A systematic approach for matching simulated and experimental polarization curves for a PEM fuel cell. (13th January 2020)

• Record Type: Journal Article
• Authors: Arif, Muhammad; Cheung, Sherman C.P.; Andrews, John
• Is Part Of: International journal of hydrogen energy. Volume 45:Number 3(2020)
• Page Start: 2206
• Page End: 2223
• Publication Date: 2020-01-13
• Subjects: Simulation modelling -- PEM fuel cells -- ANSYS PEM Fuel Cell Module -- Polarization curve -- Matching simulated and experimental curves; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2019.11.057
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: Matching simulated and experimental polarization curves is an essential step in the modelling of polymer electrolyte membrane (PEM) fuel cells, but the numerical values of many input parameters like exchange current densities, charge transfer coefficients, protonic conduction coefficient and water removal coefficient are hard to be found experimentally. In this paper, the influence of these input parameters on the performance of PEM fuel cells has been investigated using the ANSYS PEM Fuel Cell Module. The simulation results show how the exchange current densities and charge transfer coefficients influence the activation losses; membrane resistance and contact resistance between the different components of a fuel cell contribute to the ohmic losses; and the coefficient of liquid water removal affects the concentration losses. A systematic procedure to match a simulated polarization curve with an experimental curve is presented and illustrated by application to an experimental PEM fuel cell with 5 cm 2 active area. Highlights: Exchange current density and charge transfer coefficient affect activation losses. Effective water removal enhances PEM fuel cell performance at high currents. A systematic procedure is presented to match simulated and experimental V-j curves. Maximum percentage difference between simulation-4 and experimental data is 5%. The uniqueness of the best-fit input parameter values is discussed.