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Proceedings of 2009 International Workshop on Information Security and Application (IWISA 2009)

Qingdao, China, November 21-22, 2009

Editors: Feng Gao and Xijun Zhu

AP Catalog Number: AP-PROC-CS-09CN004

ISBN: 978-952-5726-06-0

Page(s): 457-460

Intelligent Energy Management for Parallel HEV Based on Driving Cycle Identification Using SVM

Zhang Liang, Zhang Xin, and Tian Yi

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Hybrid Electric Vehicles (HEV) offer the ability to significantly reduce fuel consumptions and emission. Management of energy is one of essential elements in the implementation of HEV. The parameters of HEV control strategy are always optimized on some one standardized driving cycle, but the different city has its own driving cycle. So the great advantage of parallel HEV is limited. This paper proposes an intelligent management for parallel HEV based on driving cycle identification using support vector machines (SVM). SVM is great in model identification. The intelligent energy management of parallel 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 SVM could further improve the fuel consumption and reduce emissions.

Index Terms

driving cycle sensitivity, support vector machine, control strategy, genetic

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