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Artificial intelligence-based adaptive control for vehicle-to-grid and grid-to-vehicle operations in electric vehicle charging stations

Author

Listed:
  • Vennila, C.
  • Selvakumaran, S.
  • Preetha, K.
  • Muralikrishnan, G.

Abstract

Integrating electric vehicles with the smart grid requires energy management systems that maintain a dynamic balance between supply and demand. Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) operations at electric vehicle charging stations need to be optimized using artificial intelligence-based adaptive control systems. The proposed artificial intelligence-based adaptive control scheme efficiently manages bidirectional energy flow between the grid and the electric vehicle charging stations. By utilizing empirical mode decomposition alongside long short-term memory, bidirectional long short-term memory, and the pine cone optimization algorithm, the system enhances grid stability, reduces charging costs, and optimizes energy usage. This is achieved by anticipating grid demand spikes, optimizing charging times, and considering user preferences, leading to smooth integration of electric vehicles with the power grid. The efficiency of the proposed method is validated using MATLAB, with results showing a reduction in root mean squared error by 0.97 %. The system effectively manages peak loads, increases renewable energy usage, and improves the overall reliability of the electric vehicle charging infrastructure.

Suggested Citation

  • Vennila, C. & Selvakumaran, S. & Preetha, K. & Muralikrishnan, G., 2026. "Artificial intelligence-based adaptive control for vehicle-to-grid and grid-to-vehicle operations in electric vehicle charging stations," Renewable Energy, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:renene:v:257:y:2026:i:c:s0960148125024334
    DOI: 10.1016/j.renene.2025.124769
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    References listed on IDEAS

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    1. Abdelfattah, Wael & Abdelhamid, Ahmed Sayed & Hasanien, Hany M. & Rashad, Basem Abd-Elhamed, 2024. "Smart vehicle-to-grid integration strategy for enhancing distribution system performance and electric vehicle profitability," Energy, Elsevier, vol. 302(C).
    2. Laugs, Gideon A.H. & Benders, René M.J. & Moll, Henri C., 2024. "Maximizing self-sufficiency and minimizing grid interaction: Combining electric and molecular energy storage for decentralized balancing of variable renewable energy in local energy systems," Renewable Energy, Elsevier, vol. 229(C).
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