Model Predictive Control for Energy Saving of Hybrid Trains in Catenary-Free Scenarios

This paper addresses the energy management control problem for railway systems, focusing on a hybrid train equipped with hydrogen fuel cells and lithium-ion batteries, also enhanced by regenerative braking, capable of operating both with and without a catenary connection. Initially, a detailed electrical model is developed, ensuring an accurate representation of the train. This model is then simplified for control purposes, enhancing computational efficiency while maintaining accuracy. Hence, a model predictive control (MPC) strategy with an economic cost is designed, focusing on optimizing energy management and minimizing power losses, while ensuring that the levels of the batteries and fuel cells remain within their optimal ranges. The work, relying on real data provided by the industrial partner Alstom, concludes with a comparative analysis against a heuristic approach, with satisfactory performance in terms of efficiency and reliability.

Authors

Institutions

Publication Details

Published
2025-06-24
DOI
https://doi.org/10.23919/ecc65951.2025.11187250
Primary Topic
Railway Systems and Energy Efficiency
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Model Predictive Control for Energy Saving of Hybrid Trains in Catenary-Free Scenarios

Alessio La Bella, Gian Paolo Incremona, Patrizio Colaneri, Manuel Camarda
Railway Systems and Energy Efficiency
article

Model Predictive Control for Energy Saving of Hybrid Trains in Catenary-Free Scenarios

Alessio La Bella, Gian Paolo Incremona, Patrizio Colaneri, Manuel Camarda
article en

Abstract

This paper addresses the energy management control problem for railway systems, focusing on a hybrid train equipped with hydrogen fuel cells and lithium-ion batteries, also enhanced by regenerative braking, capable of operating both with and without a catenary connection. Initially, a detailed electrical model is developed, ensuring an accurate representation of the train. This model is then simplified for control purposes, enhancing computational efficiency while maintaining accuracy. Hence, a model predictive control (MPC) strategy with an economic cost is designed, focusing on optimizing energy management and minimizing power losses, while ensuring that the levels of the batteries and fuel cells remain within their optimal ranges. The work, relying on real data provided by the industrial partner Alstom, concludes with a comparative analysis against a heuristic approach, with satisfactory performance in terms of efficiency and reliability.

Politecnico di Milano (IT)
Openalex Percentile: Top 59%
Railway Systems and Energy Efficiency
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Model Predictive Control for Energy Saving of Hybrid Trains in Catenary-Free Scenarios — Alessio La Bella, Gian Paolo Incremona, et al. · (2025) | TGRS Research Map | TGRS