A method and system for modeling wheel-ground relationships of multi-wheeled special vehicles in field environments

Abstract

This invention discloses a method and system for modeling wheel-ground relationships of multi-wheeled special vehicles in field environments, relating to the field of vehicle motion control technology. The invention includes data acquisition, model construction, parameter identification, and model verification steps. It establishes a virtual soft road surface data acquisition platform, targeting the repeated passage of multi-wheeled special vehicles on soft roads. With the goal of accurately acquiring the lateral forces on each tire, it establishes a wheel-ground contact model suitable for soft roads, thereby providing more reliable vehicle dynamic parameters for vehicle model establishment and vehicle control system operation.

Description

Technical Field

[0001] This invention relates to the field of vehicle motion control technology, and in particular to a method and system for modeling wheel-ground relationships for multi-wheeled special vehicles in field environments. Background Technology

[0002] Tire lateral force is crucial for vehicle lateral dynamics control technologies such as yaw stability control and trajectory tracking. For vehicles in off-road environments, the deformability of unstructured roads complicates wheel-ground contact relationships. The unique contact forces generated by tire sinking, such as bulldozing resistance and compaction resistance, cause tire mechanical properties to exhibit strong nonlinear characteristics. For multi-wheeled special vehicles, the interaction between the front wheels and the ground leaves ruts, altering parameters such as soil density and cohesion, directly affecting the rear wheels' ability to pass through. This repeated passage effect distorts the rear wheel lateral force characteristics, resulting in severe asymmetry. These factors collectively prevent the accurate acquisition of tire lateral force using traditional tire models for multi-wheeled special vehicles in off-road environments.

[0003] The current main method for fitting tire force is to obtain the true value of tire force during driving through experiments and compare it with the calculated value of the model. The main problems with this method are data acquisition and model correction and identification.

[0004] 1) Data Acquisition: Setting up field environments is costly, requires extensive sensor facilities, and soil parameters are difficult to measure. Environmental uncertainties introduce significant noise into true data acquisition, making it impossible to create an ideal experimental environment. Accurate experimental data is a necessary condition for establishing precise models.

[0005] 2) Model Correction and Identification: Currently, no research provides a mechanistic tire model suitable for field environments, let alone a model form that considers multiple-pass effects. The structure of semi-empirical tire models needs to be varied according to actual needs, and the parameters to be fitted further determine the model's performance.

[0006] Therefore, proposing a wheel-ground relationship modeling method and system for multi-wheeled special vehicles in the field environment to solve the problems existing in the prior art is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0007] In view of this, the present invention provides a wheel-ground relationship modeling method and system for multi-wheeled special vehicles in field environments. It establishes a virtual soft road surface data acquisition platform and, taking into account the phenomenon of multi-wheeled special vehicles passing through soft roads multiple times, aims to accurately obtain the lateral forces of each tire and establish a wheel-ground contact model and parameter set suitable for soft roads. This provides more reliable vehicle dynamic parameters for the establishment of vehicle models and the operation of vehicle control systems.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A method for modeling wheel-ground relationships for multi-wheeled special vehicles in field environments includes the following steps:

[0010] S1 Data Acquisition Steps: Build a soft road surface simulation environment, establish a driver-in-the-loop data acquisition environment in the soft road surface simulation environment, and build a data post-processing platform based on the built soft road surface driver-in-the-loop data acquisition environment.

[0011] S2 model construction steps: Adjust the parameters of the original Burckhardt coupled tire model based on lateral force according to the sign of the tire slip angle, and obtain a tire lateral force model suitable for soft road surfaces;

[0012] S3 Parameter Identification Steps: Based on the driving data of multi-wheeled special vehicles on soft road surfaces obtained in S1 and the tire lateral force model obtained in S2, the tire lateral force model parameters are fitted using the particle swarm optimization algorithm to establish the mapping relationship between soil parameters and tire lateral force model parameters.

[0013] S4 Model Validation Steps: Based on the soft road surface simulation environment, driver-in-the-loop data acquisition environment, and data post-processing platform, conduct joint simulation experiments to obtain the error between the calculated tire lateral force value and the measured tire lateral force value.

[0014] Optionally, the S1 data acquisition step in the above method may include the following:

[0015] S101 Soft Road Surface Simulation Environment Construction

[0016] Based on Bekker's theory, a simulation environment of a homogenized soft road surface composed of clay is constructed.

[0017] S102 Driver in the surrounding environment establishment

[0018] Based on the simulation environment built by S101, the steering wheel is connected to simulate the real driving input of the driver, and the real driving behavior of multi-wheeled special vehicles on the constructed soft road surface is simulated.

[0019] S103 Data Post-processing Platform Construction

[0020] The soft road surface simulation environment built on S101 and the soft road surface driver-in-the-loop data acquisition environment built on S102 transmit the data to an external record via Ethernet for post-processing.

