Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm

被引：2

作者：

Zhu, Tianjun ^{[1
,3
]}

Li, Bin ^{[2
]}

Zong, Changfu ^{[1
]}

Wu, Yang ^{[3
]}

机构：

[1] ZhaoQing Univ, Dept Elect Informat & Elect Engn, Zhaoqing 526061, Peoples R China

[2] Concordia Univ, CONCAVE Res Ctr, Dept Mech & Ind Engn, Montreal, PQ H3G 1M8, Canada

[3] Hebei Univ Engn, Coll Mech & Equipment Engn, Handan 056038, Peoples R China

来源：

2ND INTERNATIONAL CONFERENCE ON AUTOMATION, CONTROL AND ROBOTICS ENGINEERING (CACRE 2017) | 2017年 / 235卷

关键词：

D O I：

10.1088/1757-899X/235/1/012013

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Hybrid electric vehicles (HEV), compared with conventional vehicles, have complex structures and more component parameters. If variables optimization designs are carried on all these parameters, it will increase the difficulty and the convergence of algorithm program, so this paper chooses the parameters which has a major influence on the vehicle fuel consumption to make it all work at maximum efficiency. First, HEV powertrain components modelling are built. Second, taking a tandem hybrid structure as an example, genetic algorithm is used in this paper to optimize fuel consumption and emissions. Simulation results in ADVISOR verify the feasibility of the proposed genetic optimization algorithm.

引用

页数：6

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