Co-Estimation and Validation of Driving States of a 3-DOFs Vehicle Model Based on UKF Approach

Accurate acquisition of driving states is the premise of realizing active safety control (ASC) of a vehicle. This paper proposes an effective co-estimation method of vehicle driving states including the sideslip angle, yaw rate and longitudinal speed of the electric vehicle based on the Unscented Kalman Filter (UKF) algorithm. First, a three degree-of-freedoms (DOFs) nonlinear vehicle dynamics model is established as the nominal control plant. Then, based on CarSim software, the simulation results of front steer angle, longitudinal and lateral acceleration are obtained under a variety of working conditions, which are regarded as the pseudo-measured values. Finally, the joint simulation of vehicle state estimation on the sideslip angle, yaw rate and longitudinal speed is realized in the MATLAB/Simulink environment by using the pseudo-measured values and the UKF algorithm, together. The results of co-estimation show that the proposed UKF-based vehicle driving state estimation method is accurate in different working scenarios.