PEMFC state-of-health estimation using a model-based state Bayesian observer under an automotive load profile
Abstract
An estimation of the actual State of health (SoH) of the Proton Exchange Membrane Fuel Cell (PEMFC) based on a Bayesian observer model is presented. The observer model considers a degradation model which describes the electrodes platinum dissolution to characterize the energy source deterioration under dynamic operation conditions in real time. This observer model is carried out using an Unscented Kalman Filter to correct and update the SoH estimation. The proposed method is evaluated using a 1000h durability test under a dynamic highly load profile, similar to an automotive load cycling, from a 20 cells PEMFC stack.