DocumentCode
2906224
Title
Intelligent Energy Management Based on Driving Cycle Identification Using Fuzzy Neural Network
Author
Yi, Tian ; Xin, Zhang ; Liang, Zhang ; Xinn, Zhang
Author_Institution
Sch. of Mech. Electr. & Control Eng., Beijing Jiaotong Univ., Beijing, China
Volume
2
fYear
2009
fDate
12-14 Dec. 2009
Firstpage
501
Lastpage
504
Abstract
Hybrid electric vehicles (HEV) is becoming the important develop tendency of the vehicle for its better fuel economy and emission. The parameters of HEV control strategy is always optimized on some one standardized driving cycle, but the different city have its own driving cycle. So the great advantage of HEV is limited. This paper proposes an intelligent management for parallel HEV based on driving cycle identification using fuzzy neural network. Fuzzy neural network is great in model identification. The intelligent energy management of HEV identifies the driving cycle and changes the parameters of the control strategy. The applicability of the proposed intelligent control system is confirmed by simulation examples. The simulation results show that the control strategy based on driving cycle identification using fuzzy neural network could further improve the fuel consumption and reduce emissions.
Keywords
energy management systems; fuel economy; fuzzy control; fuzzy neural nets; hybrid electric vehicles; neurocontrollers; driving cycle identification; fuel economy; fuel emission; fuzzy neural network; hybrid electric vehicle control strategy; intelligent control system; intelligent energy management; parallel hybrid electric vehicle; Battery powered vehicles; Competitive intelligence; Energy management; Engines; Fuzzy control; Fuzzy neural networks; Hybrid electric vehicles; Intelligent networks; Torque; Vehicle driving; control strategy; driving cycle; fuzzy neural network; genetic;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Design, 2009. ISCID '09. Second International Symposium on
Conference_Location
Changsha
Print_ISBN
978-0-7695-3865-5
Type
conf
DOI
10.1109/ISCID.2009.271
Filename
5368792
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