A new energy vehicle electromechanical thermal fluid multi-field coupling simulation platform and a construction method

Abstract

The application discloses a new energy vehicle electromechanical thermal fluid multi-field coupling simulation platform and a construction method, relates to the simulation technical field of automobile electric control systems, and comprises the following steps: based on a bond graph mechanism, a layered model library is constructed; the model library is divided into a physical domain layer and a control domain layer; the physical domain layer comprises a mechanical library, an electrical library, a thermal library and a hydraulic library; interaction parameters transmitted between interaction ports of different component models in the physical domain layer are uniformly represented as potential variables and flow variables; in addition, a front-backstage collaborative calculation architecture is constructed; in the architecture, the front stage is used for enabling a user to build a simulation vehicle model in a dragging manner based on the layered model library and automatically establishing potential-flow connections between models, and receiving model parameter configurations and simulation task configurations input by the user; and the backstage is used for performing simulation operation according to the simulation vehicle model and the model parameter configurations and simulation task configurations input by the user. The application can realize efficient simulation platform building and simulation operation.

Description

Technical Field

[0001] This application relates to the field of automotive electronic control system simulation technology, and in particular to a multi-field coupling simulation platform for electromechanical-thermal-fluid systems of new energy vehicles and its construction method. Background Technology

[0002] New energy vehicles are multi-domain, cross-integrated systems involving multiple physical domains such as mechanical, electrical, thermal, and hydraulic. Existing simulation platforms suffer from the following problems: different modeling tools are used for each physical domain, resulting in inconsistent model formats and significant integration difficulties; the coupling relationships between the mechanical, electrical, thermal, and hydraulic domains are complex, making it difficult for existing platforms to achieve fully coupled simulations; model reusability is poor: models are bound to specific vehicle models, making reuse across different models difficult; simulation efficiency is low: large-scale parameter scanning and batch simulations are time-consuming, lacking parallel computing capabilities; and user interaction is unfriendly: model building requires programming skills, making it difficult for non-professional users to use.

[0003] Currently, existing technologies are insufficient to meet the high-efficiency simulation requirements for the development of electronic control systems for new energy vehicles. There is an urgent need for a simulation platform for automotive electronic control systems that can achieve multi-physics coupling and high-efficiency simulation. Summary of the Invention

[0004] The purpose of this application is to provide a multi-field coupling simulation platform and construction method for electromechanical-thermal-fluid systems of new energy vehicles, which can realize efficient simulation of automotive electronic control systems and environment construction.

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for constructing a multi-field coupled simulation platform for electromechanical-thermal-fluid systems in new energy vehicles, including: Based on the bond graph mechanism, a hierarchical model library is constructed. This library is divided into a physical domain layer and a control domain layer. The physical domain layer includes mechanical, electrical, thermal, and hydraulic libraries. The control domain layer includes component controllers and vehicle controllers. The interaction parameters transmitted between the interaction ports of different component models in the physical domain layer are divided into potential variables and flow variables. These interaction ports are categorized as electrical ports, mechanical ports, thermal ports, and hydraulic ports. The potential variables and flow variables of the electrical ports are voltage and current, respectively. The potential variables and flow variables of the mechanical ports are torque and speed, respectively. The potential variables and flow variables of the thermal ports are temperature and entropy flow rate, respectively. The potential variables and flow variables of the hydraulic ports are pressure and flow rate, respectively. A front-end and back-end collaborative computing architecture is constructed. In the front-end and back-end collaborative computing architecture, the front-end is used to allow users to build simulation vehicle models by dragging and dropping based on a hierarchical model library and to automatically establish potential-flow connections between models, as well as to receive user input of model parameter configuration and simulation task configuration. The back-end is used to execute simulation calculations based on the simulation vehicle models and the user input of model parameter configuration and simulation task configuration.

[0006] Secondly, this application provides a multi-field coupling simulation platform for electromechanical-thermal-fluid systems in new energy vehicles, including: A hierarchical model library is provided, divided into a physical domain layer and a control domain layer. The physical domain layer includes mechanical, electrical, thermal, and hydraulic libraries. The control domain layer includes component controllers and vehicle controllers. Interaction parameters transmitted between different model interaction ports in the physical domain layer are categorized as potential variables and flow variables. These interaction ports are classified as electrical, mechanical, thermal, and hydraulic ports. The potential and flow variables of the electrical ports are voltage and current, respectively. The potential and flow variables of the mechanical ports are torque and speed, respectively. The potential and flow variables of the thermal ports are temperature and entropy flow rate, respectively. The potential and flow variables of the hydraulic ports are pressure and flow rate, respectively. The visualization building module allows users to build simulated vehicle models by dragging and dropping based on a hierarchical model library and automatically establishes potential-current connections between different models. The parameter configuration module is used to receive user input for model parameter configuration; The simulation task module is used to receive simulation task configurations input by the user. The computing center module is used to perform simulation calculations based on the simulated vehicle model and the model parameter configuration and simulation task configuration input by the user. The results display module is used to display simulation results.

[0007] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a multi-field coupling simulation platform for electromechanical and thermo-hydraulic systems of new energy vehicles and its construction method. The first step involves constructing a hierarchical model library based on a bond graph mechanism. This model library is divided into a physical domain layer and a control domain layer. The physical domain layer includes mechanical, electrical, thermal, and hydraulic libraries; the control domain layer includes component controllers and a vehicle controller. By utilizing the bond graph mechanism, the interaction parameters transmitted between the interaction ports of different component models in the physical domain layer are uniformly represented as potential variables and flow variables. These interaction ports are further subdivided into electrical ports (voltage and current), mechanical ports (torque and speed), thermal ports (temperature and entropy flow rate), and hydraulic ports (pressure and flow rate). This step separates the physical domain layer from the control domain layer, constructing a hierarchical model library. This separates the simulation vehicle model calculation process corresponding to the physical domain layer from the actual vehicle control process corresponding to the control domain layer, reducing integration complexity and allowing users to perform real-vehicle control of various types of vehicles through a unified, standardized model construction. This reduces operational difficulty and improves model reusability. Furthermore, this step unifies the interaction parameters of the mechanical, electrical, thermal, and hydraulic domains into potential variables and flow variables based on a bond graph mechanism, thereby reducing the coupling complexity between different domains. The second step involves constructing a front-end and back-end collaborative computing architecture. In this architecture, the front-end allows users to build simulated vehicle models using a drag-and-drop interface based on a hierarchical model library, automatically establishing potential-flow connections between models, and receiving user-inputted model parameter configurations and simulation task configurations. The back-end executes simulation calculations based on the simulated vehicle models and the user-inputted model parameter and simulation task configurations. This step separates the modeling tasks, allowing users to build models in a drag-and-drop manner on the front-end, lowering the barrier to entry, while the back-end performs simulation calculations based on user-inputted model parameter and simulation task configurations, thus improving computational efficiency. Compared to existing technologies, the above-mentioned solution in this application decouples energy flows between different components and different physical domains, unifies the interaction interfaces into potential variables and flow variables, facilitates energy conservation verification, reduces the complexity of model integration and cross-domain coupling, and achieves efficient simulation platform construction and simulation calculations. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating a method for constructing a multi-field coupling simulation platform for electromechanical-thermal-fluid systems of new energy vehicles, as provided in an embodiment of this application. Figure 2 A schematic diagram of a multi-field coupling simulation platform for electromechanical-thermal-fluid systems of new energy vehicles provided in an embodiment of this application. Detailed Implementation

