Abstract
With the climate change challenges, transport network companies started to electrify their fleet to reduce CO2 emissions. However, such ecological transition brings new research challenges for dynamic electric fleet charging management under uncertainty. In this study, we address the dynamic charging scheduling management of shared ride-hailing services with public charging stations. A two-stage charging scheduling optimization approach under a rolling horizon framework is proposed to minimize the overall charging operational 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.
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Acknowledgement
The work was supported by the Luxembourg National Research Fund (C20/SC/14703944).
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Ma, TY. (2023). Dynamic Charging Management for Electric Vehicle Demand Responsive Transport. In: Nathanail, E.G., Gavanas, N., Adamos, G. (eds) Smart Energy for Smart Transport. CSUM 2022. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-031-23721-8_14
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DOI: https://doi.org/10.1007/978-3-031-23721-8_14
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