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Consumer Preferences for Electric Vehicle Charging Infrastructure Based on the Text Mining Method

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  • Yuan-Yuan Wang

    (School of Economics and Management, Beijing University of Technology, Beijing 100124, China
    Center for Energy and Environmental Policy Research, Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China
    These two authors contribute equally.)

  • Yuan-Ying Chi

    (School of Economics and Management, Beijing University of Technology, Beijing 100124, China
    These two authors contribute equally.)

  • Jin-Hua Xu

    (Center for Energy and Environmental Policy Research, Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China)

  • Jia-Lin Li

    (China Datang Group New Energy Science and Technology Research Institute, Beijing 100124, China)

Abstract

The construction of charging infrastructure has a positive effect on promoting the diffusion of new energy vehicles (NEVs). This study uses natural language processing (NLP) technology to explore consumer preferences for charging infrastructure from consumer comments posted on public social media. The findings show that consumers in first-tier cities pay more attention to charging infrastructure, and the number of comments accounted for 36% of the total. In all comments, consumers are most concerned about charging issues, national policy support, driving range, and installation of private charging piles. Among the charging modes of charging piles, direct current (DC) fast charging is more popular with consumers. The inability to find public charging piles in time to replenish power during travel or high energy consumption caused by air conditioning is the main reason for consumers’ range anxiety. Increasing battery performance, improving charging convenience, and construction of battery swap station are the main ways consumers prefer to increase driving range. Consumers’ preference for charging at home is the main reason for their high attention to the installation of private charging piles. However, the lack of fixed parking spaces and community properties have become the main obstacles to the installation of private charging piles. In addition, consumers in cities with different development levels pay different amounts of attention to each topic of charging infrastructure. Consumers in second-tier and above cities are most concerned about charging issues. Consumers in third-tier and above cities pay significantly more attention to the installation of private charging piles than consumers in fourth-tier and fifth-tier cities. Consumers in each city have almost the same amount of attention to driving range.

Suggested Citation

  • Yuan-Yuan Wang & Yuan-Ying Chi & Jin-Hua Xu & Jia-Lin Li, 2021. "Consumer Preferences for Electric Vehicle Charging Infrastructure Based on the Text Mining Method," Energies, MDPI, vol. 14(15), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4598-:d:604240
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    References listed on IDEAS

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    Cited by:

    1. Tao Shu & Zhiyi Wang & Ling Lin & Huading Jia & Jixian Zhou, 2022. "Customer Perceived Risk Measurement with NLP Method in Electric Vehicles Consumption Market: Empirical Study from China," Energies, MDPI, vol. 15(5), pages 1-23, February.
    2. Yu Feng & Xiaochun Lu, 2021. "Construction Planning and Operation of Battery Swapping Stations for Electric Vehicles: A Literature Review," Energies, MDPI, vol. 14(24), pages 1-19, December.
    3. Jin, Tao & Jiang, Yulian & Liu, Xingwen, 2023. "Evolutionary game analysis of the impact of dynamic dual credit policy on new energy vehicles after subsidy cancellation," Applied Mathematics and Computation, Elsevier, vol. 440(C).
    4. Yang, Mengqi & Lin, Boqiang, 2024. "The development of consumer preferences for electric vehicle charging infrastructure in China: Evidence from a questionnaire survey with a four-year interval," Energy, Elsevier, vol. 307(C).
    5. Gozuacik, Necip & Sakar, C. Okan & Ozcan, Sercan, 2023. "Technological forecasting based on estimation of word embedding matrix using LSTM networks," Technological Forecasting and Social Change, Elsevier, vol. 191(C).

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