Online Charge Scheduling for Electric Vehicles in Autonomous Mobility on Demand Fleets
Online Charge Scheduling for Electric Vehicles in Autonomous Mobility on Demand Fleets
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DOI：
10.1109/itsc.2019.8917101
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Nathaniel Tucker;Berkay Turan;M. Alizadeh
中科院分区：
文献类型：
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作者：
Nathaniel Tucker;Berkay Turan;M. Alizadeh
本文研究了自主移动按需电动汽车（AMoD ev）车队的在线充电调度策略。我们考虑了这样一种情况：车辆完成了行程，然后在一天中进入往返状态，车队运营商可以通过在线方式获得这些信息。在往返状态下，车辆必须安排充电，然后路由到下一个接客地点。此外，由于未知的每日乘车请求序列，该问题无法通过任何离线方法解决。因此，我们研究了一种基于原始对偶方法的在线福利最大化启发式算法，该算法分配有限的车队充电资源并重新平衡车辆，同时避免充电设施和上车地点的拥堵。我们讨论了一个竞争比结果，比较了我们的在线解决方案与千里眼离线解决方案的性能，并提供了突出我们的启发式性能的数值结果。
In this paper, we study an online charge scheduling strategy for fleets of autonomous-mobility-on-demand electric vehicles (AMoD EVs). We consider the case where vehicles complete trips and then enter a between-ride state throughout the day, with their information becoming available to the fleet operator in an online fashion. In the between-ride state, the vehicles must be scheduled for charging and then routed to their next passenger pick-up locations. Additionally, due to the unknown daily sequences of ride requests, the problem cannot be solved by any offline approach. As such, we study an online welfare maximization heuristic based on primal-dual methods that allocates limited fleet charging resources and rebalances the vehicles while avoiding congestion at charging facilities and pick-up locations. We discuss a competitive ratio result comparing the performance of our online solution to the clairvoyant offline solution and provide numerical results highlighting the performance of our heuristic.