Simultaneous and Iterative Estimation of Vehicle Ride Model States and Parameters

Abstract

Abstract With advancements in vehicle electronics and growing focus on vehicle safety systems, state and parameter estimation has become a remarkable sphere of research. In this study, the vehicle vertical dynamics states and parameters were estimated simultaneously and iteratively with relevant vehicle responses. This was achieved through vehicle tests, designed to excite the corresponding vehicle states. A linear Kalman filter was used for state estimation. Vehicle parameters were obtained as a optimal solution using an optimization algorithm. With the help of multi-DOF vehicle ride model along with real vehicle measurements, the state and parameter estimators work concurrently to obtain the results. A cleat test was performed for a Sports Utility Vehicle (SUV) in IPG CarMaker® and in reality, for evaluation of the proposed framework. The state estimation results showed upto 93% correlation from simulated data and upto 81% correlation from real time measurements. Parameter estimation produced an average error of only 9.1%. This demonstrated the efficacy of the algorithm for use in vehicle systems.