@Article{cmes.2023.025169,
AUTHOR = {Weiguang Zheng, Junzhu Zhang, Shanchao Wang, Gaoshan Feng, Xiaohong Xu, Qiuxiang Ma},
TITLE = {A Shifting Strategy for Electric Commercial Vehicles Considering Mass and Gradient Estimation},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {1},
PAGES = {489--508},
URL = {http://www.techscience.com/CMES/v137n1/52333},
ISSN = {1526-1506},
ABSTRACT = {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.},
DOI = {10.32604/cmes.2023.025169}
}