Virtual evaluation system and method for vehicle handling stability based on parameterized crosswind excitation

Abstract

The present application belongs to the technical field of vehicle engineering, and particularly relates to a virtual evaluation system for vehicle handling stability based on parameterized crosswind excitation, wherein a crosswind excitation model construction module generates a linear and sinusoidal crosswind model; a whole vehicle aerodynamics model construction module completes model building, mesh division, calculation domain and boundary condition setting based on the three-dimensional shape of the vehicle; a model integrated calculation module loads the crosswind model in the form of a moving boundary, and solves the aerodynamic six-component force through the overlapping grid technology and the finite volume method; a vehicle motion response generation module constructs a vehicle multi-body dynamics model and loads the aerodynamic six-component force; and a driving simulator integrated module converts it into a real-time vehicle model, realizes signal interaction through software-in-the-loop and hardware-in-the-loop interfaces, and provides technical support for virtual evaluation of vehicle handling stability. The present application can solve the problem of lack of stability evaluation and subjective driving experience analysis in the prior art crosswind sensitivity research.

Description

Technical Field

[0001] This invention belongs to the field of vehicle engineering technology, and in particular relates to a virtual evaluation system and method for vehicle handling stability based on parametric crosswind excitation. Background Technology

[0002] Vehicles are susceptible to the effects of natural crosswinds and environmental airflow disturbances during high-speed driving, especially in open sections of highways, bridges and elevated roads, valley entrances and exits, tunnel entrances and exits, and coastal roads. These areas are affected by terrain changes, building obstructions, and abrupt wind field changes, resulting in significant unsteady characteristics in crosswind intensity and direction. These transient crosswind loads alter the aerodynamic pressure distribution around the vehicle, generating additional lateral forces, yaw moments, and lift, leading to deviations in the vehicle's trajectory and attitude fluctuations. When the amplitude or frequency of crosswind disturbances increases, vehicle handling stability significantly decreases, requiring frequent directional corrections to maintain lane keeping, thus increasing the handling load. In severe cases, this can lead to yaw instability, increased lateral deviation, and even loss of vehicle control. Therefore, accurately assessing the aerodynamic response of vehicles under unsteady, time-varying crosswind excitation conditions and its impact on handling stability has become a key technical issue in vehicle safety design and performance evaluation.

[0003] Currently, research on the handling stability of automobiles in crosswind environments mainly relies on the following types of testing and simulation methods, but all of them have varying degrees of engineering limitations and technical bottlenecks: 1. Real vehicle road test Current real-vehicle crosswind road tests typically rely on fixed wind turbine arrays to apply constant or quasi-steady-state wind speed input to the vehicle. This method directly applies physical wind turbines to a real vehicle, thereby obtaining the vehicle's dynamic response, and is a necessary means of traditionally evaluating crosswind stability. However, its inherent limitations are obvious: (1) Poor repeatability: Natural wind fields are difficult to completely isolate, and the test process is affected by climate change, resulting in poor data consistency; (2) Insufficient safety: Simulating extreme crosswinds in a real vehicle has potential risks, especially when the vehicle is close to the stability limit area, which poses a threat to the safety of the test personnel and the vehicle. (3) High cost and long preparation period: The site, wind turbine array and test organization costs are high, which is not conducive to early development and rapid verification.

[0004] (4) Single excitation mode: Wind turbine arrays can usually only simulate uniform crosswinds, and the wind field is relatively idealized, which is different from the complex crosswind characteristics in the actual road environment.

[0005] 2. Wind tunnel test In wind tunnel testing, the relative wind direction angle experienced by the vehicle can be changed by using a balance turntable, thereby measuring the aerodynamic force changes under different yaw angles. This method has high accuracy in aerodynamic calibration, but it has the following limitations: (1) It mainly obtains static or quasi-steady-state aerodynamic characteristics, but it is difficult to reproduce the dynamic aerodynamic loads of wind speed changing rapidly with time in real roads; (2) Lacking the influence of tires, suspension, and driver, it cannot reflect the comprehensive dynamic response of the vehicle under actual driving conditions; (3) It is essentially an open-loop calibration and cannot achieve crosswind excitation. Aerodynamics Vehicle movement Dynamic closed-loop coupling between driver operations.

[0006] 3. Numerical Calculation Independent simulations were developed using computational fluid dynamics (CFD) or multibody dynamics (MBD) software. Most studies use quasi-steady-state aerodynamic lookup tables or constant crosswind velocities as input, which cannot realistically simulate the impact of time-varying unsteady crosswinds on vehicles. Furthermore, the prediction accuracy of complex crosswind conditions on the transient dynamic response of vehicles remains significantly uncertain, and its effectiveness and reliability for assessing crosswind stability in real-world vehicles lack sufficient validation. In addition, this method lacks a closed-loop "driver-vehicle" interaction, failing to assess the driver's handling load and subjective experience under real crosswind disturbances, resulting in a significant discrepancy with actual driving scenarios.

[0007] In summary, traditional real-vehicle testing, wind tunnel testing, and open-loop simulation methods are all insufficient to systematically reproduce the time-varying crosswinds that are prevalent in real-world roads. These traditional methods not only fail to provide controllable, repeatable, and safe crosswind excitation inputs, but also lack a closed-loop environment with driver participation, thus making them inadequate for supporting vehicle stability assessment and subjective driving experience analysis under extreme or rapidly changing crosswind conditions. Summary of the Invention

[0008] The technical problem solved by this invention is to provide a virtual evaluation system and method for vehicle handling stability based on parametric crosswind excitation, so as to solve the problem that crosswind sensitivity research in the prior art lacks stability assessment and subjective driving experience analysis.

[0009] The basic solution provided by this invention is a virtual evaluation system for vehicle handling stability based on parametric crosswind excitation, comprising a whole vehicle aerodynamic model construction module, a crosswind excitation model construction module, a model integration and calculation module, a vehicle motion response generation module, and a driving simulator integration module, wherein: The crosswind excitation model building module is used to build linear crosswind models and sinusoidal crosswind models; The vehicle aerodynamic model construction module constructs a vehicle aerodynamic model based on the vehicle's 3D shape model, and divides the model into meshes, sets computational domains and boundary conditions. The boundary conditions include: a pressure inlet boundary condition at the front of the computational domain, a pressure outlet boundary condition at the rear of the computational domain, crosswind inlet boundary conditions and pressure outlet boundary conditions on both sides of the computational domain, a pressure outlet boundary condition at the top of the computational domain, a moving wall boundary condition at the bottom of the computational domain, and a no-slip wall boundary condition on the vehicle surface. The model integration calculation module is used to load the crosswind inlet boundary conditions of the whole vehicle aerodynamic model into the linear crosswind model and the sinusoidal crosswind model in the form of dynamic boundary, and at the same time, it uses overlapping mesh technology and finite volume method to calculate the aerodynamic six components. The vehicle motion response generation module constructs a vehicle multibody dynamics model based on vehicle parameters and uses the aerodynamic six-component force obtained from the model integration calculation module as input, which is then applied to the hard points of the vehicle multibody dynamics model. The driving simulator integration module is used to convert a vehicle multibody dynamics model with six aerodynamic forces into a real-time vehicle model that can be run on the driving simulator, and exchanges signals through software-in-the-loop and hardware-in-the-loop interfaces.

