Online Estimation of Vehicle Inertial Parameters for Improving Chassis Control Systems

Vehicle chassis control systems aim at increasing vehicle safety and performance, while ensuring superior passenger comfort. Nearly all control algorithms are sensitive to the inertial parameters of a vehicle. As the vehicle mass, the moments of inertia, and the centre of gravity (COG) position can change significantly during operation, an accurate online estimation of these properties could substantially improve the performance of an active system. This paper presents an innovative algorithm for the online estimation of the inertial parameters of a road-vehicle. Using low-frequent suspension displacement signals and suspension stiffness characteristics, the vehicle mass and horizontal COG position are estimated. A Monte Carlo method determines the most probable mass distribution. Based on the assigned passenger weight, anthropometric data sets allow to calculate the inertial properties of every passenger, eventually resulting in the inertial parameters of the complete, loaded vehicle. The accuracy of the proposed algorithm is validated by means of a test campaign on an accurate kinematics and compliance testrig.