Digital Twin Approach for PEM Fuel Cell Degradation Prognostics Using Equivalent Electrical Circuit Model and Polarization Curves

This paper presents the development of a digital twin (DT) for predicting the degradation of proton exchange membrane fuel cells (PEMFCs). The digital twin integrates an electrical equivalent circuit model with polarization curve data from IEEE prognostics and health management (PHM). Various polarization data sets are considered to study the model parameter estimation and performance degradation over time. Moreover, the effects of various losses on the aging behavior of PEMFC are quantified. The variation of model parameters with time is modeled and incorporated by leveraging the curve-fitting technique to develop a digital twin for the PEMFC stack. The digital twin model is effectively used to generate the aging data and study or predict the aging behavior of PEMFC. The digital twin provides performance predictions and facilitates prognostics of PEMFC degradation, enabling optimized operation and maintenance strategies.