Browse Publications Technical Papers 2020-01-1174
2020-04-14

Effect of geometry variations in a polymer electrolyte membrane fuel cell 2020-01-1174

Water transport at high current densities is of main concern for polymer electrolyte membrane fuel cells. The water content of the membrane has to be high enough to provide maximum electrical conductivity and thus optimal stack performance. Dry-out may also lead to membrane degradation. However, a too high level of humidity leads to cell flooding, blocking the air and fuel flows to the catalyst sites and thus the reactions, resulting in a drop in efficiency. Fuel cells water transport physics requires further investigation due to its complexity [1,2] and numerical modelling can improve the fundamental understanding of the phenomena. In this work, an optimization algorithm is used to optimize a fuel cells geometry to improve the temperature distribution and the pressure drop. In addition, the effect of the several geometric configurations on the water management is discussed. The PEM fuel cell is modelled in Siemens Simcenter STAR-CCM+ [3]. Anode and cathode GDL and catalyst layers are modelled as porous media, with electrochemical reactions in the catalyst layer. The membrane is modelled as a solid block including proton and water transport with electro-osmotic drag as well as ohmic heating. A two-phase approach is used to model the gas mixture and liquid water transport in the catalyst layer, GDL and channels. The calibration is performed with the SHERPA algorithm [4] that simultaneously uses several optimization algorithms to increase robustness and efficiency. A study of the effect of catalyst layer thickness on the current-voltage characteristics is also provided. [1] Tabuchi et al. Effects of heat and water transport on the performance of polymer electrolyte membrane fuel cell under high current density operation. Electroch Acta (2010) [2] Kotaka et al. Impact of Interfacial Water Transport in PEMFCs on Cell Performance. Electroch Acta (2014) [3] STAR-CCM+ User Guide. Version 13.06. (2018) [4] Chase N et al. A Benchmark Study of Optimization Search Algorithms. Red Cedar Technology (2018)

SAE MOBILUS

Subscribers can view annotate, and download all of SAE's content. Learn More »

Access SAE MOBILUS »

Attention: This item is not yet published. Pre-Order to be notified, via email, when it becomes available.
Members save up to 18% off list price.
Login to see discount.
Special Offer: With TechSelect, you decide what SAE Technical Papers you need, when you need them, and how much you want to pay.
X