Analysing PEM Fuel Cell Parameters and Predicting them using Machine Learning Algorithms

Author Reg. ID: TIJER_151735

Published Paper Id: TIJER2403122

Published In: Volume 11 Issue 3, March-2024

DOI: http://doi.one/10.1729/Journal.38708

Abstract: Proton Exchange Membrane Fuel Cell (PEMFC) also known as Polymer Electrolyte Membrane Fuel Cell is one of the cleanest and most efficient energy conversion devices. PEMFC is expected to play a very big role in future energy solutions. PEM Fuel Cells work by converting the chemical energy of a fuel which is mainly hydrogen directly into electrical energy which can be used for running various electrical appliances. This is achieved by the reaction of hydrogen with oxygen over a catalyst such as Platinum and water is obtained as the by-product. PEM Fuel Cells are very promising electrochemical devices because they are highly efficient, have high power density and low emissions. Machine learning is a methodology that trains a certain model to obtain a certain data-fitting model based on the existing data fed to it and uses this model to execute predictions with high nonlinear problem forecasting accuracy and with higher computational efficiency. Machine learning models are widely used to predict parameters of various energy conversion devices. PEM Fuel Cell is also one of those devices with parameters like fuel cell performance, efficiency being accurately predicted by machine learning algorithms.

Keywords: Energy, Fuel Cell, Hydrogen, Machine Learning, PEM

Downloads: 000177

Page No: a899-a910

Country: Azadpur, Delhi, India

Research Area: Science and Technology

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJER2403122

Published Paper PDF: https://tijer.org/TIJER/papers/TIJER2403122

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