@inproceedings{0aee2ef9f9c243179a60da8c43b0b3b5,
title = "Optimization of fuzzy controller based on genetic algorithm",
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 = "Control strategy, Genetic algorithm, Hybrid electric vehicle, Optimization",
author = "Shichun Yang and Li Ming and Bin Xu and Bin Guo and Chuangao Zhu",
year = "2010",
doi = "10.1109/ISDEA.2010.159",
language = "英语",
isbn = "9780769542126",
series = "Proceedings - 2010 International Conference on Intelligent System Design and Engineering Application, ISDEA 2010",
publisher = "IEEE Computer Society",
pages = "21--28",
booktitle = "Proceedings - 2010 International Conference on Intelligent System Design and Engineering Application, ISDEA 2010",
address = "美国",
}