Placing BEV Charging Infrastructure: Influencing Factors, Metrics, and Their Influence on Observed Charger Utilization
Abstract
:1. Introduction and Motivation
- A detailed overview of existing influencing factors and associated metrics concerning the demand-oriented placement of PEVCI.
- A list of 15 core aspects regarding the placement of PEVCI, representing the most commonly addressed groups of influencing factors.
- A taxonomy of 9 lines of argumentation, describing the mechanisms by which charging demand is influenced by the identified factors.
- A case study on the city of Hamburg’s 1023 public charging points to evaluate the presumed link between influencing factors, metrics, and observed charging utilization.
- An pre-processed open source data set of the Hamburg’s public charging station usage over the period of four weeks.
- The identification of further research directions.
2. Related Literature and Problem Statement
2.1. Related Literature
2.1.1. Practical Guidelines
2.1.2. User Surveys
2.1.3. Expert Interviews
2.1.4. Optimization and Assessment
2.1.5. Reviews
2.2. Problem Statement and Concept Description
3. Toward a Charging Site Evaluation Framework
3.1. General Approach
3.2. Influencing Factors, Core Aspects, and Lines of Argumentation
3.2.1. Collection, Categorization, and Clustering of Factors—Steps 1–3
3.2.2. Lines of Argumentation—Step 4
3.2.3. Linking Argumentation Codes with Core Aspects—Step 5
3.3. Metrics
3.4. Charging Site Evaluation Framework: Conclusions
4. Hamburg Case Study
4.1. Introduction
4.2. Spatial Data and Metrics
4.3. Charging Data Set
4.3.1. Pre-Processing
4.3.2. Exploratory Data Analysis
- Occupancy: the share of occupied chargers at a given time.
- Connection ratio: the proportion of time a charger is occupied.
- Daily transactions: the number of transactions per charging station and day.
- Duration: the duration of a transaction.
4.4. Correlations of Spatial Metrics and Charge Point Usage
5. Summary
6. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
Ac | Activity |
AcHo | Quality–Home |
AcWo | Quality–Work |
AcPOI | Quality–Points of Interest |
AcPT | Quality–Public Transport |
Av | Availability |
AvCo | Availability–Coverage |
AvAR | Availability–Access Restriction |
BEV | Battery Electric Vehicle |
CC | City Center |
CIS | Charging Infrastructure |
CP | Charging Point |
EV | Electric Vehicle |
ISM | Interpretive Structural Modeling |
MICMAC | Cross-Impact Matrix Multiplication Applied to Classification |
PEVCI | Public Electric Vehicle Charging Infrastructure |
POI | Point of Interest |
PROMETHEE | Preference Ranking Organization Method for Enrichment Evaluation |
Qu | Quality |
QuRe | Quality–Reachability |
QuRed | Quality–Redundancy |
QuAt | Quality–Attractiveness |
SU | Suburbs |
UR | Urban Residential |
VIKOR | Multicriteria Optimization and Compromise Solution |
WA | Working Area |
References
- United Nations. Paris Agreement: 2015. 2015. Available online: https://treaties.un.org/pages/ViewDetails.aspx?src=TREATY&mtdsg_no=XXVII-7-d&chapter=27&clang=_en (accessed on 1 February 2022).
- Rockström, J.; Gaffney, O.; Rogelj, J.; Meinshausen, M.; Nakicenovic, N.; Schellnhuber, H.J. A roadmap for rapid decarbonization. Science 2017, 355, 1269–1271. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hardman, S.; Tal, G. Driving the Market for Plug-in Vehicles—Developing PEV Charging Infrastrcuture for Consumers; Institute of Transportation Studies: Berkeley, CA, USA, 2017. [Google Scholar]
- Stadt Hamburg. Masterplan Ladeinfrastruktur. 2014. Available online: https://www.hamburg.de/contentblob/4479262/dcabd1a0157d6ac7c2ab1bfb06b22dc7/data/masterplan-ladeinfrastruktur.pdf (accessed on 1 February 2022).
- NOW GmbH. Tank-und Ladeinfrastruktur—Öffentlicher Bereich—Kommunen—Standortkriterien. 2022. Available online: https://www.now-gmbh.de/foerderung/foerderprogramme/ladeinfrastruktur/ (accessed on 20 February 2022).