[0010] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0011] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0012] In one exemplary embodiment, such as Figure 1 As shown, a method for constructing a multi-field coupled simulation platform for electromechanical-thermal-fluid systems in new energy vehicles is provided, including the following steps 101 to 102. Wherein: Step 101: Based on the bond graph mechanism, construct a hierarchical model library; the hierarchical model library is divided into a physical domain layer and a control domain layer; the physical domain layer includes mechanical, electrical, thermal, and hydraulic libraries; the control domain layer includes component controllers and vehicle controllers; the interaction parameters transmitted between the interaction ports of different component models in the physical domain layer are divided into potential variables and flow variables; the interaction ports are divided into electrical ports, mechanical ports, thermal ports, and hydraulic ports; the potential variables and flow variables of the electrical ports are voltage and current, respectively; the potential variables and flow variables of the mechanical ports are torque and speed, respectively; the potential variables and flow variables of the thermal ports are temperature and entropy flow rate, respectively; the potential variables and flow variables of the hydraulic ports are pressure and flow rate, respectively.

[0013] Step 102: Construct a front-end and back-end collaborative computing architecture. In the front-end and back-end collaborative computing architecture, the front-end is used to allow users to build simulated vehicle models by dragging and dropping based on a hierarchical model library and to automatically establish potential-flow connections between models, as well as to receive user input of model parameter configurations and simulation task configurations. The back-end is used to execute simulation calculations based on the simulated vehicle models and the user input of model parameter configurations and simulation task configurations.

[0014] In step 101 above, the potential variable of the thermal port is temperature in K, the flow variable is entropy flow rate in (W / K), and the power bond of the thermal port is thermal power, which is the product of the potential variable (temperature) and the flow variable (entropy flow rate) in W.

[0015] In step 102 above, the front-end and back-end collaborative computing architecture achieves high-speed data transmission through shared memory and transmits control commands through COM port commands.

[0016] As an optional implementation, the mechanical library includes engine models, drive motor models, clutch models, transmission models, differential models, wheel models, and vehicle body models; the electrical library includes power battery models, generator models, power electronic device models, and DC-DC converter models; the thermal library includes heating device models, thermostat models, radiator models, cooling water pump models, and battery pack thermal management models; and the hydraulic library includes hydraulic pump models, hydraulic valve group models, and coolant circuit models.

[0017] As an optional implementation, in the mechanical library: the engine model has mechanical ports and thermal ports; the drive motor model has electrical ports, mechanical ports and thermal ports; the clutch model, transmission model, differential model, wheel model and vehicle body model each have mechanical ports.

[0018] In the electrical library: the power battery model has electrical ports and thermal ports; the generator model has mechanical ports and electrical ports; the power electronics model and the DC-DC converter model each have electrical ports.

[0019] In the thermal storage: the heating device model has a thermal port; the thermostat model and the radiator model have thermal ports and hydraulic ports respectively; the cooling water pump model has mechanical ports and hydraulic ports; the battery pack thermal management model has thermal ports and hydraulic ports.

[0020] In the hydraulic system: the hydraulic pump model has mechanical ports and hydraulic ports; the hydraulic valve group model and the coolant circuit model each have hydraulic ports.

[0021] As an optional implementation, the simulation calculation includes single-step calculation mode, matrix calculation mode, and component variable calculation mode.

[0022] As an optional implementation, the model parameter configuration is obtained through a parameter identification method based on historical evaluation data.

[0023] In another exemplary embodiment, such as Figure 2 As shown, a multi-field coupling simulation platform for electromechanical-thermal-fluid systems in new energy vehicles is provided, including: A hierarchical model library is provided, comprising a physical domain layer and a control domain layer. The physical domain layer includes mechanical, electrical, thermal, and hydraulic libraries. The control domain layer includes component controllers and vehicle controllers. Interaction parameters transmitted between different model interaction ports in the physical domain layer are categorized as potential variables and flow variables. These interaction ports are classified as electrical, mechanical, thermal, and hydraulic ports. The potential and flow variables of the electrical ports are voltage and current, respectively. The potential and flow variables of the mechanical ports are torque and speed, respectively. The potential and flow variables of the thermal ports are temperature and entropy flow rate, respectively. The potential and flow variables of the hydraulic ports are pressure and flow rate, respectively.

[0024] The visualization building module allows users to build simulated vehicle models by dragging and dropping based on a hierarchical model library and automatically establishes potential-current connections between different models.

[0025] The parameter configuration module is used to receive user input for model parameter configuration.

[0026] The simulation task module is used to receive simulation task configurations input by the user.

[0027] The computing center module (equivalent to the backend) is used to perform simulation calculations based on the simulated vehicle model and the model parameter configuration and simulation task configuration input by the user.

[0028] The results display module is used to display simulation results.

[0029] The visualization module, parameter configuration module, simulation task module, and result display module are all front-end modules.

[0030] Using the simulation platform provided in this embodiment, during the multiphysics coupling simulation under extreme working conditions of high and low temperatures, the entropy flow rate signal (flow variable) output by the thermal port is transmitted to the cooling system (the component controller corresponding to the control domain layer). The cooling system adjusts the coolant flow rate according to the magnitude of the entropy flow rate, thereby reducing the ambient temperature. Then, the temperature signal (potential variable) is fed back to the electrical port, forming a potential-flow paired coupling closed loop.

