Parameter Identification of a Quasi-3D PEM Fuel Cell Model by Numerical Optimization

Abstract

Polymer electrolyte membrane fuel cells (PEMFCs) supplied with green hydrogen from renewable sources are a promising technology for carbon dioxide-free energy conversion. Many mathematical models to describe and understand the internal processes have been developed to design more powerful and efficient PEMFCs. Parameterizing such models is challenging, but indis-pensable to predict the species transport and electrochemical conversion accurately. Many material parameters are unknown, or the measurement methods required to determine their values are ex-pensive, time-consuming, and destructive. This work shows the parameterization of a quasi-3D PEMFC model using measurements from a stack test stand and numerical optimization algorithms. Differential evolution and the Nelder–Mead simplex algorithm were used to optimize eight material parameters of the membrane, cathode catalyst layer (CCL), and gas diffusion layer (GDL). Measurements with different operating temperatures and gas inlet pressures were available for optimization and validation. Due to the low operating temperature of the stack, special attention was paid to the temperature dependent terms in the governing equations. Simulations with optimized parameters predicted the steady-state and transient behavior of the stack well. Therefore, valuable data for the characterization of the membrane, the CCL and GDL was created that can be used for more detailed CFD simulations in the future.