Smart and Resilient EV Charging in SDN-Enhanced Vehicular Edge Computing Networks

Jiajia Liu, Hongzhi Guo, Jingyu Xiong, Nei Kato, Jie Zhang, Yanning Zhang

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Smart grid delivers power with two-way flows of electricity and information with the support of information and communication technologies. Electric vehicles (EVs) with rechargeable batteries can be powered by external sources of electricity from the grid, and thus charging scheduling that guides low-battery EVs to charging services is significant for service quality improvement of EV drivers. The revolution of communications and data analytics driven by massive data in smart grid brings many challenges as well as chances for EV charging scheduling, and how to schedule EV charging in a smart and resilient way has inevitably become a crucial problem. Toward this end, we in this paper leverage the techniques of software defined networking and vehicular edge computing to investigate a joint problem of fast charging station selection and EV route planning. Our objective is to minimize the total overhead from users' perspective, including time and charging fares in the whole process, considering charging availability and electricity price fluctuation. A deep reinforcement learning (DRL) based solution is proposed to determine an optimal charging scheduling policy for low-battery EVs. Besides, in response to dynamic EV charging, we further develop a resilient EV charging strategy based on incremental update, with EV drivers' user experience being well considered. Extensive simulations demonstrate that our proposed DRL-based solution obtains near-optimal EV charging overhead with good adaptivity, and the solution with incremental update achieves much higher computation efficiency than conventional game-theoretical method in dynamic EV charging.

Original languageEnglish
Article number8892573
Pages (from-to)217-228
Number of pages12
JournalIEEE Journal on Selected Areas in Communications
Volume38
Issue number1
DOIs
Publication statusPublished - 2020 Jan

Keywords

  • Smart grid
  • charging scheduling
  • deep reinforcement learning
  • electric vehicle
  • vehicular edge computing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Smart and Resilient EV Charging in SDN-Enhanced Vehicular Edge Computing Networks'. Together they form a unique fingerprint.

  • Cite this