A Literature Review on the Charging Behaviour of Private Electric Vehicles
Source abstract
Abstract
Electric mobility is one of the ways of containing greenhouse gas and local pollutants emissions in urban areas. Nevertheless, the massive introduction of battery-powered electric vehicles (EVs) is introducing some concerns related to their energy demand. Modelling vehicle usage and charging behavior is essential for charge demand forecasting and energy consumption estimation. Therefore, it is crucial to understand how the charging decisions of EV owners are influenced by different factors, ranging from the charging infrastructure characteristics to the users’ profiles. This review examines the approaches used to investigate charging behavior and highlights the trends and differences between the results, remarking on any gaps worthy of further investigation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Electric Vehicles and Infrastructure · Advanced Battery Technologies Research · Transportation and Mobility Innovations
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Laboratory of Systems and Technologies for Sustainable Mobility, ENEA C.R. Casaccia, via Anguillarese 301, 00123 Roma, Italy
Funding
Ministry of Environment and Energy Security
References (100+)
Show source-provided references
1. Un-Noor, F., Padmanaban, S., Mihet-Popa, L., Mollah, M.N., and Hossain, E. (2017). A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development. Energies, 10.10.20944/preprints201705.0090.v1 ↗
2. Loganathan, M.K., Tan, C.M., Mishra, B., Msagati, T.A.M., and Snyman, L.W. (2019, January 17–19). Review and selection of advanced battery technologies for post 2020 era electric vehicles. Proceedings of the 2019 IEEE Transportation Electrification Conference (ITEC-India), Bengaluru, India.10.1109/ITEC-India48457.2019.ITECINDIA2019-254 ↗
3. International Energy Agency (2023, July 14). Global EV Outlook 2023: Catching up with Climate Ambitions. Available online: www.iea.org.
4. Why are charging stations associated with electric vehicle adoption? Untangling effects in three United States metropolitan areas10.1016/j.erss.2022.102663 ↗
5. Deb, S., Kalita, K., and Mahanta, P. (2017, January 21–23). Review of impact of electric vehicle charging station on the power grid. Proceedings of the 2017 International Conference on Technological Advancements in Power and Energy (TAP Energy), Kollam, India.10.1109/TAPENERGY.2017.8397215 ↗
6. Impacts of grid integration of solar PV and electric vehicle on grid stability, power quality and energy economics: A review10.1049/iet-esi.2019.0047 ↗
7. (2017). Electric Vehicle Conductive Charging System—Part 1: General Requirements (Standard No. IEC 61851-1:2017). Available online: https://webstore.iec.ch/publication/33644.
8. Ultra-fast charging of electric vehicles: A review of power electronics converter, grid stability and optimal battery consideration in multi-energy systems10.1016/j.segan.2023.101112 ↗
9. Assessment of charging technologies, infrastructure and charging station recommendation schemes of electric vehicles: A review10.1016/j.asej.2022.101938 ↗
10. A Comprehensive Review on the Integration of Electric Vehicles for Sustainable Development10.1155/2022/3868388 ↗
11. Understanding the generation mechanism of BEV drivers’ charging demand: An exploration of the relationship between charging choice and complexity of trip chaining patterns10.1016/j.tra.2022.02.007 ↗
12. Vanhaverbeke, L., Verbist, D., Barrera, G., VUB-MOBI Electromobility Research Centre, Csukas, M., and FIER (2023, July 14). Consumer Monitor 2022. Available online: https://alternative-fuels-observatory.ec.europa.eu/system/files/documents/2023-06/2022%20EAFO_CountryReport_EU.pdf.
13. Fast-charging station here, please! User criteria for electric vehicle fast-charging locations10.1016/j.trf.2016.04.013 ↗
14. Consumer preferences for electric vehicles: A literature review10.1080/01441647.2016.1230794 ↗
15. Assessing the socio-demographic, technical, economic and behavioral factors of Nordic electric vehicle adoption and the influence of vehicle-to-grid preferences10.1016/j.rser.2019.109692 ↗
16. Psychological and behavioral factors affecting electric vehicle adoption and satisfaction: A comparative study of early adopters in China and Korea10.1016/j.trd.2019.09.009 ↗
17. Febransyah, A. (2021). Predicting Purchase Intention towards Battery Electric Vehicles: A Case of Indonesian Market. World Electr. Veh. J., 12.10.3390/wevj12040240 ↗
18. Self-image motives for electric vehicle adoption: Evidence from China10.1016/j.trd.2022.103383 ↗
19. A systematic review of the evidence on plug-in electric vehicle user experience10.1016/j.trd.2019.01.008 ↗
20. How might potential future plug-in electric vehicle buyers differ from current “Pioneer” owners?10.1016/j.trd.2016.05.015 ↗
21. Nunes, M.L., Fernandes, J.F.P., Oliveira, J., and Branco, P.J.C. (2022, January 2–3). Power factor signature analysis for disaggregation of EV charging loads from aggregated power. Proceedings of the CIRED Porto Workshop 2022: E-Mobility and Power Distribution Systems, Hybrid Conference, Porto, Portugal.10.1049/icp.2022.0654 ↗
22. Taha FAl Vincent, T., and Bitar, E. (2022). An Efficient Method for Quantifying the Aggregate Flexibility of Plug-in Electric Vehicle Populations. arXiv.
