Health status analysis of PEMFCs based on degradation mechanism model and data-driven method

Abstract

Proton exchange membrane fuel cells (PEMFCs), as an environmentally friendly energy conversion device, are considered an important solution to achieve the net-zero pathway. However, the widespread commercialization of PEMFC is still hindered by limitations in reliability and durability. This article combines the degradation mechanism model with data-driven optimization algorithms to achieve degradation mechanisms and internal health state analysis of PEMFCs. Firstly, a semi-empirical degradation model for PEMFCs is constructed and validated with experimental data from the polarization curve. Afterward, based on the 4750 h degradation test data, the aging factors are fitted based on genetic algorithms, including leakage current, electrochemical active surface area (ECSA), ion impedance, contact resistance, oxygen diffusion coefficient, and concentration loss coefficient. The health status of key components is reflected by these aging factors. This article provides an in-depth analysis of the aging mechanism observed in PEMFCs during long-term operation, thereby establishing a fundamental understanding of the health status pertaining to crucial components within PEMFCs.