[0010] Furthermore, the crosswind excitation model construction module constructs the linear crosswind model and the sinusoidal crosswind model specifically as follows: The characteristics of typical road wind environment were analyzed and differentiated from strong crosswind road tests. Crosswind was simplified into two basic models: linear and sinusoidal. A linear crosswind model is constructed based on linear crosswinds, and its expression is:

[0011] A sinusoidal crosswind model is constructed based on sinusoidal crosswinds, and its expression is:

[0012] in, For a moment crosswind speed, This refers to the peak wind speed of the gust. This marks the beginning of the stable phase of gusts. This marks the end of the stable phase of the gusts. , Indicates the duration of rise / fall. .

[0013] Furthermore, in the vehicle aerodynamics model construction module, the spatial range of the computational domain is proportionally set according to the vehicle's external dimensions. The setting of the computational domain includes: The length along the vehicle's forward direction is set to 20 times the vehicle length; the length along the rear side of the vehicle is 10 times the vehicle length; the distance from the side wind inlet to the windward side of the vehicle is 20 times the vehicle width; the distance from the side wind outlet to the leeward side of the vehicle is 20 times the vehicle width; the height of the computational domain along the Z-axis is 10 times the vehicle height, and the computational domain blockage ratio is less than 5%.

[0014] By setting the above-mentioned scaled computational domain, the incoming flow is fully developed and the backflow interference is reduced, thereby reducing the impact of wall blockage effect on the aerodynamic calculation results and improving the accuracy and stability of the whole vehicle aerodynamic numerical simulation.

[0015] Furthermore, the overlapping mesh technology in the model integration calculation module includes a main region mesh and a sub-region mesh; wherein, the main region mesh is a static background mesh covering the entire computational domain and is used to describe far-field flow; the sub-region mesh is a dynamic mesh that encloses the aerodynamic model body of the whole vehicle and its surrounding near-field flow field and is used to perform localized and refined calculations of the flow near the vehicle body. The main region grid and sub-region grid transmit flow field information through interpolation calculation, wherein the fluid domain mass conservation equation is:

[0016] The momentum conservation equation in the fluid domain is:

[0017] As the mesh volume changes, the following space conservation law holds:

[0018] in, For time; For fluid density; To calculate the static pressure of the fluid within the computational domain, used to characterize the normal stress state of the fluid micro-element; is the dynamic viscosity coefficient of a fluid, used to characterize the shear viscosity effect produced by a fluid under the action of a velocity gradient; Let these represent the boundary area element and the volume element that control the volume, respectively. This represents the surface normal vector of the control volume; Represents the velocity vector. For vehicle speed; Indicates the control volume Volume fraction within, Indicates time The total derivative, Indicates the boundary surface of the control volume The area integral on, Indicates control volume Boundary surface, Indicates flow rate In the normal vector Rate of change in direction.

[0019] Furthermore, the vehicle motion response generation module specifically constructs a vehicle multibody dynamics model based on vehicle parameters as follows: Based on vehicle geometric parameters, mass parameters, and suspension layout parameters, a multibody dynamics model of the vehicle, including a body model, suspension model, steering model, and tire model, is constructed. The vehicle multibody dynamics model is achieved through... The test data were calibrated; the tire model adopted the Magic Formula or MF-Tyre model, which has lateral stiffness, longitudinal slip, road adhesion coefficient and nonlinear saturation characteristics; the suspension model adopted the high-frequency bushing model, which has force transmission path flexibility and frequency response characteristics; the steering model includes power assist mechanism, return characteristics and handling delay elements to reproduce driver operation feedback.

[0020] Furthermore, in the driving simulator integration module, the driving simulator includes a cockpit, a motion platform, a visual system, and a high-performance computer group; The driving simulator integration module converts the vehicle multibody dynamics model loaded with aerodynamic six-component forces into a real-time vehicle model that can be run on the driving simulator through model order reduction and real-time solution algorithms.

[0021] Furthermore, it also includes a virtual evaluation module, which is used to receive the vehicle dynamic response of the driving simulator under linear crosswind and sinusoidal crosswind, and to evaluate the vehicle when it encounters crosswind disturbance at high speed according to preset evaluation indicators. The preset evaluation indicators include lateral acceleration, yaw rate, steering wheel angle, and lateral displacement.

[0022] The virtual evaluation method for vehicle handling stability based on parameterized crosswind excitation, applied to the aforementioned virtual evaluation system for vehicle handling stability based on parameterized crosswind excitation, includes: S1: The linear crosswind model is used to characterize the monotonic or approximately uniform change process of wind speed over a longer time scale, and the sinusoidal crosswind model is used to characterize the time-varying wind field with periodic or pulsating characteristics, thereby realizing a parameterized description of crosswind excitation from both the time domain and frequency domain. S2: Based on the 3D model of the vehicle's exterior, construct the vehicle dynamics model, and divide it into meshes, set the computational domain and boundary conditions; the boundary conditions include the pressure inlet boundary conditions at the front of the computational domain (along the direction of vehicle travel), the pressure outlet boundary conditions at the rear of the computational domain, the crosswind inlet boundary conditions and pressure outlet boundary conditions on both sides of the computational domain, the pressure outlet boundary conditions at the top of the computational domain, the moving wall boundary conditions at the bottom of the computational domain, and the non-slip wall boundary conditions on the vehicle surface; S3: Load the crosswind inlet boundary conditions of the whole vehicle aerodynamic model into the form of a linear crosswind model and a sinusoidal crosswind model in the form of a dynamic boundary. At the same time, the whole vehicle aerodynamic model uses overlapping mesh technology and finite volume method to calculate the aerodynamic six components. S4: Construct a vehicle multibody dynamics model based on vehicle parameters, and load the aerodynamic six-component forces obtained from the model integration calculation module as input into the vehicle multibody dynamics model; S5: Converts the vehicle multibody dynamics model with six aerodynamic forces into a real-time vehicle model that can be run on the driving simulator, and exchanges signals through software-in-the-loop and hardware-in-the-loop interfaces. S6: Receives the vehicle dynamic response from the driving simulator under linear crosswind and sinusoidal crosswind, and evaluates the vehicle when it encounters crosswind disturbance at high speed according to preset evaluation indicators.

[0023] The principle and advantages of this invention are as follows: In the technical solution of this application, the core logic is parameterized crosswind accurate reproduction - multiphysics model coupling - closed-loop evaluation and verification, and virtual evaluation of vehicle crosswind handling stability is achieved through modular collaboration; wherein: The crosswind excitation model construction module uses mathematical modeling to reproduce the dynamic changes of crosswinds on real roads, including two typical unsteady crosswind models: linear crosswind and sinusoidal crosswind. It divides the crosswind into three stages based on the time dimension: "rise-stable-fall," and precisely defines the crosswind velocity at different times using formulas. This parametric design allows for adjustment of peak wind speed. Stable period of time to Parameters such as rise / fall duration ST can be used to flexibly generate controllable and repeatable crosswind excitations, solving the problem that natural wind fields are difficult to reproduce and uncontrollable in traditional real vehicle tests.

