Vehicle-to-grid communication system for electric vehicle charging

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.

[1]  Ahmed Yousuf Saber,et al.  Intelligent unit commitment with vehicle-to-grid —A cost-emission optimization , 2010 .

[2]  Pedro Faria,et al.  An optimal scheduling problem in distribution networks considering V2G , 2011, 2011 IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG).

[3]  James D. Hamilton Time Series Analysis , 1994 .

[4]  Maode Ma,et al.  UBAPV2G: A Unique Batch Authentication Protocol for Vehicle-to-Grid Communications , 2011, IEEE Transactions on Smart Grid.

[5]  Yu Wang,et al.  Routing in vehicular ad hoc networks: A survey , 2007, IEEE Vehicular Technology Magazine.

[6]  S. S. Venkata,et al.  Coordinated Charging of Plug-In Hybrid Electric Vehicles to Minimize Distribution System Losses , 2011, IEEE Transactions on Smart Grid.

[7]  Dae-Ki Kang,et al.  Ensemble with neural networks for bankruptcy prediction , 2010, Expert Syst. Appl..

[8]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[9]  Manuel Graña,et al.  Morphological neural networks and vision based simultaneous localization and mapping , 2007, Integr. Comput. Aided Eng..

[10]  Alberto Vale,et al.  Navigation strategies for cooperative localization based on a particle-filter approach , 2007, Integr. Comput. Aided Eng..

[11]  Paolo Santi,et al.  Vehicle-to-Vehicle Communication: Fair Transmit Power Control for Safety-Critical Information , 2009, IEEE Transactions on Vehicular Technology.

[12]  Aboelmagd Noureldin,et al.  Implementation methodology of embedded land vehicle positioning using an integrated GPS and multi sensor system , 2010, Integr. Comput. Aided Eng..

[13]  Kamalrulnizam Abu Bakar,et al.  Inter-Vehicle Communication Protocols for Multimedia Transmission , 2010 .

[14]  C. Maihofer,et al.  A survey of geocast routing protocols , 2004, IEEE Communications Surveys & Tutorials.

[15]  Brad Karp,et al.  GPSR: greedy perimeter stateless routing for wireless networks , 2000, MobiCom '00.

[16]  Sreejit Chakravarty,et al.  Dynamic filter weights neural network model integrated with differential evolution for day-ahead price forecasting in energy market , 2011, Expert Syst. Appl..

[17]  Farshid Keynia,et al.  Day-ahead electricity price forecasting by modified relief algorithm and hybrid neural network , 2010 .

[18]  Brad Karp,et al.  Greedy Perimeter Stateless Routing for Wireless Networks , 2000 .

[19]  C. Rodriguez,et al.  Energy price forecasting in the Ontario competitive power system market , 2004, IEEE Transactions on Power Systems.

[20]  Willett Kempton,et al.  Vehicle-to-grid power fundamentals: Calculating capacity and net revenue , 2005 .