Machine learning optimization of operating parameters to achieve high power density and efficiency of polymer electrolyte membrane fuel cell

Kaiser, Rashed; Park, Jong-Chun

doi:10.1016/j.ces.2024.120309

Chemical Engineering Science

2024

DOI: 10.1016/j.ces.2024.120309

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Machine learning optimization of operating parameters to achieve high power density and efficiency of polymer electrolyte membrane fuel cell

Rashed Kaiser,

Jong-Chun Park

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Machine learning optimization of operating parameters to achieve high power density and efficiency of polymer electrolyte membrane fuel cell

Cited by 2 publications

References 66 publications

Insights into the pore structure effect on the mass transfer of fuel cell catalyst layer via combining machine learning and multiphysics simulation

Insights into the pore structure effect on the mass transfer of fuel cell catalyst layer via combining machine learning and multiphysics simulation

Optimization of cathode catalyst layer composition for PEMFC based on an integrated approach of numerical simulation, surrogate model, multi-objective genetic algorithm and evaluation strategy

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