NDLI: Research on parallel hybrid electric vehicle control strategy and GA optimization

Content Provider	IEEE Xplore Digital Library
Author	Liu Han Gao Jun-Tao
Copyright Year	2011
Description	Author affiliation: School of Automation and Information Engineering, Xi'an University of Technology, 710048, China (Liu Han; Gao Jun-Tao)
Abstract	In this paper, based on the deterministic rule-based control strategy, controller parameters are optimized by genetic algorithms for parallel hybrid electric vehicle. Compared with previous results, this approach can effectively reduce fuel consumption and emissions without sacrificing vehicle performance. Additionally, the contrast experiments between this approach and fuzzy control strategy have also been done. The fuel consumption and emissions of fuzzy control strategy is better than that of optimized deterministic rule-based control strategy. Because the deterministic rule-based control strategy can keep the battery constantly charging or discharging and the motor often work, the fuzzy control strategy often makes the engine work in high efficiency areas or low-emission zones, so it is worse than deterministic rule-based control strategy in terms of vehicle performance.
Starting Page	5082
Ending Page	5087
File Size	402826
Page Count	6
File Format	PDF
ISBN	9781457706776
ISSN	21612927
e-ISBN	9789881725592
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Fuzzy control Torque Parametric optimization GA System-on-a-chip Hybrid electric vehicles Fuels PHEV Control strategy Genetic algorithms
Content Type	Text
Resource Type	Article

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