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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Bibliographic Details
Published in:	Applied energy Vol. 313; p. 118835
Main Authors:	Benaggoune, Khaled, Yue, Meiling, Jemei, Samir, Zerhouni, Noureddine
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.05.2022 Elsevier
Subjects:	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
Online Access:	Get full text
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