An Adaptive Energy Management System for Electric Vehicles Based on Driving Cycle Identification and Wavelet TransformReportar como inadecuado




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State Key Laboratory of Automotive Simulation and Control, Jilin University, 5988 People Street, Changchun 130022, China



These authors contributed equally to this work.





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Academic Editor: K.T. Chau

Abstract Since driving cycle greatly affects load power demand, driving cycle identification DCI is proposed to predict power demand that can be expected to prepare for the power distribution between battery and supercapacitor. The DCI is developed based on a learning vector quantization LVQ neural network method, which is assessed in both training and validation based on the statistical data obtained from six standard driving cycles. In order to ensure network accuracy, characteristic parameter and slide time window, which are two important factors ensuring the network accuracy for onboard hybrid energy storage system HESS applications in electric vehicles, are discussed and designed. Based on the identification results, Multi-level Haar wavelet transform Haar-WT is proposed for allocating the high frequency components of power demand into the supercapacitor which could damage battery lifetime and the corresponding low frequency components into the battery system. The proposed energy management system can better increase system efficiency and battery lifetime compared with the conventional sole frequency control. The advantages are demonstrated based on a randomly generated driving cycle from the standard driving cycle library via simulation. View Full-Text

Keywords: electric vehicle; energy management system; driving cycle identification; Haar wavelet transform electric vehicle; energy management system; driving cycle identification; Haar wavelet transform





Autor: Qiao Zhang † and Weiwen Deng †,*

Fuente: http://mdpi.com/



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