[0021] Optionally, the S2 model construction steps described above may include the following:

[0022] The Burckhardt original coupled tire model based on lateral forces is as follows:

[0023]

[0024] in, Indicates the lateral force of the tire. This indicates the vertical force acting on the tire. Indicates slip ratio, Indicates the tire slip angle. This represents the parameters to be fitted in the model;

[0025] Under soft road conditions, there is a nonlinear relationship between tire lateral force and tire slip angle, and the tire force of the rear wheel exhibits asymmetry. The parameters of the original Burckhardt coupled tire model based on lateral force are adjusted according to the sign of the tire slip angle, without retaining... The model is simplified while retaining parameters related to the tire slip angle, resulting in a tire lateral force model suitable for soft road surfaces, as follows:

[0026]

[0027] in, d Indicates the tire force hysteresis coefficient. The first derivative of the tire slip angle. b Indicates lateral force bias. This indicates the tire slip angle.

[0028] The above method, optionally, includes the following specific steps for S3 parameter identification:

[0029] S301 Particle Swarm Optimization Initialization:

[0030] Tire force hysteresis coefficient And lateral force paralysis The tire lateral force model parameters were manually adjusted. As the optimized particle of PSO, i.e. In addition, due to the effects of multiple passes and load transfer, the model parameters are fitted based on the positive and negative segments of the tire slip angle.

[0031] The initial total number of particles is set to N Set the maximum number of iterations to The initial position and velocity of each particle are initialized using an initialization function, resulting in... ,in, For the first The initial positions of the particles; For the first The initial velocity of each particle;

[0032] S302 calculates the fitness function:

[0033] In each iteration, the fitness function needs to be evaluated based on the particle's current position. The fitness function is as follows:

[0034]

[0035] in, Indicates the first The first particle The fitness function value of each iteration. Indicates that due to the first The particle in the first The value of the wheel is based on the formula Generate a series of tire lateral force values. This represents the actual values ​​collected based on the simulation environment built using S11;

[0036] S303 updates particle position and velocity:

[0037] In each iteration, the historical best position of the particle in the current iteration number is obtained by calculating the fitness function value for each particle. Global optimal position of particle swarm Based on the gradient direction of the fitness function, calculate the latest velocity and latest position of the particle in the current iteration:

[0038]

[0039]

[0040] in, Indicates the first The particle in the first The position of the wheel; Indicates the first The particle in the first Wheel speed; Indicates the inertia factor; Indicates the learning factor; Represents the number of seeds, which is Random numbers between;

[0041] S304 Adaptive Weighting Coefficients:

[0042] The adaptive weights automatically adjust the inertia weight factor based on the convergence degree and fitness value. When the fitness value is less than the average fitness value, the inertia weight factor is as follows:

[0043]

[0044] When the fitness value is greater than the average fitness value, the inertia weighting factor is as follows:

[0045]

[0046] in, Indicates the inertia weighting factor. These represent the maximum and minimum values ​​of the inertia weighting factor, respectively. This represents the current fitness value. These represent the average fitness value and the minimum fitness value, respectively.

[0047] S305 determines whether the termination condition has been met:

[0048] During the iteration process, the cycle is repeated according to S302-S304, and the iteration number is... Reaching the maximum number of iterations At that time, the optimal particle position under the current working condition is obtained, which is the optimal model parameter under that working condition. This means that the model parameters have been identified.

[0049] S306 parameter set establishment:

[0050] Soil parameters change after each vehicle passes, and these changes are calculated using a slip function, as follows:

[0051]

[0052] in, Indicates soil density, Indicates the parameters to be fitted. Indicates the slip ratio of the wheel. Indicates the number of times it passes;

[0053] Cohesion and shear modulus The properties also change, and the calculation formula is as follows:

[0054]

[0055]

[0056] in, This indicates that the wheels passed through the same patch of soil. The cohesion of the soil after that, This indicates that the wheels passed through the same patch of soil. The subsequent soil shear modulus;

[0057] Based on the data acquisition environment established in S1, tire force data under different combinations of soil parameters are collected. By iterating through S301-S305, the tire lateral force model established in S2 is identified, and the parameter set of soil parameters and tire lateral force model parameters is obtained.

[0058] The above methods can be optionally used for S401 co-simulation experiments:

[0059] Start the co-simulation and collect tire slip angle and tire force signals;

[0060] S402 lateral tire force real-time calculation:

[0061] Based on the tire lateral force model obtained from S202 and the tire lateral force model parameters obtained from S3, the lateral force of each tire is calculated in real time.

[0062] S403 Error Calculation:

[0063] Based on the calculated tire lateral force value obtained from S402, the error between the calculated tire lateral force value and the measured tire lateral force value is calculated using the normalized root mean square error formula:

[0064]

[0065] in, This represents the true value of tire force collected through a simulation platform. This represents the calculated value obtained through the proposed model. n This indicates the number of sampling points.

[0066] A wheel-ground relationship modeling system for multi-wheeled special vehicles in field environments, used to implement the wheel-ground relationship modeling method for multi-wheeled special vehicles in field environments described above, includes a data acquisition module, a model building module, a parameter identification module, and a model verification module connected in sequence.

[0067] The data acquisition module builds a soft road surface simulation environment, establishes a driver-in-the-loop data acquisition environment within the soft road surface simulation environment, and builds a data post-processing platform based on the established driver-in-the-loop data acquisition environment for soft road surfaces.

