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 to achieve robust long-term prediction.
By clicking the following link, you can find this paper:
https://www.sciencedirect.com/science/article/abs/pii/S0306261921012307?via%3Dihub