Vehicle mass estimation based on vehicle vertical dynamics using a multi-model filter

Abstract

Vehicle mass estimation is an important task to compute the input parametrization for various advanced driver assistance systems. Further, detecting when a trailer is present or even the mass distribution between vehicle and trailer is of interest for various systems. Thus, we discuss the influence between vehicle and trailer of different approaches and finally yield the mass distribution over the vehicle-trailer-combination by linking mass estimates from longitudinal and vertical dynamics. This work investigates a multi-model approach for vehicle mass estimation based on common sensor signals for vertical dynamics as they are available in modern vehicle suspension systems with no need of a calibrated reference. For tracking the suspension dynamics, we partly use a Kalman filter with a linear dynamic system matrix. However, it is not feasible to gain consistent filter behavior for all possible mass hypotheses. Thus, we apply modifications to the common multi-model approach and define an evaluation function for the model probabilities to overcome the consistency issue and speed up computation time.