Journal
JOURNAL OF POWER SOURCES
Volume 448, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.jpowsour.2019.227591
Keywords
Degradation trend; Stacked long short-term memory; Fuel cells
Funding
- Ministry of Science and Technology, Taiwan [MOST-107-2221-E011-100-MY3]
Ask authors/readers for more resources
Proton exchange membrane fuel cell (PEMFC) systems have numerous applications such as transportation, portable power generation, and military. In this study, we propose a stacked long-short term memory (S-LSTM) model for fitting the degradation of a PEMFC system. Moreover, the proposed model provides the remaining useful life (RUL) prediction. A stacked LSTM architecture with dropout parameters can improve the prediction accuracy of the fuel cell degradation. We optimize the hyper parameters of the S-LSTM model using a differential evolution algorithm. The ageing test conditions of two PEMFC systems are carried by a fixed current and a ripple current, respectively. The results indicate that the S-LSTM model outperforms the other models in the RUL prediction of the PEMFC degradation in terms of mean absolute percent error and root mean square error.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available