Real-Time Center of Gravity Estimation for Intelligent Connected Vehicle Based on HEKF-EKF

Abstract

The vehicle center of gravity estimation is the key technology to the vehicle active safety system in intelligent connected vehicles. In this study, an integrated estimation approach for center of gravity (CG) combining Huber Extended Kalman Filter and Extended Kalman Filter (HEKF-EKF) is proposed. First, HEKF algorithm is used to estimate the distance between the CG and the front axle at the current time. Then, the CG height obtained by HEKF and EKF algorithms is weighted to obtain the optimal estimate value. Finally, the results show that the algorithm’s estimation convergence time is 2 s, its longitudinal position estimation error is less than 2%, and its center of gravity height estimation error is less than 3%. The longitudinal and vertical positions of the vehicle CG can be accurately estimated using this method. This method can help advance the development of active safety technology.