Reinforcement Learning–Fuzzy Logic Hybrid Model for Smart Charging Optimization in Electric Vehicle Network

Abstract

The significance of Smart Charging Optimisation in EV Network is underscored by the fact that the fast uptake of PEV offers a game-changing chance to lessen reliance on oil-based fuels and encourage the use of low-carbon energy. Charging solutions that are both efficient and effective reduce operational costs while simultaneously improving grid stability and environmental sustainability. By utilising preprocessing approaches such KNN-based imputation, Z-score outlier identification, and Min-Max scaling to guarantee data quality, this study develops an advanced optimisation framework utilising the EV Smart Charging Dataset. Integrating smart grid technology, finding the best location for stations, making the most of renewable energy, and considering regulatory consequences are all important considerations that are analysed using PCA. To further enhance adaptability compared to traditional static systems, a new energy-aware model based on FLRL is presented. This model takes into consideration the rate of energy consumption and residual battery energy as inputs and employs a dynamic membership function. Energy efficiency and charge optimisation are both much improved by the suggested dynamic FLRL method, according to the simulation findings. The model's usefulness for intelligent EV network management is validated by experimental data, which show that it outperforms traditional FL, RL, and GNN techniques with an accuracy of 95.34% and a MAE of 4.5%.