Optimization of Gradient Catalyst Layers in PEMFCs Based on Neural Network Models

The high cost of platinum (Pt) catalysts impedes the widespread commercialization of proton exchange membrane fuel cells (PEMFCs). Reducing Pt loading will increase local oxygen transport resistance (RPtO2) and decrease performance. Due to the oxygen transport resistance, the reactants in the cathode catalyst layer (CCL) are not evenly distributed. The gradient structure can cooperate with the unevenly distributed reactants in CL to enhance the Pt utilization. In this work, a one-dimensional gradient CCL model considering RPtO2 is established, and the optimal gradient structure is optimized by combining the artificial neural network (ANN) model and the genetic algorithm (GA). The optimal structure parameters of non-gradient CCL are lCL equal to 8.86 μm, rC equal to 36.82 nm, and I/C equal to 0.48, with the objective of maximum current density (Imax); lCL equal to 4.24 μm, rC equal to 36.60 nm, and I/C equal to 0.76, with the objective of maximum power density (Pmax). For the gradient CCL, the best gradient distribution enables Pt loading to increase from the membrane (MEM) side to the gas diffusion layer (GDL) side and the ionomer volume fraction to decrease from the MEM side to the GDL side.