Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/108421
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Title: Mathematical modeling of fuel cells fed with an electrically rechargeable liquid fuel
Authors: Shi, X 
Huo, X 
Esan, OC 
Pan, Z 
Yun, L 
An, L 
Zhao, TS
Issue Date: Oct-2023
Source: Energy and AI, Oct. 2023, v. 14, 100275
Abstract: Lately, utilizing a novel electrically rechargeable liquid fuel (e-fuel), a fuel cell has been designed and fabricated, which is demonstrated to achieve a much better performance than alcoholic liquid fuel cells do. However, its current performance, which thus hampers its wide application, demands further improvement to meet up with industrial requirement. Therefore, to attain a better performance for this system, an in-depth understanding of the complex physical and chemical processes within this fuel cell is essential. To this end, in this work, a two-dimensional transient model has been developed to gain an extensive knowledge of a passive e-fuel cell and analyze the major factors limiting its performance. The effects of various structural parameters and operating conditions are studied to identify the underlying performance-limiting factors, where deficient mass transport is found to be one of the major causes. The increment of anode porosity and thickness are found to be effective methods of improving the cell performance. This study therefore provides insights on achieving further performance advancement of the fuel cell in the future.
Keywords: E-fuel
Liquid fuel cells
Mathematical modeling
Operation conditions
Structural parameters
Publisher: Elsevier BV
Journal: Energy and AI 
EISSN: 2666-5468
DOI: 10.1016/j.egyai.2023.100275
Rights: © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The following publication Shi, X., Huo, X., Esan, O. C., Pan, Z., Yun, L., An, L., & Zhao, T. S. (2023). Mathematical modeling of fuel cells fed with an electrically rechargeable liquid fuel. Energy and AI, 14, 100275 is available at https://doi.org/10.1016/j.egyai.2023.100275.
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