TY  - EJOU
AU  - Zheng, Weiguang 
AU  - Zhang, Junzhu 
AU  - Wang, Shanchao 
AU  - Feng, Gaoshan 
AU  - Xu, Xiaohong 
AU  - Ma, Qiuxiang 

TI  - A Shifting Strategy for Electric Commercial Vehicles Considering Mass and Gradient Estimation
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2023
VL  - 137
IS  - 1
SN  - 1526-1506

AB  - The extended Kalman filter (EKF) algorithm and acceleration sensor measurements were used to identify vehicle mass and road gradient in the work. Four different states of fixed mass, variable mass, fixed slope and variable slope were set to simulate real-time working conditions, respectively. A comprehensive electric commercial vehicle shifting strategy was formulated according to the identification results. The co-simulation results showed that, compared with the recursive least square (RLS) algorithm, the proposed algorithm could identify the real-time vehicle mass and road gradient quickly and accurately. The comprehensive shifting strategy formulated had the following advantages, e.g., avoiding frequent shifting of vehicles up the hill, making full use of motor braking down the hill, and improving the overall performance of vehicles.
KW  - EKF algorithm; electric commercial vehicle; vehicle mass; road gradient; comprehensive shifting strategy

DO  - 10.32604/cmes.2023.025169