[0068] The model building module is used to adjust the parameters of the original Burckhardt coupled tire model based on the lateral force according to the sign of the tire slip angle, and correct it to obtain a tire lateral force model suitable for soft road surfaces.

[0069] The parameter identification module is used to fit the tire lateral force model parameters using the particle swarm optimization algorithm based on the driving data of multi-wheeled special vehicles on soft road surfaces obtained by the data acquisition module and the tire lateral force model obtained by the model building module, and to establish the mapping relationship between soil parameters and tire lateral force model parameters.

[0070] Model validation is used to conduct joint simulation experiments based on a soft road surface simulation environment, a driver-in-the-loop data acquisition environment, and a data post-processing platform to obtain the error between the calculated tire lateral force value and the measured tire lateral force value.

[0071] As can be seen from the above technical solution, compared with the prior art, the present invention provides a method and system for modeling wheel-ground relationships of multi-wheeled special vehicles in field environments, which has the following beneficial effects:

[0072] This invention establishes a virtual soft road surface data acquisition platform. Targeting the phenomenon of multi-wheeled special vehicles repeatedly passing over soft roads, it aims to accurately acquire the lateral forces of each tire. It establishes a wheel-ground contact model and parameter set suitable for soft roads, thereby providing more reliable vehicle dynamics parameters for vehicle model building and vehicle control system operation. By considering the impact of multiple passages on the lateral force characteristics of the front and rear wheels, the tire force output by the model can effectively improve the accuracy of the vehicle dynamics model, enabling more accurate prediction of vehicle motion states, providing high-quality input to the controller, ultimately achieving better control performance and ensuring driving safety. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0074] Figure 1 This is a flowchart of a wheel-ground relationship modeling method for multi-wheeled special vehicles in field environments disclosed in this invention;

[0075] Figure 2 This is a schematic diagram of the wheel-ground relationship modeling system for multi-wheeled special vehicles in field environments disclosed in this invention;

[0076] Figure 3 This is a flowchart of a wheel-ground relationship modeling method for multi-wheeled special vehicles in field environments, as disclosed in an embodiment of the present invention.

[0077] Figure 4 This is a diagram showing the difference in tire force characteristics between the front and rear wheels caused by the multiple-pass effect disclosed in an embodiment of the present invention. 4a represents the change in the lateral force characteristics of the front wheel with its slip angle, and 4b represents the change in the lateral force characteristics of the rear wheel with its slip angle.

[0078] Figure 5The diagram shows the real-time fitting effect of the front and rear wheel lateral forces disclosed in the embodiment of the present invention, where 5a represents the fitting effect of the model on the front wheel lateral forces and 5b represents the fitting effect of the model on the rear wheel lateral forces. Detailed Implementation

[0079] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0080] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0081] See Figure 1 As shown, this invention discloses a method for modeling wheel-ground relationships for multi-wheeled special vehicles in field environments, comprising the following steps:

[0082] S1 Data Acquisition Steps: Build a soft road surface simulation environment, establish a driver-in-the-loop data acquisition environment in the soft road surface simulation environment, and build a data post-processing platform based on the built soft road surface driver-in-the-loop data acquisition environment.

[0083] S2 model construction steps: Adjust the parameters of the original Burckhardt coupled tire model based on lateral force according to the sign of the tire slip angle, and obtain a tire lateral force model suitable for soft road surfaces;

[0084] S3 Parameter Identification Steps: Based on the driving data of multi-wheeled special vehicles on soft road surfaces obtained in S1 and the tire lateral force model obtained in S2, the tire lateral force model parameters are fitted using the particle swarm optimization algorithm to establish the mapping relationship between soil parameters and tire lateral force model parameters.

[0085] S4 Model Validation Steps: Based on the soft road surface simulation environment, driver-in-the-loop data acquisition environment, and data post-processing platform, conduct joint simulation experiments to obtain the error between the calculated tire lateral force value and the measured tire lateral force value.

[0086] Furthermore, data acquisition experiments conducted in the field typically require significant resources and are difficult to replicate, making the creation of a virtual soft pavement data acquisition environment a necessary task. Specifically, the S1 data acquisition steps include the following:

[0087] S101 Soft Road Surface Simulation Environment Construction

[0088] Based on Bekker's theory, a simulation environment of a homogenized soft road surface composed of clay is constructed.

[0089] S102 Driver in the surrounding environment establishment

[0090] Based on the simulation environment built by S101, the steering wheel is connected to simulate the driver's real driving input (during the data acquisition process, the driver controls the vehicle in the simulation environment through the Logitech G29 steering wheel) to simulate the real driving behavior of multi-wheeled special vehicles on the constructed soft road surface.

[0091] S103 Data Post-processing Platform Construction

[0092] The soft road surface simulation environment built on S101 and the soft road surface driver-in-the-loop data acquisition environment built on S102 transmit the data to an external record via Ethernet for post-processing.

