Distance-Based Heuristic in Selecting a DC Charging Station for Electric Vehicles
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.
KeywordsElectric vehicles tour scheduler DC charging TSP variant distance-based heuristic
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- 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.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.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.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.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
- 8.Mehar, S., Remy, G.: EV-planning: Electric vehicle itinerary planning. In: International Conference on Smart Communications in Network Technologies (2013)Google Scholar
- 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
- 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
- 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.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.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