[0024] The vehicle aerodynamic model building module is based on the three-dimensional shape of the vehicle. Through mesh generation, computational domain setting, and boundary condition definition, it constructs a precise flow field calculation framework. The model integration calculation module loads parameterized crosswinds into the inlet in the form of dynamic boundaries. By combining the overlapping mesh technology and the finite volume method with the three major equations of mass conservation, momentum conservation, and space conservation, it solves the aerodynamic six components of the vehicle under dynamic crosswinds, ensuring the continuity and accuracy of aerodynamic calculations.

[0025] The vehicle motion response generation module constructs a multi-body dynamics model of the entire vehicle based on the actual vehicle structural parameters. The multi-body dynamics model includes a body subsystem, a suspension subsystem, a steering subsystem, and a tire subsystem, which is used to describe the kinematic constraint relationship and dynamic coupling characteristics between various vehicle components. The aerodynamic six-component force obtained from aerodynamic calculations is input as a time-varying external load into the multi-body dynamics model and applied to the vehicle's center of mass and corresponding points of application. Through the coupling solution of dynamic equations, the mechanical response calculation of the aerodynamic load and the vehicle structural system is realized, thereby driving the vehicle to generate dynamic motion responses such as lateral displacement, yaw rate, sideslip angle, and lateral acceleration.

[0026] The driving simulator integration module transforms the multibody model into a real-time model that can run on the simulator by reducing the model order and solving it in real time. It realizes closed-loop interaction between the driver, vehicle and crosswind with the help of the cockpit and visual system. The virtual evaluation module quantifies the vehicle response during the interaction process based on preset indicators, and captures the driver's operation feedback to achieve a comprehensive evaluation that combines objective data and subjective experience.

[0027] The advantages are: 1. Compared to real vehicle testing which is affected by natural wind and wind tunnel testing which can only simulate quasi-steady-state wind, the parameterized crosswind model can accurately control the wind speed change pattern, and the loading process is completely controllable. Under the same parameters, a consistent wind field can be repeatedly generated, which greatly improves the consistency and reliability of the evaluation data. 2. Traditional real-vehicle simulation of extreme crosswinds poses safety risks, while this solution reproduces extreme crosswinds in a virtual environment, eliminating concerns about loss of control of the real vehicle and covering crosswind testing needs across all scenarios from normal to extreme. 3. Unlike traditional open-loop numerical simulation, the driving simulator integrates modules to enable real-time driver intervention, and can simultaneously evaluate vehicle dynamic response and driver load, which is more in line with real driving scenarios and avoids the disconnect between simulation and actual experience. Attached Figure Description

[0028] Figure 1 This is a functional block diagram of an embodiment of the present invention; Figure 2 This is a flowchart of an embodiment of the present invention. Detailed Implementation

[0029] The following detailed description illustrates the specific implementation method: Traditional real-vehicle testing, wind tunnel testing, and open-loop simulation methods are all insufficient to systematically reproduce the time-varying crosswinds that are prevalent in real-world roads. These traditional methods not only fail to provide controllable, repeatable, and safe crosswind excitation inputs, but also lack a closed-loop environment with driver participation, thus making them inadequate for supporting vehicle stability assessment and subjective driving experience analysis under extreme or rapidly changing crosswind conditions.

[0030] In response, this application innovates on three levels: crosswind generation mechanism, load solution method, and vehicle handling stability evaluation method, in order to solve the technical defects of existing crosswind sensitivity studies in three aspects: uncontrollable crosswind excitation, insufficient coupling between aerodynamic load and vehicle dynamic response, and lack of a closed-loop evaluation environment with the driver in the loop.

[0031] To achieve the above objectives, the technical solution adopted by the present invention generally includes the following sequentially connected steps or modules: First, at the level of crosswind excitation generation, this invention parametrically models the complex and ever-changing unsteady crosswind process in real roads, and uniformly describes the dynamic evolution law of crosswind through linear and sinusoidal crosswind models, so that crosswind excitation no longer depends on natural wind or physical wind turbines, but can be accurately generated through parameter configuration, and can generate various predetermined modes of unsteady crosswind excitation in a repeatable and precisely controllable manner.

[0032] Secondly, the parameterized crosswind is loaded into the vehicle aerodynamic calculation as a time-dependent dynamic boundary condition. By combining overlapping mesh technology and the finite volume method, the aerodynamic six components of the vehicle under unsteady crosswind are solved in real time. This aerodynamic load is then introduced into the vehicle multibody dynamics model as a time-varying input, realizing the dynamic consistency coupling between crosswind excitation, aerodynamic response and vehicle motion response. This breaks through the limitations of existing crosswind research, which is mostly based on quasi-steady-state aerodynamics or open-loop simulation.

[0033] Finally, this invention integrates a vehicle dynamics model containing unsteady aerodynamic loads into a driving simulator, constructing a closed-loop interactive environment of crosswind, vehicle, and driver. This allows vehicle handling stability assessment to not only obtain objective indicators such as lateral acceleration and yaw rate, but also to simultaneously reflect the driver's handling load and subjective experience, thus forming a new virtual assessment mode that combines objective performance and subjective feeling. This solves the problem that crosswind sensitivity research in the prior art lacks stability assessment and subjective driving experience analysis. In response, the virtual evaluation system for vehicle handling stability based on parameterized crosswind excitation proposed in this application has the following basic embodiments. Figure 1 As shown: This includes a whole-vehicle aerodynamic model building module, a crosswind excitation model building module, a model integration and calculation module, a vehicle motion response generation module, a driving simulator integration module, and a virtual evaluation module, among which: The crosswind excitation model construction module is used to construct linear and sinusoidal crosswind models. Specifically, considering the high spatiotemporal randomness and complexity of crosswinds in the natural environment, directly using measured wind field data for simulation analysis presents problems such as unreproducible operating conditions and difficulty in extracting key scenarios. Therefore, this invention, from an engineering application perspective, reasonably simplifies natural crosswinds through equivalent methods.

[0034] The core idea of ​​simplification lies in stripping away the highly random turbulent details in natural wind fields and extracting and enhancing the deterministic change patterns that dominate the dynamic timescale when acting on vehicle systems. By analyzing the characteristics of typical road wind environments and performing differentiated characterization with strong crosswind road tests, crosswinds are simplified into two basic models: linear and sinusoidal.

[0035] Linear crosswind models characterize the monotonic, approximately uniform change in wind speed over a long period, serving as a benchmark model for describing gradual changes. These models precisely correspond to the transition process of a vehicle entering / leaving a generalized wind zone. For example, when approaching the middle of a large bridge or a mountain pass wind zone, the spatial gradient of the background wind speed will exhibit a linear function of time during vehicle passage. In control theory, linearly varying inputs (ramp signals) are standard inputs for testing a system's steady-state tracking capability and error cancellation performance. Applying them to crosswind scenarios effectively evaluates the ability of vehicle control systems (such as ESP and active rear-wheel steering) to suppress slowly increasing wind disturbances, as well as the progressive steering compensation required by the driver to maintain the path. This is crucial for studying driver workload and comfort.

