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.

燃料电池退化是氢燃料电池汽车的主要挑战之一，可以通过机器学习等稳健的预测技术来解决。在这项研究中，考虑了一个特定的质子交换膜燃料电池堆，并导入实验数据来预测电池堆的未来行为。此外，还考虑了四种不同的预测神经网络算法，并选择了深度神经网络。此外，Simcenter Amesim 软件具有动态仿真能力，可计算实时燃料消耗、燃料电池退化和发动机性能。最后，为了更好地了解燃料电池退化如何影响燃料消耗和生命周期排放，生命周期评估作为一种潜在工具使用 GREET 软件进行。结果表明，退化的质子膜燃料电池堆在新欧洲驾驶循环中可导致燃料消耗增加14.32%，在FTP-75驾驶循环中增加13.9%。生命周期评估分析结果表明，燃料电池退化对燃料消耗和总排放有显着影响。结果表明，具有预测退化的燃料电池将多排放 26.4% 的二氧化碳₂排放比质子交换膜燃料电池无退化。