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Time filter

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Ibrahim M.,Laboratory of Mathematics of Besancon | Ibrahim M.,University of Franche Comte | Jemei S.,University of Franche Comte | Wimmer G.,Laboratory of Mathematics of Besancon | And 4 more authors.
International Journal of Hydrogen Energy | Year: 2015

The Wavelet Transform (WT) is a mathematical method used to represent a given signal in different scales. This method is also used to extract the different frequency bands (or different frequency components) from a signal.In this paper, wavelet transform in the domain of energy management of Electrical Vehicles (EV) is considered. A method for choosing the mother wavelet and the decomposition level will be presented.Taking into account the damages caused by the extreme variations of the power demand on the power sources, this paper shows how Wavelet Transform can help eliminating this negative impact. © 2015 Hydrogen Energy Publications, LLC. Source


Ibrahim M.,Laboratory of Mathematics of Besancon | Ibrahim M.,University of Franche Comte | Jemei S.,University of Franche Comte | Wimmer G.,Laboratory of Mathematics of Besancon | Hissel D.,University of Franche Comte
Electric Power Systems Research | Year: 2016

Hybrid electric vehicles are one of the most promising solutions for reducing pollution and fuel consumption. However, their propulsion system comprises a number of different onboard power sources with different dynamic characteristics, meaning that some strategy is required for sharing power between them that takes their characteristics into account. In this paper, a new real time energy management strategy for battery/ultra-capacitor hybrid vehicles is proposed. This strategy is based on sharing the total power between the onboard power systems, namely the battery and the ultra-capacitors, using a Nonlinear Auto-Regressive Neural Network (NARNN) as a time series prediction model, and Discrete Wavelet Transform (DWT) as a time-frequency filter. The objective of this strategy is to lengthen the life of the battery. We simulated this new strategy using actual data from a military hybrid vehicle. The results were found to be promising and show the robustness of the proposed method. 2016 Elsevier B.V. All rights reserved. Source


Ibrahim M.,Laboratory of Mathematics of Besancon | Ibrahim M.,University of Franche Comte | Antoni U.,EIfER | Steiner N.Y.,University of Franche Comte | And 6 more authors.
Energy Procedia | Year: 2015

In order to exploit all the benefits from the Proton Exchange Membrane Fuel Cell (PEMFC) technology and to gain a deeper understanding of operating faults during fuel cell operations, Investigation of the origins of faults is necessary. In this work, a diagnosis approach consisting of a method using signal-based pattern recognition is proposed. It is aimed at a minimization of efforts and costs in acquisition and evaluation of data for diagnostic purposes. All information needed to locate the faults is drawn from the recorded fuel cell output voltage, since certain phenomena leave characteristic patterns in the voltage signal. A signal analysis tool, namely the Wavelet Transform (WT), is employed to identify different patterns or faults signatures. The approach has been applied to voltage data recorded on a PEMFC suffering from dysfunctions related to inappropriate humidity levels inside the cell (two different faults are simulated: flooding and drying out). Characteristic features in the output voltage signals were outlined, so a distinction of several states of health was accomplished. The results show the efficiency of the proposed approach, and the WT can be considered as a reliable method to localize the dysfunctions. A comparison between the Discrete Wavelet Transform (DWT) and the Continuous Wavelet Transform (DWT) has shown that the DWT is more efficient in detecting and localizing faults in fuel cells. © 2015 The Authors. Published by Elsevier Ltd. Source

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