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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Bibliographic Details
Published in:Energy conversion and management Vol. 248; p. 114793
Main Authors: Raeesi, Mehrdad, Changizian, Sina, Ahmadi, Pouria, Khoshnevisan, Alireza
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 15.11.2021
Elsevier Science Ltd
Subjects:
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 Access:Get full text
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Summary:•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 degraded fuel cell vehicles.•Using real driving cycle for a better dynamic simulation. Fuel cell degradation is one of the main challenges of hydrogen fuel cell vehicles, which can be solved by robust prediction techniques like machine learning. In this research, a specific Proton-exchange membrane fuel cell stack is considered, and the experimental data are imported to predict the future behavior of the stack. Besides, four different prediction neural network algorithms are considered, and Deep Neural Network is selected. Furthermore, Simcenter Amesim software is used with the ability of dynamic simulation to calculate real-time fuel consumption, fuel cell degradation, and engine performance. Finally, to better understand how fuel cell degradation affects fuel consumption and life cycle emission, lifecycle assessment as a potential tool is carried out using GREET software. The results show that a degraded Proton-exchange membrane fuel cell stack can result in an increase in fuel consumption by 14.32 % in the New European driving cycle and 13.9 % in the FTP-75 driving cycle. The Life Cycle Assessment analysis results show that fuel cell degradation has a significant effect on fuel consumption and total emission. The results show that a fuel cell with a predicted degradation will emit 26.4 % more CO2 emissions than a Proton-exchange membrane fuel cell without degradation.
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ISSN:0196-8904
1879-2227
DOI:10.1016/j.enconman.2021.114793