Abstract
The growing adoption of electric vehicles (EVs) poses new challenges for the planning and management of charging infrastructures (CIs). This paper proposes a methodology to estimate the sufficiency of EV charging infrastructures in a given area of study (AOS) containing public and private buildings, using open-source data and a case study of Lindau (Bodensee), Germany. The methodology consists of two main steps: first, applying the attractiveness factor concept from travel models to cluster buildings according to their potential EV users; second, classifying charging stations based on their location and occupancy rate. To reach our desired result, we compare the number of charging hours needed by EVs arriving at each building cluster with the number of available charging stations in each station cluster, and identify any gaps or surpluses. The paper demonstrates the feasibility and applicability of the methodology using data from the city Lindau (Bodensee) as an example. The paper also discusses the limitations and assumptions of the methodology, and suggests future directions for developing a machine-learning based tool that could support optimal placement of new charging stations.
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Hinweise zur Schätzung des Verkehrsaufkommens von Gebietstypen (2006)
Abdi, H., Mohammadi-ivatloo, B., Javadi, S., Khodaei, A.R., Dehnavi, E.: Chapter 7 - energy storage systems. In: Gharehpetian, G., Mousavi Agah, S.M. (eds.) Distributed Generation Systems, Butterworth-Heinemann, pp. 333–368 (2017). https://doi.org/10.1016/B978-0-12-804208-3.00007-8, https://www.sciencedirect.com/science/article/pii/B9780128042083000078
Bosserhoff, D.: Programm VER_bau: Abschätzung des verkehrsaufkommens durch vorhaben der bauleitplanung mit excel-tabellen am PC (2003). https://www.dietmar-bosserhoff.de/index.html
Calearo, L., Marinelli, M., Ziras, C.: A review of data sources for electric vehicle integration studies. Renew. Sustain. Energy Rev. 151, 111518 (2021)
Center of automotive management: electromoility report 2022 (2022)
Detlef Borscheid, Kraftfahrt-Bundesamt: Prognose: Mehr als elf Millionen Elektroautos und Plug-Ins bis 2030. Autohaus (2020)
Draz, M., Albayrak, S.: A power demand estimator for electric vehicle charging infrastructure. In: 2019 IEEE Milan PowerTec, pp. 1–6. IEEE (2019). https://doi.org/10.1109/PTC.2019.8810659
Elattar, H.: Open-data methodology for optimizing the allocations of Charging Stations
Elattar, H., Von Tüllenburg, F., Wöllmann, S., Valdes, J.: Evaluating the fulfilment rate of charging demand for electric vehicles using open-source data. In: Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management. SCITEPRESS - Science and Technology Publications, Prague, Czech Republic, pp. 159–166 (2023). https://doi.org/10.5220/0011849400003473, https://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0011849400003473
Friese, P.A., Michalk, W., Fischer, M., Hardt, C., Bogenberger, K.: Charging point usage in Germany-automated retrieval, analysis, and usage types explained. Sustainability 13(23), 13046 (2021)
Hecht, C., Das, S., Bussar, C., Sauer, D.U.: Representative, empirical, real-world charging station usage characteristics and data in Germany. ETransportation, 6, 100079(2020). https://doi.org/10.1016/j.etran.2020.100079
Hernández-Moreno, A., et al.: Transient traffic energy-use analysis employing video-tracking and microscopic modeling techniques: a case study using electric and combustion engine vehicles. Energy Sci. Eng. 10(7), 2022–2034 (2022). https://doi.org/10.1002/ese3.1148
