Remaining Useful Life Prediction for Proton Exchange Membrane Fuel Cell Using Stochastic Fusion Filtering

Abstract With the advantages of high power generation efficiency, low pollution and noise, Proton exchange membrane fuel cell (PEMFC) is regarded as a promising energy source for electric vehicles and stationary power stations. However, performance degradation is inevitable when PEMFC is in operating, which makes the estimation of remaining useful life (RUL) a crucial problem to assure the stability and security of PEMFC. In this paper, the RUL of PEMFC is estimated by stochastic fusion filtering approach with the dataset provided in IEEE PHM 2014 Data Challenge. The total voltage dataset as well as each cell degradation dataset are fused to get more accurate results. The simulation results demonstrate the good characteristics of this method.