Constraint-Based Charging Scheduler Design for Electric Vehicles
This paper proposes an efficient charging scheduler for electric vehicles and measures its performance, aiming at reducing peak power consumption while satisfying the diverse constraints specified in each charging request. Upon the arrival of a charging request via the underlying vehicle network, the scheduler builds the feasible schedule based on the activation time, the deadline, and the power load profile of each charging task, which is practically nonpreemptive. During the search space expansion of a backtracking algorithm, each step checks the constraint imposed on peak load, completion time, number of chargers, and precedence relation between tasks to prune unnecessary branches. The performance measurement result obtained from the prototype implementation reveals that the proposed scheme reduces the execution time by 80 %, achieves the peak load reduction by 11 %, and improves the schedulability by 5 %, compared with uncoordinated and list scheduling schemes for the given parameter set.
KeywordsExecution Time Time Slot Electric Vehicle Smart Grid Schedule Scheme
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- 1.Gellings, C.W.: The Smart Grid: Enabling Energy Efficiency and Demand Response. CRC Press (2009)Google Scholar
- 2.Korean Smart Grid Institute, http://www.smartgrid.or.kr/eng.html
- 4.Markel, T., Simpson, A.: Plug-in Hybrid Electric Vehicle Energy Storage System Design. In: Advanced Automotive Battery Conference (2006)Google Scholar
- 5.Spees, K., Lave, L.: Demand Response and Electricity Market Efficiency. The Electricity Journal, 69–85 (2007)Google Scholar
- 6.Sanzhong, B., Yu, D., Lukic, S.: Optimum Design of an EV/PHEV Charging Station with DC Bus and Storage System. In: IEEE Energy Conversion Congress and Exposition, pp. 1178–1184 (2010)Google Scholar
- 7.Kaplan, S.M., Sissine, F.: Smart Grid: Modernizing Electric Power Transmission and Distribution; Energy Independence, Storage and Security. TheCapitol.Net (2009)Google Scholar
- 8.Schweppe, H., Zimmermann, A., Grill, D.: Flexible In-vehicle Stream Processing with Distributed Automotive Control Units for Engineering and Diagnosis. In: IEEE 3rd International Symposium on Industrial Embedded Systems, pp. 74–81 (2008)Google Scholar
- 9.Ipakchi, A., Albuyeh, F.: Grid of the Future. IEEE Power & Energy Magazine, 52–62 (2009)Google Scholar
- 12.Tremblay, O., Dessaint, L.: Experimental Validation of a Battery Dynamic Model for EV Applications. World Electric Vehicle Journal 3 (2009)Google Scholar
- 13.Caramanis, M., Foster, J.M.: Management of Electric Vehicle Charging to Mitigate Renewable Generation Intermittency and Distribution Network Congestion. In: 48th IEEE Conference on Decision and Control, pp. 4717–4722 (2009)Google Scholar
- 14.Midlam-Mohler, S., Ewing, S., Marano, V., Guezennec, Y., Rizzoni, G.: PHEV Fleet Data Collection and Analysis. In: IEEE Vehicle Power and Propulsion Conference, pp. 1205–1210 (2009)Google Scholar