[0031] As an optional implementation, in the visualization construction module, the power signal is identified by a first color, and the control signal is identified by a second color; the power signal is composed of potential variables and current variables.

[0032] As an optional implementation, the visualization construction module also includes a signal verification area for color-coding the signal connection status, wherein a correct connection is green and an incorrect connection is red.

[0033] As an optional implementation, the computing center module supports single-step computing mode, matrix computing mode, and component variable computing mode.

[0034] As an optional implementation, the hierarchical model library also supports model version management, which is used to record model update history and support model backtracking.

[0035] To aid those skilled in the art in understanding, the following embodiments further illustrate the point.

[0036] Existing simulation platforms suffer from the following problems: 1) Difficult model integration: Different modeling tools are used for models in different physical domains, resulting in inconsistent model formats and making integration difficult; 2) Insufficient multi-physics coupling: Strong coupling relationships exist between mechanical, electrical, thermal, and hydraulic domains (e.g., motor electromagnetic torque-heat generation-coolant flow), making it difficult for existing platforms to achieve fully coupled energy flow simulation; 3) Poor model reusability: Models are bound to specific vehicle models, and parameters and topologies are fixed, making it difficult to reuse them across different vehicle models; 4) Low simulation efficiency: Large-scale parameter scanning (e.g., energy management strategy optimization under NEDC / WLTC conditions) simulations are time-consuming, and there is a lack of a parallel computing architecture with front-end and back-end separation; 5) Unfriendly user interaction: Model building requires programming skills, making it difficult for non-professional simulation engineers to use; 6) Insufficient accuracy verification: Simulation results lack systematic comparison and verification with real vehicle data.

[0037] To address the problems of difficult model integration, insufficient multiphysics coupling, and low simulation efficiency in existing simulation platforms, this embodiment constructs a technical system consisting of "layered model library architecture - bond graph connection mechanism - front-end and back-end collaborative computing - accuracy verification system": First, a layered model library architecture is established, dividing the model into physical and control domains; second, bond graph modeling rules are used to connect multiphysics models; then, a front-end and back-end collaborative computing architecture is designed, with the front-end enabling visual configuration and the back-end enabling model integration and simulation calculation; finally, an accuracy verification system is established to ensure simulation reliability.

[0038] This embodiment mainly achieves the following objectives: (1) Establish a hierarchical model library architecture to achieve standardization and reusability of mechanical, electrical, thermal and hydraulic four-domain models; (2) Adopt the bond graph modeling rules (multi-domain coupling signal interface mechanism) to unify the definition of potential / flow variables, realize the automatic transfer and superposition of cross-domain energy flow, and seamlessly connect multi-physics field models; (3) Design a front-end and back-end collaborative computing architecture and a visual drag-and-drop building environment to improve simulation efficiency and user experience and reduce the threshold for model building; (4) Establish a parameter identification method based on evaluation data to ensure the reliability of simulation results and support multi-physics field coupling simulation under extreme working conditions of high and low temperatures.

[0039] (I) Overall architecture design.

[0040] The simulation platform includes a model library module, a visualization building module, a parameter configuration module, a simulation task module, a computing center module, and a result display module. These six modules work together to achieve a closed-loop process of "model building - parameter configuration - simulation calculation - result display".

[0041] (II) Layered model library architecture design.

[0042] 2.1) Establish a hierarchical model library architecture, dividing the model library into two layers: the physical domain and the control domain.

[0043] 2.1.1) The physical domain model library is divided into mechanical library, electrical library, thermal library and hydraulic library.

[0044] Model input / output (energy flow) signal classification: Mechanical domain - torque / speed, Electrical domain - voltage / current, Thermal domain - temperature / entropy / flow rate, Hydraulic domain - pressure / flow rate. Internal data processing: Output is calculated through mechanistic equations or lookup table algorithms. Cross-domain coupling: Automatic transfer of energy flow and information flow is achieved through standardized interfaces.

[0045] a. Machine depot: Includes models of: engine, drive motor, clutch, transmission (DCT / AMT / AT / CVT), differential, wheels, and vehicle body.

[0046] The input signals for each model come from: the pedal opening of the driver model, the torque command of the controller, and the state variables of this model from the previous moment.

[0047] Input signal definition: Load torque (N·m, potential variable), requested rotational speed (rad / s, stream variable).

[0048] Data processing: through the torque balance equation Calculate the output, where, The moment of inertia is expressed in units of ( ). ; It represents the rotational angular velocity of the motor, with the unit being rad / s, which is the flow rate at the mechanical port; This represents the input torque, in units of... ; Represents load torque, in units of ; This represents the friction loss torque, in units of... .

[0049] Output signal: Actual rotational angular velocity (Stream variable, unit is) Output torque (Potential variable, unit is) ); Coupling relationship: with the electrical domain via τ em The electromagnetic torque of the motor and ω (the rotational angular velocity of the motor) are coupled together with the heat power generated by efficiency losses in the thermal domain. Q loss coupling.

[0050] b. Electrical warehouse: It includes models of power batteries (including thermal coupling interfaces), generators, power electronic devices, DC-DC converters, etc.

[0051] Input signal source: Current demand I from the motor controller req Temperature feedback T in the thermal domain cell .

[0052] Input signal definitions: current demand (current) and bus voltage (potential).

[0053] Data processing: through equivalent circuit equations Calculate, where, This indicates the battery terminal voltage, measured in V. Indicates polarization voltage; Indicates the actual operating current, in units of (A); This represents the temperature-dependent ohmic internal resistance, expressed in Ω. This represents the polarization voltage, with units of (V). This indicates the temperature of the battery itself, in degrees Celsius (°C).

[0054] Cross-domain coupling: via Joule heating The computation is coupled with the thermodynamic domain through electromagnetic conversion. The computational and mechanical domains are coupled.

[0055] c. Thermal storage: Includes PTC heaters, thermostats (temperature regulators), radiators, cooling water pumps, battery pack thermal management models, engine compartment thermal management models, etc.

[0056] Input signal source: Heat generation power Q in the electrical domain gen The coolant flow rate q and temperature T in the hydraulic domain in ; Input signal definition: entropy flow rate Q (flow variable), temperature T (potential variable).

