Model-Based Vehicle Roll Moment of Inertia Estimation

A variety of control functions are used in modern vehicles to stabilize the vehicle dynamics. These can be improved with precise information of time-varying parameters. The vehicle mass, center of gravity height and the roll moment of inertia are significant for vehicle roll dynamics as they characterize the static and dynamic behavior of the roll motion. These parameters not only affect the driving behavior but can also increase the risk of rollover. Since the roll moment of inertia is particularly significant for transient roll dynamics, the main contribution of this paper is to develop a model-based estimation algorithm to estimate the roll moment of inertia. This paper therefore presents an Unscented Kalman Filter for a simultaneous state and parameter estimation. By using a nonlinear vehicle model which represents the roll, pitch and vertical dynamics, the effects of the center of gravity height and additional masses on the inertia are taken into account. In order to improve the estimation results, an activation condition based on a linear single track model and an underlying observability analysis is presented. Based on that, a precise parameter estimation with a deviation of less than 5 % to the nominal parameter is achieved.