A novel experimental based statistical study for water management in proton exchange membrane fuel cells

M. Hasheminasab, M.J. Kermani, S.S. Nourazar and M.H. Khodsiani

Applied Energy, 2020, vol. 264, issue C, No S0306261920302257

Abstract: Water management is a key issue in low-temperature PEMFCs. For proper water management, processes leading to dehydration or flooding of cells should be clearly understood, hence prevented. Here, the effects of Anode Stoichiometry (AST), Cathode Stoichiometry (CST) and inlet gas temperature (T*) on the cell performance and water management in PEMFC are experimentally studied. Two methods, namely (i) direct- and (ii) indirect-method are employed to monitor water accumulation in cathode flow channels of a multi-path-parallel-serpentine PEMFC. Flow visualization served as direct method (item (i)), in which recorded videos of transparent channels were analyzed via an image-processing algorithm to monitor water coverage ratio (WCR). Simultaneously an inlet-to-exit pressure drop coefficient (Φ) was defined for indirect-method (item (ii)). AST, CST and T* were taken as input to provide three experimental based correlations for generated electrical power, WCR and Φ as a response. According to the results, an increase in AST reduces water-content in cathode channels; decreases performance also raises the sensitivity of generated power to variations in other operating-parameters. However, an increase in CST and T* reduces water contents in cathode channels also increases the cell power and lowers sensitivity of the power to the variation of other operating-parameters. It is observed that strong connectivity exists between WCR and Φ. This is verified by obtaining a high Pearson correlation coefficient of 0.887 between WCR and Φ. It is demonstrated that dehydration and flooding could be prevented when 3 ≤ WCR ≤ 4, from which appropriate operating-parameters AST, CST and T* could be retrieved.

Keywords: Performance enhancement of PEMFC; Water management; Two-phase flow; Design of Experiment (DOE); Direct optical visualization; Digital image processing (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (13)

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DOI: 10.1016/j.apenergy.2020.114713

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