[0057] Data processing: through thermal resistance network equations Calculate the temperature change, where, Heat capacity, measured in J / K, is used to represent the overall heat storage capacity of the battery pack and cooling system. This refers to the coolant temperature, expressed in °C, which is the radiator return water temperature. T is the thermal resistance, expressed in K / W, representing the overall heat transfer resistance between the battery body and the coolant; T is the battery body temperature, expressed in °C. The battery's heat output is represented by W; t represents time, represented by s.

[0058] Cross-domain coupling: Affecting the internal resistance R of the electrical port through temperature feedback int ( ) is coupled with the electrical domain and with the hydraulic domain through heat dissipation requirements.

[0059] d. The hydraulic system includes: hydraulic pump, hydraulic valve assembly, and coolant circuit model.

[0060] Input signal source: Temperature signal from the thermal domain Pump speed command from the controller .

[0061] Input signal definition: pressure P (potential variable), flow rate q (flow variable).

[0062] Data processing: through pump characteristic equations and pipeline resistance equation Calculate; where, This indicates the coolant flow rate, in units of (m³). 3 / s); This indicates the pump displacement, in units of (m³). 3 / rev); Indicates volumetric efficiency; This indicates the pipeline pressure difference, expressed in Pa.

[0063] Cross-domain coupling: through flow-heat transfer relationship Coupled with the thermal domain; where, This refers to the heat dissipation power of the radiator.

[0064] 2.1.2) The control domain model library is divided into component controllers and vehicle controllers.

[0065] The component controllers include: motor controller MCU, battery management system BMS, engine controller ECU, and fuel cell controller FCU.

[0066] The vehicle controller includes: Energy Management System (EMS) and Thermal Management System (TMS).

[0067] Vehicle Controller (VCU / EMS): It has only a control signal port, receives driver requirements and the status of each subsystem, and calculates power distribution commands through energy management algorithms (rule-based or MPC model predictive control); Motor controller (MCU): has a control-physical hybrid port, receiving torque commands from the VCU. t cmdIt outputs a PWM switching signal to the power electronic device and simultaneously receives feedback on the actual motor speed ω. Battery Management System (BMS): Receives electrical domain sensor signals (cell voltage) V cell ,temperature T cell The SOC is estimated using Kalman filtering, and the available power limit is output. P limit To VCU; Engine Controller (ECU): Receives torque requests and determines the fuel injection quantity and ignition advance angle using a lookup table method.

[0068] The layered model library architecture supports plug-and-play design: Unified interface specifications for all component models: Defines standard input and output signals (bond graph mechanism); Configurable parameters: Supports XML configuration files; Model version management: Records update history.

[0069] Component models can be constructed using parameter identification methods based on test data. For example, the open-circuit voltage-SOC relationship curve can be determined through data fitting algorithms, and key parameters such as battery internal resistance and polarization resistance can be identified through the least squares method, achieving a high degree of consistency between the model and the dynamic response of the actual vehicle.

[0070] (iii) Bond graph connection mechanism.

[0071] 3.1) Multiphysics connections are achieved using bond graph modeling rules. Models in the physics domain model library are interconnected via power bonds to transfer energy flow. A multi-domain coupling signal interface mechanism is implemented, using standardized signal interfaces to connect multiphysics (cross-domain) models. This defines signals for the mechanical domain (torque / speed), electrical domain (voltage / current), thermodynamic domain (temperature / entropy / flow rate), and hydraulic domain (pressure / flow rate). Energy and information flows are transferred between models via signal lines, enabling power conservation calculations and automatic superposition. Energy flow is transferred via power signal lines (e.g., purple markings), and control commands are transferred via control signal lines (e.g., green markings).

[0072] The following is an explanation of the basic concepts of bond graphs: Potential variables (Effort): In the mechanical domain, it is torque; in the electrical domain, it is voltage; in the thermodynamic domain, it is temperature; and in the hydraulic domain, it is pressure.

[0073] Flow variables: rotational speed in the mechanical domain, current in the electrical domain, entropy flow rate in the thermodynamic domain, and flow rate in the hydraulic domain. Power bond: the product of potential and flow is power.

[0074] 3.2) Multi-domain coupling: 3.2.1) Mechanical-electrical coupling (taking a drive motor as an example): Application scenario and purpose: To simulate the electromagnetic torque output and speed response of the motor when a vehicle accelerates / climbs a hill, and to evaluate the power performance. In a vehicle, this is reflected as the driver pressing the pedal → VCU analyzing the torque demand → MCU controlling the motor to output torque → driving the wheels through the reducer.

[0075] Signal flow direction: The electrical port receives voltage U and current I, which are then converted by electromagnetic conversion to output a torque signal to the mechanical port. ; in, The electromagnetic torque of the motor (N·m) serves as the torque input source for the mechanical domain. This represents the number of pole pairs of the motor. For permanent magnet flux linkage (Wb); The dq-axis current (A); H is the dq axis inductance.

[0076] Mechanical port input load torque Motor speed After power balancing P elec =P mech +P loss Feedback affects the current demand of the electrical ports. In the formula, P elec The electrical power input to the motor, measured in watts (W), is calculated by multiplying the voltage and current input at the electrical port. P mech The motor outputs mechanical power, measured in watts (W), and torque is output from the mechanical port. With motor speed The product is calculated as follows: ; P loss This represents the total power loss of the motor, expressed in watts (W), calculated based on copper losses, iron losses, and mechanical friction losses.

[0077] The power requirement fed back from the mechanical load to the electrical domain is calculated as follows: in, The load torque transmitted from the mechanical domain. This refers to the motor speed. For mechanical transmission efficiency; Indicates copper loss, unit: ; Indicates iron loss .

[0078] 3.2.2) Electrical-thermal coupling (taking the thermal model of a power battery as an example).

[0079] Application scenario and purpose: To simulate the limitation of battery discharge capacity by temperature rise under fast charging / high load, evaluate the design of thermal management system, and in the vehicle, this is reflected in the BMS dynamically limiting charging and discharging power based on estimated temperature to prevent thermal runaway.

