Congestion-Aware Charging Coordination for Electric Ride-Hailing Fleets under Stochastic Demand

Charging-station capacity strongly affects the profitability of electric ride-hailing systems.

In this study, we develop a dynamic charging scheduling method that anticipates vehicles' energy needs and coordinates their charging operations with real-time energy prices to avoid long waiting time at charging stations and increase the total profit of the system.

A sequential mixed integer linear programming model is proposed to devise vehicles' day-ahead charging plans based on their experienced charging waiting times and energy consumption.

The developed charging policy is tested on a Manhattan-like study area using synthetic data drawn from NYC yellow taxi data with a fleet size of 100 vehicles given the scenarios of 3000 and 4000 customers/day.

The computational results show that our method outperforms different benchmark policies with up to +19.32% profit and +20.03% service rate for 4000 customers relative to the weakest benchmark; relative to the strongest benchmark (OptChg), the corresponding gains are +3.91% profit and +4.60% service rate.

Sensitivity analysis is conducted with different system parameters and managerial insights are discussed.