Abstract
Abstract: To help a transition of prognostics approaches toward industries, it is necessary to show that they can be adapted in every situation. Nowadays, a lot of prognostics applications focus on energy sources, among them Proton Exchange Membrane Fuel Cells (PEMFC) can be cited. Due to their wide range of applications, different prognostics adaptations should be considered. Issues coming with PEMFC used for transportation are considered in this paper. Different time scales are involved, requiring a modification of the existing approaches. This paper proposes a solution to perform short-term and long-term predictions on a PEMFC stack used in a transportation application based on particle filters. After proposing different data reductions, the adapted particle filters configuration for this use case is determined. Accurate State of Health (SoH) estimations and predictions, with high coefficient of determination, are obtained. Behavior predictions are also performed and show promising results.
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