[0080] Signal flow direction: The electrical port receives the current demand from the motor controller. I req The actual current is calculated internally using an equivalent circuit. I actual ; The current is used to generate an entropy flow rate signal via the built-in Joule heating calculation module (a data processing submodule within the battery model). It is transferred to the heat port; where: For the first i The current (A) of each RC branch is derived from the calculation of the equivalent circuit model; For the first i The resistance (Ω) of each RC branch is a function of temperature T (°C) and SOC; The generated thermal power (W) is transferred as an output to the thermal port; This indicates the ohmic internal resistance of the electrical port.

[0081] The thermal port receives the return water temperature from the cooling system. The battery body temperature was calculated using a thermal resistance network. T pack .

[0082] Simultaneously, the entropy flow rate signal output from the thermal port This information is transmitted to the cooling system, which adjusts the coolant flow rate based on the entropy flow rate, forming a paired, coupled closed loop of entropy flow rate (flow variable) output and temperature (potential variable) feedback. This temperature is determined through a temperature-internal resistance mapping function. Feedback corrects the ohmic internal resistance parameter of the electrical port, forming a closed-loop coupling; among which, This indicates the reference ohmic internal resistance at 25℃, in units of Ω. ; This represents the resistance temperature correction factor.

[0083] Some terminology definitions: The multiphysics coupling model of the power battery (hereinafter referred to as the power battery model) is an integrated model that includes an equivalent circuit sub-model and a thermal resistance network sub-model to achieve electrical-thermal coupling.

[0084] SOC (State of Charge): The battery's state of charge, the ratio of its current remaining capacity to its rated capacity, expressed using the ampere-hour integral formula. Calculate, where, Rated capacity; This represents the initial value of the charged state. Represents current. This represents the integral variable.

[0085] Open-circuit voltage Uoc: The terminal voltage of the battery in a zero-current state, which is a function of SOC and temperature. .

[0086] Battery terminal voltage Ub The actual output voltage of the battery when it is in operation. ,in, This represents the steady-state total internal resistance, expressed in Ω.

[0087] Matrix computation mode: The background solver uses a sparse matrix algorithm to combine the equations of each physical domain into a unified DAE (differential algebraic equation) system for solution, ensuring coupling accuracy and energy conservation.

[0088] 3.2.3) Thermo-hydraulic coupling (taking the power battery thermal management system as an example).

[0089] Application scenario and purpose: To simulate the coordinated control of thermostat, water pump and radiator, evaluate thermal management energy consumption and battery life under extreme temperatures, and in the vehicle, this is reflected in the TMS adjusting the water pump speed and thermostat opening according to the battery temperature to balance heat dissipation demand and pump power consumption. Signal flow direction: Thermal port output coolant temperature T coolant Drive the thermostat to operate; The thermostat opening function f(T) regulates the flow rate of the radiator branch at the hydraulic port. ; Hydraulic port output flow signal Based on convective heat transfer calculations: ; Feedback adjustment of the heat dissipation power of the thermal port; in, This represents the heat dissipation power of the radiator, measured in W, and serves as a boundary condition for the thermal domain. This refers to the density of the coolant, expressed in kg / m³. 3 ); Specific heat capacity, in units of (J / kg·K); Heat exchange efficiency is related to wind speed and flow rate; This indicates the outlet water temperature of the radiator, in °C. This indicates the inlet water temperature of the radiator, in °C.

[0090] 3.3) Other signal connections (information flow): Control signal types: switch signal, PWM signal, CAN signal; Sensor signal types: speed, temperature, pressure, current.

[0091] Energy and information flows are transmitted between models via signal lines to achieve power conservation calculations and automatic superposition.

[0092] (iv) Front-end and back-end collaborative computing architecture.

[0093] Design a collaborative computing architecture that separates the front-end and back-end: 4.1) Front-end (user interface): The front-end enables the visualization of vehicle model building, parameter configuration, and simulation task configuration.

[0094] 4.1.1) The platform interface adopts the MVC (Model-View-Controller) design pattern: Model (Model Layer): Stores software runtime information, including project configuration ID, component selection list, signal connection topology matrix, and parameter modification markers.

[0095] View (view layer): Tree-shaped control: Displays the hierarchical structure of the model library (root node "Physical Domain / Control Domain" → second-level nodes "Mechanical Library / Electrical Library / Thermal Library / Hydraulic Library" → third-level nodes specific components); Vehicle assembly area: Displays graphical representations of components and signal connections; Signal verification area: The connection status is indicated by color (green for correct, red for incorrect, and purple for power signal). Controller: Handles drag-and-drop events, automatically establishes signal connections, and synchronizes parameter modifications.

[0096] 4.1.2) Functional Modules: It should be noted that the "automatic establishment of potential-flow connections between models" refers to the automatic topology construction of energy flow signals, that is, the automatic connection of power signals (power keys composed of potential variables and flow variables); for information flow signals such as control commands and sensor feedback, the platform achieves connection through independent control signal lines (such as green indicators), and the two are distinguished by different colors in the visualization construction area.

[0097] Vehicle Model Library: Project management, vehicle model creation / opening / saving; Workbench: Drag-and-drop vehicle assembly, model connection, and signal verification; Vehicle parameters: parameter configuration, MAP drawing; Simulation tasks: operational configuration, computing center; Results Center: Results display and report generation.

[0098] The hierarchical structure of the model library is displayed through a tree control. Users can drag and drop component models to the vehicle assembly area. The platform automatically establishes energy flow signal connections and information flow signal connections (power signals are marked with purple lines, and control signals are marked with green lines), thereby realizing the visual splicing of multiphysics models.

[0099] The visual drag-and-drop assembly includes a model selection area, a vehicle model assembly area, and a signal verification area. The model selection area displays the physical domain / control domain hierarchy using a tree control; the vehicle model assembly area displays a graphical representation of the components; and the signal verification area uses color coding to indicate signal connection status (green for correct connection, red for incorrect connection). The "automatic assembly" refers to the platform automatically identifying port types and recommending connection paths based on the topology of the components dragged by the user.

[0100] The erroneous connections (marked in red) occur in the following scenarios: port type mismatch; energy flow direction conflict; unclosed topology. The signal verification zone verifies in real time using the built-in bond graph rule engine.

[0101] 4.2) Backend (Computing Engine): Model stitching: Automatically stitches models based on front-end configuration.

[0102] Simulation calculation: Using the MATLAB / Simulink solver.

[0103] Data post-processing: Result statistics and energy flow analysis.