Hummler, P., Naumzik, C., Feuerriegel, S.: Web mining to inform locations of charging stations for electric vehicles. In: Companion Proceedings of the Web Conference 2022, pp. 166–170 (2022). https://doi.org/10.1145/3487553.3524264, http://arxiv.org/abs/2203.07081, arXiv:2203.07081 [cs]
Jahn, R.M., Syré, A., Grahle, A., Schlenther, T., Göhlich, D.: Methodology for determining charging strategies for urban private vehicles based on traffic simulation results. Procedia Comput. Sci. 170, 751–756 (2020). https://doi.org/10.1016/j.procs.2020.03.160
Janowicz, K., Gao, S., McKenzie, G., Hu, Y., Bhaduri, B.: GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. Int. J. Geogr. Inf. Sci. 34(4), 625–636 (2020)
Akker, J.M. van den.: E-Vehicles: Interaction of Smart Charging and DSO Strategies. Utrecht University (2020)
Klinkhardt, C., et al.: Using OpenStreetMap as a data source for attractiveness in travel demand models. Transp. Res. Rec. 2675(8), 294–303 (2021). https://doi.org/10.1177/0361198121997415
Koch, L., et al.: Accurate physics-based modeling of electric vehicle energy consumption in the SUMO traffic microsimulator. In: 2021 IEEE International Intelligen Transportation Systems Conference ITSC, pp. 1650–1657. IEEE (2021). https://doi.org/10.1109/ITSC48978.2021.9564463
Kraftfahrt-Bundesamt: Der fahrzeugbestand am 1. januar 2022 (2022)
Mock, P.: European union co2 standards for new passenger cars and vans (2021)
Mortimer, B.J., Hecht, C., Goldbeck, R., Sauer, D.U., De Doncker, R.W.: Electric vehicle public charging infrastructure planning using real-world charging data. World Electr. Veh. J. 13(6), 94 (2022)
Neufert, E., Neufert, P., Kister, J.: Architects’ data. Wiley-Blackwell, Chichester, West Sussex, UK ; Ames, Iowa, 4th ed edn. (2012), oCLC: ocn775329524
Pagany, R., Marquardt, A., Zink, R.: Electric charging demand location model-a user- and destination-based locating approach for electric vehicle charging stations. Sustainability 11(8), 2301 (2019)
Pinjari, A.R., Bhat, C.R.: CHAPTER 17. Activity-based Travel Demand Analysis
Schlote, A., Crisostomi, E., Kirkland, S., Shorten, R.: Traffic modelling framework for electric vehicles. Int. J. Control 85(7), 880–897 (2021). https://doi.org/10.1080/00207179.2012.668716
Sparks, K., Thakur, G., Pasarkar, A., Urban, M.: A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation. Int. J. Geogr. Inf. Sci. 34(4), 759–776 (2020)
Valdes, J., Wuth, J., Zink, R., Schröck, S., Schmidbauer, M.: Extracting relevant points of interest from open street map to support E-mobility infrastructure models. Bavarian J. Appl. Sci 4, 323341 (2018). https://doi.org/10.25929/BJAS.V4I1.51
Westin, R.B., Manski, C.F.: Theoretical and Conceptual Developments in Demand Modelling. Routledge (1979)
Wirges, J., Linder, S., Kessler, A.: Modelling the development of a regional charging infrastructure for electric vehicles in time and space. Eur. J. Transp. Infrastruct. Res. 12(4) (2012). https://doi.org/10.18757/ejtir.2012.12.4.2976, https://journals.open.tudelft.nl/ejtir/article/view/2976
Zhou, Y., et al.: Plug-in electric vehicle market penetration and incentives: a global review. Mitig. Adapt. Strat. Glob. Change 20(5), 777–795 (2014). https://doi.org/10.1007/s11027-014-9611-2
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Elattar, H., von Tüllenburg, F., Karas, S., Valdes, J. (2024). An Open-Source Model for Estimating the Need to Expansion in Local Charging Infrastructures. In: Grueau, C., Rodrigues, A., Ragia, L. (eds) Geographical Information Systems Theory, Applications and Management. GISTAM 2023. Communications in Computer and Information Science, vol 2107. Springer, Cham. https://doi.org/10.1007/978-3-031-60277-1_5
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