Digital Twin-Assisted Optimization of Electric Vehicle Charging Infrastructure

  • Authors

    • Andrey Ershov Professor, Siberian Division Academy of Sciences, Russia Author
    • Alexey Lyapunov Professor, Moscow State University, Russia Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2025PI7F8B

    Published 05-03-2025

  • Digital Twin, Electric Vehicles, Charging Infrastructure, Smart Grid, Internet of Things, Artificial Intelligence, Machine Learning, Charging Optimization, Predictive Analytics, Sustainable Transportation

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    Digital Twin-Assisted Optimization of Electric Vehicle Charging Infrastructure. (2025). International Journal of Modern Research in Science & Engineering, 8(1), 01-16. https://doi.org/10.67228/30716357/IJMRSE-2025PI7F8B

  • Abstract

    The rapid adoption of electric vehicles (EVs) has increased the need for intelligent charging infrastructure capable of addressing challenges such as charging congestion, uneven energy distribution, grid instability, and long waiting times. Conventional charging management approaches based on static scheduling are inadequate for dynamic charging environments. This paper proposes a Digital Twin-Assisted Optimization Framework for Electric Vehicle Charging Infrastructure (DTO-EVCI) that integrates IoT, cloud computing, artificial intelligence (AI), machine learning, and optimization techniques to enable real-time monitoring, predictive analytics, and intelligent charging management. The framework synchronizes physical charging stations with a virtual digital twin, enabling accurate simulation, charging demand prediction, occupancy forecasting, optimized scheduling, predictive maintenance, and adaptive energy management. By improving charging efficiency, resource utilization, grid reliability, and renewable energy integration, the proposed framework reduces operational costs, minimizes charging delays, and supports sustainable large-scale EV deployment while contributing to smart city and carbon-neutral transportation initiatives.

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