Abstract
Coordinating the charging scheduling of electric vehicles for dynamic dial-a-ride services is challenging considering charging queuing delays and stochastic customer demand. We propose a new two-stage solution approach to handle dynamic vehicle charging scheduling to minimize the costs of daily charging operations of the fleet. The approach comprises two components: daily vehicle charging scheduling and online vehicle–charger assignment. A new battery replenishment model is proposed to obtain the vehicle charging schedules by minimizing the costs of vehicle daily charging operations while satisfying vehicle driving needs to serve customers. In the second stage, an online vehicle–charger assignment model is developed to minimize the total vehicle idle time for charges by considering queuing delays at the level of chargers. An efficient Lagrangian relaxation algorithm is proposed to solve the large-scale vehicle-charger assignment problem with small optimality gaps. The approach is applied to a realistic dynamic dial-a-ride service case study in Luxembourg and compared with the nearest charging station charging policy and first-come-first-served minimum charging delay policy under different charging infrastructure scenarios. Our computational results show that the approach can achieve significant savings for the operator in terms of charging waiting times (–74.9%), charging times (–38.6%), and charged energy costs (–27.4%). A sensitivity analysis is conducted to evaluate the impact of the different model parameters, showing the scalability and robustness of the approach in a stochastic environment.
Highlights
Electric vehicle technology has gained increasing interest amongst policymakers, the general public, and the automotive industry in response to worldwide directives to reduce CO2 emissions
We propose a two-stage solution for handling the dynamic vehicle charging scheduling problem for dynamic dial-a-ride services using EVs that is comprised of two components: vehicle charging scheduling and vehicle–charger assignment
Charging scheduling is considered on the basis of each vehicle as a battery recharge problem, which decomposes the problem into multistage decision-making to minimize the charging costs at each stage while satisfying vehicle driving needs for the stage
Summary
Electric vehicle technology has gained increasing interest amongst policymakers, the general public, and the automotive industry in response to worldwide directives to reduce CO2 emissions. Research on the electrification of ride-hailing services in the USA has shown that TNCs need to recharge e-fleets several times a day and rely primarily on DC fast chargers to minimize charging times [2]. With the increased number of electric vehicles in the fleet and the relatively limited number of public and private charging spots, the likelihood that accessible charging stations will be temporarily unavailable will soon become an issue. Existing studies mainly focus on static EV routing problems under charging infrastructure constraints, whereas research on online charging scheduling under stochastic demand is still limited [5] For this purpose, we propose an online charging scheduling model for dynamic dial-a-ride services to minimize the total charging delays and costs of the fleet under charging infrastructure constraints and stochastic customer demand
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