[0036] Sinusoidal crosswind models are used to simulate wind fields with periodic or strong pulsating characteristics and are analytical models for analyzing frequency response. Many real-world wind fields contain significant periodic components, such as periodic crosswinds generated by atmospheric vortex shedding, standing waves caused by specific terrain, or wakes from large obstacles. Sinusoidal models are idealized representations of these narrow-band spectral wind disturbances. By applying sinusoidal crosswinds of different frequencies, the amplitude-frequency and phase-frequency characteristics of vehicle yaw rate under crosswind disturbances can be accurately plotted, directly identifying the system's resonant frequency, bandwidth, and phase lag. This is instructive for optimizing suspension and steering system parameters, avoiding resonance with common wind disturbance frequencies, and designing feedforward compensation controllers. Furthermore, studying driver behavior under continuous sinusoidal disturbances can effectively assess long-term driving fatigue.

[0037] Both methods cover the dominant wind disturbance modes affecting vehicle handling stability in both the time and frequency domains. This simplification transforms complex stochastic processes into deterministic inputs with fewer parameters, repeatability, and clear physical meaning. This enables efficient and systematic crosswind stability testing and controller calibration in simulation platforms, test fields, and even wind tunnels, greatly improving development efficiency and verification coverage.

[0038] Therefore, based on the above approach, this application ultimately constructs a linear crosswind model based on linear crosswinds, with the expression as follows:

[0039] A sinusoidal crosswind model is constructed based on sinusoidal crosswinds, and its expression is:

[0040] in, For a moment crosswind speed, This refers to the peak wind speed of the gust. This marks the beginning of the stable phase of gusts. This marks the end of the stable phase of the gusts. , Indicates the duration of rise / fall. .

[0041] In this embodiment, both the linear crosswind model and the sinusoidal crosswind model follow the logic of first rising, then stabilizing, and finally falling, and the crosswind speed at different times is precisely defined by mathematical expressions. This ensures that wind speed changes are quantifiable and controllable; key parameters include: Peak gust speed, representing the maximum intensity of the crosswind, can be adjusted according to actual testing requirements, such as when simulating a level 6 crosswind. Extreme crosswinds ; The moment when the gust stabilizes. The difference between the two values ​​at the end of the gust stabilization phase determines the duration of crosswind stabilization on the vehicle. Duration of rise / fall.

[0042] Both linear and sinusoidal crosswind models have addressed the core challenges of traditional crosswind simulation through parametric design. 1. The wind speed changes in the two types of crosswind models are only determined by... , , , The four parameters determine the crosswind excitation, which can be generated repeatedly under the same parameters without relying on natural wind or complex wind turbine control. This avoids the data inconsistency problem caused by random fluctuations in natural wind in real vehicle tests and greatly improves the reliability of the test results. 2. Traditional wind tunnel tests can only simulate quasi-steady-state crosswinds with constant wind speed, while crosswinds in real roads are mostly time-varying and unsteady. The two types of crosswind models in this application accurately reproduce the dynamic characteristics of unsteady crosswinds by combining the rise / fall stroke with the steady section. They can simulate high-risk scenarios such as vehicles encountering sudden gusts at tunnel exits and pulsating crosswinds on coastal roads, filling the scenario gaps in traditional testing. 3. By adjusting the core parameters, crosswinds of different intensities, durations, and patterns of change can be flexibly generated. For example: Adjustment From conventional crosswinds to extreme crosswinds This covers the full range of crosswinds that the vehicle design must be able to handle; Adjustment From instantaneous gusts of wind To persistent crosswinds Simulate crosswind interference of different durations; Adjustment : From rapid change crosswind To slowly change crosswind It is adapted to crosswind scenarios with different rates of change.

[0043] Furthermore, the linear crosswind model of this application is applicable to scenarios where local wind speed changes rapidly due to terrain in actual roads. Due to its uniform change characteristics, it can accurately test the dynamic response of vehicles during the process of progressively increasing crosswind intensity and is suitable for evaluating the continuous response capability of vehicles under steady-state crosswind. The sinusoidal crosswind model is suitable for simulating the wake vortex of large vehicles, the periodic pulsation of sea breeze, or local periodic disturbances. It is based on the smooth change characteristics of the cosine curve and can accurately test the smooth response of vehicles under crosswinds without sudden changes.

[0044] The vehicle aerodynamics model construction module is based on the vehicle's 3D shape model. It constructs the vehicle dynamics model, divides the mesh, and sets the computational domain and boundary conditions. The boundary conditions include pressure inlet boundary conditions at the front of the computational domain (along the direction of vehicle travel), pressure outlet boundary conditions at the rear of the computational domain, crosswind inlet boundary conditions and pressure outlet boundary conditions on both sides of the computational domain, pressure outlet boundary conditions at the top of the computational domain, moving wall boundary conditions at the bottom of the computational domain, and vehicle surface defined as a non-slip wall boundary condition. In the process of constructing the vehicle dynamics model, the module first extracts key shape features related to aerodynamic characteristics based on the original 3D geometric model of the vehicle, including the outer surface of the vehicle body, chassis, tires, rearview mirrors, windows, and door handles. Redundant structures such as internal components, small holes, and decorative parts that have negligible impact on aerodynamic performance are removed. This simplification helps to unify the vehicle body coordinate system and significantly reduces the subsequent mesh size and computational cost without affecting the calculation results. Next, mesh generation is performed, creating quadrilateral surface meshes on the outer surface of the vehicle model. The mesh size is adaptively set based on local geometric features and flow gradients: in high curvature areas with drastic streamline changes (such as the front of the car and A-pillars), a denser 5-10 mm mesh is used to capture detailed flow characteristics; in areas with relatively gentle flow, such as the sides and rear of the car, a 10-20 mm surface mesh is used to optimize computational efficiency; once the surface mesh quality meets the computational requirements, a volume mesh is further generated. The volume mesh used in this application consists of two parts: a main region mesh and a sub-region mesh, wherein: The sub-region mesh covers the vehicle body and its adjacent flow field region, forming a cuboid near-field region with dimensions of 1-2 times the vehicle width, 1.5 times the vehicle length, and 1.5 times the vehicle height. This region uses a hexahedral structured mesh to ensure high accuracy in near-wall flow analysis. Furthermore, during vehicle movement, the sub-region mesh is dynamically updated along with the vehicle's movement, achieving synchronous movement between the mesh and the object. The main region grid covers the entire computational domain, that is, the entire fluid space except for the sub-regions; the main region also adopts a hexahedral structured grid, and through appropriate grid size changes, a smooth transition is achieved in the sub-region boundary layer region, thereby avoiding the influence of numerical errors and ensuring the stability and accuracy of the overall calculation; During the simulation, high-precision interpolation and transfer of flow field physical quantities (such as velocity and pressure) between the sub-region grid and the main region grid are achieved by setting an interface. Specifically, during the calculation initialization phase, the system automatically identifies the overlapping positions of the sub-region grid and the main region grid, marks the overlapping background grid cells in the main region as inactive, and creates an interpolation interface in the overlapping part of the sub-region grid and the background grid to establish a virtual data transfer channel. In this interface, the sub-region grid acts as a "donor" to provide accurate flow field solutions near the moving boundary, while the background grid acts as a "receiver" to receive this information and integrate it into the global flow field calculation. In each calculation time step, the sub-region grid undergoes rigid movement or deformation due to aerodynamic forces. The system re-determines the overlap relationship in real time, updates the interface region, and completes the conserved interpolation transfer of flow field solutions between the old and new overlapping regions through the interface, ensuring the continuity of mass, momentum, and energy fluxes.

