# Artificial Intelligence-Assisted Optimization and Multiphase Analysis of Polygon PEM Fuel Cells

arXiv: https://arxiv.org/abs/2205.06768 · HTML: https://mlsanity.com/paper/2205.06768/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 85 references that are in the map.
- First posted on arXiv 10 Apr 2022.

## Builds on (most cited first, 1 of 1)

- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)

## Abstract (arXiv)

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.