A Joint Vehicle Mass and Road Slope Estimation of Distributed Drive Electric Vehicles Considering Road Environment Factors

Abstract

This paper proposes a joint adaptive estimation method of vehicle mass and road slope considering road environment factors. This method aims at solving the problem that the accuracy of the existing vehicle mass estimation methods is easily affected by the road environment changes and the error between the calibration value of the resistance coefficient and the actual value. The vehicle mass estimation method in this paper takes into account road slope, tire rolling resistance coefficient and air resistance coefficient. The vehicle kinematics and longitudinal dynamics models are constructed by estimating the road slope in real time based on the recursive Kalman filter, and by estimating the rolling resistance and air resistance coefficients of the tire in real time based on the strong tracking extended Kalman filter. The vehicle longitudinal dynamics model is corrected in real time using the above parameter estimates, and then the vehicle mass is estimated in real time based on the recursive least square method with the forgetting factor. The sensitive parameters in the vehicle dynamics model are adaptively corrected according to the changes of the road environment, which effectively improves the accuracy and stability of the slope and vehicle mass estimation algorithm, and is applicable to a wide range of conditions.