Machine Learning-Based Methodology for Intelligent Energy Management Strategy in Heavy-Duty Fuel Cell Hybrid Electric Vehicles with Pantograph

Jose del C. Julio-Rodríguez, Pedro S. Gonzalez-Rodriguez, Stefania Matilde Amaya-Sandoval, David Sebastian Puma-Benavides, Milton Israel Quinga-Morales, Javier Milton Solís-Santamaria, Edilberto Antonio Llanes-Cedeño

This study presents a novel methodology for optimizing energy management strategies in heavy-duty Fuel Cell Hybrid Electric Vehicles (FCHEVs) with pantograph charging systems. The approach integrates machine learning (ML) techniques to predict energy demand, optimize the power distribution between the battery and fuel cell, and enhance overall efficiency. The methodology involves clustering vehicle and road data, supervised ML classification, and zonification of routes for adaptive energy management. The proposed system was validated using real-world driving data from five different routes in Germany. The results indicate a significant improvement in hydrogen consumption and fuel cell degradation compared to conventional control strategies. This research establishes a framework for advanced energy management in heavy-duty hydrogen-powered electric vehicles.