Estimation of Vehicle State and Road Coefficient for Electric Vehicle through Extended Kalman Filter and RLS Approaches

Abstract

Estimation of vehicle state (e.g., vehicle velocity and sideslip angle) and road friction coefficient is essential for electric vehicle stability control. This article proposes a novel real-time model-based vehicle estimator, which can be used for estimation of vehicle state and road friction coefficient for the distributed driven electric vehicle. The estimator is realized using the extended Kalman filter (EKF) and the recursive least squares (RLS) technique. The proposed estimation algorithm is evaluated through simulation and experimental test. Results to data indicate that the proposed approach is effective and it has the ability to provide with reliable information for vehicle active safety control.