Vehicle state estimation for advanced vehicle motion control using novel lateral tire force sensors

In this paper, new real-time methods for the lateral vehicle velocity and roll angle estimation are presented. Lateral tire forces, obtained from a multi-sensing hub (MSHub) unit, are used to estimate lateral vehicle velocity and a roll angle. In order to estimate lateral vehicle velocity, the recursive least square (RLS) algorithm is utilized based on a linear vehicle model and sensor measurements. In the roll angle estimation, the Kalman filter is designed for real-time estimation. The proposed estimation methods, RLS-based estimator and the Kalman filter, were verified by field tests on an experimental electric vehicle. Test results show that the proposed estimation methods provide better estimation performances and these methods are robust to road conditions.