Recent Advancements in Polysulfone Based Membranes for Fuel Cell (PEMFCs, DMFCs and AMFCs) Applications: A Critical Review

In recent years, ion electrolyte membranes (IEMs) preparation and properties have attracted fabulous attention in fuel cell usages owing to its high ionic conductivity and chemical resistance. Currently, perfluorinatedsulfonicacid (PFSA) membrane has been widely employed in the membrane industry in polymer electrolyte membrane fuel cells (PEMFCs); however, NafionTM suffers reduced proton conductivity at a higher temperature, requiring noble metal catalyst (Pt, Ru, and Pt-Ru), and catalyst poisoning by CO. Non-fluorinated polymers are a promising substitute. Polysulfone (PSU) is an aromatic polymer with excellent characteristics that have attracted membrane scientists in recent years. The present review provides an up-to-date development of PSU based electrolyte membranes and its composites for PEMFCs, alkaline membrane fuel cells (AMFCs), and direct methanol fuel cells (DMFCs) application. Various fillers encapsulated in the PEM/AEM moiety are appraised according to their preliminary characteristics and their plausible outcome on PEMFC/DMFC/AMFC. The key issues associated with enhancing the ionic conductivity and chemical stability have been elucidated as well. Furthermore, this review addresses the current tasks, and forthcoming directions are briefly summarized of PEM/AEMs for PEMFCs, DMFCs, AMFCs.

Multi-Variate Optimization of Polymer Electrolyte Membrane Fuel Cells in Consideration of Effects of GDL Compression and Intrusion

This study applies a comprehensive surrogate-based optimization techniques to optimize the performance of polymer electrolyte membrane fuel cells (PEMFCs). Parametric cases considering four variables are defined using latin hypercube sampling. Training and test data are generated using a multidimensional, two-phase PEMFC simulation model. Response surface approximation, radial basis neural network, and kriging surrogates are employed to construct objective functions for the PEMFC performance. There accuracies are tested and compared using root mean square error and adjusted R-square. Surrogates linked with optimization algorithms, i.e., genetic algorithm and particle swarm optimization are used to determine the optimal design points. Comparative study of these surrogates reveals that the kriging model outperforms the other models in terms of prediction capability. Furthermore, the PEMFC model simulations at the optimal design points demonstrate that performance improvements of around 56–69 mV at 2.0 A/cm2 are achieved with the optimal design compared to typical PEMFC design conditions.