Optimization of Fuzzy Control Strategy for Hybrid Electric Bus Based on Ant Colony Algorithm of Genetic Method

被引：0

作者：

Yin, An-dong ^{[1
]}

Zhao, Han

Zhang, Hui

机构：

[1] Hefei Univ Technol, Sch Machinery & Automobile Engn, Tunxi Rd 193, Hefei 230009, Peoples R China

来源：

25TH WORLD BATTERY, HYBRID AND FUEL CELL ELECTRIC VEHICLE SYMPOSIUM AND EXHIBITION PROCEEDINGS, VOLS 1 & 2 | 2010年

关键词：

hybrid; ant colony algorithm of genetic; fuzzy control; optimization;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The object is a Optimization of Fuzzy Control Strategy for a parallel hybrid electric bus. In this paper, a new algorithm is presented by using the ant colony algorithm based on genetic method (ACG) to solve the optimization problem of the fuzzy control strategy. It has many advantages, comparing the method with traditional genetic algorithm (GA) and ant colony algorithm (ACA).

引用

页码：360 / 364

页数：5

共 6 条

[1] Antoni S, 2001, HYBRID ELECT VEHICLE
[2] [丁建立 Ding Jianli], 2003, [计算机研究与发展, Journal of Computer Research and Development], V40, P1351
[3] Montazeri-Gh M, 2006, J FRANKLIN I, V343, P420, DOI 10.1016/j.jfranklin.2006.02.015
[4] Control strategies for hybrid electric vehicles: Evolution, classification, comparison, and future trends
Salmasi, Farzad Rajaei
[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2007, 56 (05) : 2393 - 2404
[5] WU Jing-bo, 2009, VEHICLE ENGINE, P44
[6] Zi Xinyun, 2008, Journal of Wuhan University of Technology (Information & Management Engineering), V30, P561

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