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Electric vehicle user classification and value discovery based on charging big data

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  • Hu, Dingding
  • Zhou, Kaile
  • Li, Fangyi
  • Ma, Dawei

Abstract

With the rapid development of electric vehicles (EVs) in recent years, it is important to understand the varied EV users for EV sector business innovation. Therefore, identifying different types of EV users and implementing differentiated marketing strategies can assist charging service enterprises in improving profitability and user loyalty. Recency, frequency and monetary (RFM) model is an important data mining method that has important practical applications in customer relationship management and direct marketing fields. To classify EV users, an integrated approach incorporating an extended RFM model, a two-stage clustering method, and the Entropy Weight Method is proposed in this study. Analysis results demonstrate that 7426 EV users are divided into six groups, namely “high value users”, “key users to maintain”, “key users to develop”, “potential users”, “new users” and “lost users”. To estimate the performances of the proposed approach, the traditional cluster algorithm and fuzzy c-means method are compared with the improved entropy-cluster algorithm by using the intraclass method. The results indicate that the proposed approach is more robust than other methods. Finally, we develop related marketing strategies for each group of EV users to assist charging service enterprises in improving their marketing effectiveness and financial performance.

Suggested Citation

  • Hu, Dingding & Zhou, Kaile & Li, Fangyi & Ma, Dawei, 2022. "Electric vehicle user classification and value discovery based on charging big data," Energy, Elsevier, vol. 249(C).
    • Handle: RePEc:eee:energy:v:249:y:2022:i:c:s0360544222006016
    DOI: 10.1016/j.energy.2022.123698
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    1. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M. & Jenkins, Nick & Carroll, Steve & Barker, Myles, 2016. "A data-driven approach for characterising the charging demand of electric vehicles: A UK case study," Applied Energy, Elsevier, vol. 162(C), pages 763-771.
    2. Wang, Hua & Zhao, De & Meng, Qiang & Ong, Ghim Ping & Lee, Der-Horng, 2019. "A four-step method for electric-vehicle charging facility deployment in a dense city: An empirical study in Singapore," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 224-237.
    3. Ke, Wenwei & Zhang, Shaojun & He, Xiaoyi & Wu, Ye & Hao, Jiming, 2017. "Well-to-wheels energy consumption and emissions of electric vehicles: Mid-term implications from real-world features and air pollution control progress," Applied Energy, Elsevier, vol. 188(C), pages 367-377.
    4. Ji, Zhenya & Huang, Xueliang, 2018. "Plug-in electric vehicle charging infrastructure deployment of China towards 2020: Policies, methodologies, and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 710-727.
    5. Wang, Jian & Lv, Kangjuan & Bian, Yiwen & Cheng, Yu, 2017. "Energy efficiency and marginal carbon dioxide emission abatement cost in urban China," Energy Policy, Elsevier, vol. 105(C), pages 246-255.
    6. Li, Jingjing & Jiao, Jianling & Tang, Yunshu, 2019. "An evolutionary analysis on the effect of government policies on electric vehicle diffusion in complex network," Energy Policy, Elsevier, vol. 129(C), pages 1-12.
    7. Ma, Shao-Chao & Fan, Ying, 2020. "A deployment model of EV charging piles and its impact on EV promotion," Energy Policy, Elsevier, vol. 146(C).
    8. Zhou, Yue & Wen, Ruoxi & Wang, Hewu & Cai, Hua, 2020. "Optimal battery electric vehicles range: A study considering heterogeneous travel patterns, charging behaviors, and access to charging infrastructure," Energy, Elsevier, vol. 197(C).
    9. Bunce, Louise & Harris, Margaret & Burgess, Mark, 2014. "Charge up then charge out? Drivers’ perceptions and experiences of electric vehicles in the UK," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 278-287.
    10. Nunes, Pedro & Farias, Tiago & Brito, Miguel C., 2015. "Enabling solar electricity with electric vehicles smart charging," Energy, Elsevier, vol. 87(C), pages 10-20.
    11. Cui, Shaohua & Yao, Baozhen & Chen, Gang & Zhu, Chao & Yu, Bin, 2020. "The multi-mode mobile charging service based on electric vehicle spatiotemporal distribution," Energy, Elsevier, vol. 198(C).
    12. Yu, Hui & Chen, LuYuan & Yao, JingTao & Wang, XingNan, 2019. "A three-way clustering method based on an improved DBSCAN algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    13. Pasaoglu, Guzay & Zubaryeva, Alyona & Fiorello, Davide & Thiel, Christian, 2014. "Analysis of European mobility surveys and their potential to support studies on the impact of electric vehicles on energy and infrastructure needs in Europe," Technological Forecasting and Social Change, Elsevier, vol. 87(C), pages 41-50.
    14. Foley, Aoife & Tyther, Barry & Calnan, Patrick & Ó Gallachóir, Brian, 2013. "Impacts of Electric Vehicle charging under electricity market operations," Applied Energy, Elsevier, vol. 101(C), pages 93-102.
    15. Kim, Jae D., 2019. "Insights into residential EV charging behavior using energy meter data," Energy Policy, Elsevier, vol. 129(C), pages 610-618.
    16. Speidel, Stuart & Bräunl, Thomas, 2014. "Driving and charging patterns of electric vehicles for energy usage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 97-110.
    17. Xu, Min & Meng, Qiang & Liu, Kai & Yamamoto, Toshiyuki, 2017. "Joint charging mode and location choice model for battery electric vehicle users," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 68-86.
    18. Pagani, M. & Korosec, W. & Chokani, N. & Abhari, R.S., 2019. "User behaviour and electric vehicle charging infrastructure: An agent-based model assessment," Applied Energy, Elsevier, vol. 254(C).
    19. Morrissey, Patrick & Weldon, Peter & O’Mahony, Margaret, 2016. "Future standard and fast charging infrastructure planning: An analysis of electric vehicle charging behaviour," Energy Policy, Elsevier, vol. 89(C), pages 257-270.
    20. Huang, Wencheng & Shuai, Bin & Sun, Yan & Wang, Yang & Antwi, Eric, 2018. "Using entropy-TOPSIS method to evaluate urban rail transit system operation performance: The China case," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 292-303.
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