[0093] Furthermore, semi-empirical formulas focus on the shape characteristics of the curve to be fitted, and directly derive mathematical expressions that accurately describe the system characteristics through curve fitting. Among these, the Burckhardt model has been proven to satisfy a strongly nonlinear relationship and is commonly used in tire model building. Specifically, the S2 model construction steps include the following:

[0094] The Burckhardt original coupled tire model based on lateral forces is as follows:

[0095]

[0096] in, Indicates the lateral force of the tire. This indicates the vertical force acting on the tire. Indicates slip ratio, Indicates the tire slip angle. This represents the parameters to be fitted in the model;

[0097] Based on the data collected in S1, a significant nonlinear relationship exists between tire lateral force and tire slip angle under soft road surface conditions, and the tire force of the rear wheels exhibits obvious asymmetry. The constant parameter Burckhardt model cannot fit this relationship, and the model parameters need to be adjusted according to the sign of the tire slip angle. Based on the classic Burckhardt model, a modified Burckhardt model suitable for multi-wheeled vehicles on soft roads is obtained.

[0098] Under soft road conditions, there is a nonlinear relationship between tire lateral force and tire slip angle, and the tire force of the rear wheel exhibits asymmetry. The parameters of the original Burckhardt coupled tire model based on lateral force are adjusted according to the sign of the tire slip angle, without retaining... The simplified model representation retains parameters related to the tire slip angle.

[0099] Increase This item simulates the phenomenon of tire force hysteresis.

[0100] retention factor The magnitude of the lateral saturation force is determined by the effect of multiple passes, which leads to differences in the terrain conditions encountered by the front and rear wheels, resulting in different lateral saturation forces and thus significant differences in the parameters between the front and rear wheels.

[0101] retention factor The lateral stiffness of the tire is characterized by the slope of the origin. Due to the influence of multiple passes, the rear wheel exhibits obvious asymmetry due to the disruption of terrain parameters, and should be designed in a segmented manner.

[0102] Reserved parameters The saturation slope is determined by this parameter, especially when the axle load on the rear wheel decreases, its tire lateral force is very easy to saturate, and the influence of this parameter is extremely significant.

[0103] Add parameters , representing the value under static conditions, reflects the lateral compaction resistance of the soil.

[0104] The final tire lateral force model suitable for soft road surfaces is as follows:

[0105]

[0106] in, d Indicates the tire force hysteresis coefficient. The first derivative of the tire slip angle. b Indicates lateral force bias. This indicates the tire slip angle.

[0107] Furthermore, the particle swarm optimization algorithm randomly distributes the initial particle swarm in space and searches for the optimal solution in the entire space by simulating the foraging behavior of a population and based on the experience of the group and itself. Specifically, the details of the S3 parameter identification step are as follows:

[0108] S301 Particle Swarm Optimization Initialization:

[0109] Tire force hysteresis coefficient And lateral force paralysis The tire lateral force model parameters were manually adjusted. As the optimized particle of PSO, i.e. In addition, due to the effects of multiple passes and load transfer, the model parameters are fitted based on the positive and negative segments of the tire slip angle.

[0110] The initial total number of particles is set to N, and the maximum number of iterations is set to [value missing]. The initial position and velocity of each particle are initialized using an initialization function, resulting in... ,in, For the first The initial positions of the particles; For the first The initial velocity of each particle;

[0111] S302 calculates the fitness function:

[0112] In each iteration, the fitness function needs to be evaluated based on the particle's current position. The fitness function is as follows:

[0113]

[0114] in, Indicates the first The first particle The fitness function value of each iteration. Indicates that due to the first The particle in the first The value of the wheel is based on the formula Generate a series of tire lateral force values. This represents the actual values ​​collected based on the simulation environment built using S11;

[0115] S303 updates particle position and velocity:

[0116] In each iteration, the historical best position of the particle in the current iteration number is obtained by calculating the fitness function value for each particle. Global optimal position of particle swarm Based on the gradient direction of the fitness function, calculate the latest velocity and latest position of the particle in the current iteration:

[0117]

[0118]

[0119] in, Indicates the first The particle in the first The position of the wheel; Indicates the first The particle in the first The speed of the wheel; Indicates the inertia factor; Indicates the learning factor; Represents the number of seeds, which is Random numbers between;

[0120] S304 Adaptive Weighting Coefficients:

[0121] The inertia factor has a significant impact on the ability of the particle swarm optimization algorithm to search for optimal solutions. When the inertia factor is large, the particle swarm optimization algorithm has strong global search capabilities and fast search speed, but the accuracy of the search results is low. When the inertia factor is small, the accuracy of the search results of the particle swarm optimization algorithm is high, but the search speed is slow and it is more likely to get trapped in local optima.

[0122] The adaptive weights automatically adjust the inertia weight factor based on the convergence degree and fitness value. When the fitness value is less than the average fitness value, the inertia weight factor is as follows:

[0123]

[0124] When the fitness value is greater than the average fitness value, the inertia weighting factor is as follows:

[0125]

[0126] in, Indicates the inertia weighting factor. These represent the maximum and minimum values ​​of the inertia weighting factor, respectively. This represents the current fitness value. These represent the average fitness value and the minimum fitness value, respectively.

[0127] S305 determines whether the termination condition has been met:

[0128] During the iteration process, the cycle is repeated according to S302-S304, and the iteration number is... Reaching the maximum number of iterations At that time, the optimal particle position under the current working condition is obtained, which is the optimal model parameter under that working condition. This means that the model parameters have been identified.

