Enhancing Efficiency Prediction Accuracy in PEM Fuel Cell Hybrid Systems Using AI

The transport sector, particularly the automotive industry, is undergoing a significant transformation due to rising environmental concerns and the urgent need for sustainable solutions. Despite overall emission reductions, road traffic emissions in the European Union have increased from 16 % to 26 % in the last three decades. Similarly, the transport sector contributes substantially to global energy consumption and carbon dioxide emissions. This study explores advancements in train technology, focusing on hybrid solutions and highlighting their benefits and limitations. To further the transition to zero-emission vehicles, a hybrid powertrain integrating a PEM fuel cell system and a battery pack was analyzed using a scaled test bench. Preliminary experimental data were employed to train and evaluate a machine learning model for predicting engine efficiency. The study details the test bench setup, machine learning methodology, and results, offering insights into the potential of hybrid powertrains and AI-driven optimization for sustainable transportation