Method of Electric Powertrain Matching for Battery-powered Electric Cars

作     者：NING Guobao XIONG Lu ZHANG Lijun YU Zhuoping 

作者机构：Clean Energy Automotive Engineering Center Tongji University 

出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报（英文版）)

年 卷 期：2013年第26卷第3期

页      面：483-491页

核心收录：

学科分类：0817[工学-化学工程与技术] 08[工学] 0807[工学-动力工程及工程热物理] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0801[工学-力学（可授工学、理学学位）] 

基　　金：supported by National Basic Research Program of China(973 Program, Grant No. 2011CB711200) National Natural Science Foundation of China (Grant No. 51105278) 

主　　题：battery-powered electric vehicle electric powertrain electric driving system energy storage system optimization design 

摘      要：The current match method of electric powertrain still makes use of longitudinal dynamics, which can’t realize maximum capacity for on-board energy storage unit and can’t reach lowest equivalent fuel consumption as well. Another match method focuses on improving available space considering reasonable layout of vehicle to enlarge rated energy capacity for on-board energy storage unit, which can keep the longitudinal dynamics performance almost unchanged but can’t reach lowest fuel consumption. Considering the characteristics of driving motor, method of electric powertrain matching utilizing conventional longitudinal dynamics for driving system and cut-and-try method for energy storage system is proposed for passenger cars converted from traditional ones. Through combining the utilization of vehicle space which contributes to the on-board energy amount, vehicle longitudinal performance requirements, vehicle equivalent fuel consumption level, passive safety requirements and maximum driving range requirement together, a comprehensive optimal match method of electric powertrain for battery-powered electric vehicle is raised. In simulation, the vehicle model and match method is built in Matlab/simulink, and the Environmental Protection Agency (EPA) Urban Dynamometer Driving Schedule (UDDS) is chosen as a test condition. The simulation results show that 2.62% of regenerative energy and 2% of energy storage efficiency are increased relative to the traditional method. The research conclusions provide theoretical and practical solutions for electric powertrain matching for modern battery-powered electric vehicles especially for those converted from traditional ones, and further enhance dynamics of electric vehicles.