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• W3204144049 endingPage "114793" @default.
• W3204144049 startingPage "114793" @default.
• W3204144049 abstract "• 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 CO 2 emissions than a Proton-exchange membrane fuel cell without degradation." @default.
• W3204144049 created "2021-10-11" @default.
• W3204144049 creator A5004412317 @default.
• W3204144049 creator A5047672152 @default.
• W3204144049 creator A5056513469 @default.
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• W3204144049 date "2021-11-01" @default.
• W3204144049 modified "2023-10-17" @default.
• W3204144049 title "Performance analysis of a degraded PEM fuel cell stack for hydrogen passenger vehicles based on machine learning algorithms in real driving conditions" @default.
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• W3204144049 doi "https://doi.org/10.1016/j.enconman.2021.114793" @default.
• W3204144049 hasPublicationYear "2021" @default.
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