[0045] Next, we will set up the computational domain. In this application, the computational domain is a virtual region that includes the vehicle and all flow field spaces, specifically: X-axis (vehicle forward direction): the front end is 20 times the vehicle length from the windward side of the vehicle, and the rear end is 10 times the vehicle length from the leeward side of the vehicle. Y-axis (lateral, crosswind direction): The distance from the side of the crosswind inlet to the side of the vehicle is 20 times the vehicle width, and the distance from the side of the crosswind outlet to the side of the vehicle is 20 times the vehicle width; Z-axis (perpendicular to the ground): bottom is in contact with the ground, top is 10 times the vehicle height from the top of the vehicle; The blocking ratio is less than 5%.

[0046] Therefore, the computational domain adopts a cuboid structure, with the entire computational domain serving as the carrier of the main region grid, and nested sub-region grids inside, ensuring that the main / sub-region completely covers the computational domain.

[0047] Finally, boundary conditions are set. In this application, the boundary conditions include the crosswind inlet boundary, the flow field outlet boundary, the vehicle wall boundary, the ground boundary, the top / front / rear end faces of the computational domain, and the main-sub-region interface. The crosswind inlet boundary is a velocity inlet boundary to adapt to the dynamic boundary requirements of parameterized crosswinds. The flow field outlet boundary is a pressure outlet boundary to simulate the atmospheric environment. The vehicle wall boundary is a no-slip wall boundary to conform to real physical laws. The ground boundary is a moving wall boundary to simulate a real road surface. The front face of the computational domain is a pressure inlet boundary, and the top / rear end face of the computational domain is a pressure outlet boundary to simulate an infinite flow field environment and avoid the boundary from interfering with the flow field around the vehicle. The main-sub-region interface is an overlapping mesh interface, and a conserved interpolation algorithm is used to transfer the flow field data to ensure the continuity of the flow field between the main region and the sub-region.

[0048] The model integration calculation module is used to load the crosswind inlet boundary conditions of the whole vehicle aerodynamic model into the linear crosswind model and the sinusoidal crosswind model in the form of dynamic boundary conditions. That is, the instantaneous crosswind velocity field calculated according to the parameterized crosswind model is mapped into the time-varying velocity boundary conditions of the crosswind inlet boundary of the computational domain, thereby realizing the loading of crosswind conditions in the form of dynamic boundary conditions.

[0049] Specifically, the model integration calculation module incorporates a linear crosswind model and a sinusoidal crosswind model. These two crosswind models are used to describe the parameterized functional relationship between crosswind velocity and time. Based on the set parameters such as crosswind intensity and duration, the instantaneous inflow velocity distribution at the crosswind inlet is calculated in real time during the calculation process. The dynamic boundary condition adopts a time-dependent velocity inlet boundary condition, whose velocity magnitude and direction are updated over time according to the corresponding crosswind model, thereby forming a crosswind-excited dynamic boundary that changes over time, in order to simulate the working conditions of a vehicle encountering sudden crosswinds or periodic crosswind disturbances during actual driving. Under this dynamic boundary condition, the unsteady flow field around the vehicle is solved by combining overlapping mesh technology and the finite volume method. Furthermore, the aerodynamic six-component forces generated by the vehicle under crosswind excitation are calculated. Both the overlapping mesh technology and the finite volume method calculations are implemented using the software Star CCM+. Specifically, the overlapping mesh technology involves: Set up a main region grid and sub-region grids. The main region grid is a static grid that covers the entire computational domain, and the sub-region grid is a dynamic grid that covers the aerodynamic model of the whole vehicle and its surrounding near-field flow field. The flow field information at the region boundary is transferred by interpolation calculation of the main region grid and the sub-region grid, where the mass conservation equation of the fluid domain is:

[0050] The momentum conservation equation in the fluid domain is:

[0051] As the mesh volume changes, the following space conservation law holds:

[0052] in, For time; For fluid density; To calculate the static pressure of the fluid within the computational domain, used to characterize the normal stress state of the fluid micro-element; is the dynamic viscosity coefficient of a fluid, used to characterize the shear viscosity effect produced by a fluid under the action of a velocity gradient; Let these represent the boundary area element and the volume element that control the volume, respectively. This represents the surface normal vector of the control volume; Represents the velocity vector. For vehicle speed; Indicates the control volume Volume fraction within, Indicates time The total derivative, Indicates the boundary surface of the control volume The area integral on, Indicates control volume Boundary surface, Indicates flow rate In the normal vector Rate of change in direction; When solving using the finite volume method, the entire computational domain, including the main region grid and sub-region grids, is first divided into countless tiny grid cells. The continuous fluid control equations (mass and momentum conservation) are then transformed into discrete algebraic equations for each grid cell, yielding the control volume. The discrete algebraic equations contain the flow velocities of that grid cell and its adjacent cells. , , ),pressure( ),density( The parameters are equal, and the transport equations of the turbulence model are coupled. In this application, the turbulence model is... The model, whose transport equations include equations and The equation, The model uses existing conventional modeling techniques, which will not be explained in detail in this application; Subsequently, a time-progression method was adopted, starting from the initial moment and gradually advancing at a fixed time step. At each step, the flow velocity, pressure, density and other parameters of all control volumes were solved iteratively until the flow field reached the steady state at the current moment or met the convergence condition. A turbulence model was also introduced to simulate turbulent viscous forces to improve the accuracy of the aerodynamic six components.

[0053] At the initial moment, the flow field is static, and the flow velocities in the x, y, and z directions of all control volumes are... , , All values ​​are equal to 0, pressure is atmospheric pressure, density is assigned according to ambient air density, and turbulence model parameters are set to initial default values, for example... , The time step is set to a fixed time step according to the principle that the number of CFLs is less than 1, for example... Second; In the time-progression method, the single-time-step iteration process is a prediction-correction process: Progressing in fixed time steps, each step completes multiple iterations until convergence, where: Prediction step: based on the previous time step The flow field parameters, such as velocity and pressure, are determined, ignoring the instantaneous change in the pressure gradient at the current moment. The discrete momentum conservation equation is then solved to obtain the predicted velocity for each control volume. , , During the solution process, the wind speed value of the crosswind excitation model at the current moment is loaded synchronously, and the flow velocity boundary conditions at the crosswind inlet boundary are updated. Correction Step: Using the SIMPLE algorithm as the core, since the predicted flow velocity does not satisfy the mass conservation equation, the pressure correction equation is obtained by discretization based on the mass conservation equation. The expression of the pressure correction equation is:

[0054]

[0055] in, The principal coefficient for pressure correction in the current control volume. This is the pressure correction influence coefficient of the adjacent control volume on the current control volume. This is the pressure correction value for the current control volume. The pressure correction value for adjacent control volumes, and Simultaneous solution, For source terms, To predict flow rate, For vehicle speed, To control the volume boundary volume, The boundary normal vector. To control the volume boundary area, The fluid density is given.