[0104] 4.3) Front-end and back-end interaction: The front-end and back-end collaborative computing architecture consists of a front-end that configures parameters and simulation tasks, and a back-end that automatically stitches together models and performs simulation calculations. The back-end interacts with the model through shared memory and COM port commands. The automatic model stitching automatically generates an executable simulation model based on the signal topology configured on the front-end.

[0105] Shared memory: enables high-speed data transmission; COM port commands: enables the transmission of control instructions.

[0106] It supports multiple simulation modes, including single-step calculation mode, matrix calculation mode (which combines multi-physics domain equations into a sparse matrix for unified solution by the DAE system), and component variable calculation mode. Single-step calculation is used for debugging, matrix calculation for parameter scanning, and component variable calculation for detailed analysis.

[0107] Single-step calculation mode refers to advancing the simulation time step by step with a fixed step size. After each step, the simulation is paused and the current state variable is output. It is suitable for model debugging and algorithm verification.

[0108] Matrix computation mode refers to the background solver combining multi-physics domain equations into a sparse matrix for unified solution by the DAE system, which is suitable for large-scale parameter scanning and batch simulation.

[0109] The component variable calculation mode refers to performing detailed transient analysis on a single component model and outputting all its internal state variables, which is suitable for component-level mechanism research and fault injection testing.

[0110] (v) Simulation task configuration.

[0111] It supports various simulation conditions, such as: cyclic conditions, acceleration conditions, climbing conditions, constant speed conditions, and driving conditions; the cyclic conditions support NEDC, WLTC, and CLTC-P standard conditions, and support the import of custom conditions, as well as fixed gradient, additional mass, number of cycles, scaling ratio, and cold start configuration.

[0112] (vi) Parameter identification based on evaluation data.

[0113] The component model is constructed using a data-driven parameter identification method. The aim is to embed the dynamic response characteristics of the real vehicle into the simulation model, so that the simulation platform can replicate the energy consumption characteristics of the real vehicle under specific operating conditions, and provide a high-fidelity virtual verification environment for energy management strategy optimization.

[0114] 6.1) Battery Model Parameter Identification (Electrical Library): The correspondence between open-circuit voltage Uoc and SOC in the screening and evaluation data was investigated, and a Uoc-SOC curve was established using polynomial fitting (Matlab polyfit).

[0115] Least squares identification: Construct an equivalent circuit model of the battery, with the terminal voltage Ub = Uoc - U as the observed output and the current I as the input (where, The total voltage drop inside the battery, in units of Including ohmic internal resistance voltage drop With polarization voltage The sum of The identified parameters include the ohmic internal resistance R0, polarization resistors R1 and R2, and polarization capacitors C1 and C2, ensuring that the root mean square error between the model's dynamic response and the actual vehicle test data is less than 5%. In the second-order RC equivalent circuit model, the terminal voltage Ub is equal to the open-circuit voltage Uoc minus the total internal voltage drop U, which is determined by the ohmic internal resistance voltage drop. and polarization voltage It consists of two parts.

[0116] Temperature Correction: By analyzing test data at different temperatures, temperature coefficients (α and β) are identified, and a temperature mapping relationship between ohmic internal resistance and polarization resistance is established. ; ; in, For temperature Lower internal resistance The resistance value, for C lower internal resistance The resistance value, Internal resistance The identification temperature coefficient; For temperature Lower polarization resistor The resistance value, for Resistor C The resistance value, For resistors The identification temperature coefficient.

[0117] The functional relationship between battery open-circuit voltage and SOC is as follows: Battery terminal voltage ,in, = + + This represents the steady-state total internal resistance.

[0118] 6.2) Electrical Library - Motor Model Parameter Identification: Motor efficiency MAP (a three-dimensional data table of speed ω - torque τ - efficiency η) was obtained through bench testing, and a bilinear interpolation algorithm was used. η=f(ω,τ) Establish a lookup table model.

[0119] Identifying copper damage With iron loss ;in, This is the hysteresis loss coefficient. The eddy current loss coefficient supports the accuracy of thermal power calculations in electro-thermal coupling simulations; among which, This represents the stator resistance, measured in Ω. Indicates the temperature coefficient of resistance; This indicates the winding temperature, in °C. This represents the hysteresis loss coefficient, with units of (W·s / rad). This represents the eddy current loss coefficient, with units of (W·s). 2 / rad 2 ).

[0120] 6.3) Mechanical Library - Engine Model Parameter Identification: Based on steady-state operating point test data (speed n - torque T - fuel consumption f), the least squares method is used to fit the Willanns line model or a lookup table model. The identified parameters include frictional work Wf, pumping loss Wp, and indicated thermal efficiency ηi, establishing an accurate relationship between fuel consumption rate and power output for energy flow coupling calculations in the electrical domain.

[0121] 6.4) Parameter identification of the heat storage-radiator model: The convective heat transfer coefficient is identified using thermal balance test data (coolant flow rate q - air velocity v - heat transfer Q). (Nu is the Nusselt number, k is the thermal conductivity, and d is the characteristic length), establish the coupling relationship between flow rate, pressure, and heat transfer power.

[0122] Compared with the prior art, this application has the following significant advantages: High model reusability: Layered architecture and standardized interfaces support rapid setup of over 27 vehicle configurations (CV / EV / PHEV / FCV), improving efficiency by over 60%. Sufficient multi-physics coupling: Achieves fully coupled mechanical-electrical-thermal-fluid simulation through signal interfaces, with automatic energy flow conservation, solving the error-prone problem of manual calculations in traditional co-simulation. Reliable simulation accuracy: Simulation accuracy is verified through comparison with real vehicle test data; energy consumption error, dynamic performance error, and process data error are all less than 5%. Extreme condition coverage: Supports extreme temperature simulation of 40℃ / -7℃, enabling analysis of the impact of air conditioning heat load, battery heat loss, and component efficiency degradation on overall vehicle performance. User-friendly interface: Visual drag-and-drop setup, operable even by non-professional simulation engineers.

[0123] Example 1: Construction of a simulation platform for pure electric vehicles (EVs).

[0124] Vehicle configuration: Single-motor front-wheel drive pure electric, 100kWh battery, 150kW permanent magnet synchronous motor, single-stage reducer (transmission ratio 7.5:1).

[0125] Setup process: Model selection: Drag and drop the driver, environment, battery, drive motor, reducer, brake, and vehicle body from the model library (tree control) to the vehicle model building area.

