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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Vydáno v:	Sensors (Basel, Switzerland) Ročník 23; číslo 1; s. 166
Hlavní autoři:	Xia, Zetao, Wang, Yining, Ma, Longhua, Zhu, Yang, Li, Yongjie, Tao, Jili, Tian, Guanzhong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 24.12.2022 MDPI
Témata:	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
On-line přístup:	Získat plný text
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