[0129] S306 parameter set establishment:

[0130] Soil parameters change after each vehicle passes, and these changes are calculated using a slip function, as follows:

[0131]

[0132] in, Indicates soil density, Indicates the parameters to be fitted. Indicates the slip ratio of the wheel. Indicates the number of times it passes;

[0133] Cohesion and shear modulus The properties also change, and the calculation formula is as follows:

[0134]

[0135]

[0136] in, This indicates that the wheels passed through the same patch of soil. The cohesion of the soil after that, This indicates that the wheels passed through the same patch of soil. The subsequent soil shear modulus;

[0137] Repeated compaction of the soil causes changes in its internal parameters, which in turn alters the wheel-ground interaction relationship between the front and rear wheels of multi-wheeled special vehicles.

[0138] For mechanism-based tire models, soil parameters directly affect the calculated values. For semi-empirical tire models, changes in soil parameters will cause changes in model parameters, which in turn will affect the calculated values.

[0139] Based on the data acquisition environment established in S1, tire force data under different combinations of soil parameters are collected. By iterating through S301-S305, the tire lateral force model established in S2 is identified, and the parameter set of soil parameters and tire lateral force model parameters is obtained.

[0140] Further, S401 co-simulation experiment:

[0141] Start the co-simulation and collect tire slip angle and tire force signals;

[0142] S402 lateral tire force real-time calculation:

[0143] Based on the tire lateral force model obtained from S202 and the tire lateral force model parameters obtained from S3, the lateral force of each tire is calculated in real time.

[0144] S403 Error Calculation:

[0145] Based on the calculated tire lateral force value obtained from S402, the error between the calculated tire lateral force value and the measured tire lateral force value is calculated using the normalized root mean square error formula:

[0146]

[0147] in, This represents the true value of tire force collected through a simulation platform. This represents the calculated value obtained through the proposed model. n This indicates the number of sampling points.

[0148] and Figure 1 Corresponding to the method shown, this invention also discloses a wheel-ground relationship modeling system for multi-wheeled special vehicles in field environments, used for... Figure 1 The implementation of a wheel-ground relationship modeling method for multi-wheeled special vehicles in field environments is shown, including a data acquisition module, a model building module, a parameter identification module, and a model verification module connected in sequence.

[0149] The data acquisition module is used to build a soft road surface simulation environment, establish a driver-in-the-loop data acquisition environment in the soft road surface simulation environment, and build a data post-processing platform based on the built driver-in-the-loop data acquisition environment for soft road surfaces.

[0150] The model building module is used to adjust the parameters of the original Burckhardt coupled tire model based on lateral force according to the sign of the tire slip angle, and correct it to obtain a tire lateral force model suitable for soft road surfaces.

[0151] The parameter identification module is used to fit the tire lateral force model parameters using the particle swarm optimization algorithm based on the driving data of multi-wheeled special vehicles on soft road surfaces obtained by the data acquisition module and the tire lateral force model obtained by the model building module, and to establish the mapping relationship between soil parameters and tire lateral force model parameters.

[0152] The model validation module is used to conduct joint simulation experiments based on a soft road surface simulation environment, a driver-in-the-loop data acquisition environment, and a data post-processing platform to obtain the error between the calculated and measured values ​​of tire lateral force. Based on the validation results, a tire model suitable for multi-wheeled vehicles on soft roads is obtained. Experimental results show that the proposed model can fit the lateral forces of the front and rear wheels of the vehicle with high accuracy.

[0153] In one specific embodiment, the present invention constructs a virtual soft road surface data acquisition platform. Targeting the phenomenon of multiple passes of multi-wheeled special vehicles on soft roads, and aiming to accurately obtain the lateral forces on each tire, a wheel-ground contact model suitable for soft roads is established. This provides more reliable vehicle dynamic parameters for vehicle model creation and vehicle control system operation. See also Figure 3 As shown, the steps involved in the method of this embodiment are as follows:

[0154] Step 1: Data Collection

[0155] 1.1 Setting up a Chrono Soft Road Surface Simulation Environment

[0156] Compared to other dynamics simulation tools such as Adams and CarSim, Chrono's Soil Contact Model (SCM) stands out for its efficient data processing, providing real-time or near-real-time simulation capabilities and accurately simulating soil deformation and parameter changes resulting from vehicle-ground interaction. These characteristics make SCM an ideal platform for studying vehicle dynamics on deformable terrain.

[0157] Based on Bekker's theory, a homogenized, soft pavement composed of clay was constructed using Chrono. Its main parameters are shown in Table 1.

[0158] Table 1

[0159]

[0160] 1.2 Logitech G29 Pilot Environment Establishment

[0161] Based on the simulation environment built in step 1.1, the Logitech G29 steering wheel is connected through the official Logitech driver to simulate the real driving input of the driver, simulating the real driving behavior of multi-wheeled special vehicles on the constructed soft road surface.

[0162] 1.3 Simulink Data Post-processing Platform Setup

[0163] Based on the driver-in-the-loop data acquisition environment for soft road surfaces established in steps 1.1 and 1.2, the data is transmitted to Simulink via Ethernet for recording and post-processing.

