A New Approach to Modeling Vehicle Dynamics System Using a Block-Oriented Structure with Genetic Algorithm

This paper aims to nonlinearly model a vehicle dynamics system utilizing the Wiener-Hammerstein Structure based on a genetic ‎algorithm. System identification involves deriving mathematical models of dynamical systems from output data, test results, and ‎observations. Many modern vehicle features rely on models of underlying vehicle dynamics, with the bicycle vehicle model ‎being a commonly used, relatively simple structure in the vehicle dynamics literature. This study begins with this model ‎structure and attempts to estimate the longitudinal and lateral stiffness of a tire. In recent years, block-oriented models have been ‎employed to model nonlinear systems. Among these models, the Wiener-Hammerstein model comprises two linear blocks with ‎a static nonlinear block between them. Identification was performed using simulated data with high stiffness tires and a genetic ‎algorithm. The simulation results demonstrate the high accuracy of the proposed method.‎