4.7 Article

Mathematical modeling of direct formate fuel cells incorporating the effect of ion migration

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijheatmasstransfer.2020.120629

关键词

Fuel cells; Direct formate fuel cells; Mathematical modeling; Ion migration; Catalyst layer; Potential distribution

资金

  1. Research Grants Council of the Hong Kong Special Administrative Region, China [25211817]

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This work develops a one-dimensional mathematical model of direct formate fuel cells, incorporating mass/charge transport and electrochemical reactions. The model is able to predict fuel cell performance and provide insights into the characteristics of direct formate fuel cells, with good agreement with experimental data. Various factors affecting fuel cell performance are examined using the model.
In this work, a one-dimensional mathematical model of direct formate fuel cells is developed. The present model involves mass/charge transport and electrochemical reactions. Compared to the previous models, this model incorporates the ion migration and considers the anode catalyst layer thickness, so that this model is not only capable of predicting the polarization curves to evaluate the fuel cell performance, but also able to give more in-depth insights into the direct formate fuel cells, e.g., the concentration distributions of reactants/products, the distribution of local current density, and the distribution of electrode potential. In validation, the present model results agree well with the experimental data from the open literature. The voltage losses resulting from the anode, membrane and cathode, as well as the distribution of electrode potential are specified individually via using the present model. Moreover, the effects of the operating conditions, i.e., the feeding concentrations of reactants, and the structural design parameters, i.e., the thicknesses and porosities of diffusion layers and catalyst layers as well as the specific active surface area of catalyst layers, on the fuel cell performance are examined. (C) 2020 Elsevier Ltd. All rights reserved.

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