Temperature distribution,
mass transport, and current density are
crucial parameters to characterize the durability and output performance
of proton exchange membrane fuel cell (PEMFC), which are affected
by thermal contact resistance (TCR) and gas diffusion layer (GDL)
face permeability within both cathode and anode GDL porous jumps.
This study examined the effects of TCR and GDL face permeability on
a single PEM fuel cell’s temperature profiles, mass transport,
and cell performance using a three-dimensional, nonisothermal computational
model with an isotropic gas diffusion layer (GDL). This model calculates
the ideal thermal contact resistance by comparing the expected plate-cathode
electrode temperature difference to the numerical and experimental
literature. The combined artificial neural network-genetic algorithm
(ANN-GA) method is also applied to identify the optimum powers and
their operating conditions in six cases. Theoretical findings demonstrate
that TCR and suitable GDL face permeability must be considered to
optimize the temperature distribution and cell efficiency. TCR and
GDL face permeability lead to a 1.5 °C rise in maximum cell temperature
at 0.4 V, with a “Λ” shape in temperature profiles.
The TCR and GDL face permeability also significantly impacts electrode
heat and mass transfer. Case 6 had 1.91, 6.58, and 8.72% higher velocity
magnitudes, oxygen mass fractions, and cell performances than case
1, respectively. Besides, the combined ANN-GA method is suitable for
predicting fuel cell performance and identifying operation parameters
for optimum powers. Therefore, the findings can improve PEM fuel cell
performance and give a reference for LT-PEMFC design.