[0164] Step 2: Model Building

[0165] Semi-empirical formulas focus on the shape characteristics of the curve to be fitted and directly derive mathematical expressions that accurately describe the system characteristics through curve fitting. Among them, the Burckhardt model has been proven to satisfy strongly nonlinear relationships and is often used in tire modeling. The original Burckhardt coupled tire equations providing lateral forces are given below:

[0166]

[0167] in, It is the lateral force of the tire. It is the vertical force acting on the tire. It is the slip ratio. It is the tire slip angle. These are the parameters to be fitted in the model.

[0168] The model parameters are adjusted according to the sign of the tire slip angle. Based on the classic Burckhardt model, a modified Burckhardt model suitable for multi-wheeled vehicles on soft roads is obtained.

[0169] No reservation The simplified model expression is only related to the tire slip angle.

[0170] Increase This item simulates the phenomenon of tire force hysteresis.

[0171] retention factor The magnitude of the lateral saturation force is determined by the effect of multiple passes, which leads to differences in the terrain conditions encountered by the front and rear wheels, resulting in different lateral saturation forces and thus significant differences in the parameters between the front and rear wheels.

[0172] retention factor The lateral stiffness of the tire is characterized by the slope of the origin. Due to the influence of multiple passes, the rear wheel exhibits obvious asymmetry due to the disruption of terrain parameters, and should be designed in a segmented manner.

[0173] Reserved parameters The saturation slope is determined by this parameter, especially when the axle load on the rear wheel decreases, its tire lateral force is very easy to saturate, and the influence of this parameter is extremely significant.

[0174] Add parameters , representing the value under static conditions, reflects the lateral compaction resistance of the soil.

[0175] The final tire lateral force model is as follows:

[0176]

[0177] in, d Indicates the tire force hysteresis coefficient. The first derivative of the tire slip angle. b Indicates lateral force bias. This indicates the tire slip angle.

[0178] Step 3: Parameter Identification

[0179] Based on the driving data of special vehicles on soft road surfaces obtained in step 1 and the tire lateral force model obtained in step 2, the particle swarm optimization algorithm is used to fit the model parameters and establish the mapping relationship between soil parameters and model parameters.

[0180] The particle swarm optimization algorithm randomly distributes an initial swarm of particles in space and searches for the optimal solution in the entire space by simulating the foraging behavior of the population and based on the experience of the group and itself.

[0181] 3.1 Particle Swarm Optimization Initialization

[0182] Tire force hysteresis coefficient And lateral force paralysis The adjustment is relatively easy and can be done manually; therefore, this invention adjusts the tire model parameters. As the optimized particle of PSO, i.e. In addition, due to the effects of multiple passes and load transfer, the model parameters should be fitted piecewise based on the positive and negative values ​​of the tire slip angle.

[0183] The initial total number of particles is set to N, and the maximum number of iterations is set to [value missing]. The initial position and velocity of each particle are initialized using an initialization function, resulting in... . For the first The initial positions of the particles; For the first The initial velocity of each particle.

[0184] 3.2 Calculate the fitness function

[0185] In each iteration, the fitness function needs to be evaluated based on the particle's current position. The fitness function selected in this invention is:

[0186]

[0187] in For the first The first particle The fitness function value of each iteration. Because of the The particle in the first The value of the wheel is based on the formula Generate a series of tire lateral force values. These are the true values ​​collected based on the simulation environment built in step 1.

[0188] 3.3 Update particle position and velocity

[0189] In each iteration, by calculating the fitness function value for each particle, the historical best position of the individual particle in the current iteration can be obtained. Global optimal position of particle swarm Based on the gradient direction of the fitness function, calculate the latest velocity and latest position of the particle in the current iteration:

[0190]

[0191]

[0192] in, For the first The particle in the first The position of the wheel; For the first The particle in the first The speed of the wheel; Inertia factor; For learning factors; The number of seeds is A random number between [a certain number of points].

[0193] 3.4 Adaptive Weighting Coefficients

[0194] The inertia factor has a significant impact on the ability of the particle swarm optimization algorithm to search for optimal solutions. When the inertia factor is large, the particle swarm optimization algorithm has strong global search capabilities and fast search speed, but the accuracy of the search results is low. When the inertia factor is small, the accuracy of the search results of the particle swarm optimization algorithm is high, but the search speed is slow and it is more likely to get trapped in local optima.

[0195] Adaptive weights can automatically adjust the magnitude of the inertia weight factor based on the convergence degree and fitness value. When the fitness value is less than the average fitness value, the following applies:

[0196]

[0197] When the fitness value is greater than the average fitness value, then:

[0198]

[0199] in, As the inertia weighting factor, These are the maximum and minimum values ​​of the inertia weighting factor, respectively. The current fitness value, These are the average fitness value and the minimum fitness value, respectively.

[0200] 3.5 Determine if the termination condition has been met.

[0201] During the iteration process, steps 3.2-3.4 are repeated. When the number of iterations reaches a certain threshold... Reaching the maximum number of iterations At that time, the optimal particle position under the current working condition is obtained, which is the optimal model parameter under that working condition. This means that the model parameters have been identified.

[0202] 3.6 Parameter Set Establishment

[0203] Soil parameters change their properties with each pass; this change is a function of slip.

