A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell

Fuel cell technology has been rapidly developed in the last decade owing to its clean characteristic and high efficiency. Proton exchange membrane fuel cells (PEMFCs) are increasingly used in transportation applications and small stationary applications; however, the cost and the unsatisfying durabi...

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Vydané v:	Applied energy Ročník 313; s. 118835
Hlavní autori:	Benaggoune, Khaled, Yue, Meiling, Jemei, Samir, Zerhouni, Noureddine
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.05.2022 Elsevier
Predmet:	Automatic commercialization Computer Science Convolutional neural network Data Structures and Algorithms durability Electric power energy Engineering Sciences Fluid mechanics fuel cells markets Mechanics neural networks Physics prediction Prognostics Proton membrane exchange fuel cell Thermics Time series prediction uncertainty Proton membrane exchange fuel cell Time series prediction Convolutional neural network Prognostics
ISSN:	0306-2619, 1872-9118
On-line prístup:	Získať plný text
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