Dynamic charging management for electric vehicle demand responsive transport

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.