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

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.

Automated summary

The study uses prediction techniques such as machine learning to address the issue of fuel cell degradation in hydrogen fuel cell vehicles. By analyzing experimental data and utilizing deep neural network algorithms, the research predicts the impact of fuel cell degradation on fuel consumption and total emissions, conducting a life cycle assessment to evaluate the potential effects.