- Kindl, A.; Luchman, I.; Szabo, N.; Walther, C. SIMONE: Bedarfsorientiertes Verfahren zur Planung von Ladeinfrastruktur. Available online: https://www.forschungsinformationssystem.de/servlet/is/493096/?clsId0=276664&clsId1=0&clsId2=0&clsId3=0 (accessed on 20 February 2022).
- Deb, S.; Tammi, K.; Kalita, K.; Mahanta, P. Review of recent trends in charging infrastructure planning for electric vehicles. Wiley Interdiscip. Rev. Energy Environ. 2018, 7, 114. [Google Scholar] [CrossRef] [Green Version]
- Pagany, R.; Marquardt, A.; Zink, R. Electric Charging Demand Location Model—A User- and Destination-Based Locating Approach for Electric Vehicle Charging Stations. Sustainability 2019, 11, 2301. [Google Scholar] [CrossRef] [Green Version]
- Philipsen, R.; Schmidt, T.; Ziefle, M. A Charging Place to Be—Users’ Evaluation Criteria for the Positioning of Fast-charging Infrastructure for Electro Mobility. Procedia Manuf. 2015, 3, 2792–2799. [Google Scholar] [CrossRef] [Green Version]
- Philipsen, R.; Schmidt, T.; van Heek, J.; Ziefle, M. Fast-charging station here, please! User criteria for electric vehicle fast-charging locations. Transp. Res. Part Traffic Psychol. Behav. 2016, 40, 119–129. [Google Scholar] [CrossRef]
- Anderson, J.E.; Lehne, M.; Hardinghaus, M. What electric vehicle users want: Real-world preferences for public charging infrastructure. Int. J. Sustain. Transp. 2018, 12, 341–352. [Google Scholar] [CrossRef]
- Helmus, J.R.; Spoelstra, J.C.; Refa, N.; Lees, M.; van den Hoed, R. Assessment of public charging infrastructure push and pull rollout strategies: The case of the Netherlands. Energy Policy 2018, 121, 35–47. [Google Scholar] [CrossRef]
- Wu, H.; Niu, D. Study on Influence Factors of Electric Vehicles Charging Station Location Based on ISM and FMICMAC. Sustainability 2017, 9, 484. [Google Scholar] [CrossRef] [Green Version]
- Wu, Y.; Yang, M.; Zhang, H.; Chen, K.; Wang, Y. Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method. Energies 2016, 9, 157. [Google Scholar] [CrossRef] [Green Version]
- Zhao, H.; Li, N. Optimal Siting of Charging Stations for Electric Vehicles Based on Fuzzy Delphi and Hybrid Multi-Criteria Decision Making Approaches from an Extended Sustainability Perspective. Energies 2016, 9, 270. [Google Scholar] [CrossRef] [Green Version]
- Guo, S.; Zhao, H. Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective. Appl. Energy 2015, 158, 390–402. [Google Scholar] [CrossRef]
- Tang, Z.; Guo, C.; Hou, P.; Fan, Y. Optimal Siting of Electric Vehicle Charging Stations Based on Voronoi Diagram and FAHP Method. Energy Power Eng. 2013, 5, 1404–1409. [Google Scholar] [CrossRef]
- Erbaş, M.; Kabak, M.; Özceylan, E.; Çetinkaya, C. Optimal siting of electric vehicle charging stations: A GIS-based fuzzy Multi-Criteria Decision Analysis. Energy 2018, 163, 1017–1031. [Google Scholar] [CrossRef]
- Bi, R.; Xiao, J.; Pelzer, D.; Ciechanowicz, D.; Eckhoff, D.; Knoll, A. A simulation-based heuristic for city-scale electric vehicle charging station placement. In Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan, 16–19 October 2017; pp. 1–7. [Google Scholar] [CrossRef]
- Csonka, B.; Csiszár, C. Determination of charging infrastructure location for electric vehicles. Transp. Res. Procedia 2017, 27, 768–775. [Google Scholar] [CrossRef]
- Csiszár, C.; Csonka, B.; Földes, D.; Wirth, E.; Lovas, T. Urban public charging station locating method for electric vehicles based on land use approach. J. Transp. Geogr. 2019, 74, 173–180. [Google Scholar] [CrossRef]
- Chung, S.H.; Kwon, C. Multi-period planning for electric car charging station locations: A case of Korean Expressways. Eur. J. Oper. Res. 2015, 242, 677–687. [Google Scholar] [CrossRef]
- He, Y.; Kockelman, K.M.; Perrine, K.A. Optimal locations of U.S. fast charging stations for long-distance trip completion by battery electric vehicles. J. Clean. Prod. 2019, 214, 452–461. [Google Scholar] [CrossRef]
