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@article{169810,
        author = {Shubham Gade and Amita Singh and Shubham Sarote},
        title = {Efficient H-net model-based slot assignment solution to accelerate the EV charging station searching process},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {6},
        pages = {2590-2597},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169810},
        abstract = {The extensive growth in the manufacturing of lithium-ion batteries and their usage in electric vehicles has led to a surge in the sales of electric vehicles, surpassing previous records. Electric vehicles offer numerous advantages over fossil fuel vehicles, including decreased air and noise pollution, lower maintenance costs, among other benefits. However, there are also growing issues associated with this, such as the unavailability of EV charging stations when they are needed. To address this issue, there are numerous mobile applications that can locate the closest EV charging stations based on their proximity to GPS coordinates. Very few of the systems consider the availability of the charging slots along with the nearest stations. However, there are only a few systems that rely solely on finger counting to allocate slots at EV charging stations effectively. Therefore, this paper proposes utilizing the H-Net model, a Hungarian neural network, to allocate EV charging slots efficiently, enabling the charging of more vehicles in a single day and preventing congestion. This method, in conjunction with shortest distance estimation, quickly identifies the nearest EV charging station with an available slot, thereby enhancing the congestion-free charging process.},
        keywords = {Neural Networks, EV Charging station location, Slot Allocation, Hungarian neural network, Euclidean distance.},
        month = {September},
        }