Reference : Mixed hybrid and electric bus dynamic fleet management in urban networks: a model pre...
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Multidisciplinary, general & others
Computational Sciences
http://hdl.handle.net/10993/38831
Mixed hybrid and electric bus dynamic fleet management in urban networks: a model predictive control approach
English
Rinaldi, Marco mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Picarelli, Erika mailto [Roma Tre University]
Laskaris, Georgios mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Viti, Francesco mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Jan-2019
Yes
International
98th Annual Meeting of the Transportation Research Board (TRB)
from 14-1-2019 to 17-1-2019
Washington, D.C.
USA
[en] Dynamic bus fleet management ; e-bus charging schedule ; MILP ; MPC
[en] Reducing pollutant emissions and promoting sustainable mobility solutions, including Public Transport, are increasingly becoming key objectives for policymakers worldwide. In order to jointly achieve these goals, careful consideration should be put on the operational cost and management of PT services, in order to promote the adoption of green mobility solutions and advanced management techniques by operators. In this work we develop a dynamic fleet management approach for next generation Public Transportation systems, considering the instance of mixed electric / hybrid fleet. Our objective is that of investigating to what extent electrification, coupled with optimal fleet management, can yield operational cost savings for PT operators, explicitly considering real-time disturbances, including delays, service disruptions etc. We propose a Mixed Integer Linear Program to address the problem of optimal scheduling of a mixed fleet of electric and hybrid / non-electric buses, and employ it as predictor in a Model Predictive Control approach.
Test results based upon a real-life scenario showcase how the proposed approach is indeed capable of
yielding a sizable reduction in operational costs, even when considerable disturbances arise from the
underlying system.
Fonds National de la Recherche - FnR
Researchers ; Professionals
http://hdl.handle.net/10993/38831
FnR ; FNR11349329 > Francesco Viti > eCoBus > Electrified Cooperative Bus System > 01/07/2017 > 30/06/2020 > 2016

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