- Wu, F.; Sioshansi, R. A stochastic flow-capturing model to optimize the location of fast-charging stations with uncertain electric vehicle flows. Transp. Res. Part D Transp. Environ. 2017, 53, 354–376. [Google Scholar] [CrossRef]
- Gkatzoflias, D.; Drossinos, Y.; Zubaryeva, A.; Zambelli, P.; Dilara, P.; Thiel, C. Optimal allocation of electric vehicle charging infrastructure in cities and regions: Scientific analysis or review. JRC Rep. 2016. [Google Scholar] [CrossRef]
- Namdeo, A.; Tiwary, A.; Dziurla, R. Spatial planning of public charging points using multi-dimensional analysis of early adopters of electric vehicles for a city region. Technol. Forecast. Soc. Chang. 2014, 89, 188–200. [Google Scholar] [CrossRef] [Green Version]
- Pagani, M.; Korosec, W.; Chokani, N.; Abhari, R.S. User behaviour and electric vehicle charging infrastructure: An agent-based model assessment. Appl. Energy 2019, 254, 113680. [Google Scholar] [CrossRef]
- 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. 2012, 12, 391–416. [Google Scholar] [CrossRef]
- Chen, T.D.; Kockelman, K.M.; Khan, M. Locating Electric Vehicle Charging Stations. Transp. Res. Rec. J. Transp. Res. Board 2013, 2385, 28–36. [Google Scholar] [CrossRef]
- Efthymiou, D.; Antoniou, C.; Tyrinopoylos, Y.; Mitsakis, E. Spatial Exploration of Effective Electric Vehicle Infrastructure Location. Procedia Soc. Behav. Sci. 2012, 48, 765–774. [Google Scholar] [CrossRef] [Green Version]
- Frade, I.; Ribeiro, A.; Gonçalves, G.; Antunes, A.P. Optimal Location of Charging Stations for Electric Vehicles in a Neighborhood in Lisbon, Portugal. Transp. Res. Rec. J. Transp. Res. Board 2011, 2252, 91–98. [Google Scholar] [CrossRef] [Green Version]
- 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. Transp. Res. Part C Emerg. Technol. 2016, 67, 131–148. [Google Scholar] [CrossRef]
- Lam, A.Y.S.; Leung, Y.W.; Chu, X. Electric Vehicle Charging Station Placement: Formulation, Complexity, and Solutions. IEEE Trans. Smart Grid 2014, 5, 2846–2856. [Google Scholar] [CrossRef] [Green Version]
- Zhang, H.; Hu, Z.; Xu, Z.; Song, Y. An Integrated Planning Framework for Different Types of PEV Charging Facilities in Urban Area. IEEE Trans. Smart Grid 2016, 7, 2273–2284. [Google Scholar] [CrossRef]
- Hodgson, M.J. A Flow-Capturing Location-Allocation Model. Geogr. Anal. 1990, 22, 270–279. [Google Scholar] [CrossRef]
- He, F.; Yin, Y.; Zhou, J. Deploying public charging stations for electric vehicles on urban road networks. Transp. Res. Part C Emerg. Technol. 2015, 60, 227–240. [Google Scholar] [CrossRef]
- Cruz-Zambrano, M.; Corchero, C.; Igualada-Gonzalez, L.; Bernardo, V. Optimal location of fast charging stations in Barcelona: A flow-capturing approach. In Proceedings of the 2013 10th International Conference on the European Energy Market (EEM), Stockholm, Sweden, 27–31 May 2013; pp. 1–6. [Google Scholar] [CrossRef] [Green Version]
- Upchurch, C.; Kuby, M. Comparing the p-median and flow-refueling models for locating alternative-fuel stations. J. Transp. Geogr. 2010, 18, 750–758. [Google Scholar] [CrossRef]
- Shareef, H.; Islam, M.M.; Mohamed, A. A review of the stage-of-the-art charging technologies, placement methodologies, and impacts of electric vehicles. Renew. Sustain. Energy Rev. 2016, 64, 403–420. [Google Scholar] [CrossRef]
- Zhang, Y.; Liu, X.; Zhang, T.; Gu, Z. Review of the Electric Vehicle Charging Station Location Problem. In Dependability in Sensor, Cloud, and Big Data Systems and Applications; Wang, G., Bhuiyan, M.Z.A., de Di Capitani Vimercati, S., Ren, Y., Eds.; Communications in Computer and Information Science; Springer: Singapore, 2019; Volume 1123, pp. 435–445. [Google Scholar] [CrossRef]
- Pagany, R.; Ramirez Camargo, L.; Dorner, W. A review of spatial localization methodologies for the electric vehicle charging infrastructure. Int. J. Sustain. Transp. 2019, 13, 433–449. [Google Scholar] [CrossRef] [Green Version]
