Mathematics - Optimization and Control; ride-hailing; electric vehicle; dynamic charging management; capacitated charging network; time-of-use energy prices; optimization
Abstract :
[en] Effective utilization of charging station capacity plays an important role in
enhancing the profitability of ride-hailing systems using electric vehicles.
Existing studies assume constant energy prices and uncapacitated charging
stations or do not explicitly consider vehicle queueing at charging stations,
resulting in over-optimistic charging infrastructure utilization. In this
study, we develop a dynamic charging scheduling method (named CongestionAware)
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 obtained charging plans are adapted within the day in response
to vehicles' energy needs and charging station congestion. The developed
charging policy is tested using NYC yellow taxi data in a Manhattan-like study
area with a fleet size of 100 vehicles given the scenarios of 3000 and 4000
customers per day. The computational results show that our CongestionAware
policy outperforms different benchmark policies with up to +15.06% profit and
+19.16% service rate for 4000 customers per day. Sensitivity analysis is
conducted with different system parameters and managerial insights are
discussed.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
