Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems Under Demand and Supply Uncertainties
Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems Under Demand and Supply Uncertainties
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DOI：
10.1109/tits.2023.3237804
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发表时间：
2022-11
影响因子：
8.5
通讯作者：
Sihong He;Zhili Zhang;Shuo Han;Lynn Pepin;Guang Wang;Desheng Zhang;J. Stankovic;Fei Miao
中科院分区：
文献类型：
--
作者：
Sihong He;Zhili Zhang;Shuo Han;Lynn Pepin;Guang Wang;Desheng Zhang;J. Stankovic;Fei Miao
电动汽车（ev）由于其经济和社会效益而迅速被采用。自动按需移动（AMoD）系统也接受了这一趋势。然而，电动汽车的充电时间长、充电频率高，给电动汽车AMoD系统的高效管理带来了挑战。电动汽车AMoD系统复杂的动态充电和移动过程使得需求和供给的不确定性在车辆平衡算法设计中显得尤为突出。在这项工作中，我们设计了一种数据驱动的分布式鲁棒优化（DRO）方法来平衡电动汽车的移动服务和充电过程。优化目标是在乘客出行需求不确定和电动汽车供应不确定的情况下，使最坏情况下的期望成本最小化。然后，我们提出了一种新的分布不确定性集构造算法，该算法保证生成的参数以给定的概率包含在期望的置信区域中。为了解决所提出的DRO - AMoD - EV平衡问题，我们导出了一个等效的计算可处理的凸优化问题。基于出租车系统的实际电动汽车数据，与不考虑不确定性的解决方案相比，我们的解决方案平均总平衡成本降低了14.49%，平均出行公平性和充电公平性分别提高了15.78%和34.51%。
Electric vehicles (EVs) are being rapidly adopted due to their economic and societal benefits. Autonomous mobility-on-demand (AMoD) systems also embrace this trend. However, the long charging time and high recharging frequency of EVs pose challenges to efficiently managing EV AMoD systems. The complicated dynamic charging and mobility process of EV AMoD systems makes the demand and supply uncertainties significant when designing vehicle balancing algorithms. In this work, we design a data-driven distributionally robust optimization (DRO) approach to balance EVs for both the mobility service and the charging process. The optimization goal is to minimize the worst-case expected cost under both passenger mobility demand uncertainties and EV supply uncertainties. We then propose a novel distributional uncertainty sets construction algorithm that guarantees the produced parameters are contained in desired confidence regions with a given probability. To solve the proposed DRO AMoD EV balancing problem, we derive an equivalent computationally tractable convex optimization problem. Based on real-world EV data of a taxi system, we show that with our solution the average total balancing cost is reduced by 14.49%, and the average mobility fairness and charging fairness are improved by 15.78% and 34.51%, respectively, compared to solutions that do not consider uncertainties.