ABSTRACT Proton exchange membrane fuel cells (PEMFCs) are a promising clean energy technology; however, their long‐term degradation remains a critical challenge, limiting their commercialization and operational efficiency. This paper presents a simplified semi‐empirical model for predicting degradation trends of PEMFC. The proposed model combines an equivalent electrical circuit framework with polarization curve data. The Levenberg–Marquardt (L–M) optimization algorithm is utilized to estimate the degradation model parameters across different polarization datasets over time, while empirical time‐dependent relationships are determined using curve‐fitting techniques. To evaluate the end of life of PEMFC, the model is applied under constant loading conditions (FC‐CL) and quasi‐dynamic loading (FC‐QDL) using the IEEE Prognostics and Health Management (PHM) dataset. In addition, aging data for the polarization curve is generated up to the estimated lifetime to analyze the evolution of polarization characteristics over time. Furthermore, the end‐of‐life (EOL) of PEMFC is analyzed, along with the impact of parameter uncertainty on EOL prediction. Finally, a comparative analysis of the proposed model with the long short‐term memory (LSTM)‐based machine learning approach is studied.
Kumar et al. (Sun,) studied this question.