Abstract
Among other variables, smart tyre systems are capable of determining tyre contact forces. Combining this information with the signals obtained from conventional vehicle sensors, e.g. inertial measurement units, wheel speed sensors and steering wheel angle sensors, improves the estimation accuracy of the states used by the vehicle dynamics controllers of production cars. This study assesses the performance improvement brought by the vertical and longitudinal tyre contact force signals, obtained through smart tyres, to an unscented Kalman filter (UKF) for vehicle speed and sideslip angle estimation, based on a nonlinear vehicle dynamics model. Two UKF designs, excluding and including smart tyre information, are compared by using experimental data from a purposely sensorised high-performance passenger car, along a comprehensive set of manoeuvres. The results show average ∼60% and ∼37% reductions of the root mean square values of the normalised vehicle speed and sideslip angle estimation errors for the filter design receiving the smart tyre information. In comparison with the more conventional UKF design without tyre force inputs, the smart tyre based estimator is also characterised by significantly enhanced robustness and adaptability to typical variations of vehicle and tyre parameters, such as the tyre-road friction coefficient.