Generative AI-Empowered Vehicle-Grid Interaction Large Models: Classification, Research Review and Application Dilemmas

Jiayi Gao

doi:10.54254/2755-2721/2025.GL28734

Applied and Computational EngineeringOpen access

Generative AI-Empowered Vehicle-Grid Interaction Large Models: Classification, Research Review and Application Dilemmas

Research Article

Open Access

Generative AI-Empowered Vehicle-Grid Interaction Large Models: Classification, Research Review and Application Dilemmas

Jiayi Gao ^1*

¹ University of Hong Kong

^*Corresponding author: u3612822@connect.hku.hk

Published on 28 October 2025

ACE Vol.201

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-493-9

ISBN (Online): 978-1-80590-494-6

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Abstract

With the rapid development of electric vehicles (EVs) and the deepening demand for smart grid construction, vehicle-grid interaction (VGI) has become a key link to optimize energy allocation and ensure grid stability. However, traditional VGI models face challenges in processing multi-modal, time-series VGI data and adapting to diverse application scenarios. This paper focuses on the construction and application of generative AI-enabled VGI large models, adopting a systematic review method. It classifies these models along the dimensions of technical architecture (e.g., Transformer variants, multi-modal fusion frameworks) and application scenarios (e.g., peak shaving and valley filling, virtual power plant collaboration), and systematically reviews research progress in both categories. Key research points include the core characteristics and application boundaries of different types of generative AI-based VGI large models, as well as the technical bottlenecks and practical obstacles in their current development. This paper concludes that Transformer-based models excel in time-series VGI data modeling, while multi-modal fusion models enhance interaction accuracy; in application scenarios, models show significant value in load regulation and resource aggregation but face challenges such as data privacy and computational efficiency.

Keywords:

Generative Artificial Intelligence, Vehicle-Grid Interaction (VGI), Virtual Power Plant, Peak Shaving and Valley Filling.

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References

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References

[1]. Nadimi, R., & Goto, M. (2025). Vehicle grid integration planning tool: Novel approach in case of Tokyo. Applied Energy, 399, 126509. https: //doi.org/10.1016/j.apenergy.2025.126509

[2]. Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2023). Generative AI. Business & Information Systems Engineering, 66(1), 111–126. https: //doi.org/10.1007/s12599-023-00834-7‌

[7]. Rodrig Dekordevi, Ilker. (2025). Business models for vehicle-grid integration: a literature review and european case studies. Polimi.it. https: //hdl.handle.net/10589/234689

[13]. Cao, C., Wu, Z., & Chen, B. (2020). Electric Vehicle–Grid Integration with Voltage Regulation in Radial Distribution Networks. Energies, 13(7), 1802. https: //doi.org/10.3390/en13071802

Cite this article

Gao,J. (2025). Generative AI-Empowered Vehicle-Grid Interaction Large Models: Classification, Research Review and Application Dilemmas. Applied and Computational Engineering,201,23-28.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-FMCE 2025 Symposium: Semantic Communication for Media Compression and Transmission

ISBN: 978-1-80590-493-9(Print) / 978-1-80590-494-6(Online)

Editor: Anil Fernando

Conference website: https://2025.conffmce.org/glasgow.html

Conference date: 24 October 2025

Series: Applied and Computational Engineering

Volume number: Vol.201

ISSN: 2755-2721(Print) / 2755-273X(Online)

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