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	Forecast of Driving Load of Hybrid Electric Vehicles by using Discrete Cosine Transform and Support Vector Machine
	Yang, Han ; Huang, Xi ; Tan, Ying ; He, Xingui
	2008
关键词	KERNEL
英文摘要	As advances in green automotives, hybrid electric vehicle (HEV) has being given more and more attention in recent years. The power management control strategy of REV is the key problem that determines the efficiency and pollution emission level of the HEV, which requires the forecast of driving load situation of REV in advance. This paper proposes an efficient approach for forecasting the driving load of the HEV by using Discrete Cosine Transform (DCT) and Support Vector Machine (SVM). The DCT is used to extract features from raw data, and reduce the dimensionality of feature which will result in an efficient SVM classification. The SVM is used to classify the current driving load into one of five presetting levels of the driving load of the REV. In such way, we can predict the driving load efficiently and accurately, which leads to a reasonable control to the HEV and gives as a high efficiency and low emission level as possible. Finally, a number of experiments are conducted to verify the! validity of our proposed approach. Compared to current methods, our proposed approach gives a considerably promising performance through extensive experiments and comparison tests.; Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Engineering, Electrical & Electronic; CPCI-S(ISTP); 0
语种	英语
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/293469]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Yang, Han,Huang, Xi,Tan, Ying,et al. Forecast of Driving Load of Hybrid Electric Vehicles by using Discrete Cosine Transform and Support Vector Machine. 2008-01-01.

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