23. Patil, P., Kazemzadeh, K., and Bansal, P. (2022). Integration of charging behavior into infrastructure planning and management of electric vehicles: A systematic review and framework. SSRN Electron. J.10.2139/ssrn.4132436 ↗
24. Amara-Ouali, Y., Goude, Y., Massart, P., Poggi, J.-M., and Yan, H. (2021). A Review of Electric Vehicle Load Open Data and Models. Energies, 14.10.1145/3447555.3466568 ↗
25. A review on charging behavior of electric vehicles: Data, model, and control10.1007/s11768-020-0048-8 ↗
26. A review of consumer preferences of and interactions with electric vehicle charging infrastructure10.1016/j.trd.2018.04.002 ↗
27. How much charging infrastructure do electric vehicles need? A review of the evidence and international comparison10.1016/j.trd.2019.10.024 ↗
28. Are travel surveys a good basis for EV models? Validation of simulated charging profiles against empirical data10.1016/j.apenergy.2020.115318 ↗
29. Iqbal, M.N., Kütt, L., Lehtonen, M., Millar, R.J., Püvi, V., Rassõlkin, A., and Demidova, G.L. (2021). Travel Activity Based Stochastic Modelling of Load and Charging State of Electric Vehicles. Sustainability, 13.10.3390/su13031550 ↗
30. Gao, H., Yang, L., Zhang, A., and Sheng, M. (2021). Analysis of Urban Electric Vehicle Trip Rule Statistics and Ownership Prediction. Symmetry, 13.10.3390/sym13112052 ↗
31. Electrification of personal vehicle travels in cities—Quantifying the public charging demand10.1016/j.etran.2021.100125 ↗
32. Building-centric investigation into electric vehicle behavior: A survey-based simulation method for charging system design10.1016/j.energy.2023.127010 ↗
33. Capturing diversity in electric vehicle charging behaviour for network capacity estimation10.1016/j.trd.2021.102762 ↗
34. Pagany, R., Marquardt, A., and Zink, R. (2019). Electric Charging Demand Location Model—A User- and Destination-Based Locating Approach for Electric Vehicle Charging Stations. Sustainability, 11.10.3390/su11082301 ↗
35. Coincidence Factors for Domestic EV Charging From Driving and Plug-In Behavior10.1109/TTE.2021.3088275 ↗
36. Grid Loading Due to EV Charging Profiles Based on Pseudo-Real Driving Pattern and User Behavior10.1109/TTE.2019.2921854 ↗
37. Modeling the charging and route choice behavior of BEV drivers10.1016/j.trc.2015.09.008 ↗
38. Analysis of the effect of charging needs on battery electric vehicle drivers’ route choice behaviour: A case study in the Netherlands10.1016/j.trd.2019.102206 ↗
39. Forecasting electricity demand of electric vehicles by analyzing consumers’ charging patterns10.1016/j.trd.2018.02.009 ↗
40. Jabeen, F., Olaru, D., Smith, B., Braunl, T., and Speidel, S. (2013, January 2–4). Electric vehicle battery charging behaviour: Findings from a driver survey. Proceedings of the Australasian Transport Research Forum 2013, Brisbane, Australia.
