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Generative AI-driven framework for estimating future electric vehicle usage

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  • Chen, Xiaowei
  • Hamim, Omar Faruqe
  • Moras, Bruno Cesar Krause
  • Gkritza, Konstantina
  • Ukkusuri, Satish V.

Abstract

In the rapidly advancing field of electric vehicle (EV) adoption and infrastructure planning, the scarcity of detailed and comprehensive datasets poses significant challenges for effective decision-making. This study introduces a cutting-edge framework leveraging generative AI, specifically, Sequential Generative Adversarial Networks, to synthesize realistic survey and travel sequence data. The framework’s Data Fusion module integrates socio-demographic attributes with travel behaviors, creating enriched synthetic datasets that capture multi-dimensional insights. Using Indiana as a case study, the research demonstrates key applications, including future EV adoption projections and charging demand estimation for residential and public stations. Results indicate an 18-fold increase in EVs by 2031 under optimistic scenarios, with existing charging stations meeting less than 50 % of demand. By addressing critical data gaps, this generative AI-driven approach provides actionable insights for strategic EV infrastructure development, enabling informed policy-making and promoting sustainable mobility solutions.

Suggested Citation

  • Chen, Xiaowei & Hamim, Omar Faruqe & Moras, Bruno Cesar Krause & Gkritza, Konstantina & Ukkusuri, Satish V., 2025. "Generative AI-driven framework for estimating future electric vehicle usage," Applied Energy, Elsevier, vol. 401(PB).
    • Handle: RePEc:eee:appene:v:401:y:2025:i:pb:s0306261925013807
    DOI: 10.1016/j.apenergy.2025.126650
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    References listed on IDEAS

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