- Pallonetto, F.; Galvani, M.; Torti, A.; Vantini, S. A Framework for Analysis and Expansion of Public Charging Infrastructure under Fast Penetration of Electric Vehicles. World Electr. Veh. J. 2020, 11, 18. [Google Scholar] [CrossRef] [Green Version]
- Wagner, S.; Götzinger, M.; Neumann, D. Optimal Location of Charging Stations in Smart Cities: A Points of Interest Based Approach. In International Conference on Information Systems. 2013, Volume 3, pp. 2838–2855. Available online: https://aisel.aisnet.org/icis2013/proceedings/BreakthroughIdeas/12 (accessed on 1 February 2022).
- Bian, C.; Li, H.; Wallin, F.; Avelin, A.; Lin, L.; Yu, Z. Finding the optimal location for public charging stations—A GIS-based MILP approach. Energy Procedia 2019, 158, 6582–6588. [Google Scholar] [CrossRef]
- Wirges, J. Planning the Charging Infrastructure for Electric Vehicles in Cities and Regions; KIT Scientific Publishing: Karlsruhe, Germany, 2016. [Google Scholar] [CrossRef]
- Brost, W.; Funke, T.; Philipsen, R.; Brell, T.; Ziefle, M. Integrated Model Approach STELLA Method of Site Identification for Charging Infrastructure. IFAC-PapersOnLine 2018, 51, 206–211. [Google Scholar] [CrossRef]
- Dong, J.; Liu, C.; Lin, Z. Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data. Transp. Res. Part C Emerg. Technol. 2014, 38, 44–55. [Google Scholar] [CrossRef] [Green Version]
- Friese, P.A.; Michalk, W.; Fischer, M.; Hardt, C.; Bogenberger, K. Charging Point Usage in Germany—Automated Retrieval, Analysis, and Usage Types Explained. Sustainability 2021, 13, 13046. [Google Scholar] [CrossRef]
- Funke, S.Á.; Gnann, T.; Plötz, P. Addressing the Different Needs for Charging Infrastructure: An Analysis of Some Criteria for Charging Infrastructure Set-up. In E-Mobility in Europe; Leal Filho, W., Kotter, R., Eds.; Green Energy and Technology, Springer International Publishing: Cham, Switzerland, 2015; pp. 73–90. [Google Scholar] [CrossRef]
- Helmus, J.; van den Hoed, R. Key Performance Indicators of Charging infrastructure. World Electr. Veh. J. 2016, 8, 733–741. [Google Scholar] [CrossRef] [Green Version]
- Huang, K.; Kanaroglou, P.; Zhang, X. The design of electric vehicle charging network. Transp. Res. Part D Transp. Environ. 2016, 49, 1–17. [Google Scholar] [CrossRef] [Green Version]
- Jordán, J.; Palanca, J.; del Val, E.; Julian, V.; Botti, V. A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations. Appl. Sci. 2018, 8, 313. [Google Scholar] [CrossRef] [Green Version]
- Luo, L.; Gu, W.; Zhou, S.; Huang, H.; Gao, S.; Han, J.; Wu, Z.; Dou, X. Optimal planning of electric vehicle charging stations comprising multi-types of charging facilities. Appl. Energy 2018, 226, 1087–1099. [Google Scholar] [CrossRef]
- Maase, S.; Dilrosun, X.; Kooi, M.; van den Hoed, R. Performance of Electric Vehicle Charging Infrastructure: Development of an Assessment Platform Based on Charging Data. World Electr. Veh. J. 2018, 9, 25. [Google Scholar] [CrossRef] [Green Version]
- Niels, T.; Gerstenberger, M.; Bogenberger, K.; Hessel, C.; Gigl, A.; Wagner, K. Model-based optimization of public charging infrastructure planning in rural areas. Transp. Res. Procedia 2019, 41, 342–353. [Google Scholar] [CrossRef]
- Pevec, D.; Babic, J.; Kayser, M.A.; Carvalho, A.; Ghiassi-Farrokhfal, Y.; Podobnik, V. A data-driven statistical approach for extending electric vehicle charging infrastructure. Int. J. Energy Res. 2018, 42, 3102–3120. [Google Scholar] [CrossRef]
- Raposo, J.; Rodrigues, A.; Silva, C.; Dentinho, T. A multi-criteria decision aid methodology to design electric vehicles public charging networks. AIP Adv. 2015, 5, 057123. [Google Scholar] [CrossRef] [Green Version]
- Shirmohammadli, A.; Vallée, D. Developing a location model for fast charging infrastructure in urban areas. Int. J. Transp. Dev. Integr. 2017, 1, 159–170. [Google Scholar] [CrossRef]
