On-Line Event-Driven Scheduling for Electric Vehicle Charging via Park-and-Charge

Kong, Fanxin; Xiang, Qiao; Kong, Linghe; Li, Xue

doi:10.1109/rtss.2016.016

Cited by 32 publications

(23 citation statements)

References 28 publications

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“…There are two different charging scheduling strategies for EVs, i.e., centralized and decentralized ones [18,24,47]. Among existing EV charging research, most of them aim to optimize single EV fleets, while some others cooperatively optimize multiple heterogeneous EV fleets.…”

Section: Related Workmentioning

confidence: 99%

“…Much existing research [7,24,25,27,50] focuses on how to choose the optimal locations for charging stations and how to assign charging points in each station to minimize the charging time of EVs considering some passenger demands or cost constraints. However, to our knowledge, almost all these works [7,25,28,41,44,[50][51][52] only consider one fleet (e.g., only e-taxis [7,25] or e-buses [44]).…”

Section: Introductionmentioning

confidence: 99%

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sharedCharging

Wang

Zhang

et al. 2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Our society is witnessing a rapid vehicle electrification process. Even though being environmental-friendly, electric vehicles have not reached their full potentials due to prolonged charging time. Moreover, unbalanced spatiotemporal charging demand/supply along with the uneven number of charging stations between heterogeneous fleets make electric vehicle management more challenging, e.g., surplus charging stations across a city for electric buses but limited charging stations in some regions for electric taxis, which severely limit the charging performance of the whole electric vehicle network in a city. In this paper, we first analyze a large-scale real-world dataset from two heterogeneous electric vehicle fleets in the Chinese city Shenzhen. We investigate their mobility and charging patterns and then verify the practicability and necessity of shared charging. Based on the insights we found, we design a generic real-time shared charging scheduling system called sharedCharging to improve overall charging efficiency for heterogeneous electric vehicle fleets. Our sharedCharging also considers sophisticated real-world constraints, e.g., station spaces, availability of charging points, real-time timetable guarantee, etc. More importantly, we take the electric bus and electric taxi fleets as a concrete example of heterogeneous electric vehicle fleets given their different operating patterns. We implement and evaluate sharedCharging with streaming data from over 13,000 electric taxis and 16,000 electric buses, coupled with the charging station data in the Chinese city Shenzhen, which is the largest public electric vehicle network in the world. The evaluation results demonstrate that the proposed sharedCharging reduces the waiting time by 63.5% and reduces the total charging time by 15% on average for e-taxis.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

sharedCharging

Wang

Zhang

et al. 2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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“…Kong et al [17] studies deadline-aware rate control in the context of demand response. Kong et al [12] employ the event-driven model and borrow real-time scheduling theories to schedule EV charging loads for park-and-charge. Similarly, Yorie et al [41] also borrows an algorithm from real-time scheduling, which is the least-laxity-first algorithm that first charges the most urgent vehicles.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, serving more charging demand may lower the charging rates of EVs at the stations. However, most existing works study the two operations separately and focus on either of charging rate control (e.g., [8]- [12]) or charging demand balancing (e.g., [13]- [15]). The most closely related work [16], which considers both operations together, however, simply overlooks the coupling relation between the two operations.…”

Section: Introductionmentioning

confidence: 99%

Joint Rate Control and Demand Balancing for Electric Vehicle Charging

Kong

Liu²,

Lee

2018

2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)

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Charging stations have become indispensable infrastructure to support the rapid proliferation of electric vehicles (EVs). The operational scheme of charging stations is crucial to satisfy the stability of the power grid and the quality of service (QoS) to EV users. Most existing schemes target either of the two major operations: charging rate control and demand balancing. This partial focus overlooks the coupling relation between the two operations and thus causes the degradation on the grid stability or customer QoS. A thoughtful scheme should manage both operations together. A big challenge to design such a scheme is the aggregated uncertainty caused by their coupling relation. This uncertainty accumulates from three aspects: the renewable generators co-located with charging stations, the power load of other (or non-EV) consumers, and the charging demand arriving in the future. To handle this aggregated uncertainty, we propose a stochastic optimization based operational scheme. The scheme jointly manages charging rate control and demand balancing to satisfy both the grid stability and user QoS. Further, our scheme consists of two algorithms that we design for managing the two operations respectively. An appealing feature of our algorithms is that they have robust performance guarantees in terms of the prediction errors on these three aspects. Simulation results demonstrate the efficacy of the proposed operational scheme and also validate our theoretical results. Disciplines Computer Engineering | Computer Sciences Comments

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“…The first is residential charging stations, where an EV owner plugs in when he or she at home, and the EV usually charges overnight [3,18]. The second context is park-and-charge, i.e., charging while parked, where a parking lot is equipped with charging function and EVs can receive recharging while parked [19,20]. This scenario encourages EV users to take advantage of nearby facilities, such as parking stations including parking at malls, working places and airports.…”

Section: Charging Infrastructure Overviewmentioning

confidence: 99%