Energy Management for a Fuel Cell Hybrid Electrical Vehicle
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | Energy Management for a Fuel Cell Hybrid Electrical Vehicle |
| Type de publication | Conference Paper |
| Year of Publication | 2014 |
| Auteurs | Ibrahim M, Wimmer G, Jemei S, Hissel D |
| Conference Name | IECON 2014 - 40TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY |
| Publisher | Inst Elect & Elect Engineers; IEEE Ind Elect Soc |
| Conference Location | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| ISBN Number | 978-1-4799-4032-5 |
| Mots-clés | ARIMA, Energy management, hybid electrical vehicles, prediction methods, wavelet transforms |
| Résumé | In order to perform an energy management strategy in hybrid electrical vehicles containing fuel cells, based on a power supply linking ultra-capacitors, batteries and fuel cells, time series prediction based on wavelet transform and auto-regressive integrated moving average is proposed in this paper. By wavelet denoising, the noise is removed from a part of the signal, by the auto-regressive integrated moving average method; a modeling and a prediction are done and thanks to the wavelet transform, the different frequency bands existing in the signal are attributed to the different power sources on board. The low frequency signal is attributed to the fuel cell and/or the batteries and the high frequency signal to the UC. Simulation results show the efficiency of the proposed method. |