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

World Electric Vehicle Journal, vol. 17, pp. 279

Abstract

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.

Authors 7

1. Jose del C. Julio-Rodríguez Aachen Faculty of Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Faculty of Mechanical Engineering, RWTH Aachen University, 52074 Aachen, Germany

2. Pedro S. González-Rodríguez

   Tecnológico de Monterrey

   Affiliation as printed

   School of Engineering and Sciences, Tecnologico de Monterrey, Puebla Campus, Puebla 72453, Mexico

3. STEFANÍA MATILDE AMAYA SANDOVAL

   Universidad de las Fuerzas Armadas ESPE

   Affiliation as printed

   Department of Energy and Mechanics, Universidad de las Fuerzas Armadas ESPE, Campus Latacunga, Latacunga 050101, Ecuador

4. David Sebastian Puma-Benavides

   Tecnológico de Monterrey

   Affiliation as printed

   School of Engineering and Sciences, Tecnologico de Monterrey, Puebla Campus, Puebla 72453, Mexico

5. Milton Israel Quinga-Morales

   Escuela Superior Politécnica del Chimborazo

   Affiliation as printed

   Automotive Engineering Program, Faculty of Mechanical Engineering, Escuela Superior Politécnica de Chimborazo (ESPOCH), Panamericana Sur km 1½, Riobamba 060155, Ecuador

6. Javier Milton Solís Santamaria

   Escuela Superior Politécnica del Chimborazo

   Affiliation as printed

   Automotive Engineering Program, Faculty of Mechanical Engineering, Escuela Superior Politécnica de Chimborazo (ESPOCH), Panamericana Sur km 1½, Riobamba 060155, Ecuador

7. Edilberto Antonio Llanes Cedeño

   Universidad Internacional SEK

   Affiliation as printed

   Faculty of Architecture and Engineering, Universidad Internacional SEK (UISEK), Albert Einstein S/N y 5ta Transversal, Quito 170104, Ecuador