REC: Predictable Charging Scheduling for Electric Taxi Fleets

Zheng Dong, Cong Liu, Yanhua Li, Jie Bao, Yu Gu, Tian He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Due to the energy security concern, our society is witnessing a surge of EV fleet applications, e.g., public EV taxi fleet systems. A major issue impeding an even more widespread adoption of EVs is range anxiety, which is due to several factors including limited battery capacity, limited availability of battery charging stations, and long charging time compared to traditional gasoline vehicles. By analyzing our accessible real-world EV taxi system-wide datasets, we observe that current EV taxi drivers often suffer from unpredictable, long waiting times at charging stations, due to temporally and spatially unbalanced utilization among charging stations. This is mainly because current taxi fleet management system simply rely on taxi drivers to make charging decisions. In this paper, In this paper, we develop REC, a Real-time Ev Charging scheduling framework for EV taxi fleets, which informs each EV taxi driver at runtime when and where to charge the battery. REC is able to analytically guarantee predictable and tightly bounded waiting times for all EVs in the fleet and temporally/spatially balanced utilization among charging stations, if each driver follows the charging decision made by REC. Moreover, REC can further efficiently handle real-life issues, e.g., allowing a taxi driver to charge at its preferred charging station while still guaranteeing balanced charging station utilization.We have extensively evaluated REC using our accessible real-world EV taxi system-wide datasets. Experimental results show that REC is able to address the unpredictability and unbalancing issues existing in current EV taxi fleet systems, yielding predictable and tightly bounded waiting times, and equally important, temporally/spatially balanced charging station utilization.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE Real-Time Systems Symposium, RTSS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages287-296
Number of pages10
ISBN (Electronic)9781538614143
DOIs
StatePublished - Jan 31 2018
Event38th IEEE Real-Time Systems Symposium, RTSS 2017 - Paris, France
Duration: Oct 5 2017Oct 8 2017

Publication series

NameProceedings - Real-Time Systems Symposium
Volume2018-January
ISSN (Print)1052-8725

Other

Other38th IEEE Real-Time Systems Symposium, RTSS 2017
CountryFrance
CityParis
Period10/5/1710/8/17

Keywords

  • Charging-Scheduling
  • Electric-Taxi-Fleets
  • Real-time-Scheduling
  • Suspending-task-model
  • predictability

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  • Cite this

    Dong, Z., Liu, C., Li, Y., Bao, J., Gu, Y., & He, T. (2018). REC: Predictable Charging Scheduling for Electric Taxi Fleets. In Proceedings - 2017 IEEE Real-Time Systems Symposium, RTSS 2017 (pp. 287-296). (Proceedings - Real-Time Systems Symposium; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RTSS.2017.00034