[0126] Signal connection: Motor electrical port ↔ Battery electrical port (voltage-current power signal, purple wire).

[0127] Motor mechanical port ↔ Reducer input port (torque-speed signal).

[0128] Battery thermal port ↔ Cooling system thermal port (temperature-entropy flow rate signal).

[0129] Parameter configuration: Import battery parameter identification results (R0=0.061Ω, R1=0.030Ω, C1=471.5F), import motor efficiency MAP.

[0130] Operating condition configuration: Select NEDC / WLTC / CLTC-P cyclic operating condition, configure fixed slope 0%, number of cycles 3, and enable cold start.

[0131] Simulation calculation: The background automatically assembles the model and executes matrix calculation mode.

[0132] Result verification: NEDC energy consumption: Simulated 11.51kWh / 100km, measured 11.8kWh / 100km, error 2.5% (less than 5%).

[0133] Motor operating point: mainly within 3000rpm and 40kW, with an average efficiency of 85%.

[0134] High temperature conditions (40℃): Energy consumption increases by 5.08 kWh / 100km, mainly due to increased air conditioning cooling (1.43 kW) and increased battery heat loss (6.94 kJ).

[0135] Example 2: Plug-in hybrid electric vehicle (PHEV) simulation.

[0136] Vehicle configuration: P2 configuration, 1.5L engine, 80kW electric motor, 6DCT transmission, 15kWh battery.

[0137] Setup process: Model selection: Select engine, motor, battery, 6DCT transmission, clutch, and vehicle controller.

[0138] Torque coupling: The engine and motor are connected in parallel via a mechanical signal interface, and the torque is transmitted to the transmission via a clutch. The power key connection satisfies the following: τ_eng + τ_motor = τ_trans ;in, τ_eng This represents the engine output torque (N·m), which is the output signal of the mechanical domain engine model. tmotor : Motor output torque (N·m), which is the output signal of the mechanical domain drive motor model; t trans The input shaft torque requested by the transmission (N·m) reflects the mechanical torque superposition characteristics of the P2 configuration parallel hybrid system. The mechanical ports follow the bond graph rules of potential (torque) superposition and flow (speed) equality to ensure power conservation.

[0139] Energy management configuration: Motor priority is set during the CD (energy consumption) phase, and engine operating conditions are optimized during the CS (energy retention) phase, with a starting power threshold of 10kW; Extreme condition simulation: -7℃ low temperature: NEDC fuel consumption increased by 11.39L / 100km due to increased air conditioning heating (8kW) and battery heat loss (178.25kJ).

[0140] 0-100 km / h acceleration: 10.2s at -7℃ and 9.7s at 25℃. Low temperatures cause a decrease in battery output power (from 46.9kW to 41kW).

[0141] Results verification: CLTC-P fuel consumption simulation is 5.44L / 100km, actual measurement is 5.35L / 100km, error is 1.7%; engine operating points are concentrated below 2500rpm, torque is 20-160Nm.

[0142] Example 3: Fuel Cell Vehicle (FCV) Simulation.

[0143] Vehicle configuration: 80kW fuel cell + 5kWh battery, single motor front drive.

[0144] Setup process: Model selection: Select fuel cell system models (including stack, anode, cathode, thermal management), battery, motor, and vehicle controller.

[0145] Multi-domain coupling: Electrical domain: The output voltage of the fuel cell stack decreases as power increases (350V→300V, consistent with the polarization curve).

[0146] Thermal zone: The stack temperature is maintained at 65-75℃, and the coolant flow rate is regulated by a thermostat.

[0147] Hydraulic domain: The speed of the cooling water pump is proportional to the power of the fuel cell.

[0148] Control domain: Power allocation strategy based on rules and query maps.

[0149] Thermal management strategy: The thermostat controls the distribution of large and small circulation flow based on the coolant temperature, and the heat dissipation of the radiator is proportional to the airflow.

[0150] Durability optimization: By optimizing the voltage control strategy using wavelet algorithm, the single-cell voltage decay was reduced from 14.5μV to 10.6μV (a reduction of 26.7%).

[0151] Results verification: The simulated hydrogen consumption of WLTC is 0.78 kg / 100 km, and the actual measured value is 0.79 kg / 100 km, with an error of 1.3%; the average efficiency of the fuel cell is 53.4%, and the average efficiency of the motor is 85.5%.

[0152] The following embodiment provides a multi-field coupling simulation platform for electromechanical-thermal-fluid systems in new energy vehicles, including: Model Library Module: Provides a hierarchical model library, including physical domain models and control domain models. The physical domain includes mechanical, electrical, thermal, and hydraulic libraries, while the control domain includes component controllers and vehicle controllers, supporting plug-and-play signal interfaces.

[0153] Visualization module: Enables drag-and-drop vehicle model building, supports model selection, model connection, and signal verification, and provides a workbench interface that displays a graphical representation of the model and its connection relationships. Models are selected using a tree-like control, and multi-domain signal connections are automatically established in the vehicle model building area.

[0154] Parameter configuration module: Enables parameter configuration for component models and supports importing parameter identification results based on evaluation data, including battery SOC-OCV curves and motor efficiency MAP diagrams.

[0155] Simulation Task Module: Enables configuration of simulation conditions, supporting configuration of cyclic conditions, acceleration conditions, climbing conditions, constant speed conditions, and driving conditions, and provides a condition library and custom condition import.

[0156] The computation center module enables automatic model assembly and simulation calculations, supporting single-step calculation, matrix calculation, and component variable calculation modes. It provides calculation progress display and result storage. The matrix calculation mode refers to the background solver using a sparse matrix algorithm to process coupled differential equation systems. .

[0157] Where M() is the mass matrix, also known as the system inertia (capacitance) parameter matrix; A vector of state variables; Time, in seconds; For system dynamics functions; The input variable vector ensures real-time energy conservation in multiphysics coupling.

[0158] Results Display Module: Displays simulation results, including result summary tables, simulation reports, component transient processes, energy consumption analysis, component operating point distribution, energy flow playback, and supports result comparison and report export.

[0159] The model library module supports model version management, records model update history, and supports model backtracking and version comparison.

[0160] The computing center module supports extreme operating condition simulation, including high temperature conditions of 40℃ and low temperature conditions of -7℃, and can analyze the impact of air conditioning heat load, battery heat loss and motor efficiency degradation on the energy consumption of the whole vehicle.

