Energy Management of Dual-Source Propelled Electric Vehicle using Fuzzy Controller Optimized via Genetic Algorithm

被引：0

作者：

Arani, S. Khoobi ^{[1
,2
]}

Niasar, A. Halvaei ^{[1
]}

Zadeh, A. Haji ^{[3
]}

机构：

[1] Univ Kashan, Dept Elect & Comp Engn, Kashan, Iran

[2] Univ Shahrood, Dept Elect Engn, Shahrood, Iran

[3] Aalborg Univ, Dept Energy Technol, Aalborg, Denmark

来源：

2016 7TH POWER ELECTRONICS AND DRIVE SYSTEMS & TECHNOLOGIES CONFERENCE (PEDSTC) | 2016年

关键词：

Electric Vehicle (EV); Energy Management; Fuzzy Controller; Genetic Algorithm (GA); ADVISOR; CONTROL STRATEGY; HYBRID; OPERATOR;

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：

Energy and power distribution between multiple energy sources of electric vehicles (EVs) is the main challenge to achieve optimum performance from EV. Fuzzy inference systems are powerful tools due to nonlinearity and uncertainties of EV system. Design of fuzzy controllers for energy management of EV relies too much on the expert experience and it may lead to suboptimal performance. This paper develops an optimized fuzzy controller using genetic algorithm (GA) for an electric vehicle equipped with two power bank including battery and supercapacitor. The model of EV and optimized fuzzy controller are simulated in ADVISOR software. Developed method has been implemented on standard driving cycles and simulation results show the decrease on consumed power by developed controller compared with standard fuzzy controller.

引用

页码：338 / 343

页数：6

共 30 条

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