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.

The dynamic performance (DP) of fuel cells (FC) is closely related to the transient characteristics of FC vehicles, especially under variable load conditions. The uneven pore structure of the porous layers (PLs) lead to obvious anisotropy in heat and mass transport (MT), which will make the transport capacity significantly different in two directions. The resulting uneven distribution of temperature and reactant concentration will seriously affect the FC’s DP. In order to study the influence of the PLs’ anisotropic MT properties, the model considers the effect of PLs’ structure on its MT capacity, the heat exchanges of FC, the gas, liquid and dissolved phases of water, the actual agglomerate structure of the catalyst layer, and the time-dependent changes in momentum, energy, species and water transport. Select voltage, oxygen molar concentration, membrane temperature and water content as indicators to characterize the DP. Using the current density as the input load, we investigate the effects of the anisotropic electrical, thermal and proton conductivity, diffusivity, permeability of the PLs on the FC’s DP. The results show that the influence of proton conductivity is the largest, the influence of electrical conductivity and diffusivity is equal and obvious, and the influence of permeability is the least.