Quality-of-Service Aware Battery Swapping Navigation and Pricing for Autonomous Mobility-on-Demand System

被引:23
|
作者
Ding, Zhaohao [1 ]
Tan, Wenrui [1 ]
Lu, Wenbing [1 ]
Lee, Wei-Jen [2 ]
机构
[1] North China Elect Power Univ, Sch Elect & Elect Engn, Beijing 102206, Peoples R China
[2] Univ Texas Arlington, Energy Syst Res Ctr, Arlington, TX 76019 USA
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
Batteries; Pricing; Quality of service; Navigation; Optimization; Costs; Routing; Autonomous mobility-on-demand (AMoD); battery swapping pricing; electric vehicles (EVs); queue; ELECTRIC VEHICLES; CHARGING STATIONS; OPERATION; TRANSPORTATION; NETWORK; MODEL;
D O I
10.1109/TII.2022.3172985
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The autonomous mobility-on-demand (AMoD) system is regarded as a promising shared mobility method for the sustainable transportation system. In addition, battery swapping could become an efficient energy-refueling means for electric vehicles (EVs). The quality-of-service aware battery swapping price determined by the battery swapping stations (BSSs) considers the interaction between AMoD fleet and BSSs, which is formulated as a bilevel optimization problem in this article. The interdependence between two systems is bridged by the swapping price that is both time-varying and location-varying. In the upper level, the swapping pricing is optimized to reflect the battery inventory so that the EV fleet could be navigated for the BSS strategically, the queue time would be reduced consequently. BSS operator optimizes swapping pricing scheme and battery charging management with the objective of minimizing the cost induced by the queue process and battery charging. In the lower level, a unified network flow model incorporating queue procedure in BSSs is proposed in order to characterize the operational decision of AMoD fleet. The AMoD operator develops fleet scheduling strategies with the objective of maximizing the AMoD system profit. An iteration-based algorithm is applied to attain the results and the effectiveness of the algorithm is validated by the real-world data from New York.
引用
收藏
页码:8247 / 8257
页数:11
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