What Lies behind Idle Connection Time in Fast-Charging Public Stations: Evidence from Changshu, China
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 8
Abstract
Understanding charging vehicles, charging stations, and built environment concerning idle connection times significantly guided the management of charging infrastructure. However, the interplay between these factors had remained incompletely understood. This study addressed this gap by investigating public charging stations in Changshu, Suzhou, China, as a case study. The random forest regression and partial dependence plots were employed to explore the nonlinear relationships between idle connection times of vehicles at public fast-charging stations and the built environment, charging stations, and charging vehicles. The exploration encompassed two typical scenarios: workdays and weekends. The findings reveal the distinct influences of various factors in different scenarios. Notably, catering service points of interests in the proximity of charging stations, significantly impact the idle connection time on both workdays and weekends. Furthermore, government groups and residential areas have a notable influence on idle connection times during workdays. Shopping service and Leisure sport have a significant impact on idle connection time during the weekends. Variables such as the charging start time and charged energy also exhibit significant effects. Importantly, these influencing factors demonstrate heterogeneity and exhibit different threshold effects. This research can offer valuable insights to planning authorities and charging facility operators for formulating strategies to enhance charging infrastructure utilization.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.
Acknowledgments
This research was funded by the National Key R&D Program of China (2018YFB1600900 and 2018YFE0120100) and China Scholarship Council (202306090195).
Author contributions: The authors confirm contribution to the article as follows: X. Zhou: Study conception and design. X. Zhou and Y. Ji: Data collection. X. Zhou and X. Ding: Analysis and interpretation of results. X. Zhou, X. Ding, and Y. Ji: Draft manuscript preparation. All authors reviewed the results and approved the final version of the manuscript.
References
Almaghrebi, A., F. Al Juheshi, K. James, N. Aljuhaishi, and M. Alahmad. 2021. “PEVs idle time prediction at public charging stations using machine-learning methods.” In Proc., 2021 IEEE Transportation Electrification Conf. & Expo (ITEC), 1–5. New York: IEEE.
Ashkrof, P., G. C. C. A. de Almeida Correia, and B. Van Arem. 2020. “Analysis of the effect of charging needs on battery electric vehicle drivers’ route choice behaviour: A case study in the Netherlands.” Transp. Res. Part D Transp. Environ. 78 (Jan): 102206. https://doi.org/10.1016/j.trd.2019.102206.
Bergstra, J., and Y. Bengio. 2012. “Random search for hyper-parameter optimization.” J. Mach. Learn. Res. 13 (2): 281–305.
Bi, H., Z. Ye, and H. Zhu. 2022. “Examining the nonlinear impacts of built environment on ridesourcing usage: Focus on the critical urban sub-regions.” J. Cleaner Prod. 350 (May): 131314. https://doi.org/10.1016/j.jclepro.2022.131314.
Brady, J., and M. O’Mahony. 2016. “Modelling charging profiles of electric vehicles based on real-world electric vehicle charging data.” Sustainable Cities Soc. 26 (Oct): 203–216. https://doi.org/10.1016/j.scs.2016.06.014.
Breiman, L. 2001. “Random forests.” Mach. Learn. 45 (Oct): 5–32. https://doi.org/10.1023/A:1010933404324.
Brooker, R. P., and N. Qin. 2015. “Identification of potential locations of electric vehicle supply equipment.” J. Power Sources 299 (Dec): 76–84. https://doi.org/10.1016/j.jpowsour.2015.08.097.
Cervero, R. 2002. “Built environments and mode choice: Toward a normative framework.” Transp. Res. Part D Transp. Environ. 7 (4): 265–284. https://doi.org/10.1016/S1361-9209(01)00024-4.
Chakraborty, D., S. Hardman, and G. Tal. 2020. “Why do some consumers not charge their plug-in hybrid vehicles? Evidence from Californian plug-in hybrid owners.” Environ. Res. Lett. 15 (8): 084031. https://doi.org/10.1088/1748-9326/ab8ca5.
Chen, C., Y. Song, X. Hu, and I. G. Guardiola. 2020. “Analysis of electric vehicle charging behavior patterns with function principal component analysis approach.” J. Adv. Transp. 2020 (Nov): 1–12. https://doi.org/10.1155/2020/8850654.
China Electric Vehicle Charging Infrastructure Promotion Alliance. 2021. The development report on charging infrastructure in China for the fiscal year 2020-2021. Shanghai, China: China Electric Vehicle Charging Infrastructure Promotion Alliance.
