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Exploring Electric Vehicle Patent Trends through Technology Life Cycle and Social Network Analysis

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  • Yuan Chen

    (Department of Mechanical-Computer-Industrial Engineering, Graduate School, Kangwon National University, 346 Jungang-ro, Samcheok-si 25913, Gangwon-do, Republic of Korea)

  • Seok Swoo Cho

    (Division of Mechanical Design Engineering, Kangwon National University, 346 Jungang-ro, Samcheok-si 25913, Gangwon-do, Republic of Korea)

Abstract

In response to environmental and energy challenges, electric vehicles (EVs) have re-emerged as a viable alternative to internal combustion engines. However, existing research lacks a comprehensive analysis of the technology life cycle of EVs in both global and South Korean contexts and offers limited strategic guidance. This study introduces a novel approach to address these gaps by integrating the S-curve model with social network analysis (SNA), time series analysis, and core applicant layouts. The study specifically utilizes the logistic curve to model technology growth. It applies SNA methods, including International Patent Classification (IPC) co-occurrence analysis and the betweenness centrality metric, to identify the stages of technological development and sustainable research directions for EVs. By analyzing patent data from 2004 to 2023, the study reveals that EV technologies have reached the saturation phase globally and in South Korea, with South Korea maintaining a two-year technological advantage. The research identifies sustainable research directions, including fast charging technology and charging infrastructure, battery monitoring and management, and artificial intelligence (AI) applications. Additionally, the study also determined the sustainability of these research directions by examining the sustainability challenges faced by EVs. These insights offer a clear view of EV technology trends and future directions, guiding stakeholders.

Suggested Citation

  • Yuan Chen & Seok Swoo Cho, 2024. "Exploring Electric Vehicle Patent Trends through Technology Life Cycle and Social Network Analysis," Sustainability, MDPI, vol. 16(17), pages 1-27, September.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7797-:d:1473237
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    References listed on IDEAS

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    1. Janghyeok Yoon & Sungchul Choi & Kwangsoo Kim, 2011. "Invention property-function network analysis of patents: a case of silicon-based thin film solar cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(3), pages 687-703, March.
    2. Yuan, Xiaodong & Cai, Yuchen, 2021. "Forecasting the development trend of low emission vehicle technologies: Based on patent data," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    3. Gilsing, Victor & Nooteboom, Bart & Vanhaverbeke, Wim & Duysters, Geert & van den Oord, Ad, 2008. "Network embeddedness and the exploration of novel technologies: Technological distance, betweenness centrality and density," Research Policy, Elsevier, vol. 37(10), pages 1717-1731, December.
    4. Wellik, T.K. & Griffin, J.R. & Kockelman, K.M. & Mohamed, M., 2021. "Utility-transit nexus: Leveraging intelligently charged electrified transit to support a renewable energy grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    5. Rehman, Faheem Ur & Islam, Md. Monirul & Miao, Qing, 2023. "Environmental sustainability via green transportation: A case of the top 10 energy transition nations," Transport Policy, Elsevier, vol. 137(C), pages 32-44.
    6. Adamuthe, Amol C. & Thampi, Gopakumaran T., 2019. "Technology forecasting: A case study of computational technologies," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 181-189.
    7. Maleki, Ali & Rosiello, Alessandro, 2019. "Does knowledge base complexity affect spatial patterns of innovation? An empirical analysis in the upstream petroleum industry," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 273-288.
    8. Rupp, Matthias & Handschuh, Nils & Rieke, Christian & Kuperjans, Isabel, 2019. "Contribution of country-specific electricity mix and charging time to environmental impact of battery electric vehicles: A case study of electric buses in Germany," Applied Energy, Elsevier, vol. 237(C), pages 618-634.
    9. Ta-Shun Cho & Hsin-Yu Shih, 2011. "Patent citation network analysis of core and emerging technologies in Taiwan: 1997–2008," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 795-811, December.
    10. Caviggioli, Federico, 2016. "Technology fusion: Identification and analysis of the drivers of technology convergence using patent data," Technovation, Elsevier, vol. 55, pages 22-32.
    11. Park, Jongyong & Lee, Hakyeon & Park, Yongtae, 2009. "Disembodied knowledge flows among industrial clusters: A patent analysis of the Korean manufacturing sector," Technology in Society, Elsevier, vol. 31(1), pages 73-84.
    12. John E. Butler, 1988. "Theories of technological innovation as useful tools for corporate strategy," Strategic Management Journal, Wiley Blackwell, vol. 9(1), pages 15-29, January.
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