Vehicle dynamics performance assessment by development of model-supported testing

The research field of virtual sensing looks for techniques to provide access to difficult-to-measure quantities. This is done by combining existing measurement data with model information. The presented work focuses on the estimation of the vehicle sideslip angle and of the forces exchanged at road level. Accurate estimation of vehicle motion during handling maneuvers can be achieved by the development of model-supported testing. The presented approach uses both a fixed parameter linear tire model, and an adaptive linear tire model together in an Extended Kalman Filter. The former model is used to provide an additional virtual measurement, and a reliable reference during straight driving, whereas the latter accounts for variable tire behavior. A phase error in the estimated sideslip angle, due to vehicle motion being considered only on the horizontal plane, is corrected by considering the contribution of the roll rate. The proposed approach provides deeper insight in vehicle performance with a reduced test setup, through the enhancement of low qualities responses and the estimation of additional quantities.