Fischer, M., C. Hardt, W. Michalk, and K. Bogenberger. 2022. “Charging or idling: Method for quantifying the charging and the idle time of public charging stations.” In Proc., TRB 101st Annual Meeting Compendium of Papers. Washington, DC: Transportation Research Board.
Helmus, J. R., M. H. Lees, and R. van den Hoed. 2020. “A data driven typology of electric vehicle user types and charging sessions.” Transp. Res. Part C Emerging Technol. 115 (Jun): 102637. https://doi.org/10.1016/j.trc.2020.102637.
Hu, D., L. Huang, C. Liu, Z. Liu, and M. Ge. 2022. “Data driven optimization for electric vehicle charging station locating and sizing with charging satisfaction consideration in urban areas.” IET Renewable Power Gener. 16 (12): 2630–2643. https://doi.org/10.1049/rpg2.12382.
Hu, X., Y. Cao, T. Peng, R. Gao, and G. Dai. 2021. “Nonlinear influence model of built environment of residential area on electric vehicle miles traveled.” World Electric Vehicle J. 12 (4): 247. https://doi.org/10.3390/wevj12040247.
IEA. 2021. Global EV Outlook 2021. Paris: IEA.
Jabeen, F., D. Olaru, B. Smith, T. Braunl, and S. Speidel. 2013. “Electric vehicle battery charging behaviour: Findings from a driver survey.” In Proc., Australasian Transport Research Forum (Vol. 1). Brisbane, Australia: Australasian Transport Research Forum.
Latinopoulos, C., A. Sivakumar, and J. W. Polak. 2017. “Response of electric vehicle drivers to dynamic pricing of parking and charging services: Risky choice in early reservations.” Transp. Res. Part C Emerging Technol. 80 (Jul): 175–189. https://doi.org/10.1016/j.trc.2017.04.008.
Lee, J. H., D. Chakraborty, S. J. Hardman, and G. Tal. 2020. “Exploring electric vehicle charging patterns: Mixed usage of charging infrastructure.” Transp. Res. Part D Transp. Environ. 79 (Feb): 102249. https://doi.org/10.1016/j.trd.2020.102249.
Liu, Y., Y. Hong, and C. Hu. 2019. “Research on big data mining technology of electric vehicle charging behavior.” Elektronika Ir Elektrotechnika 25 (6): 55–61. https://doi.org/10.5755/j01.eie.25.6.24827.
Lu, T., E. Yao, F. Jin, and Y. Yang. 2022. “Analysis of incentive policies for electric vehicle adoptions after the abolishment of purchase subsidy policy.” Energy 239 (Part B): 122136. https://doi.org/10.1016/j.energy.2021.122136.
Lucas, A., G. Prettico, M. G. Flammini, E. Kotsakis, G. Fulli, and M. Masera. 2018. “Indicator-based methodology for assessing EV charging infrastructure using exploratory data analysis.” Energies 11 (7): 1869. https://doi.org/10.3390/en11071869.
Mead, I. 2017. International energy outlook 2017. Washington, DC: US Energy Information Administration.
Motoaki, Y., and M. G. Shirk. 2017. “Consumer behavioral adaption in EV fast charging through pricing.” Energy Policy 108 (Sep): 178–183. https://doi.org/10.1016/j.enpol.2017.05.051.
Pan, L., E. Yao, and D. Mackenzie. 2019. “Modeling EV charging choice considering risk attitudes and attribute non-attendance.” Transp. Res. Part C Emerging Technol. 102 (May): 60–72. https://doi.org/10.1016/j.trc.2019.03.007.
Peng, T., J. Chen, K. Liu, Z. Qiu, Z. Fu, and Y. Huang. 2023. “Examining the relationship between built environment and urban parking demand from the perspective of travelers.” J. Cleaner Prod. 385 (Jan): 135766. https://doi.org/10.1016/j.jclepro.2022.135766.
Rao, R., H. Cai, and M. Xu. 2018. “Modeling electric taxis’ charging behavior using real-world data.” Int. J. Sustainable Transp. 12 (6): 452–460. https://doi.org/10.1080/15568318.2017.1388887.
Sadeghianpourhamami, N., N. Refa, M. Strobbe, and C. Develder. 2018. “Quantitive analysis of electric vehicle flexibility: A data-driven approach.” Int. J. Electr. Power Energy Syst. 95 (Feb): 451–462. https://doi.org/10.1016/j.ijepes.2017.09.007.
Siddique, C., F. Afifah, Z. Guo, and Y. Zhou. 2022. “Data mining of plug-in electric vehicles charging behavior using supply-side data.” Energy Policy 161 (Feb): 112710. https://doi.org/10.1016/j.enpol.2021.112710.