[0161] In some embodiments, the platform supports multiple vehicle configurations, including pure electric vehicles (EVs), plug-in hybrid electric vehicles (PHEVs), and conventional fuel vehicles (CVs), through a "modular topology configuration algorithm"; it defines power flow paths by combining bitmasks of the configuration identifier Vehicle_Type_ID; it automatically instantiates adjacency matrices and verifies power conservation through a signal topology adaptive generation algorithm; and it automatically loads corresponding energy management strategies through a control strategy adaptation layer.

[0162] In addition to new energy vehicles, this solution also supports model simulation of traditional fuel vehicles.

[0163] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0164] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for constructing a multi-field coupled simulation platform for electromechanical-thermal-fluid systems in new energy vehicles, characterized in that, include: A hierarchical model library is constructed based on the bond graph mechanism; The hierarchical model library is divided into a physical domain layer and a control domain layer. The physical domain layer includes mechanical, electrical, thermal, and hydraulic libraries. The control domain layer includes component controllers and vehicle controllers. The interaction parameters transmitted between the interaction ports of different component models in the physical domain layer are divided into potential variables and flow variables. The interaction ports are divided into electrical ports, mechanical ports, thermal ports, and hydraulic ports. The potential variables and flow variables of the electrical ports are voltage and current, respectively. The potential variables and flow variables of the mechanical ports are torque and speed, respectively. The potential variables and flow variables of the thermal ports are temperature and entropy flow rate, respectively. The potential variables and flow variables of the hydraulic ports are pressure and flow rate, respectively. The mechanical library includes engine models, drive motor models, clutch models, transmission models, differential models, wheel models, and vehicle body models; the electrical library includes power battery models, generator models, power electronic device models, and DC-DC converter models; the thermal library includes heating device models, thermostat models, radiator models, cooling water pump models, and battery pack thermal management models; the hydraulic library includes hydraulic pump models, hydraulic valve group models, and coolant circuit models. In the mechanical library: the engine model has mechanical and thermal ports; the drive motor model has electrical, mechanical, and thermal ports; the clutch model, transmission model, differential model, wheel model, and vehicle body model each have mechanical ports; In the electrical library: the power battery model has electrical and thermal ports; the generator model has mechanical and electrical ports; the power electronics model and the DC-DC converter model each have electrical ports; In the thermal storage: the heating device model has a thermal port; the thermostat model and the radiator model have thermal ports and hydraulic ports respectively; the cooling water pump model has mechanical ports and hydraulic ports; the battery pack thermal management model has thermal ports and hydraulic ports. In the hydraulic system: the hydraulic pump model has mechanical ports and hydraulic ports; the hydraulic valve group model and the coolant circuit model each have hydraulic ports; Build a front-end and back-end collaborative computing architecture; In the aforementioned front-end and back-end collaborative computing architecture, the front-end is used to allow users to build simulated vehicle models by dragging and dropping based on a hierarchical model library and to automatically establish potential-flow connections between models, as well as to receive user input of model parameter configurations and simulation task configurations. The background system is used to execute simulation calculations based on the simulated vehicle model and the user-input model parameter configuration and simulation task configuration. The simulation calculations include: single-step calculation mode, matrix calculation mode, and component variable calculation mode. The single-step calculation mode refers to advancing the simulation time step by step with a fixed step size, pausing after each step and outputting the current state variable, which is suitable for model debugging and algorithm verification. The matrix calculation mode refers to the background solver combining multi-physics domain equations into a sparse matrix and solving it uniformly in a DAE system, which is suitable for large-scale parameter scanning and batch simulation. The component variable calculation mode refers to performing detailed transient analysis on a single component model and outputting all its internal state variables, which is suitable for component-level mechanism research and fault injection testing.

2. The method for constructing a multi-field coupled simulation platform for electromechanical-thermal-fluid systems of new energy vehicles according to claim 1, characterized in that, The model parameter configuration is obtained through a parameter identification method based on historical evaluation data.

3. A multi-field coupling simulation platform for electromechanical-thermal-fluid systems in new energy vehicles, characterized in that, The new energy vehicle electromechanical-thermal-fluid multi-field coupling simulation platform is used to implement the construction method of the new energy vehicle electromechanical-thermal-fluid multi-field coupling simulation platform as described in any one of claims 1-2, including: A hierarchical model library is provided, divided into a physical domain layer and a control domain layer. The physical domain layer includes mechanical, electrical, thermal, and hydraulic libraries. The control domain layer includes component controllers and vehicle controllers. Interaction parameters transmitted between different model interaction ports in the physical domain layer are categorized as potential variables and flow variables. These interaction ports are classified as electrical, mechanical, thermal, and hydraulic ports. The potential and flow variables of the electrical ports are voltage and current, respectively. The potential and flow variables of the mechanical ports are torque and speed, respectively. The potential and flow variables of the thermal ports are temperature and entropy flow rate, respectively. The potential and flow variables of the hydraulic ports are pressure and flow rate, respectively. The visualization building module allows users to build simulated vehicle models by dragging and dropping based on a hierarchical model library and automatically establishes potential-current connections between different models. The parameter configuration module is used to receive user input for model parameter configuration; The simulation task module is used to receive simulation task configurations input by the user. The computing center module is used to perform simulation calculations based on the simulated vehicle model and the model parameter configuration and simulation task configuration input by the user. The results display module is used to display simulation results.

4. The multi-field coupling simulation platform for electromechanical-thermal-fluid systems of new energy vehicles according to claim 3, characterized in that, In the visualization module, power signals are identified by a first color, and control signals are identified by a second color; the power signals are composed of potential variables and current variables.

5. The multi-field coupling simulation platform for electromechanical-thermal-fluid systems of new energy vehicles according to claim 4, characterized in that, The visualization module also includes a signal verification area, which uses color to indicate the signal connection status, with green for a correct connection and red for an incorrect connection.

6. The multi-field coupling simulation platform for electromechanical-thermal-fluid systems of new energy vehicles according to claim 3, characterized in that, The computing center module supports single-step computing mode, matrix computing mode, and component variable computing mode.

7. The multi-field coupling simulation platform for electromechanical-thermal-fluid systems of new energy vehicles according to claim 3, characterized in that, The hierarchical model library also supports model version management, which is used to record model update history and support model backtracking.

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