Title :
Energy management for a fuel cell hybrid electrical vehicle
Author :
Ibrahim, Mona ; Wimmer, Genevieve ; Jemei, Samir ; Hissel, Daniel
Author_Institution :
Math. Lab. of Besancon, Univ. of Franche Comte, Besancon, France
Abstract :
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.
Keywords :
energy management systems; fuel cell vehicles; hybrid electric vehicles; moving average processes; supercapacitors; time series; wavelet transforms; battery; energy management; frequency band; fuel cell hybrid electrical vehicle; power supply linking ultracapacitor; time series prediction; transform autoregressive integrated moving average method; wavelet denoising; wavelet transform; Energy management; Mathematical model; Predictive models; Time series analysis; Vehicles; Wavelet transforms; ARIMA; energy management; hybid electrical vehicles; prediction methods; wavelet transforms;
Conference_Titel :
Industrial Electronics Society, IECON 2014 - 40th Annual Conference of the IEEE
DOI :
10.1109/IECON.2014.7049092
