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arXiv · 2412.09978

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

Tai-Yu Ma·Richard D. Connors·Francesco Viti
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Abstract

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.

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Tai-Yu Ma, Richard D. Connors, Francesco Viti. 2024-12-13. Congestion-Aware Charging Coordination for Electric Ride-Hailing Fleets under Stochastic Demand. https://arxiv.org/abs/2412.09978

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