Extended Kalman Filter-Based Vehicle State Estimation for Direct Yaw Moment Control Systems

Abstract

This paper presents a vehicle state parameter estimation method based on an extended Kalman filter with a three-degree-of-freedom vehicle dynamic model, and a simulation platform for the vehicle direct yaw moment control system is built using Carsim/Simulink to estimate the longitudinal vehicle speed, vehicle sideslip angle, and yaw angle respectively in real-time. The simulations were carried out at different vehicle speeds with steering wheel input angular step signals and modeled and estimated values are given for longitudinal vehicle speed, vehicle slip angle, and yaw angle, respectively. Simulation results show that the extended Kalman filter method is well for estimating vehicle state parameters with high accuracy and provides the necessary sensory information for the direct yaw control system of vehicles.