- Straka, M.; Buzna, L. Preprocessing of GIS data for electric vehicle charging stations analysis and evaluation of the predictors significance. Transp. Res. Procedia 2019, 40, 1583–1590. [Google Scholar] [CrossRef]
- Straka, M.; de Falco, P.; Ferruzzi, G.; Proto, D.; van der Poel, G.; Khormali, S.; Buzna, L. Predicting Popularity of Electric Vehicle Charging Infrastructure in Urban Context. IEEE Access 2020, 8, 11315–11327. [Google Scholar] [CrossRef]
- Straka, M.; Carvalho, R.; van der Poel, G.; Buzna, L. Analysis of Energy Consumption at Slow Charging Infrastructure for Electric Vehicles. IEEE Access 2021, 9, 53885–53901. [Google Scholar] [CrossRef]
- Wolbertus, R.; Kroesen, M.; van den Hoed, R.; Chorus, C. Fully charged: An empirical study into the factors that influence connection times at EV-charging stations. Energy Policy 2018, 123, 1–7. [Google Scholar] [CrossRef] [Green Version]
- Statistisches Bundesamt. Zensus 2011: Bevölkerung im 100 Meter-Gitter. 2011. Available online: https://www.zensus2011.de/DE/Home/Aktuelles/DemografischeGrunddaten.html?nn=559100 (accessed on 18 January 2022).
- Geofabrik.de. OSM-Downloadserver: Hamburg. 2022. Available online: https://download.geofabrik.de/europe/germany/hamburg.html (accessed on 18 January 2022).
- Stadt Hamburg. Transparenzportal Hamburg: Elektro Ladestandorte Hamburg. 2022. Available online: https://suche.transparenz.hamburg.de/dataset/elektro-ladestandorte-hamburg26 (accessed on 18 January 2022).
- Stadt Hamburg. Transparenzportal Hamburg: Flächennutzungsplan Hamburg. 2022. Available online: https://suche.transparenz.hamburg.de/dataset/flachennutzungsplan-hamburg25 (accessed on 18 January 2022).
- van der waerden, P.; Timmermans, H.; de Bruin-Verhoeven, M. Car drivers’ characteristics and the maximum walking distance between parking facility and final destination. J. Transp. Land Use 2017, 10, 1–11. [Google Scholar] [CrossRef] [Green Version]
- Luxen, D.; Vetter, C. Real-time routing with OpenStreetMap data. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems—GIS ’11, Chicago, IL, USA, 1–4 November 2011; Cruz, I., Agrawal, D., Eds.; ACM Press: New York, NY, USA, 2011; p. 513. [Google Scholar] [CrossRef]
- Hecht, C.; Das, S.; Bussar, C.; Sauer, D.U. Representative, empirical, real-world charging station usage characteristics and data in Germany. ETransportation 2020, 6, 100079. [Google Scholar] [CrossRef]
Core Aspect | Frequency |
---|---|
Accessibility | 8 |
Centrality | 4 |
Connectivity to public transport | 6 |
Coverage | 10 |
Dwell Characteristics | 11 |
Land Use | 10 |
Other CIS | 12 |
POI | 23 |
Parking Situation | 16 |
Residential Population | 45 |
Safety | 5 |
Surrounding Street Network | 11 |
Traffic Flow | 11 |
Visibility | 5 |
Working Population | 9 |
Accessibility | Centrality | Connectivity to Public Transport | Coverage | Dwell Characteristics | Land Use | Other CIS | POI | Parking Situation | Residential Population | Safety | Surrounding Street Network | Traffic Flow | Visibility | Working Population | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Anderson et al. [11] | x | – | – | – | – | x | – | x | – | – | – | x | – | – | – |
Bi et al. [19] | – | – | – | – | x | – | x | – | – | – | – | – | – | – | – |
Bian et al. [44] | – | – | – | – | – | x | x | – | – | – | – | – | x | – | – |
Brost et al. [46] | – | – | – | – | x | – | x | x | – | – | – | – | x | – | – |
Chen et al. [29] | – | – | – | x | x | x | x | – | x | x | – | x | – | – | x |
Chung, Kwon [22] | – | – | – | – | – | – | – | – | – | – | – | – | x | – | – |
Csiszár et al. [21] | – | – | – | – | x | – | x | – | x | x | – | – | – | – | – |
Csonka, Csiszár [20] | – | – | – | – | x | – | x | – | – | x | – | – | – | – | – |
Deb et al. [7] | – | – | – | x | – | – | – | – | – | – | – | – | x | – | – |
Dong et al. [47] | – | – | – | – | – | – | – | x | x | – | – | – | – | – | – |
Efthymiou et al. [30] | – | – | – | x | – | – | – | x | – | x | – | – | – | – | – |
