Electric bus charging station location selection problem with slow and fast charging

Journal Article (2025)
Author(s)

Konstantinos Gkiotsalitis (National Technical University of Athens)

Dimitrios Rizopoulos (National Technical University of Athens)

Marilena Merakou (National Technical University of Athens)

Christina Iliopoulou (University of Patras)

Tao Liu (Southwest Jiaotong University)

O Cats (TU Delft - Transport and Planning)

Department
Transport and Planning
DOI related publication
https://doi.org/10.1016/j.apenergy.2024.125242
More Info
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Publication Year
2025
Language
English
Department
Transport and Planning
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Volume number
382
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Abstract

To facilitate the shift from conventional to electric buses, the required charging infrastructure must be deployed. This study models the charging station location selection problem for fixed-line public transport services consisting of electric buses. The model considers the deadheading time of electric buses between the final stop of their trip and the locations of the potential charging stations with the objective of minimizing vehicle running costs. The problem is solved at a strategic level; therefore, several parameters of day-to-day operations, such as deadheading distances, are included as aggregate data considering their average values. In addition, it considers different charger types (slow and fast), which are subject to a day-ahead scheduling of the charging sessions of the buses. The developed model is a mixed-integer nonlinear program, which is reformulated as a mixed-integer linear program and can be solved efficiently for large networks with more than 1940 bus trips and 336 charging installation options. The model is applied in the Athens metropolitan area, demonstrating its potential as a decision support tool for selecting charging station locations and charger types in large public transport networks.

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