A Hybrid Prognostic Method for Proton-Exchange-Membrane Fuel Cell with Decomposition Forecasting Framework Based on AEKF and LSTM

Durability and reliability are the major bottlenecks of the proton-exchange-membrane fuel cell (PEMFC) for large-scale commercial deployment. With the help of prognostic approaches, we can reduce its maintenance cost and maximize its lifetime. This paper proposes a hybrid prognostic method for PEMFC...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 23; H. 1; S. 166
Hauptverfasser:	Xia, Zetao, Wang, Yining, Ma, Longhua, Zhu, Yang, Li, Yongjie, Tao, Jili, Tian, Guanzhong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 24.12.2022 MDPI
Schlagworte:	Accuracy Aging Algorithms Decomposition degradation prediction Forecasting Fuel cell industry Fuel cells hybrid method Investigations Kalman filters Loess Machine learning Methods Neural networks Power electronics prognostics proton-exchange-membrane fuel cell remaining useful life Trends hybrid method remaining useful life degradation prediction proton-exchange-membrane fuel cell prognostics
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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