Erbaş et al. [18] | – | – | – | – | – | – | x | x | – | x | – | x | – | – | – |
Frade et al. [31] | – | – | – | x | – | – | – | – | x | x | – | – | – | – | x |
Friese et al. [48] | – | – | – | – | – | x | – | – | – | x | – | – | – | – | – |
Funke et al. [49] | x | – | – | – | – | – | – | – | – | – | x | – | – | x | – |
Gkatzoflias et al. [25] | – | – | x | – | – | – | – | x | x | x | – | – | – | – | – |
Guo, Zhao [16] | – | – | – | – | – | – | – | – | – | – | – | x | – | – | – |
He et al. [32] | x | – | – | – | – | – | – | – | – | x | – | – | – | x | – |
Helmus, van den Hoed [50] | – | – | – | x | – | – | x | – | – | – | – | – | – | – | – |
Huang [51] | – | x | – | – | – | – | – | x | – | x | – | – | – | – | – |
Jordán et al. [52] | – | – | – | – | x | – | – | – | – | x | – | – | x | – | – |
Kindl et al. [6] | x | – | x | – | – | – | – | x | x | – | x | – | x | x | – |
Lam et al. [33] | – | – | – | x | – | – | – | – | – | x | – | – | – | – | – |
Luo et al. [53] | – | – | – | – | – | x | – | – | – | – | – | – | – | – | – |
Maase et al. [54] | – | – | – | – | – | – | – | – | x | – | – | – | x | – | – |
NOW GmbH [5] | x | x | x | – | x | – | – | – | x | – | x | – | x | x | – |
Namdeo et al. [26] | – | x | x | – | – | – | – | – | – | x | – | – | – | – | x |
Niels et al. [55] | – | – | – | – | – | – | – | x | – | x | – | – | – | – | x |
Pagani et al. [27] | – | – | – | – | – | – | – | – | – | x | – | – | – | – | – |
Pagany et al. [41] | – | – | – | x | x | – | – | – | – | – | – | – | x | – | – |
Pagany et al. [8] | – | – | – | x | x | – | – | x | – | – | – | – | – | – | – |
Pallonetto et al. [42] | – | – | – | – | – | – | – | x | – | – | – | – | – | – | – |
Pevec et al. [56] | – | – | – | – | – | – | x | x | – | – | – | – | – | – | – |
Phillipsen et al. [10] | x | – | – | – | – | – | – | x | – | – | x | – | – | – | – |
Raposo et al. [57] | – | – | – | – | – | x | – | – | x | x | – | – | – | – | – |
Shirmohammadly, Vallée [58] | – | – | – | – | – | – | x | – | x | – | – | – | – | – | – |
Stadt Hamburg [4] | x | x | x | – | – | – | – | – | x | – | – | x | – | x | – |
Straka, Buzna [59] | – | – | – | – | – | – | – | x | – | x | – | – | – | – | – |
Straka et al. [60] | – | – | – | – | – | – | x | x | – | x | – | – | – | – | x |
Straka et al. [61] | – | – | – | – | – | – | – | – | – | x | – | – | – | – | x |
Tang et al. [17] | – | – | – | – | – | – | – | – | – | – | – | x | – | – | – |
Wagner et al. [43] | – | – | – | – | – | – | – | x | – | – | – | – | – | – | – |
Wirges [45] | – | – | x | – | – | – | – | x | x | x | – | – | – | – | x |
Wolbertus et al. [62] | – | – | – | – | – | – | – | – | – | x | – | – | – | – | – |
Wu et al. [14] | – | – | – | – | – | x | x | – | – | – | – | x | – | – | – |
Wu, Niu [13] | – | – | – | – | – | x | – | – | – | – | – | – | x | – | – |
Zhang et al. [34] | – | – | – | – | – | – | – | – | – | x | – | – | – | – | – |
Zhao, Li [15] | – | – | – | x | – | – | – | – | – | x | – | x | – | – | – |
Core Aspect | Influencing Factors |
---|---|
Accessibility | accessibility, public access, unrestricted access |
Centrality | position within main road network, regional centers, centrality |
Connectivity to public transport | park-and-ride locations, relevance of public transportation, park and ride, connection to public transport, connectivity to public transport, proximity to public transport |
Coverage | distance destination—charging station, coverage, POI distance from parking, charging station range, service radius, walking distance |
Dwell Characteristics | parking daytime, expected staying period, dwelling time, visiting frequency, destination type, dwell time, natural parking locations, parking duration, average time spent in area by citizens, dwell time relevant for charging |
Land Use | regional structure, area characterization, parking duration, area attribute, land use type |
