Research On Tire Model Parameter Identification And Vehicle Load Spectrum Extraction In Virtual Testing Ground

The development cost of extracting load data through measuring the six component force excitation model of the wheel center remains high.Therefore,the method of extracting load spectra through 3D digital road surface combined with multi body dynamics model simulation has gradually become the mainstream in the engineering field.However,the characteristic parameters characterizing the nonlinearity of tires are constrained by relevant optimization algorithms during the identification stage,becoming an important factor affecting the accuracy of load spectrum extraction in virtual test field simulation.Therefore,this article takes a B-class vehicle as the research object and uses improved particle swarm optimization algorithm to identify tire model parameters;Based on the reinforcement of road testing in the experimental field,the virtual experimental field simulation method and displacement reverse engineering method are applied to extract the load spectrum simulation.The simulation results are compared and analyzed with the measured data,achieving the effectiveness verification of the virtual experimental field simulation method.(1)Based on Logistic Tent chaotic mapping,the particle swarm optimization algorithm is improved from the aspects of initial population redistribution,nonlinear inertia weight factor adjustment,and particle adaptive update strategy optimization.The algorithm fitting accuracy and convergence efficiency are verified through six algorithm test functions,providing an algorithm foundation for tire model parameter identification.(2)Using the improved particle swarm optimization algorithm for tire model parameter identification under different test conditions,conducting synchronous identification of relevant optimization algorithms and comparing the accuracy of the results,obtaining the most accurate tire characteristic parameters,and synchronously converting them into the tire attribute files required for building the vehicle’s multi body model.(3)Conduct fatigue durability enhancement road vehicle vibration response testing on the testing ground,collect response signals from the main monitoring points of the vehicle,and preprocess the collected signals to ensure their effectiveness.Collect road surface elevation data from the test site and use Open CRG technology to construct a digital road surface model.The multi body model of the entire vehicle was built through real vehicle data and tire attribute files,and suspension K&C tests and vibration table tests were conducted to achieve model tuning.The relative error between the final simulation results and the measured signal RMS was less than 20%,and the entire vehicle model met the requirements of virtual test field simulation.(4)Simulate and extract the load spectrum of the virtual test field under different vehicle speed conditions,and iteratively solve the displacement reverse method.Compare and analyze the simulation results with the measured data in terms of signal time and frequency domains.The results show that the simulation accuracy of the virtual test field is slightly lower than that of the displacement reverse method,but the overall trend of the signal time domain,frequency domain,and step through counting curves is basically consistent under all operating conditions.The relative error of RMS is less than 27%,which meets the accuracy requirements for extracting load spectra using simulation technology.This study has convenient and practical features in the application of tire model parameter identification and virtual test field simulation to extract road load spectra,which helps relevant enterprises reduce costs and improve efficiency in the early stage of vehicle development.