A novel nonlinear observer for inertial parameters of lightweight electric vehicle through adaptive dual unscented Kalman filter

Abstract

Due to the drastic reduction of vehicle weights and body size of lightweight electric vehicles (LEVs), the effects of inertia parameter variation in LEV control system become much more pronounced and have to be systematically estimated. This paper proposes a novel nonlinear observer operates in parallel with an adaptive dual unscented Kalman filter (ADUKF) to synchronously estimate LEV inertial parameters and fundamental states including the vehicle mass, yaw moment of inertia, as well as vehicle velocity, vehicle sideslip angle. The observer only uses real-time measurements from in-wheel motor and other multi-sensors in a standard car. The LEV dynamics estimation model considering payload variations is established, local observability of ADUKF observer is derived via differential geometry theory. The simulation results with a high-fidelity, CarSim®, full-vehicle model show that the proposed ADUKF observer can effectively estimate vehicle inertial parameters and states under different payloads.