Other CIS | effect of nearby charging, other installed stations, coverage, other charging stations, charging capacity, service radius, existing charging infrastructure, distance to other charging stations, number of charging points, proximity to other charging sites |
POI | number of wholesale, shops, hotels, restaurants and catering facilities, proximity to public access buildings, proximity to shopping and food areas, shopping, candidate site at POI, destinations (POI), number of business services, min. distance to financial related OSM amenity, POI quality of stay, POI, presence of food, museum and health POI, number of culture, recreation and other services, proximity to petrol station, sports and culture, hotels, possibility to spend charging time on other activities, places where people spend time, presence of shopping centers and commercial areas, dual use |
Parking Situation | parking places, parking lot availability, availability of parking space, parking pressure, parking demand, charging post usage, parking spaces, available parking space, public parking demand, number of parking lots, parking potential, parking areas, big car parks |
Residential Population | family size, residential area type, number of multi family houses, home places, residential dwelling type, population in area, early adopters, vehicle ownership, number of households with a monthly income with at most EUR 900, service area population, newly built houses, number of one person households, education level, nighttime demand, residents, parking demand, general population, private charging spot possession rate, residential location of early adopters, number of BEV, BEV ownership in service area, buying power per inhabitant, residents professional habit, average income, age, number of persons in one-person households from 45 to 65 years, percentage of working population working in the industry, population, proportion of residential area, residents receiving social assistance, street parkers, income, residential statistics, parking pressure, gender, share of residential BEV, number of cars per square kilometer, residents with high income, residential population, number of persons in one-person households from 25 to 45 years, parking nighttime |
Safety | safety, safety for me, safety for my car, safety and ease of use |
Surrounding Street Network | road conditions, reachability, proximity to main roads, traffic convenience, proximity to junctions, parking demand, convenience of transportation, main roads, lane crossings, road type |
Traffic Flow | average traffic in area, traffic volume of early adopters, flow captured, electric vehicle flow, traffic frequency of individual mobility/private cars, traffic volume, average traffic flow, traffic flow, vehicle flow, traffic volume relevant for charging, traffic density |
Visibility | visibility |
Working Population | businesses with high income, car commuter destinations, general working places, parking demand, daytime demand, early adopter working places, proportion of business and industrial area, places of employment |
Core Aspect | Argumentation Code | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Ac | AcHo | AcPOI | AcPT | AcWo | AvAR | AvCo | Qu | QuAt | QuRe | QuRed | |
Accessibility | – | – | – | – | – | 3 | – | 1 | – | 5 | – |
Centrality | – | – | 4 | – | – | – | – | – | 4 | 4 | – |
Connectivity to public transport | – | – | – | 6 | – | – | – | – | – | 1 | – |
Coverage | – | – | 2 | – | – | – | 10 | – | – | – | 1 |
Dwell Characteristics | 8 | – | 3 | – | – | – | – | – | – | – | – |
Land Use | 8 | 1 | – | – | 1 | – | – | – | 6 | – | – |
Other CIS | – | – | – | – | – | – | 11 | – | – | – | 9 |
POI | – | – | 23 | – | – | – | – | – | 5 | – | – |
Parking Situation | 16 | – | – | – | – | – | – | – | 16 | – | – |
Residential Population | – | 45 | – | – | – | – | – | – | 1 | – | – |
Safety | – | – | – | – | – | – | – | – | 5 | – | – |
Surrounding Street Network | 4 | – | – | 1 | – | – | – | – | 4 | 11 | – |