Sierzchula, W. 2014. “Factors influencing fleet manager adoption of electric vehicles.” Transp. Res. Part D Transp. Environ. 31 (Aug): 126–134. https://doi.org/10.1016/j.trd.2014.05.022.
Struben, J., and J. D. Sterman. 2008. “Transition challenges for alternative fuel vehicle and transportation systems.” Environ. Plann. B: Plann. Des. 35 (6): 1070–1097. https://doi.org/10.1068/b33022t.
van der Kam, M., W. van Sark, and F. Alkemade. 2020. “Multiple roads ahead: How charging behavior can guide charging infrastructure roll-out policy.” Transp. Res. Part D Transp. Environ. 85 (Aug): 102452. https://doi.org/10.1016/j.trd.2020.102452.
Wen, Y., D. Mackenzie, and D. R. Keith. 2016. “Modeling the charging choices of battery electric vehicle drivers by using stated preference data.” Transp. Res. Rec. 2572 (1): 47–55. https://doi.org/10.3141/2572-06.
Wolbertus, R., and B. Gerzon. 2018. “Improving electric vehicle charging station efficiency through pricing.” J. Adv. Transp. 2018 (Sep): 4831951. https://doi.org/10.1155/2018/4831951.
Wolbertus, R., M. Kroesen, R. Hoed, and C. G. Chorus. 2018a. “Policy effects on charging behaviour of electric vehicle owners and on purchase intentions of prospective owners: Natural and stated choice experiments.” Transp. Res. Part D Transp. Environ. 62 (Jul): 283–297. https://doi.org/10.1016/j.trd.2018.03.012.
Wolbertus, R., M. Kroesen, R. Van Den Hoed, and C. Chorus. 2018b. “Fully charged: An empirical study into the factors that influence connection times at EV-charging stations.” Energy Policy 123 (Dec): 1–7. https://doi.org/10.1016/j.enpol.2018.08.030.
Wolbertus, R., and R. van den Hoed. 2017. “Charging station hogging: A data-driven analysis.” In Proc., 30th Int. Electric Vehicle Symp. & Exhibition. Amsterdam, Netherlands: HvA Research Database.
Xu, M., Q. Meng, K. Liu, and T. Yamamoto. 2017. “Joint charging mode and location choice model for battery electric vehicle users.” Transp. Res. Part B Methodol. 103 (Sep): 68–86. https://doi.org/10.1016/j.trb.2017.03.004.
Xydas, E., C. Marmaras, L. M. Cipcigan, N. Jenkins, S. Carroll, and M. Barker. 2016. “A data-driven approach for characterising the charging demand of electric vehicles: A UK case study.” Appl. Energy 162 (Jan): 763–771. https://doi.org/10.1016/j.apenergy.2015.10.151.
Yang, H., Q. Zhang, M. Helbich, Y. Lu, D. He, D. Ettema, and L. Chen. 2022. “Examining non-linear associations between built environments around workplace and adults’ walking behaviour in Shanghai, China.” Transp. Res. Part A Policy Pract. 155 (Jan): 234–246. https://doi.org/10.1016/j.tra.2021.11.017.
Yang, Y., E. Yao, Z. Yang, and R. Zhang. 2016. “Modeling the charging and route choice behavior of BEV drivers.” Transp. Res. Part C Emerging Technol. 65 (Apr): 190–204. https://doi.org/10.1016/j.trc.2015.09.008.
Yu, H., L. Zhang, C. Liu, P. Wang, Y. Ren, and C. Yang. 2019. “Prediction of electric vehicles charging behavior based on the data of connected vehicles.” In Proc., CICTP 2019, 4000–4011. Reston, VA: ASCE.
Yue, W. A., B. Eya, and P. C. Long. 2020. “Electric vehicle drivers’ charging behavior analysis considering heterogeneity and satisfaction.” J. Cleaner Prod. 286 (Mar): 124982. https://doi.org/10.1016/j.jclepro.2020.124982.
Zhou, X., M. Lv, and Y. Ji. 2023. “Evaluating the nonlinear impacts of built environment on online car-hailing: Focus on the whole day.” Transp. Res. Rec. 2678 (4): 365403485. https://doi.org/10.1177/03611981231186595.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 8 • August 2024
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© 2024 American Society of Civil Engineers.
History
Received: Sep 24, 2023
Accepted: Feb 23, 2024
Published online: May 22, 2024
Published in print: Aug 1, 2024
Discussion open until: Oct 22, 2024
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