Enhancing IoT-Enabled Electric Vehicle Efficiency: Smart Charging Station and Battery Management Solution

Rapid evolutions of the Internet of Electric Vehicles (IoEVs) are reshaping and modernizing transport systems, yet challenges remain in energy efficiency, better battery aging, and grid stability. Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand, thus increasing energy costs and battery aging. This study proposes a smart charging station with an AI-powered Battery Management System (BMS), developed and simulated in MATLAB/Simulink, to increase optimality in energy flow, battery health, and impractical scheduling within the IoEV environment. The system operates through real-time communication, load scheduling based on priorities, and adaptive charging based on battery mathematically computed State of Charge (SOC), State of Health (SOH), and thermal state, with bidirectional power flow (V2G), thus allowing EVs’ participation towards grid stabilization. Simulation results revealed that the proposed model can reduce peak grid load by 37.8%; charging efficiency is enhanced by 92.6%; battery temperature lessened by 4.4°C; SOH extended over 100 cycles by 6.5%, if compared against the conventional technique. By this way, charging time was decreased by 12.4% and energy costs dropped by more than 20%. These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.