Advertisement

Distance-Based Heuristic in Selecting a DC Charging Station for Electric Vehicles

  • Junghoon Lee
  • Gyung-Leen Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8875)

Abstract

This paper proposes a suboptimal tour-and-charging scheduler for electric vehicles which need to select a DC charging station on their single day trips. As a variant of the traveling salesman problem, the tour scheduler finds a visiting order for a given set of destinations and one of any charging stations. To reduce the search space stemmed from a larger number of candidate stations, our distance-based heuristic finds first the nearest destination from each charging station, and calculates the distance between them. Then, m′ out of the whole m candidates will be filtered according to the distance. The reduced number of candidates, namely, m′, combined with constraint processing on the waiting time, significantly cuts down the execution time for tour schedule generation. The performance measurement result obtained from a prototype implementation reveals that the proposed scheme just brings at most 4.1 % increase in tour length and its accuracy is at least 0.4 with 5 picks, for the given parameter selection.

Keywords

Electric vehicles tour scheduler DC charging TSP variant distance-based heuristic 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Timpner, J., Wolf, L.: Design and evaluation of charging station scheduling strategies for electric vehicles. IEEE Transactions on Intelligent Transportation Systems 15(2), 579–588 (2014)CrossRefGoogle Scholar
  2. 2.
    Mischinger, S., Hennings, W., Strunz, K.: Integration of surplus wind energy by controlled charging of electric vehicles. In: 3rd IEEE PES Innovative Smart Grid Technologies Europe (2012)Google Scholar
  3. 3.
    Botsford, C., Szczepanek, A.: Fast charging vs. slow charging: Pros and cons for the new age of electric vehicles. In: International Battery Hybrid Fuel Cell Electric Vehicle Symposium (2009)Google Scholar
  4. 4.
    Veneri, O., Capasso, C., Ferraro, L., Pizzo, A.: Performance analysis on a power architecture for EV ultra-fast charging stations. In: International Conference on Clean Electrical Power, pp. 183–188 (2013)Google Scholar
  5. 5.
    Vedova, M., Palma, E., Facchinetti, T.: Electric load as real-time tasks: An application of real-time physical systems. In: International Wireless Communications and Mobile Computing Conference, pp. 1117–1123 (2011)Google Scholar
  6. 6.
    Shim, V., Tan, K., Tan, K.: A hybrid estimation of distribution algorithm for solving the multi-objective multiple traveling salesman problem. In: IEEE World Congress on Computational Intelligence (2012)Google Scholar
  7. 7.
    Ramchrun, S., Vytelingum, R., Rogers, A., Jennings, N.: Putting the ‘smarts’ into the smart grid: A grand challenge for artificial intelligence. Communications of the ACM 55(4), 86–97 (2012)CrossRefGoogle Scholar
  8. 8.
    Mehar, S., Remy, G.: EV-planning: Electric vehicle itinerary planning. In: International Conference on Smart Communications in Network Technologies (2013)Google Scholar
  9. 9.
    Bessler, S., Grønbæk, J.: Routing EV users towards an optimal charging plan. In: International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium (2012)Google Scholar
  10. 10.
    Vansteenwegen, P., Souffriau, W., Berghe, G., Oudheusden, D.: The City trip planner: An expert system for tourists. Expert Systems with Applications 38, 6540–6546 (2011)CrossRefGoogle Scholar
  11. 11.
    Andrandt, A., Andersen, P., Pedersen, A., You, A., Poulsen, B., O’Cornel, N., Østergaard, J.: Prediction and optimization methods for electric vehicle charging schedules in the Edison project. IEEE Transactions on Smart Grid, 111–119 (2011)Google Scholar
  12. 12.
    Ortega-Vazquez, M., Bouffard, F., Silva, V.: Electric vehicle aggregator/system operator coordination for charging scheduling and services procurement. IEEE Transactions on Power Systems 28(2), 1806–1815 (2013)CrossRefGoogle Scholar
  13. 13.
    Kisacikoglu, M., Ozpineci, B., Tolbert, L.: EV/PHEV bidirectional charger assessment for V2G reactive power operation. IEEE Transactions on Power Electronics 28(12), 5717–5727 (2013)CrossRefGoogle Scholar
  14. 14.
    Hamidi, A., Weber, L., Nasiri, A.: EV charging station integrating renewable energy and second-life battery. In: International Conference on Renewable Energy Research and Applications, pp. 1217–1221 (2013)Google Scholar
  15. 15.
    Lee, J., Park, G.: A tour recommendation service for electric vehicles based on a hybrid orienteering model. In: ACM Symposium on Applied Computing, pp. 1652–1654 (2013)Google Scholar
  16. 16.
    Lee, J., Park, G.: DC charger selection scheme for electric vehicle-based tours visiting multiple destinations. In: ACM Research in Applied Computation Symposium (to appear, 2014)Google Scholar
  17. 17.
    Lee, J., Park, G.-L.: Design of a multi-day tour-and-charging scheduler for electric vehicles. In: Ramanna, S., Lingras, P., Sombattheera, C., Krishna, A. (eds.) MIWAI 2013. LNCS, vol. 8271, pp. 108–118. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Junghoon Lee
    • 1
  • Gyung-Leen Park
    • 1
  1. 1.Dept. of Computer Science and StatisticsJeju National UniversityRepublic of Korea

Personalised recommendations