[0056] Based on predicted flow rate Solve the pressure correction equation to obtain the pressure correction value. Then use the pressure correction value. Correcting initial pressure To obtain the corrective pressure at the current moment. ,in A pressure correction relaxation factor, ranging from 0.3 to 0.7, is used to avoid iterative oscillations. The predicted velocity is then corrected using the velocity correction formula to obtain the current velocity. The velocity correction formula for the x-direction is as follows: , The x-direction velocity correction is given by the formulas for the y- and z-directions, which are identical to those for the x-direction, with only geometric parameters being adapted. This results in the corrected velocity. , , ; After completing the single time step iteration, proceed to the next time step and repeat the above process until the entire crosswind excitation cycle is covered, including lift, stabilization, and descent. The flow field parameters at each step are dynamically adjusted according to the vehicle motion and crosswind speed changes.

[0057] Based on the corrected velocity field, the turbulence model is solved. equations and Equations, and according to and Derivation of turbulent viscosity coefficient:

[0058] in, This is an empirical constant with a value of 0.09. For fluid density, for The dissipation rate obtained by solving the equation for Turbulent kinetic energy obtained by solving the equation; Let be the turbulent viscosity coefficient, and Substituting the viscous term into the above momentum equation allows us to simulate the effect of turbulence on fluid momentum, specifically the vortices at the rear of the vehicle and the areas of intense turbulence around the tires. Significantly increased viscosity, better matching the characteristics of real flow fields.

[0059] The quality of the flow field grid and the adaptation of the turbulence model to capture the flow field details in key areas can ensure that the magnitude, direction and dynamic response of the aerodynamic six components are consistent with the real scene, thereby improving the accuracy of the aerodynamic six components.

[0060] After the flow field calculation converges, the components of the pressure and viscous force of all wall elements of the vehicle body are extracted along the X, Y, and Z coordinate axes. The magnitude of the pressure of each wall element is related to the pressure P of that element and the element area. The product, directed along the element normal vector (pointing outwards from the vehicle body), is decomposed into three coordinate components:

[0061]

[0062]

[0063] in, The pressure component is along the x-axis. The pressure component along the y-axis. The pressure component along the z-axis. , , These are the components of the element's normal vector along the x, y, and z axes; the negative sign indicates that the pressure direction is opposite to the normal vector. The magnitude of the viscous force in each wall element is equal to the viscous shear stress of that element. With unit area The product, oriented along the unit tangent vector (consistent with the airflow direction), is decomposed into three coordinate vectors:

[0064]

[0065]

[0066] in The x-axis viscous force component is... The viscous force component along the y-axis. The z-axis viscous force component. , , The components of the unit tangent vector along the x, y, and z axes; Then, global vector integration is performed on the pressure and viscous force components of all wall elements to obtain three aerodynamic components: Along the X-axis: Air resistance ; Along the Y-axis: Lateral force ; Along the Z-axis: Lift ; Using the vehicle's center of mass as the moment center, vector integration is performed on the force and lever arm of each wall element along the X, Y, and Z coordinate axes to obtain three moment components: Tilting moment about the X-axis ; Pitch moment about the Y-axis ; Yaw moment around the Z-axis .

[0067] in, , , The center coordinates of the current wall element. , , The coordinates of the vehicle's center of gravity.

[0068] The calculated aerodynamic six components , , , , , The output is based on a time series and serves as input to the subsequent vehicle motion response generation module, which is then loaded into the vehicle multibody dynamics model.

[0069] The vehicle motion response generation module constructs a vehicle multibody dynamics model based on vehicle parameters and loads the aerodynamic six-component force obtained from the model integration calculation module into the vehicle multibody dynamics model as input. In this embodiment, the aerodynamic six-component forces output by the model integration calculation module need to be preprocessed. The preprocessing includes data synchronization processing, coordinate system transformation processing, and data smoothing processing. Data synchronization processing aligns the six-component force data according to the time step of the flow field calculation to ensure consistency with the calculation step of the vehicle multibody dynamics model. Coordinate system transformation processing converts the global flow field coordinate system of the aerodynamic six-component forces in the flow field into the vehicle body coordinate system of the vehicle multibody dynamics model to ensure that the load direction is consistent with the actual force direction of the vehicle body. Data smoothing processing removes high-frequency noise from data with local fluctuations in the flow field through low-pass filtering to avoid pseudo-vibration in the vehicle multibody dynamics model after loading.

[0070] The process of constructing the vehicle multibody dynamics model is as follows: A multibody dynamics model of the vehicle was established using CarSim software. Specifically, based on the vehicle's geometric parameters, mass parameters, and suspension layout parameters, the parameters of subsystems such as the body, suspension, steering, and tires were input according to CarSim's vehicle modeling process, and the system assembly was completed, thereby forming a vehicle dynamics model that can be used for time-domain simulation.

[0071] The vehicle body model establishes a rigid body based on the vehicle's curb weight, center of gravity position, and moment of inertia parameters. The suspension model is constructed based on the structural form, geometric layout parameters, spring stiffness, shock absorber damping, and equivalent stiffness and damping parameters of the front and rear suspensions to characterize the flexibility of the suspension force transmission path and its frequency response characteristics. The tire model uses the Magic Formula or MF-Tyre model, featuring lateral stiffness, longitudinal slip characteristics, road adhesion coefficient, and nonlinear saturation parameters to describe the tire's mechanical response under crosswind disturbance conditions. The steering model is implemented through the CarSim steering system module, including the power assist mechanism, return-to-center characteristics, and handling delay elements. The power assist mechanism simulates the amplification and adjustment of the driver's steering input by setting a power assist gain or power assist characteristic curve that varies with vehicle speed. At lower speeds, greater power assist is needed to reduce steering force, while at higher speeds, the power assist should be appropriately reduced to maintain vehicle stability and responsiveness. The specific setting method is as follows:

[0072] in, For vehicle speed, For vehicle speed The boost during the time, This is for maximum assist gain (which is typically achieved at low vehicle speeds). To assist in the gain attenuation coefficient.

[0073] The self-centering characteristic is formed by the self-centering torque output from the tire model and the equivalent friction and damping parameters of the steering system. It is used to reproduce the natural self-centering tendency of the vehicle under lateral disturbances. The specific self-centering torque model is expressed as follows:

[0074] in, To correct the torque, The coefficient of friction, It is a lateral force. For the radius of the steering wheel, This is the equivalent damping coefficient. This refers to the steering angular velocity.

[0075] The steering delay element describes the dynamic hysteresis effect between the driver's steering wheel input and the actual steering angle of the front wheels. It is achieved by setting a pure time delay and a first-order inertial hysteresis element, which can be specifically expressed as:

[0076] in, Input the steering wheel angle. It is a time variable; This is the equivalent steering angle of the front wheels; The steering gear ratio is between 0.05 and 0.08. To manipulate the delay time, a commonly used empirical range is 0.02-0.20s; The first-order lag time constant of the steering system is typically within the empirical range of 0.01-0.20 s. The complex frequency variable in the Laplace transform domain is used to characterize the dynamic characteristics of the first-order inertial lag element. The above parameters can be identified through actual vehicle steering response tests and input into the CarSim steering model to ensure that the time-domain and frequency-domain response characteristics of the steering system are consistent with those of the actual vehicle.

[0077] The vehicle multibody dynamics model is obtained through The test data were calibrated to ensure that the model's lateral dynamic response was consistent with that of the real vehicle.

