@Article{cmes.2026.081630,
AUTHOR = {Antonio Gimeno, Emilio Larrodé},
TITLE = {Digital Twin–Based Analysis of Energy Management Strategies for Heavy-Duty Fuel Cell–Battery Electric Vehicles Using a Hybrid Deterministic Decision Framework},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {147},
YEAR = {2026},
NUMBER = {3},
PAGES = {0--0},
URL = {http://www.techscience.com/CMES/v147n3/67907},
ISSN = {1526-1506},
ABSTRACT = {This paper presents the development of a digital twin of a heavy-duty electric truck powered by a hybrid energy system based on a hydrogen fuel cell and a battery pack. The objective of the model is to analyze different energy management strategies and to determine how the power demand of a real route can be shared between both energy sources, while keeping the fuel cell within safe operating limits to preserve its service life. The digital twin simulates vehicle dynamics, traction, and regenerative braking, and the main operational constraints of the fuel cell, including minimum and maximum power limits, power variation constraints, and startup conditions. The input data are obtained from real driving routes, using telemetry data recorded from conventional diesel trucks. These data provide realistic speed, acceleration, and slope profiles, which are used to reconstruct the power and energy demand of the vehicle and to simulate the behavior of an equivalent fuel cell–battery electric truck. Several rule-based energy management strategies are implemented and compared using indicators such as hydrogen consumption, minimum battery state of charge, unused generated energy, and fuel cell power variability. The results show that strategies limiting rapid fuel cell power changes provide a better balance between hydrogen efficiency, battery protection, and smoother fuel cell operation. The proposed digital twin offers a practical framework to evaluate hybrid energy management strategies under realistic operating conditions and to support future intelligent energy management developments.},
DOI = {10.32604/cmes.2026.081630}
}