Mathematical modeling of fuel cells fed with an electrically rechargeable liquid fuel

Shi, Xinyi; Huo, Xiaoyu; Esan, Oladapo Christopher; Pan, Zhefei; Liu, Yun; An, Le; Zhao, Tianshou

doi:10.1016/j.egyai.2023.100275

Energy and AI

2023

DOI: 10.1016/j.egyai.2023.100275

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Mathematical modeling of fuel cells fed with an electrically rechargeable liquid fuel

Xinyi Shi

Xiaoyu Huo

Oladapo Christopher Esan

et al.

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Cited by 6 publications

References 52 publications

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Overall Design of a Gradient‐Ordered Membrane Electrode Assembly for Direct Liquid Fuel Cells

Pan,

Xie,

Zhang

et al. 2024

Adv Funct Materials

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The direct liquid fuel cell (DLFC) constitutes a promising energy conversion system that directly conveys the chemical energy of liquid fuels into electrical energy. In certain DLFCs, gas is produced as a product of electrochemical reactions during operation. However, the accumulation of gas inside the porous electrode can significantly hinder the transport of reactants, leading to the failure of active sites and severe concentration loss. To address this issue, a gradient‐ordered membrane electrode assembly (MEA) is designed and fabricated, consisting of a dual‐gradient diffusion layer that comprises a pore‐size gradient and a wettability gradient as well as a catalyst layer constructed by nanoneedle catalyst. This MEA promptly removes the produced gas and delivers the fresh solution, thereby enhancing the cell power output and stability. The fuel cell with the gradient‐ordered MEA achieves a remarkable peak power density of 177 mW cm−2 and a discharging time of 19 h, which are more than four times and 30 times, respectively, higher than those of the conventional MEA.

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Overall Design of a Gradient‐Ordered Membrane Electrode Assembly for Direct Liquid Fuel Cells

Pan,

Xie,

Zhang

et al. 2024

Adv Funct Materials

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Recent understanding on pore scale mass transfer phenomena of flow batteries: Theoretical simulation and experimental visualization

Shi,

2025

Current Opinion in Electrochemistry

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Machine learning and Bayesian optimization for performance prediction of proton-exchange membrane fuel cells

Echabarri,

Do,

et al. 2024

Energy and AI

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Mathematical modeling of fuel cells fed with an electrically rechargeable liquid fuel

Cited by 6 publications

References 52 publications

Overall Design of a Gradient‐Ordered Membrane Electrode Assembly for Direct Liquid Fuel Cells

Overall Design of a Gradient‐Ordered Membrane Electrode Assembly for Direct Liquid Fuel Cells

Recent understanding on pore scale mass transfer phenomena of flow batteries: Theoretical simulation and experimental visualization

Machine learning and Bayesian optimization for performance prediction of proton-exchange membrane fuel cells

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