More accurate information about the basic vehicle parameters can improve the dynamic control functions of a vehicle. Methods for online estimation of the mass, the rolling resistance, the aerodynamic drag coefficient, the yaw inertia and the longitudinal position of the centre of gravity of an electric hybrid vehicle is therefore proposed. The estimators use the standard vehicle sensor set and the estimate of the electric motor torque. No additional sensors are hence required and no assumptions are made regarding the tyre or the vehicle characteristics. Consequently, all information about the vehicle is available to the estimator.

The estimators are evaluated using both simulations and experiments. Estimations of the mass, the rolling resistance and the aerodynamic drag coefficient are based on a recursive least-squares method with multiple forgetting factors. The mass estimate converged to within 3% of the measured vehicle mass for the test cases with sufficient excitation that were evaluated. Two methods to estimate the longitudinal position of the centre of gravity and the yaw inertia are also proposed. The first method is based on the equations of motion and was found to be sensitive to the measurement and parameter errors. The second method is based on the estimated mass and seat-belt indicators.

This estimator is more robust and reduces the estimation error in comparison with that obtained by assuming static parameters. The results show that the proposed method improves the estimations of the inertial parameters. Hence, it enables online non-linear tyre force estimators and tyre-model-based tyre–road friction estimators to be used in production vehicles.