[0204]

[0205] in, Indicates soil density, These are the parameters to be fitted. It is the slip ratio of the wheel. It is determined by the number of times.

[0206] Similarly, cohesion and shear modulus It also has similar characteristics:

[0207]

[0208]

[0209] Repeated compaction of the soil causes changes in its internal parameters, which in turn alters the wheel-ground interaction relationship between the front and rear wheels of multi-wheeled special vehicles.

[0210] For mechanism-based tire models, soil parameters directly affect the calculated values. For semi-empirical tire models, changes in soil parameters will cause changes in model parameters, which in turn will affect the calculated values.

[0211] Based on the data acquisition environment established in step 1, tire force data under different combinations of soil parameters are collected. Steps 3.1-3.5 are repeated to identify the model established in step 2 and obtain the parameter set of soil parameters and model parameters.

[0212] Step 3: Model Validation

[0213] Based on the lateral force models of the front and rear tires obtained in step 3, and based on the experimental conditions such as soft road surface, driver in-loop, and data post-processing platform established in step 1, a Chrono-Simulink co-simulation experiment was conducted.

[0214] 4.1 Joint Simulation Experiment

[0215] Initiating a Chrono-Simulink co-simulation, in this embodiment, the vehicle travels at a speed on a soft road surface. The system performs serpentine maneuvers, and while road conditions remain unchanged, it primarily collects tire slip angle and tire force signals.

[0216] 4.2 Real-time calculation of lateral tire forces

[0217] Based on the tire model expression obtained in step 2.2 and the model parameters obtained in steps 3.1-3.5, Simulink is used to calculate the lateral force of each tire in real time.

[0218] 4.3 Error Calculation

[0219] Based on the tire force calculation value obtained in step 4.2, and referring to the normalized root mean square error calculation formula, the percentage error between the calculated value and the measured tire force value is calculated:

[0220] .

[0221] The Burckhardt model for multi-wheeled vehicles on soft surfaces, built in step 2, lacks an integral form compared to the traditional Bekker model, resulting in a simpler structure suitable for controller design. This model also considers the influence of the front wheel-ground interaction on the rear wheel tire force characteristics, enabling a more accurate fit of tire forces and characterization of vehicle dynamics. Completed experimental results are as follows: Figure 4 and Figure 5 As shown, from Figure 4 As can be seen from 4a and 4b, the lateral force characteristics of the front and rear wheels show significant differences due to the multiple-pass effect, with the rear wheel exhibiting more pronounced hysteresis and a faster saturation speed; from Figure 5 As can be seen from 5a and 5b, the proposed model can calculate the lateral forces of the front and rear wheels in real time with high accuracy.

[0222] For the system or system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and relevant details can be found in the description of the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0223] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for modeling wheel-ground relationships of multi-wheeled special vehicles in field environments, characterized in that, Includes the following steps: S1 Data Acquisition Steps: Build a soft road surface simulation environment, establish a driver-in-the-loop data acquisition environment in the soft road surface simulation environment, and build a data post-processing platform based on the built soft road surface driver-in-the-loop data acquisition environment. S2 model construction steps: Adjust the parameters of the original Burckhardt coupled tire model based on lateral force according to the sign of the tire slip angle, and obtain a tire lateral force model suitable for soft road surfaces; S3 Parameter Identification Steps: Based on the driving data of multi-wheeled special vehicles on soft road surfaces obtained in S1 and the tire lateral force model obtained in S2, the tire lateral force model parameters are fitted using the particle swarm optimization algorithm to establish the mapping relationship between soil parameters and tire lateral force model parameters. S4 Model Validation Steps: Based on the soft road surface simulation environment, driver-in-the-loop data acquisition environment, and data post-processing platform, conduct joint simulation experiments to obtain the error between the calculated tire lateral force value and the measured tire lateral force value; The S2 model construction steps specifically include the following: The Burckhardt original coupled tire model based on lateral forces is as follows: in, Indicates the lateral force of the tire. This indicates the vertical force acting on the tire. Indicates slip ratio, Indicates the tire slip angle. This represents the parameters to be fitted in the model; Under soft road conditions, there is a nonlinear relationship between tire lateral force and tire slip angle, and the tire force of the rear wheel exhibits asymmetry. The parameters of the original Burckhardt coupled tire model based on lateral force are adjusted according to the sign of the tire slip angle, without retaining... The model is simplified while retaining parameters related to the tire slip angle, resulting in a tire lateral force model suitable for soft road surfaces, as follows: in, d Indicates the tire force hysteresis coefficient. The first derivative of the tire slip angle. b Indicates lateral force bias. This indicates the tire slip angle.

2. The method for modeling wheel-ground relationships of multi-wheeled special vehicles in field environments according to claim 1, characterized in that, The S1 data acquisition steps specifically include the following: S101 Soft Road Surface Simulation Environment Construction Based on Bekker's theory, a simulation environment of a homogenized soft road surface composed of clay is constructed. S102 Driver in the surrounding environment establishment Based on the simulation environment built by S101, the steering wheel is connected to simulate the real driving input of the driver, and the real driving behavior of multi-wheeled special vehicles on the constructed soft road surface is simulated. S103 Data Post-processing Platform Construction The soft road surface simulation environment built on S101 and the soft road surface driver-in-the-loop data acquisition environment built on S102 transmit the data to an external record via Ethernet for post-processing.