41. Modelling electric vehicles use: A survey on the methods10.1016/j.rser.2016.10.005 ↗
42. Dunckley, J., and Tal, G. (2016). Plug-in Electric Vehicle Multi-State Market and Charging Survey, EPRI.
43. Real-world charging behavior and preferences of electric vehicles users in Germany10.1080/15568318.2022.2147041 ↗
44. Plenter, F., von Hoffen, M., Chasin, F., Benhaus, S., Matzner, M., Paukstadt, U., and Becker, J. (2018, January 11–14). Quantifying Consumers’ Willingness to Pay for Electric Vehicle Charging. Proceedings of the 2018 IEEE 20th Conference on Business Informatics (CBI), Vienna, Austria.10.1109/CBI.2018.00030 ↗
45. How do people value electric vehicle charging service? A gamified survey approach10.1016/j.jclepro.2018.11.032 ↗
46. Economic and environmental impacts of providing renewable energy for electric vehicle charging—A choice experiment study10.1016/j.apenergy.2016.07.121 ↗
47. Do I need to charge right now? Tailored choice architecture design can increase preferences for electric vehicle smart charging10.1016/j.enpol.2022.112818 ↗
48. Anticipating PEV buyers’ acceptance of utility controlled charging10.1016/j.tra.2015.09.004 ↗
49. What do consumers think of smart charging? Perceptions among actual and potential plug-in electric vehicle adopters in the United Kingdom10.1016/j.erss.2019.101318 ↗
50. Charging behavior modeling of battery electric vehicle drivers on long-distance trips10.1016/j.trd.2022.103490 ↗
51. Sun, X., Yamamoto, T., and Morikawa, T. (2014, January 12–14). The Timing of Mid-trip Electric Vehicle Charging. Proceedings of the Transportation Research Board 93rd Annual Meeting, Washington, DC, USA. Compendium of Papers.
52. Joint charging mode and location choice model for battery electric vehicle users10.1016/j.trb.2017.03.004 ↗
53. Modeling the Charging Choices of Battery Electric Vehicle Drivers by Using Stated Preference Data10.3141/2572-06 ↗
54. Wang, Y.-Y., Chi, Y.-Y., Xu, J.-H., and Li, J.-L. (2021). Consumer Preferences for Electric Vehicle Charging Infrastructure Based on the Text Mining Method. Energies, 14.10.3390/en14154598 ↗
55. Consumer preferences for public charging infrastructure for electric vehicles10.1016/j.tranpol.2019.05.017 ↗
56. Electric vehicles’ impacts on residential electric local profiles—A stochastic modelling approach considering socio-economic, behavioural and spatial factors10.1016/j.apenergy.2018.10.010 ↗
57. Daily electric vehicle charging load profiles considering demographics of vehicle users10.1016/j.apenergy.2020.115063 ↗
58. Latinopoulos, C., Sivakumar, A., and Polak, J. (2017, January 8–12). Modeling electric vehicle charging behaviour: What is the relationship between charging location, driving and range anxiety?. Proceedings of the Transportation Research Board 96th Annual Meeting, Washington, DC, USA. 17p.
59. Are consumers in China’s major cities happy with charging infrastructure for electric vehicles?10.1016/j.apenergy.2022.120082 ↗
60. Estimating charging demand from the perspective of choice behavior: A framework combining rule-based algorithm and hybrid choice model10.1016/j.jclepro.2022.134262 ↗
61. Real-time data from mobile platforms to evaluate sustainable transportation infrastructure10.1038/s41893-020-0533-6 ↗
62. Electric vehicle drivers’ charging behavior analysis considering heterogeneity and satisfaction10.1016/j.jclepro.2020.124982 ↗
63. Nicholas, M., Tal, G., and Turrentine, T.S. (2017). Advanced Plug-in Electric Vehicle Travel and Charging Behavior Interim Report, The Regents of the University of California.
64. Exploring electric vehicle charging patterns: Mixed usage of charging infrastructure10.1016/j.trd.2020.102249 ↗
65. Understanding charging behaviour of electric vehicle users10.1016/j.trf.2013.09.002 ↗
66. Interacting with limited mobility resources: Psychological range levels in electric vehicle use10.1016/j.tra.2012.10.010 ↗
67. Philipsen, R., Brell, T., Biermann, H., and Ziefle, M. (2019). Under Pressure—Users’ Perception of Range Stress in the Context of Charging and Traditional Refueling. World Electr. Veh. J., 10.10.3390/wevj10030050 ↗
68. Investigation on Range Anxiety and Safety Buffer of Battery Electric Vehicle Drivers10.1155/2018/8301209 ↗
69. Modeling EV charging choice considering risk attitudes and attribute non-attendance10.1016/j.trc.2019.03.007 ↗
70. Hardinghaus, M., Anderson, J.E., Nobis, C., Stark, K., and Vladova, G. (2022). Booking Public Charging: User Preferences and Behavior towards Public Charging Infrastructure with a Reservation Option. Electronics, 11.10.20944/preprints202207.0445.v1 ↗
