Fuzzy Logic based Vehicle-To-Grid Optimization for Electric Vehicle

With the speedy advancements in EVs (Electric Vehicles) and their incorporations into power grid, the V2G (Vehicle-To-Grid) concept has emerged as an encouraging solution for cost-effective utilization of energy, and grid stability. This research is presenting an optimization method for EV charging using fuzzy logic-based method aiming on reduction of cost and enhancement in battery life. A fuzzy inference system (Mamdani type) has been developed, using MATLAB, with three input parameters viz. electricity price, SOC (state of charge), and grid demand for determining an optimal charging decision. The fuzzy rules and membership functions have been formulated systematically for ensuring and intelligent charging scheme which balances the grid demand and economic benefits. The developed model has been evaluated utilizing 20 test cases based on a secondary data set pertaining to real world fluctuations in charging conditions. The results indicated that the developed fuzzy model exhibits noteworthy reduction in electricity expenses and an extended battery life by reducing the charging cycles appreciably. The findings of this research underscore the efficacy of fuzzy Logic in optimization of EV charging, guaranteeing economic advantages for users, along with maintaining the grid stability.