Joint estimation method of sideslip angle and tyre–road adhesion coefficient using extended Kalman filter and unscented Kalman filter

This paper proposes a joint estimator for the sideslip angle and road adhesion coefficient of distributed drive electric vehicles based on Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), building upon the traditional Kalman filter algorithm. The estimator integrates a vehicle's 8-degree-of-freedom model with an improved Dugoff tyre model. By transforming the front wheel angle into lateral and longitudinal excitations, it addresses the potential issue of non-converging estimates during the collaborative estimation process. Through model simulations and real-vehicle experiments, it has been verified that the collaborative estimation system can effectively correct and predict parameters during the synchronous estimation process of the two estimators, achieving fast and accurate estimation. This proves the effectiveness of the road adhesion coefficient estimation method.