Our paper entitled « Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells » has been published in Applied Energy. In this study, we tried to extract the fuel cell health indicator in dynamic operating conditions via the parameter identification of a degradation&dynamic input-output model. In addition, we also investigate the symbolic-type deep-learning … Lire la suite

Paper « Degradation Identification and Prognostics of Proton Exchange Membrane Fuel Cell Under Dynamic Load », first-authored by Meiling has been accepted by Control Engineering Practice