Affiliations: [a] Department of Information Media, University of Suwon, Gyeonggi-do, Korea | [b] Department of Electrical Engineering, University of Incheon, Incheon, Korea | [c] Department of Computer Science and Information Engineering, Inha University, Incheon, Korea | [d] Department of Multimedia Engineering, Hannam University, Daejeon, Korea
Correspondence:
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Corresponding author: Hak-Man Kim, Department of Electrical Engineering, University of Incheon, 12-1 Sondo-dong, Yeonsu-gu, Incheon, 406-840, Korea. E-mail: hmkim@incheon.ac.kr.
Abstract: Recently, the attention on electric vehicle (EV)/plug-in hybrid electric vehicle (PHEV) has been growing. The EV/PHEV will be one of important electric loads from the viewpoint of smart grid in near future. It is anticipated that the EV/PHEV will affect the load pattern of power grids. For this reason, the effective management of the EV/PHEV based on the information and communications technologies will be a major function of smart grid. For EV/PHEV applications, a user interface device equipped on EVs/PHEVs allows the driver to receive instructions or seek advice to manage EV's/PHEV's battery charging/discharging process. In this paper, we present a design of vehicle-grid communications system. To improve the performance of the system, we customize our communication protocol for distributing EV/PHEV's charging information reliably. Also, we model a one-step ahead nonlinear predictor of the charge or discharge price using a neural network ensemble technique. In the experiments, we verify the performance of our protocol with respect to the data delivery ratio and the number of message forwarding. We also compare the price prediction accuracy using the real energy price data, compared with the conventional methods.
Keywords: Electric vehicle, geocasting, neural network, predictor, time series, vehicle-to-grid communication