[0078] When determining the position and loading method of the pre-processed aerodynamic six components in the vehicle multibody dynamics model, lift force air resistance The pressure distribution is based on the vehicle body's pressure distribution characteristics. These characteristics refer to the spatial distribution features of the pressure field and wall shear stress field on the vehicle body surface, obtained through numerical flow field calculations. They reflect the differences in the contribution of different vehicle body regions to the overall aerodynamic force. Specifically, the outer surface of the vehicle body is divided into front, middle, and rear regions according to aerodynamic characteristics. The pressure and shear stress on the surface of each region are integrated to obtain the contribution ratio of each region to the total lift and drag. Then, based on the contribution ratio of each region, the total lift is distributed... and air resistance The load is equivalently distributed to the front, middle and rear of the vehicle body, and the distributed load is applied to the equivalent pressure center of the corresponding area, thereby ensuring that the load distribution in terms of force and moment is consistent with the flow field calculation results.

[0079] Lateral force Yaw moment Pitch moment Lateral tilting moment When loading, the coupling effect of the pressure distribution on the side surface of the vehicle body and the lateral force of the tires must be considered simultaneously, where the lateral force... Primarily acts on the center of pressure on the side of the vehicle body, yaw moment Pitch moment Lateral tilting moment The torque is then applied to the vehicle's center of gravity in the form of a torque vector to ensure that the steering effect of the torque around the corresponding axis is consistent with that of the actual vehicle.

[0080] The driving simulator integration module is used to convert a vehicle multibody dynamics model with six aerodynamic forces into a real-time vehicle model that can be run on the driving simulator, and to exchange signals through software-in-the-loop and hardware-in-the-loop interfaces; the driving simulator includes a cockpit, motion platform, visual system and high-performance computer group.

[0081] In this embodiment, the driving simulator integration module transforms the vehicle multibody dynamics model loaded with aerodynamic six-component forces into a model that the driving simulator can respond to in real time through "model reduction + real-time adaptation + interface development," realizing a closed-loop interaction between the driver, vehicle, and crosswind. Specifically: The original vehicle multibody dynamics model contains various fine sub-models, resulting in high computational complexity. By using the modal truncation method, core dynamic characteristics such as body roll and pitch modes are preserved, while high-frequency redundant modes, such as micro-vibrations of components, are eliminated. This reduces the model's degrees of freedom from thousands to hundreds, ensuring real-time computational efficiency.

[0082] The reduced vehicle multibody dynamics model was then imported into the simulator real-time simulation platform, and a real-time solver was configured to ensure that the model outputs the vehicle dynamic response every 10ms to match the real-time nature of the driver's input. Next, we develop the software and hardware interfaces. The software-in-the-loop interface synchronizes the vehicle's dynamic response to the visual system, generating corresponding changes in road perspective, wind noise effects, etc. When the driver manipulates the hardware, such as turning the steering wheel, the hardware-in-the-loop interface converts the manipulation signal into input parameters that the model can recognize. The vehicle response output by the model is then fed back to the hardware through the interface, realizing an immersive experience such as seat vibration and steering wheel force feedback. The model integration module communicates with the driving simulator in real time, synchronizing the wind speed data of the crosswind model to the vehicle multibody dynamics model according to the time step, ensuring that the crosswind excitation is consistent with the timing of driver operation and vehicle response.

[0083] The virtual evaluation module is used to receive the vehicle's dynamic response under linear crosswind and sinusoidal crosswind conditions from the driving simulator, and to evaluate the vehicle when it encounters crosswind disturbances at high speed according to preset evaluation indicators. The preset evaluation indicators include lateral acceleration, yaw rate, steering wheel angle, and lane departure. In addition to the above objective indicators, subjective indicators are also included: a driver rating questionnaire covering vehicle controllability, steering response sensitivity, and crosswind interference resistance.

[0084] The evaluation process of the virtual evaluation module includes four steps: data acquisition, indicator extraction, indicator quantification, and level determination. Among them, data acquisition mainly involves receiving vehicle state variable data output by the driving simulator. The data includes, but is not limited to, lateral acceleration, yaw rate, steering wheel angle, and vehicle center of gravity trajectory. At the same time, the vehicle speed, crosswind speed, and wind direction angle are recorded synchronously to form a time series of operating condition and response data.

[0085] The extraction of indicators includes the statistics and classification of objective and subjective indicators.

[0086] The quantification of indicators mainly targets subjective indicators. The subjective scoring data is standardized to eliminate differences in subjective evaluation scales among different drivers. A subjective evaluation statistical model is constructed based on the scoring results of multiple drivers to form a subjective evaluation benchmark interval. Finally, the virtual assessment module outputs an assessment report, which includes crosswind scenario parameters, objective indicator calculation results and qualification judgments, driver subjective scores, and comprehensive evaluation conclusions.

[0087] like Figure 2 As shown, in another embodiment of this example, a virtual evaluation method for vehicle handling stability based on parametric crosswind excitation is further included, applied to the aforementioned virtual evaluation system for vehicle handling stability based on parametric crosswind excitation, comprising: S1: Analyze the characteristics of typical road wind environment and differentiate them from strong crosswind road tests, and construct linear crosswind model and sinusoidal crosswind model; S2: Based on the 3D model of the vehicle's shape, construct the vehicle dynamics model, and divide it into meshes, set the computational domain and boundary conditions; the boundary conditions include the pressure inlet boundary conditions at the front of the computational domain, the pressure outlet boundary conditions at the rear of the computational domain, the crosswind inlet boundary conditions and pressure outlet boundary conditions on both sides of the computational domain, the pressure outlet boundary conditions at the top of the computational domain, the moving wall boundary conditions of the ground at the bottom of the computational domain, and the non-slip wall boundary conditions of the vehicle surface; S3: Load the crosswind inlet boundary conditions of the whole vehicle aerodynamic model into the form of a linear crosswind model and a sinusoidal crosswind model in the form of a dynamic boundary. At the same time, the whole vehicle aerodynamic model uses overlapping mesh technology and finite volume method to calculate the aerodynamic six components. S4: Construct a vehicle multibody dynamics model based on vehicle parameters, and load the aerodynamic six-component forces obtained from the model integration calculation module as input into the vehicle multibody dynamics model; S5: Converts the vehicle multibody dynamics model with six aerodynamic forces into a real-time vehicle model that can be run on the driving simulator, and exchanges signals through software-in-the-loop and hardware-in-the-loop interfaces. S6: Receives the vehicle dynamic response from the driving simulator under linear crosswind and sinusoidal crosswind, and evaluates the vehicle when it encounters crosswind disturbance at high speed according to preset evaluation indicators.

