Electric vehicles (EVs) are gaining rapid popularity, necessitating the development of advanced and environmentally sustainable charging infrastructures.This study presents an AI-driven optimisation framework for designing a smart charging control device installed alongside distribution transformers.The proposed system leverages artificial intelligence algorithms to predict energy demand, analyse user behaviour and dynamically adjust charging schedules in real time to improve efficiency.By minimising operational costs, reducing grid congestion, and enhancing energy utilisation, the system maximises overall efficiency and sustainability.Integrating reinforcement learning and predictive analytics further enables adaptive responses to the evolving needs of EV users.Additionally, the system promotes the use of renewable energy sources such as solar and wind power to minimise environmental impact.Experimental results confirm the system's effectiveness in stabilising the grid, optimising energy distribution, and lowering consumer charging costs, demonstrating its scalability and eco-friendly potential within existing infrastructure.
Shi et al. (Thu,) studied this question.
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