Traffic Flow | 11 | – | – | – | – | – | – | – | 6 | – | – |
Visibility | – | – | – | – | – | – | – | – | 5 | – | – |
Working Population | – | – | – | – | 9 | – | – | – | – | – | – |
Mentioned Metrics | Core Aspect |
---|---|
Accessibility | – |
Centrality | – |
Connectivity to public transport | number of parking places |
Coverage | euclidean distance between charger and POI, total area (sqm) in walking distance of a charging point, census block center reachable from charger within willing/accepted to walk distance, maximum distance between charging stations within range of EV |
Dwell Characteristics | parking duration at POI, parking duration depending on trip activity, sum of destination type specific charging demand in 250 m hexagon |
Land Use | number of commercial buildings, parking duration depending on destination land use type, number of detached or row houses, number of all buildings |
Other CIS | mean walking distance between charging stations, neighboring charging infrastructure in willing to walk distance, number of chargers weighted by hexagon distance, sum of other charging points in 500 m radius |
POI | attractivity index, number of POI in 200 m radius, area of surface, sum of POI in 500 m radius, number of visitors |
Parking Situation | average parking prices for daily paid parking, candidate site is parking garage, capacity of parking, number of cars parked during day/night at thoroughfare parking and in parking lots, number of parking places, available at candidate site |
Residential Population | population density, number of households without garages, number of vehicles per sqkm, population in 250 m Hexagon, number of households with two cars, number of high income households, population density in m cell, number of resident BEV in census block, population in 250 m hexagon, number of inhabitants, number of registered residents, main residential building type in 250m hexagon, sum of residents in 200 m radius, average income in 200m radius, number of cars, population in area × BEV penetration rate |
Safety | – |
Surrounding Street Network | transit access and network connectivity |
Traffic Flow | number of unique users |
Visibility | – |
Working Population | employment density, student density, number of employee-owned BEV in census block, number of employees |
Core Aspect | Represented Argumentation Codes | Metric | Remark | Data Source |
---|---|---|---|---|
Residential Population | AcHo | Population: No. of residents (500 m radius) | – | Census [63] |
POI | AcPOI, QuAt | POI: Type-specific POI counts (300 m radius) | – | OSM [64] |
Parking Situation | – | – | Location and capacity of on-street parking unavailable | – |
Surrounding Street Network | QuRe | Reachability: Area of 3 min driving isochrone around coordinate | – | OSM [64] |
Traffic Flow | – | – | Traffic flow data unavailable | – |
Coverage | AvCo, QuRed | Catchment Area: Area of associated Voronoi cell | – | OD HH [65] |
Dwell Characteristics | AcPOI, AcWo | Business Area Share: Share of business area (500 m radius) | – | OD HH [66] |
Other CIS | AvCo, QuRed | Other CP: No. of competing CP (500 m radius) | – | OD HH [65] |
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Adenaw, L.; Krapf, S. Placing BEV Charging Infrastructure: Influencing Factors, Metrics, and Their Influence on Observed Charger Utilization. World Electr. Veh. J. 2022, 13, 56. https://doi.org/10.3390/wevj13040056
Adenaw L, Krapf S. Placing BEV Charging Infrastructure: Influencing Factors, Metrics, and Their Influence on Observed Charger Utilization. World Electric Vehicle Journal. 2022; 13(4):56. https://doi.org/10.3390/wevj13040056
Chicago/Turabian StyleAdenaw, Lennart, and Sebastian Krapf. 2022. "Placing BEV Charging Infrastructure: Influencing Factors, Metrics, and Their Influence on Observed Charger Utilization" World Electric Vehicle Journal 13, no. 4: 56. https://doi.org/10.3390/wevj13040056