[0088] The above are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A virtual evaluation system for vehicle handling stability based on parametric crosswind excitation, characterized in that: It includes a whole vehicle aerodynamics model building module, a crosswind excitation model building module, a model integration and calculation module, a vehicle motion response generation module, and a driving simulator integration module, among which: The crosswind excitation model building module is used to build linear crosswind models and sinusoidal crosswind models; The vehicle aerodynamic model construction module constructs a vehicle aerodynamic model based on the vehicle's 3D shape model, and divides the model into meshes, sets computational domains and boundary conditions. The boundary conditions include: a pressure inlet boundary condition at the front of the computational domain, a pressure outlet boundary condition at the rear of the computational domain, crosswind inlet boundary conditions and pressure outlet boundary conditions on both sides of the computational domain, a pressure outlet boundary condition at the top of the computational domain, a moving wall boundary condition at the bottom of the computational domain, and a no-slip wall boundary condition on the vehicle surface. The model integration calculation module is used to load the crosswind inlet boundary conditions of the whole vehicle aerodynamic model into the linear crosswind model and the sinusoidal crosswind model in the form of dynamic boundary, and at the same time, it uses overlapping mesh technology and finite volume method to calculate the aerodynamic six components. The vehicle motion response generation module constructs a vehicle multibody dynamics model based on vehicle parameters and uses the aerodynamic six-component force obtained from the model integration calculation module as input, which is then applied to the hard points of the vehicle multibody dynamics model. The driving simulator integration module is used to convert a vehicle multibody dynamics model with six aerodynamic forces into a real-time vehicle model that can be run on the driving simulator, and exchanges signals through software-in-the-loop and hardware-in-the-loop interfaces.

2. The virtual evaluation system for vehicle handling stability based on parametric crosswind excitation according to claim 1, characterized in that: The crosswind excitation model construction module constructs linear crosswind models and sinusoidal crosswind models specifically as follows: The characteristics of typical road wind environment were analyzed and differentiated from strong crosswind road tests. Crosswind was simplified into two basic models: linear and sinusoidal. A linear crosswind model is constructed based on linear crosswinds, and its expression is: A sinusoidal crosswind model is constructed based on sinusoidal crosswinds, and its expression is: in, For a moment crosswind speed, This refers to the peak wind speed of the gust. This marks the beginning of the stable phase of gusts. This marks the end of the stable phase of the gusts. , Indicates the duration of rise / fall. .

3. The virtual evaluation system for vehicle handling stability based on parametric crosswind excitation according to claim 1, characterized in that: In the vehicle aerodynamics model construction module, the spatial range of the computational domain is proportionally set according to the vehicle's external dimensions. The computational domain setting includes: The length along the vehicle's forward direction is set to 20 times the vehicle length; the length along the rear side of the vehicle is 10 times the vehicle length; the distance from the side wind inlet to the windward side of the vehicle is 20 times the vehicle width; the distance from the side wind outlet to the leeward side of the vehicle is 20 times the vehicle width; the height of the computational domain along the Z-axis is 10 times the vehicle height, and the computational domain blockage ratio is less than 5%.

4. The virtual evaluation system for vehicle handling stability based on parametric crosswind excitation according to claim 1, characterized in that: The overlapping mesh technology in the model integration calculation module includes a main region mesh and a sub-region mesh; wherein, the main region mesh is a static background mesh covering the entire computational domain and used to describe far-field flow; the sub-region mesh is a dynamic mesh that encloses the aerodynamic model body of the whole vehicle and the surrounding near-field flow field and is used to perform localized and refined calculations of the flow near the vehicle body. The main region grid and sub-region grid transmit flow field information through interpolation calculation, wherein the fluid domain mass conservation equation is: The momentum conservation equation in the fluid domain is: As the mesh volume changes, the following space conservation law holds: in, For time; For fluid density; To calculate the static pressure of the fluid within the computational domain, used to characterize the normal stress state of the fluid micro-element; is the dynamic viscosity coefficient of a fluid, used to characterize the shear viscosity effect produced by a fluid under the action of a velocity gradient; Let these represent the boundary area element and the volume element that control the volume, respectively. This represents the surface normal vector of the control volume; Represents the velocity vector. For vehicle speed; Indicates the control volume Volume fraction within, Indicates time The total derivative, Indicates the boundary surface of the control volume The area integral on, Indicates control volume Boundary surface, Indicates flow rate In the normal vector Rate of change in direction.

5. The virtual evaluation system for vehicle handling stability based on parametric crosswind excitation according to claim 1, characterized in that: The vehicle motion response generation module specifically constructs a vehicle multibody dynamics model based on vehicle parameters as follows: Based on vehicle geometric parameters, mass parameters, and suspension layout parameters, a multibody dynamics model of the vehicle, including a body model, suspension model, steering model, and tire model, is constructed. The vehicle multibody dynamics model is achieved through... The test data were calibrated; the tire model adopted the MagicFormula or MF-Tyre model, which has lateral stiffness, longitudinal slip, road adhesion coefficient and nonlinear saturation characteristics; the suspension model adopted the high-frequency bushing model, which has force transmission path flexibility and frequency response characteristics; the steering model includes power assist mechanism, return characteristics and handling delay elements to reproduce driver operation feedback.

6. The virtual evaluation system for vehicle handling stability based on parametric crosswind excitation according to claim 5, characterized in that: The driving simulator integrated module includes a cockpit, motion platform, visual system, and high-performance computer group. The driving simulator integration module converts the vehicle multibody dynamics model loaded with aerodynamic six-component forces into a real-time vehicle model that can be run on the driving simulator through model order reduction and real-time solution algorithms.

7. The virtual evaluation system for vehicle handling stability based on parametric crosswind excitation according to claim 6, characterized in that: It also includes a virtual evaluation module, which is used to receive the vehicle dynamic response of the driving simulator under linear crosswind and sinusoidal crosswind, and to evaluate the vehicle when it encounters crosswind disturbance at high speed according to preset evaluation indicators. The preset evaluation indicators include lateral acceleration, yaw rate, steering wheel angle, and lateral displacement.

8. A virtual evaluation method for vehicle handling stability based on parametric crosswind excitation, applied to the virtual evaluation system for vehicle handling stability based on parametric crosswind excitation as described in any one of claims 1-7, characterized in that: include: S1: Analyze the characteristics of typical road wind environment and differentiate them from strong crosswind road tests, and construct linear crosswind model and sinusoidal crosswind model; S2: Based on the 3D model of the vehicle's shape, construct the vehicle dynamics model, and divide it into meshes, set the computational domain and boundary conditions; the boundary conditions include the pressure inlet boundary conditions at the front of the computational domain, the pressure outlet boundary conditions at the rear of the computational domain, the crosswind inlet boundary conditions and pressure outlet boundary conditions on both sides of the computational domain, the pressure outlet boundary conditions at the top of the computational domain, the moving wall boundary conditions of the ground at the bottom of the computational domain, and the non-slip wall boundary conditions of the vehicle surface; S3: Load the crosswind inlet boundary conditions of the whole vehicle aerodynamic model into the form of a linear crosswind model and a sinusoidal crosswind model in the form of a dynamic boundary. At the same time, the whole vehicle aerodynamic model uses overlapping mesh technology and finite volume method to calculate the aerodynamic six components. S4: Construct a vehicle multibody dynamics model based on vehicle parameters, and load the aerodynamic six-component forces obtained from the model integration calculation module as input into the vehicle multibody dynamics model; S5: Converts the vehicle multibody dynamics model with six aerodynamic forces into a real-time vehicle model that can be run on the driving simulator, and exchanges signals through software-in-the-loop and hardware-in-the-loop interfaces. S6: Receives the vehicle dynamic response from the driving simulator under linear crosswind and sinusoidal crosswind, and evaluates the vehicle when it encounters crosswind disturbance at high speed according to preset evaluation indicators.

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