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Forecast of driving load of hybrid electric vehicles by using discrete cosine transform and Support Vector Machine

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4 Author(s)
Jian Yang ; State Key Laboratory of Machine Perception, Peking University, Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Beijing 100871, China ; Xi Huang ; Ying Tan ; Xingui He

As advances in green automotives, hybrid electric vehicle (BEV) has being given more and more attention in recent years. The power management control strategy of BEV is the key problem that determines the efficiency and pollution emission level of the BEV, which requires the forecast of driving load situation of BEV in advance. This paper proposes an efficient approach for forecasting the driving load of the BEV 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 BEV In such way, we can predict the driving load efficiently and accurately, which leads to a reasonable control to the BEV 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.

Published in:

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

Date of Conference:

1-8 June 2008