Dynamic charging management for electric vehicle demand responsive transport

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With the climate change challenges, transport network companies started to electrify their fleet to reduce CO2 emissions. However, such ecological tran-sition brings new research challenges for dynamic electric fleet charging management under uncertainty. In this study, we address the dynamic charg-ing scheduling management of shared ride-hailing services with public charg-ing stations. A two-stage charging scheduling optimization approach under a rolling horizon framework is proposed to minimize the overall charging op-erational costs of the fleet, including vehicles’ access times, charging times, and waiting times, by anticipating future public charging station availability. The charging station occupancy prediction is based on a hybrid LSTM (Long short-term memory) network approach and integrated into the proposed online vehicle-charger assignment. The proposed methodology is applied to a realistic simulation study in the city of Dundee, UK. The numerical studies show that the proposed approach can reduce the total charging waiting times of the fleet by 48.3% and the total charged amount of energy of the fleet by 35.3% compared to a need-based charging reference policy.
Original languageEnglish
Title of host publicationSmart Energy for Smart Transport.
Subtitle of host publicationProceedings of the 6th Conference on Sustainable Urban Mobility, CSUM2022, August 31-September 2, 2022, Skiathos Island, Greece
EditorsEftihia G. Nathanail, Nikolaos Gavanas, Giannis Adamos
Place of PublicationCham
Number of pages12
ISBN (Electronic)978-3-031-23721-8
ISBN (Print)978-3-031-23720-1
Publication statusPublished - 11 Mar 2023

Publication series

Name Lecture Notes in Intelligent Transportation and Infrastructure (LNITI)
ISSN (Print)2523-3440
ISSN (Electronic)2523-3459


  • demand-responsive transport
  • memory neural network
  • long short-term
  • electric vehicle
  • charging management
  • charg-ing station occupancy

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