DocumentCode :
2657277
Title :
The application of fuzzy-neural network on control strategy of hybrid vehicles
Author :
Rongguang, Chen ; Chunsheng, Li ; Xia, Meng ; Yongguang, Yu
Author_Institution :
Dept. of Electron. Inf. Eng., Beihang Univ., Beijing
fYear :
2008
fDate :
16-18 July 2008
Firstpage :
281
Lastpage :
284
Abstract :
In order to increase the fuel economy and decrease the emissions of hybrid vehicles, firstly a fuzzy logic control system is presented in this paper. In parallel hybrid vehicles, the whole required torque comes from internal combustion engine and motor engine respectively. Based on the desired torque for driving and state of charge, the fuzzy logic control system determines how the power splits between the dual sources, which is the key point for hybrid vehicles. Then, adaptive neural-fuzzy inference system (ANFIS) method is applied to optimize fuzzy logic control system based on the data of driving cycle. The main contribution of this paper is well application of fuzzy-neural network to improve original control system, which minimized the fuel consumption and emissions. The simulation results show very good performance of the proposed method.
Keywords :
adaptive control; automobiles; fuzzy control; fuzzy neural nets; fuzzy reasoning; hybrid electric vehicles; internal combustion engines; neurocontrollers; torque; adaptive neural-fuzzy inference system; control strategy; fuzzy logic control; fuzzy-neural network; internal combustion engine; motor engine; parallel hybrid vehicles; torque; Adaptive control; Adaptive systems; Control systems; Fuel economy; Fuzzy logic; Internal combustion engines; Optimization methods; Programmable control; Torque control; Vehicle driving; Control strategy; Fuzzy logic controller; Hybrid electric vehicles;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference, 2008. CCC 2008. 27th Chinese
Conference_Location :
Kunming
Print_ISBN :
978-7-900719-70-6
Electronic_ISBN :
978-7-900719-70-6
Type :
conf
DOI :
10.1109/CHICC.2008.4604992
Filename :
4604992
Link To Document :
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