Prediction of voltage degradation trend for a proton exchange membrane fuel cell city bus on roads

Abstract Prognostics and Health Management (PHM) is among the most significant and effective technologies to improve the durability of a proton exchange membrane (PEM) fuel cell system. This paper deals with the prediction issues of the degradation trend for PEM fuel cells equipped in a city bus. First, three aging parameters are extracted from a multi-parameter voltage model, and two of them are selected to represent the degradation of electronic and ionic resistance separately for the first time. Then the parameters are initialized by harmony search (HS) algorithm with an improved objective function, and updated by resorting to the particle filtering (PF) algorithm. Subsequently, Bayesian ridge regression (BRR) and Gaussian progress regression (GPR) are utilized to establish the relationship between the operating time and aging parameters. We categorized the input of the regression models into two classes: the total operating time and the cumulative time of four operating conditions. The results indicate that the latter performs better than the former in characterizing the future trend of aging parameters. Moreover, it is observed that BRR is more attractive since its computational time is far less than that of GPR while the mean absolute error (MAE) is no more than 8.5 mV.