TY - JOUR
T1 - Smart and Resilient EV Charging in SDN-Enhanced Vehicular Edge Computing Networks
AU - Liu, Jiajia
AU - Guo, Hongzhi
AU - Xiong, Jingyu
AU - Kato, Nei
AU - Zhang, Jie
AU - Zhang, Yanning
N1 - Funding Information:
Manuscript received May 27, 2019; revised August 30, 2019; accepted October 2, 2019. Date of publication November 6, 2019; date of current version January 31, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61801360, Grant 61771374, Grant 61771373, and Grant 61601357, and in part by the Fundamental Research Fund for the Central Universities under Grant 310201905200001, Grant 3102019PY005, Grant JB181506, Grant JB181507, and Grant JB181508. (Corresponding authors: Jiajia Liu; Hongzhi Guo.) J. Liu, H. Guo, and Y. Zhang are with the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710072, China (e-mail: liujiajia@nwpu.edu.cn; hongzhi.guo@nwpu.edu.cn).
Publisher Copyright:
© 1983-2012 IEEE.
PY - 2020/1
Y1 - 2020/1
N2 - 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.
AB - 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.
KW - Smart grid
KW - charging scheduling
KW - deep reinforcement learning
KW - electric vehicle
KW - vehicular edge computing
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U2 - 10.1109/JSAC.2019.2951966
DO - 10.1109/JSAC.2019.2951966
M3 - Article
AN - SCOPUS:85074866325
SN - 0733-8716
VL - 38
SP - 217
EP - 228
JO - IEEE Journal on Selected Areas in Communications
JF - IEEE Journal on Selected Areas in Communications
IS - 1
M1 - 8892573
ER -
