Abstract
The fast development of smart-grid technologies and applications calls for new means to meet the transportation and environment requirements of the next trend of mainstream vehicles. Electric vehicle (EV), which has been regarded as an important replacement for present gasoline-based vehicle, is expected to greatly reduce the carbon emissions meanwhile offer acceptable transportation ability. However, most of present market-level electric vehicle heavily rely its capacity-constrained battery which can only support limited driving range. Although there have been many pioneer works focusing on ameliorating the driving experience of EVs through tuning the placement of charging infrastructure, most of them do not consider the heterogeneity of vehicle movement in different scenarios. In this paper, starting from a fine-grained analysis of a real-world vehicle trace, a charging station placement algorithm considering the installation cost, traffic flow and battery capacity, called EVReal, is proposed. In comparing its performance with other representative algorithms, EVReal outperforms the others in various metrics.
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Coin-or branch and cut. https://projects.coin-or.org/Cbc. Accessed 6 Jun 2015
Gnu linear programming kit. http://www.gnu.org/software/glpk/glpk.html. Accessed 6 Jun 2016
Amici, R., Bonola, M., Bracciale, L., Loreti, P., Rabuffi, A., Bianchi, G.: Performance assessment of an epidemic protocol in VANET using real traces. In: Proceedings of MoWNeT (2014)
Bae, S., Kwasinski, A.: Spatial and temporal model of electric vehicle charging demand. TSG
Eisel, M., Schmidt, J., Kolbe, L.: Finding suitable locations for charging stations. In: Proceedings of IEVC (2014)
Haberman, R.: Mathematical models: mechanical vibrations, population dynamics, and traffic flow
Lam, A., Leung, Y.W., Chu, X.: Electric vehicle charging station placement: formulation, complexity, and solutions. TSG
Nilsson, M.: Electric vehicles: the phenomenon of range anxiety. ELVIRE
Piórkowski, M., Sarafijanovic-Djukic, N., Grossglauser, M.: A parsimonious model of mobile partitioned networks with clustering. In: Proceedings of COMSNETS (2009)
Sanchez-Martin, P., Sanchez, G., Morales-Espana, G.: Direct load control decision model for aggregated EV charging points. TPS
Tuttle, D., Baldick, R.: The evolution of plug-in electric vehicle-grid interactions. TSG (2012)
Wang, G., Xu, Z., Wen, F., Wong, K.P.: Traffic-constrained multiobjective planning of electric-vehicle charging stations. TPD
Wang, S., Fan, C., Hsu, C.H., Sun, Q., Yang, F.: A vertical handoff method via self-selection decision tree for internet of vehicles. IEEE Syst. J. 10(3) (2016)
Wang, S., Lei, T., Zhang, L., Hsu, C.H., Yang, F.: Offloading mobile data traffic for qos-aware service provision in vehicular cyber-physical systems. Future Gener. Comput. Syst. 61, 118–127 (2016)
Wang, X., Yuen, C., Hassan, N.U., An, N., Wu, W.: Electric vehicle charging station placement for urban public bus systems. IEEE TITS PP(99) (2016)
Zheng, Y., Dong, Z.Y., Xu, Y., Meng, K., Zhao, J.H., Qiu, J.: Electric vehicle battery charging/swap stations in distribution systems: comparison study and optimal planning. TPS
Zheng, Y., Liu, Y., Yuan, J., Xie, X.: Urban computing with taxicabs. In: Proceedings of UbiComp (2011)
Zhu, H., Chang, S., Li, M., Naik, K., Shen, S.: Exploiting temporal dependency for opportunistic forwarding in urban vehicular networks. In: Proceedings of INFOCOM (2011)
Zhu, Y., Wu, Y., Li, B.: Trajectory improves data delivery in urban vehicular networks. TPDS (2014)
Zi-fa, L., Wei, Z., Xing, J., Ke, L.: Optimal planning of charging station for electric vehicle based on particle swarm optimization. In: Proceedings of ISGT Asia (2012)
Acknowledgements
This research was supported in part by U.S. NSF grants NSF-1404981, IIS-1354123, CNS-1254006, and Microsoft Research Faculty Fellowship 8300751.
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Yan, L., Shen, H., Li, S., Huang, Y. (2016). Electrical Vehicle Charging Station Deployment Based on Real World Vehicle Trace. In: Hsu, CH., Wang, S., Zhou, A., Shawkat, A. (eds) Internet of Vehicles – Technologies and Services. IOV 2016. Lecture Notes in Computer Science(), vol 10036. Springer, Cham. https://doi.org/10.1007/978-3-319-51969-2_5
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DOI: https://doi.org/10.1007/978-3-319-51969-2_5
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