Real-Time Parameter Estimation of a Fuel Cell for Remaining Useful Life Assessment

Abstract

This article presents a real-time parameter estimation of a proton exchange membrane fuel cell (PEMFC). The proposed strategy estimates online the PEMFC's resistance, since it is directly correlated to its remaining useful life assessment. The estimation of the PEMFC's parameters is a difficult task to undertake due to various uncertainties, like temperature and aging, that lead to a drift in parameters and limit the performance of the overall energy system. Therefore, online system identification is essential to track online the PEMFC's time-varying parameters. Unlike other identification techniques, the proposed strategy is based on a simple yet accurate PEMFC's model and adjusts its parameters in real-time using a Lyapunov-based adaptation law, which yields guaranteed stability. Experiments are conducted on a 500-W Horizon PEMFC and results along with a comparison against the well-known Kalman filter highlight the effectiveness of the proposed approach, which is instrumental for its numerous applications, such as the energy management of hybrid fuel cell vehicles.