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

• Published in: IEEE journal on selected areas in communications Vol. 38; no. 1; pp. 217 - 228
• Main Authors: Liu, Jiajia, Guo, Hongzhi, Xiong, Jingyu, Kato, Nei, Zhang, Jie, Zhang, Yanning
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Batteries; charging scheduling; Computer simulation; deep reinforcement learning; Dynamic scheduling; Edge computing; electric vehicle; Electric vehicle charging; Electric vehicles; Electricity; Electricity pricing; Machine learning; Processor scheduling; Rechargeable batteries; Route planning; Route selection; Schedules; Scheduling; Smart grid; Software-defined networking; Variation; Vehicle dynamics; vehicular edge computing
• DOI: 10.1109/JSAC.2019.2951966
• ISSN: 0733-8716

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.