Vehicle virtual sensing : estimation of the longitudinal velocity and tyre force

Abstract

In this project, a first contact with virtual sensing is explained. Denoting the importance of sensors information, of understanding vehicle dynamics and the influence the estimators have when modeling a vehicle. Two different vehicle models are presented, Bicycle and Four-wheel model, together with a linear and a nonlinear estimators, Kalman Filter and Extended Kalman Filter, respectively. At the end of the project, two approaches have been carried out in order to determine the correct functionality of those estimators. Finally, a more detailed approach has been done in which the Extended Kalman Filter is evaluated. In this case, the vehicle is not modelled, and those estimated IMU parameters have been replaced by real data provided by McLaren Automotive LTD