Synergistic Framework for Fuel Cell Mass Transport Optimization: Coupling Reduced-Order Models with Machine Learning Surrogates

Shixin Li, Qingshan Liu () and Yisong Chen ()
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Shixin Li: Automotive Engineering Research Institute, Shaanxi Automobile Group Co., Ltd., Xi’an 710200, China
Qingshan Liu: School of Automobile, Chang’an University, Xi’an 710064, China
Yisong Chen: School of Automobile, Chang’an University, Xi’an 710064, China

Energies, 2025, vol. 18, issue 10, 1-19

Abstract: Facing the complex coupled process of thermal mass transfer and electrochemical reaction inside fuel cells, the development of a one-dimensional model is an efficient solution to study the influence of mass transfer property parameters on the transfer and reaction process, which can effectively balance the computational efficiency and accuracy. Firstly, a one-dimensional two-phase non-isothermal parametric model is established to capture the performance and state of fuel cell quickly. Then, a sensitivity analysis is performed on various mass transfer parameters of the membrane electrode assembly. Subsequently, a neural network surrogate model and genetic algorithm are combined to optimize the mass transfer property parameters globally. The impact of these parameters on the thermal and mass transfer within the fuel cell is analyzed. The results show that the maximum error between the calculation results of the developed numerical model and the experimental results is 3.87%, and the maximum error between the predicted values of the trained surrogate model and the true values is 0.15%. The mass transfer characteristics of the gas diffusion layer have the most significant impact on the performance of the fuel cell. After optimizing the mass transfer characteristic parameters, the net power density of the fuel cell increased by 5.51%. The combination of the one-dimensional model, the surrogate model, and the genetic algorithm can effectively improve the optimization efficiency.

Keywords: proton exchange membrane fuel cell; one-dimensional parametric model; porous layer properties; mass and heat transfer; surrogate model; performance enhancement (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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