Performance analysis of a degraded PEM fuel cell stack for hydrogen passenger vehicles based on machine learning algorithms in real driving conditions

•Implementation of several ML techniques to investigate performance of degraded FCV.•The Deep Neural Network algorithm has the most accurate prediction among the other.•Dynamic simulation of a fresh & degraded FCV considering environmental aspects.•The life cycle assessment for the fresh and deg...

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Veröffentlicht in:	Energy conversion and management Jg. 248; S. 114793
Hauptverfasser:	Raeesi, Mehrdad, Changizian, Sina, Ahmadi, Pouria, Khoshnevisan, Alireza
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 15.11.2021 Elsevier Science Ltd
Schlagworte:	administrative management Algorithms Artificial neural networks Carbon dioxide Carbon dioxide emissions Computer programs computer software Deep neural network algorithms Degradation Degradation effects Driving conditions Electric vehicles Emission analysis Emissions energy conversion energy use and consumption Fuel cells Fuel consumption Fuel technology hydrogen hydrogen fuel cells Hydrogen fuels Learning algorithms Life cycle analysis Life cycle assessment Life cycles Machine learning Neural networks prediction Proton exchange membrane fuel cells Proton-exchange membrane fuel cell Protons Software Proton-exchange membrane fuel cell Life cycle assessment Degradation effects Machine learning Deep neural network algorithms
ISSN:	0196-8904, 1879-2227
Online-Zugang:	Volltext
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