A Shifting Strategy for Electric Commercial Vehicles Considering Mass and Gradient Estimation

作     者：Weiguang Zheng Junzhu Zhang Shanchao Wang Gaoshan Feng Xiaohong Xu Qiuxiang Ma 

作者机构：School of Mechanical and Electrical EngineeringGuilin University of Electronic TechnologyGuilin541004China Commercial Vehicle Technology CenterDongfeng Liuzhou Automobile Co.Ltd.Liuzhou545005China School of Mechanical and Automotive EngineeringGuangxi University of Science and TechnologyLiuzhou545005China 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2023年第137卷第10期

页      面：489-508页

核心收录：

学科分类：082304[工学-载运工具运用工程] 08[工学] 080204[工学-车辆工程] 0802[工学-机械工程] 0823[工学-交通运输工程] 

基　　金：funded by the Innovation-Driven Development Special Fund Project of Guangxi,Grant No.Guike AA22068060 the Science and Technology Planning Project of Liuzhou,Grant No.2021AAA0112 the Liudong Science and Technology Project,Grant No.20210117 

主　　题：EKF algorithm electric commercial vehicle vehicle mass road gradient comprehensive shifting strategy 

摘      要：The extended Kalman filter (EKF) algorithm and acceleration sensor measurements were used to identify vehiclemass and road gradient in the work. Four different states of fixed mass, variable mass, fixed slope and variableslope were set to simulate real-time working conditions, respectively. A comprehensive electric commercial vehicleshifting 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-timevehicle mass and road gradient quickly and accurately. The comprehensive shifting strategy formulated had thefollowing advantages, e.g., avoiding frequent shifting of vehicles up the hill, making full use ofmotor braking downthe hill, and improving the overall performance of vehicles.