71. Budnitz, H., Meelen, T., and Schwanen, T. (2022). Residential Neighbourhood Charging of Electric Vehicles: An exploration of user preferences. SocArXiv.10.31235/osf.io/fsv7n ↗
72. On the ease of being green: An investigation of the inconvenience of electric vehicle charging10.1016/j.apenergy.2019.114090 ↗
73. Improving Electric Vehicle Charging Station Efficiency through Pricing10.1155/2018/4831951 ↗
74. Response of electric vehicle drivers to dynamic pricing of parking and charging services: Risky choice in early reservations10.1016/j.trc.2017.04.008 ↗
75. Can public slow charging accelerate plug-in electric vehicle sales? A simulation of charging infrastructure usage and its impact on plug-in electric vehicle sales for Germany10.1080/15568318.2018.1489016 ↗
76. A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts10.1016/j.apenergy.2015.01.144 ↗
77. Driving and charging patterns of electric vehicles for energy usage10.1016/j.rser.2014.07.177 ↗
78. Corchero, C., Gonzalez-Villafranca, S., and Sanmarti, M. (2014, January 17–19). European electric vehicle fleet: Driving and charging data analysis. Proceedings of the 2014 IEEE International Electric Vehicle Conference (IEVC), Florence, Italy.10.1109/IEVC.2014.7056144 ↗
79. Planning for electric vehicle needs by coupling charging profiles with urban mobility10.1038/s41560-018-0136-x ↗
80. An investigation into usage patterns of electric vehicles in Ireland10.1016/j.trd.2015.12.013 ↗
81. An Investigation of Battery Electric Vehicle Driving and Charging Behaviors Using Vehicle Usage Data Collected in Shanghai, China10.1177/0361198118759015 ↗
82. INL (2023, July 14). Nissan Leaf Vehicle Summary Report, Available online: https://avt.inl.gov/sites/default/files/pdf/EVProj/EVProjectNissanLeafQ42013.pdf.
83. Märtz, A., Langenmayr, U., Ried, S., Seddig, K., and Jochem, P. (2022). Charging Behavior of Electric Vehicles: Temporal Clustering Based on Real-World Data. Energies, 15.10.3390/en15186575 ↗
84. Daina, N., and Polak, J.W. (2016, January 1–4). Hazard based modelling of electric vehicles charging patterns. Proceedings of the 2016 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific), Busan, Republic of Korea.10.1109/ITEC-AP.2016.7513002 ↗
85. Charge timing choice behavior of battery electric vehicle users10.1016/j.trd.2015.04.007 ↗
86. Fast-charging station choice behavior among battery electric vehicle users10.1016/j.trd.2016.03.008 ↗
87. Heterogeneous hazard model of PEV users charging intervals: Analysis of four year charging transactions data10.1016/j.trc.2017.06.022 ↗
88. Unraveling User Type Characteristics: Towards a Taxonomy for Charging Infrastructure10.3390/wevj7040589 ↗
89. Smart EV Charging Strategies Based on Charging Behavior10.3389/fenrg.2022.773440 ↗
90. A data driven typology of electric vehicle user types and charging sessions10.1016/j.trc.2020.102637 ↗
91. Research on Big Data Mining Technology of Electric Vehicle Charging Behaviour10.5755/j01.eie.25.6.24827 ↗
92. Generating electric vehicle load profiles from empirical data of three EV fleets in Southwest Germany10.1016/j.jclepro.2017.02.150 ↗
93. Steenbrink, M., Jochemsen, T., Piersma, N., van der Mei, R., and Dugunji, E. (2023, November 28). A Prediction Model for the Demand of Charging Stations for Electric Vehicles in the Municipality of Amsterdam. Available online: https://research.hva.nl/en/publications/a-prediction-model-for-the-demand-of-charging-stations-for-electr.
94. Scalable probabilistic estimates of electric vehicle charging given observed driver behavior10.1016/j.apenergy.2021.118382 ↗
95. Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption10.1038/s41560-022-01105-7 ↗
96. The investigation of the major factors influencing plug-in electric vehicle driving patterns and charging behaviour10.1016/j.rser.2014.10.058 ↗
97. Chakraborty, D., Bunch, D.S., Lee, J., and Tal, G. (2020). What Factors Drive Commuters’ Demand for Electric Vehicle Charging Infrastructure?, UC Office of the President, University of California Institute of Transportation Studies, University of California.
98. Kim, D., Kwon, D., Han, J., Lee, S.M., Elkosantini, S., and Suh, W. (2023). Data-Driven Model for Identifying Factors Influencing Electric Vehicle Charging Demand: A Comparative Analysis of Early- and Maturity-Phases of Electric Vehicle Programs in Korea. Appl. Sci., 13.10.3390/app13063760 ↗
99. Impact of incentives for greener battery electric vehicle charging—A field experiment10.1016/j.enpol.2021.112752 ↗
100. Forecasting the EV charging load based on customer profile or station measurement?10.1016/j.apenergy.2015.10.184 ↗