Enhancing Prognostics of PEM Fuel Cells with a Dual-Attention LSTM Network for Remaining Useful Life Estimation: A Deep Learning Model
Ahmed Darwish
Abstract:The Proton Exchange Membrane Fuel Cell (PEMFC) presents itself as a viable and effective technology to consider for transportation purposes. An essential aspect in the realm of electric vehicles is the crucial evaluation of the deterioration of the PEMFC stack. This paper proposed a data-driven deep learning framework that combines a Long Short-Term Memory (LSTM), self-attention, and scaled dot-product attention mechanism in order to enhance the precision of Remaining Useful Life (RUL) prediction for Proton Ex… Show more
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