Coordinated vehicle dispatching and charging scheduling for an electric ride-hailing fleet under charging congestion and dynamic prices

TL;DR

This paper introduces CongestionAware, a dynamic charging scheduling method for electric ride-hailing fleets that optimizes vehicle charging in real-time to reduce congestion and increase profitability.

Contribution

It develops a novel mixed integer linear programming model for day-ahead and real-time charging coordination considering congestion and dynamic prices.

Findings

Up to 15.06% profit increase with CongestionAware.

Up to 19.16% improvement in service rate.

Effective reduction of charging station waiting times.

Abstract

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.