Artificial Intelligence-Assisted Optimization and Multiphase Analysis of Polygon PEM Fuel Cells
This work addresses performance improvement in fuel cell design, likely for energy or engineering applications, and appears incremental as it builds on existing optimization methods.
The study tackled optimizing polygon-shaped PEM fuel cells by developing hexagonal and pentagonal models, resulting in output current density increases of 21.8% and 39.9% compared to a base model.
This article presents new hexagonal and pentagonal PEM fuel cell models. The models have been optimized after achieving improved cell performance. The input parameters of the multi-objective optimization algorithm were pressure and temperature at the inlet, and consumption and output powers were the objective parameters. The output data of the numerical simulation has been trained using deep neural networks and then modeled with polynomial regression. The target functions have been extracted using the RSM (Response Surface Method), and the targets were optimized using the multi-objective genetic algorithm (NSGA-II). Compared to the base model, the optimized Pentagonal and Hexagonal models increase the output current density by 21.8% and 39.9%, respectively.