Online estimation of inertial parameter for lightweight electric vehicle using dual unscented Kalman filter approach

Accurate knowledge of vehicle inertial parameters (e.g. vehicle mass and yaw moment of inertia) is essential to manage vehicle potential trajectories and improve vehicle active safety. For lightweight electric vehicles (LEVs), whose control performance of dynamics system can be substantially affected due to the drastic reduction of vehicle weights and body size, such knowledge is even more critical. This study proposes a dual unscented Kalman filter (DUKF) approach, where two UKFs run in parallel to simultaneously estimate vehicle states and parameters such as vehicle velocity, vehicle sideslip angle, and inertial parameters. The proposed method only utilises real-time measurements from torque information of in-wheel motor and sensors in a standard car. The four-wheel non-linear vehicle dynamics model considering payload variations is developed, local observability of the DUKF observer is analysed and derived via differential geometry theory. To address the non-linearities in vehicle dynamics, the DUKF and dual extended Kalman filter (DEKF) are also presented and compared. Simulations with various manoeuvres are carried out using the platform of MATLAB/Simulink-Carsim(R). Simulation results of MATLAB/Simulink-Carsim(R) show that the proposed DUKF method can effectively estimate inertial parameters of LEV under different payloads. Moreover, the investigation reveals that the proposed DUKF approach has better performance of estimating vehicle inertial parameters compared with the DEKF method.