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With the climate change challenges, transport network companies started toelectrify their fleet to reduce CO2 emissions. However, such an ecologicaltransition brings new research challenges for dynamic electric fleet chargingmanagement under uncertainty. In this study, we address the dynamic chargingscheduling management of shared ride-hailing services with public chargingstations. A two-stage charging scheduling optimization approach under a rollinghorizon framework is proposed to minimize the overall charging operationalcosts of the fleet, including vehicles' access times, charging times, andwaiting times, by anticipating future public charging station availability. Thecharging station occupancy prediction is based on a hybrid LSTM (Longshort-term memory) network approach and integrated into the proposed onlinevehicle-charger assignment. The proposed methodology is applied to a realisticsimulation study in the city of Dundee, UK. The numerical studies show that theproposed approach can reduce the total charging waiting times of the fleet by48.3% and the total charged the amount of energy of the fleet by 35.3% comparedto a need-based charging reference policy.

Dynamic charging management for electric vehicle demand responsive transport