With the advantages of high efficiency, light weight and non-pollution, Proton Exchange Membrane Fuel Cells (PEMFCs) can be used in portable devices, transportation and distributed power supply systems. Nevertheless, the durability and cost are two key barriers for their large-scale commercialization in light-duty vehicle transportation applications. It is necessary to predict the future State of Health (SoH) and future behaviors. Then the operating parameters can be optimized with time or Condition-based Maintenance (CBM) can be activated to extend its life. Prognostics have the ability to estimate the Remaining Useful Life (RUL) before the failure occurs. It seems to be a great solution to deal with the durable issue of PEMFCs. PEMFCs have wide range of applications. Besides, PEMFCs have the property of multi-physics, multi-scales, and nonlinearity. Moreover, the degradation phenomenon has a relationship with the mission profiles and the external disturbances. Then all the degradation mechanisms of the various fuel cells components can hardly be completely understood. Three kinds of prognostic methods are commonly distinguished: model-based, data-driven, and hybrid method. Developing novel and efficient methods to improve the prognostic accuracy, to decrease the computational burden, and to reinforce the robustness and dynamics are what should be done in the next steps.