Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{177010,
        author = {Aditya Kumar and Anish Kumar Singh and Aditya Yadav and Chirag Dixit and Sunil Kumar Chaudhary},
        title = {Fast Charging Of Electric Vehicle Using Artificial Intellegence},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {520-525},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177010},
        abstract = {The increasing adoption of electric vehicles (EVs) has driven the need for efficient and reliable fast-charging infrastructure to reduce charging times and enhance user experience. This project proposes an AI-driven approach to optimize the fast-charging process of electric vehicles. The system utilizes machine learning algorithms to dynamically adjust charging parameters based on factors such as battery state of health (SoH), temperature, historical usage patterns, and real-time grid conditions. By using predictive analytics, the AI model predicts the optimum charge rate, prevents the battery from degrading, and thus improves energy efficiency. It also includes a smart scheduling feature that would be applied to balance demand on the grid and avoid peak load. The solution, with the proposed design, shall reduce charging time, prolong lifespan of the battery, and promote grid stability. Simulation and testing are conducted to validate the system effectiveness of the proposed AI-based charging system over conventional fast-charging techniques. This research opens avenues for more intelligent, sustainable, and user-friendly EV charging solutions.},
        keywords = {EVs,KWh,Charging time,Fast charging},
        month = {May},
        }