3. The method for modeling wheel-ground relationships of multi-wheeled special vehicles in field environments according to claim 1, characterized in that, The specific steps of S3 parameter identification are as follows: S301 Particle Swarm Optimization Initialization: Tire force hysteresis coefficient And lateral force paralysis The tire lateral force model parameters were manually adjusted. As the optimized particle of PSO, i.e. In addition, due to the effects of multiple passes and load transfer, the model parameters are fitted based on the positive and negative segments of the tire slip angle. The initial total number of particles is set to N, and the maximum number of iterations is set to [value missing]. The initial position and velocity of each particle are initialized using an initialization function, resulting in... ,in, For the first The initial positions of the particles; For the first The initial velocity of each particle; S302 calculates the fitness function: In each iteration, the fitness function needs to be evaluated based on the particle's current position. The fitness function is as follows: in, Indicates the first The first particle The fitness function value of each iteration. Indicates that due to the first The particle in the first The value of the wheel is based on the formula Generate a series of tire lateral force values. This represents the lateral force of the tire, based on the actual value collected from the simulation environment built in S1; S303 updates particle position and velocity: In each iteration, the historical best position of the particle in the current iteration number is obtained by calculating the fitness function value for each particle. Global optimal position of particle swarm Based on the gradient direction of the fitness function, calculate the latest velocity and latest position of the particle in the current iteration: in, Indicates the first The particle in the first The position of the wheel; Indicates the first The particle in the first Wheel speed; Indicates the inertia factor; Indicates the learning factor; Represents the number of seeds, which is Random numbers between; S304 Adaptive Weighting Coefficients: The adaptive weights automatically adjust the inertia weight factor based on the convergence degree and fitness value. When the fitness value is less than the average fitness value, the inertia weight factor is as follows: When the fitness value is greater than the average fitness value, the inertia weighting factor is as follows: in, Indicates the inertia weighting factor. These represent the maximum and minimum values ​​of the inertia weighting factor, respectively. This represents the current fitness value. These represent the average fitness value and the minimum fitness value, respectively. S305 determines whether the termination condition has been met: During the iteration process, the cycle is performed according to S302-S304, and the iteration number is... Reaching the maximum number of iterations At that time, the optimal particle position under the current working condition is obtained, which is the optimal model parameter under that working condition. This means that the model parameters have been identified. S306 parameter set establishment: Soil parameters change after each wheel passes, and these changes are calculated using a slip function, as follows: in, Indicates soil density, Indicates the parameters to be fitted. Indicates the slip ratio of the wheel. Indicates the number of times it passes; Cohesion and shear modulus The properties also change, and the calculation formula is as follows: in, This indicates that the wheels passed through the same patch of soil. The cohesion of the soil after that, This indicates that the wheels passed through the same patch of soil. The subsequent soil shear modulus; Based on the data acquisition environment established in S1, tire force data under different combinations of soil parameters are collected. By iterating through S301-S305, the tire lateral force model established in S2 is identified, and the parameter set of soil parameters and tire lateral force model parameters is obtained.

4. The method for modeling wheel-ground relationships of multi-wheeled special vehicles in field environments according to claim 3, characterized in that, S401 Joint Simulation Experiment: Start the co-simulation and collect tire slip angle and tire force signals; S402 lateral tire force real-time calculation: Based on the tire lateral force model obtained from S202 and the tire lateral force model parameters obtained from S3, the lateral force of each tire is calculated in real time. S403 Error Calculation: Based on the calculated tire lateral force value obtained from S402, the error between the calculated tire lateral force value and the measured tire lateral force value is calculated using the normalized root mean square error formula: in, This represents the true value of tire force collected through a simulation platform. This represents the calculated value obtained through the proposed model. n This indicates the number of sampling points.

5. A wheel-ground relationship modeling system for multi-wheeled special vehicles in field environments, characterized in that, The implementation of the wheel-ground relationship modeling method for multi-wheeled special vehicles in the field environment as described in any one of claims 1-4 includes a data acquisition module, a model building module, a parameter identification module, and a model verification module connected in sequence. The data acquisition module builds a soft road surface simulation environment, establishes a driver-in-the-loop data acquisition environment within the soft road surface simulation environment, and builds a data post-processing platform based on the established driver-in-the-loop data acquisition environment for soft road surfaces. The model building module is used to adjust the parameters of the original Burckhardt coupled tire model based on lateral force according to the sign of the tire slip angle, and correct it to obtain a tire lateral force model suitable for soft road surfaces. The parameter identification module is used to fit the tire lateral force model parameters using the particle swarm optimization algorithm based on the driving data of multi-wheeled special vehicles on soft road surfaces obtained by the data acquisition module and the tire lateral force model obtained by the model building module, and to establish the mapping relationship between soil parameters and tire lateral force model parameters. The model verification module is used to conduct joint simulation experiments based on the soft road surface simulation environment, the driver-in-the-loop data acquisition environment, and the data post-processing platform to obtain the error between the calculated tire lateral force value and the measured tire lateral force value.

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