Developing a Location Model for Fast Charging Infrastructure in Urban Areas

Developing a Location Model for Fast Charging Infrastructure in Urban Areas

A. Shirmohammadli D. Vallée

Institute of Urban and Transport Planning, RWTH Aachen University, Germany

Available online: 
31 January 2017
| Citation



The potential of reducing greenhouse gases in transport sector attracted different groups to promote electric vehicles (EVs) as a component of sustainable mobility development. However, studies assert that the usage of EV is currently limited mainly to short-distance trips and the users are only those who have the opportunity of charging their car privately at home or workplaces. This research highlights the lack of public charging stations and tries to develop a demand-oriented location model for finding the optimal location of fast charging stations

(FCSs) from a user’s point of view. In urban areas the users can make use of activity time of their daily routine activities such as supermarket shopping for charging the battery of their EVs. Therefore, the proposed location model focuses on the interaction between people’s travel behaviour and urban infrastructure. First, the potential of a facility for installation of FCS is determined by means of its different attributes such as number of attracted motorized individual trips, opening hours and parking lot availability, activity time of users in different facilities in relation to the charging time and synergy effect of closely allied facilities. In the

second step, the study area is zoned and the calculated potential for facilities is transferred to the relevant zones, considering users’ maximum detour acceptance, catchment area of facilities as well as spatial impact of existing charging stations. The input data, which rely mainly on open source and publically accessible data, are analysed and depicted as different georeferenced layers in the geographical information system (ArcGIS Software). The proposed location model aims to cover the growing demand for public FCS of current EV users as well as one step forward to increase the acceptance of electro mobility among potential users.


Electro mobility, fast charging station, location model, urban areas


[1]  Frenzel, I., Jarass, J., Trommer, S. & Lenz, B., Erstnutzer von Elektrofahrzeugen in Deutschland. Nutzerprofile, Anschaffung, Fahrzeugnutzung, 2015.

[2] Wang, G., Xu, Z., Wen, F. & Wong, K.P., Traffic-constrained multiobjective planning of electric-vehicle charging stations. IEEE Transactions on Power Delivery, 28(4), pp. 2363–2372, 2013. DOI: 10.1109/TPWRD.2013.2269142.

[3]  Mohsenzadeh, A., Pang, C., Pazouki, S. & Haghifam, M., Optimal siting and sizing of electric vehicle public charging stations considering smart distribution network reliability. In North American Power Symposium (NAPS), 2015 (pp. 1–6). IEEE. 2015.

[4] Bayram, I. S., Michailidis, G., Devetsikiotis, M. & Granelli, F., Electric power allocation in a network of fast charging stations. IEEE Journal on Selected Areas in Communications, 31(7), pp. 1235–1246, 2013. DOI: 10.1109/JSAC.2013.130707.

[5] Wirges, J., Linder, S. & Kessler, A., Modelling the development of a regional charging infrastructure for electric vehicles in time and space. European Journal of Transport and Infrastructure Research, 12(12), 391–416, 2012.

[6] Schroeder, A. & Traber, T., The economics of fast charging infrastructure for electric vehicles. Energy Policy, 43, pp. 136–144, 2012. DOI: 10.1016/j.enpol.2011.12.041.

[7] Zhu, Z. H., Gao, Z. Y., Zheng, J. F. & Du, H. M., Charging station location problem of plug-in electric vehicles. Journal of Transport Geography, 52, pp. 11–22, 2016. DOI: 10.1016/j.jtrangeo.2016.02.002.

[8] Sathaye, N. & Kelley, S., An approach for the optimal planning of electric vehicle infrastructure for highway corridors. Transportation Research Part E: Logistics and Transportation Review, 59, pp. 15–33, 2013. DOI: 10.1016/j.tre.2013.08.003.

[9] Zhang, L., Shaffer, B., Brown, T. & Samuelsen, G.S., The optimization of DC fast charging deployment in California. Applied Energy, 157, pp. 111–122, 2015. DOI: 10.1016/j.apenergy.2015.07.057.

[10]  Li, S., Huang, Y. & Mason, S.J., A multi-period optimization model for the deployment of public electric vehicle charging stations on network. Transportation Research Part C: Emerging Technologies, 65, pp. 128–143, 2016. DOI: 10.1016/j.trc.2016.01.008.

[11] Morrissey, P, Weldon, P. & O’Mahony, M., Informing the Strategic Rollout of Fast Electric Vehicle Charging Networks with User Charging Behaviour Data Analysis, Proceedings of the 95th Annual Meeting of the Transportation Research Board, National Academy of Sciences, Washington D.C. 2016. 

[12] Baouche, F., Billot, R., Trigui, R. & Faouzi, E., Efficient allocation of electric vehicles charging stations: optimization model and application to a dense urban network. Intelligent Transportation Systems Magazine, IEEE, 6(3), pp. 33–43, 2014. DOI: 10.1109/MITS.2014.2324023.

[13] Zhu, Z.H., Gao, Z.Y., Zheng, J.F. & Du, H.M., Charging station location problem of plug-in electric vehicles. Journal of Transport Geography, 52, pp. 11–22, 2016. DOI: 10.1016/j.jtrangeo.2016.02.002.

[14]  He, S.Y., Kuo, Y.H. & Wu, D., Incorporating institutional and spatial factors in the selection of the optimal locations of public electric vehicle charging facilities: A case study of Beijing, China. Transportation Research Part C: Emerging Technologies, 67, pp. 131–148, 2016.

[15] Giménez-Gaydou, D. A., Ribeiro, A. S., Gutiérrez, J., & Antunes, A. P. (2016). Optimal location of battery electric vehicle charging stations in urban areas: A new approach. International Journal of Sustainable Transportation, 10(5), 393–405.

[16] Bosserhoff: Integration von Verkehrsplanung und räumliche Planung- Teil 2: Grundsätze und Umsetzung, Heft 42 der Schriftenreihe der Hessischen Straßen – und Verkehrsverwaltung, Wiesbaden 2000.

[17] Vogt, W. & Bosserhoff, D., Hinweise zur Schätzung des Verkehrsaufkommens von Gebietstypen (Vol. 147), FGSV Verlag, 2006

[18]  Hayes, J.G. & Davis, K., Simplified electric vehicle powertrain model for range and energy consumption based on EPA coast-down parameters and test validation by Argonne National Lab data on the Nissan Leaf. In Transportation Electrification Conference and Expo (ITEC), 2014 IEEE (pp. 1–6). IEEE. 2014.

[19] Verband Deutscher Verkehrsunternehmen, Verkehrserschließung und Verkehrsangebot im ÖPNV, VDV Schriften, Heft 4, Köln 6/2001.