Title :
Optimization of Fuzzy Controller Based on Genetic Algorithm
Author :
Yang, Shichun ; Li, Ming ; Xu, Bin ; Guo, Bin ; Zhu, Chuangao
Author_Institution :
Sch. of Transp. Sci. & Eng., Beihang Univ., Beijing, China
Abstract :
The power required to drive the Hybrid electric generated by combination of internal combustion engine and electric motor. To make the power train of the hybrid electric vehicle as efficient as possible, proper management of the different energy elements is essential. This task is completed by the hybrid electric vehicle control strategy. A genetic-fuzzy control strategy is proposed for Hybrid electric vehicle in this paper. The genetic-fuzzy controller is a fuzzy logic controller that is tuned by a genetic algorithm. The objective of optimization is to decrease fuel consumption and emissions in two different test cycles NEDC and UDDS, the results demonstrate that compared with fuzzy logic control strategy, genetic-fuzzy control strategy can get better control effects. The effectiveness of this approach can reduce fuel consumption and emissions without sacrificing vehicle performance.
Keywords :
electric motors; fuzzy control; genetic algorithms; hybrid electric vehicles; internal combustion engines; machine control; power transmission (mechanical); electric motor; fuzzy logic controller; genetic algorithm; genetic fuzzy control strategy; hybrid electric vehicle control strategy; internal combustion engine; power train; Fuels; Fuzzy control; Gallium; Genetic algorithms; Genetics; Hybrid electric vehicles; Optimization; Genetic algorithm; Hybrid Electric Vehicle; Optimization; control strategy;
Conference_Titel :
Intelligent System Design and Engineering Application (ISDEA), 2010 International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4244-8333-4
DOI :
10.1109/ISDEA.2010.159