Prognostics of Proton Exchange Membrane Fuel Cells Using A Model-based Method

Proton exchange membrane fuel cells (PEMFCs) are becoming one of the most popular future power technologies with the advantages including low operating temperature, high power conversion efficiency, and zero pollution. However, there are still many shortcomings restricting the large scale commercialization of PEMFCs, such as the short lifetime and high cost. Accurate prognostics of PEMFCs are very significant for its life and cost management. This paper presents an efficient semi-empirical model-based prognostics method estimating the health state and the remaining useful life of PEMFCs based on the adaptive unscented Kalman filter (AUKF) algorithm. AUKF algorithm can automatically adjust the system process covariance and observation covariance according to the estimation residuals, and it also solves the initial parameters setting problem of conventional UKF algorithm. Finally, simulation results demonstrate that the proposed model-based prognostics method using the AUKF algorithm has better prognostics performance than using the UKF algorithm. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.