] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0138] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0139] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. A coupled environment concrete cabin thermal simulation optimization system, characterized in that: The system comprises: An environmental modeling module is used to construct an externally coupled environmental model based on the collected historical meteorological data, terrain data, and real-time weather data of the target deployment site; the historical meteorological data includes wind speed data; and the terrain data includes soil thermal conductivity; A structural modeling module is used to establish a three-dimensional model of the concrete tank according to the received concrete tank configuration file; the concrete tank configuration file includes concrete tank configuration data and heat source configuration data; a thermal simulation analysis module, configured to perform computational fluid dynamics and finite element analysis based on the three-dimensional concrete cabin model in combination with the environmental model to obtain simulation data inside the concrete cabin; the simulation data inside the concrete cabin includes cabin temperature distribution and energy consumption of the concrete cabin; The optimization scheduling module is used to generate an optimal solution set based on the multi-objective algorithm according to the simulation data in the concrete cabin and the charging efficiency response; the optimal solution set includes the concrete cabin configuration optimization data.

2. The system according to claim 1, wherein: An externally coupled environmental model is constructed based on the collected historical meteorological data, terrain data, and real-time weather data of the target deployment site, including: A regression model is used to obtain thermal boundary conditions using the historical meteorological data; the thermal boundary conditions include an outdoor temperature change curve; Derivation of a boundary layer wind speed vector field using the Reynolds-averaged Navier-Stokes equations on the terrain data and the wind speed data; The geothermal conduction boundary condition is constructed based on the soil thermal conductivity; An external coupling environment model is generated according to the thermal boundary condition, the boundary layer wind speed vector field and the ground heat conduction boundary condition.

3. The system according to claim 2, characterized in that: The structural modeling module includes model units and embedding units; The model unit is used to parse the concrete cabin configuration data using a parametric modeling language and establish a three-dimensional geometric model of the concrete cabin; the concrete cabin configuration data includes wall thickness, cavity ratio, and insulation layer material parameters; the three-dimensional geometric model of the concrete cabin includes each layer of material and corresponding material characteristic parameters; the material characteristic parameters include thermal conductivity, specific heat capacity, and density; The embedding unit is used to map the heat source configuration data based on the embedded nodes of the three-dimensional geometric model of the concrete cabin to obtain the three-dimensional model of the concrete cabin; the heat source configuration data includes the heating equipment type, installation coordinates, power characteristics and startup threshold.

4. The system according to claim 3, characterized in that The method of performing computational fluid dynamics and finite element analysis based on the three-dimensional model of the concrete cabin in combination with the environmental model to obtain simulation data inside the concrete cabin includes: Generate a multi-region non-structural network based on the concrete cabin three-dimensional model; the multi-region non-structural network includes each layer of structure and corresponding physical parameters; Finite element analysis is used to simulate heat conduction of the multi-region unstructured network to obtain the temperature distribution in the cabin; Using a computational fluid dynamics method, the air thermal convection in the three-dimensional concrete cabin model under the environmental model is simulated to obtain the convective heat flux; The view factor method is used to simulate the radiation heat exchange between the three-dimensional concrete cabin model and the environmental model to obtain the thermal radiation heat flux; Obtaining the energy consumption of the concrete cabin according to the cabin temperature distribution, the convective heat flux, and the thermal radiation heat flux; The temperature distribution in the cabin and the energy consumption of the concrete cabin are determined as simulation data in the concrete cabin.

5. The system according to claim 4, characterized in that: The temperature distribution in the cabin is obtained by the following heat conduction control equation: Among them, ρ is the density of the material characteristic parameters; c p is the specific heat capacity among the material characteristic parameters; k is the thermal conductivity among the material characteristic parameters; T is the temperature distribution in the cabin; Q is the heat source in the cabin; The convective heat flux is obtained by the following formula: q conv =h(T surface -T air ) Among them, q conv is the convective heat flux; h is the convective heat transfer coefficient, which is determined by the boundary layer wind speed vector field; T surface is the concrete bulkhead surface temperature; T air is the outdoor temperature; The thermal radiation heat flux is obtained by the following formula: Among them, q rad,i is the net thermal radiation heat flux per unit area of the i-th surface; ∈ i is the surface emissivity of the i-th surface; σ is the Stefan-Boltzmann constant; T i 、T j is the absolute temperature of the i-th surface and the j-th surface; F ij is the viewing angle factor from the i-th surface to the j-th surface.

6. The system according to claim 1, wherein: The multi-objective algorithm is based on the simulation data in the concrete cabin and the charging efficiency response, and generates an optimal solution set, including: Performing regional analysis based on the simulation data in the concrete cabin to obtain regional analysis parameters; the regional analysis parameters include heat loss locations, temperature uneven locations, and heat source redundant locations; obtaining a battery charging efficiency curve corresponding to the charging efficiency according to the temperature distribution in the cabin; A non-dominated sorting genetic algorithm is used to generate a Pareto front solution set based on the regional analysis parameters and the battery charging efficiency curve; the non-dominated sorting genetic algorithm includes a preset low energy consumption target, a temperature fluctuation target, and a temperature threshold target; The concrete cabin configuration data and the heat source configuration data are optimized according to the Pareto front solution set.

7. The system according to claim 6, characterized in that: The battery charging efficiency curve is generated by the following formula: Where T is the temperature distribution in the cabin; η chg is the battery charging efficiency at the corresponding temperature; T min is the lowest critical temperature; k is the fitting parameter; η0 is the initial battery charging efficiency.

8. A coupled environment concrete cabin thermal simulation optimization method, characterized in that: The method comprises: Building an externally coupled environmental model based on the collected historical meteorological data, terrain data, and real-time weather data of the target deployment site; the historical meteorological data includes wind speed data; the terrain data includes soil thermal conductivity; Establishing a three-dimensional model of the concrete tank according to the received concrete tank configuration file; the concrete tank configuration file includes concrete tank configuration data and heat source configuration data; Computational fluid dynamics and finite element analysis are performed based on the three-dimensional concrete cabin model and the environmental model to obtain simulation data inside the concrete cabin; the simulation data inside the concrete cabin includes temperature distribution inside the cabin and energy consumption of the concrete cabin; Based on a multi-objective algorithm, an optimal solution set is generated according to the simulation data in the concrete cabin and the charging efficiency response; the optimal solution set includes the concrete cabin configuration optimization data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to claim 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 8 are implemented.

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