Development of Energy Management Systems for Electric Vehicle Charging Stations Associated With Batteries: Application to a Real Case

Abstract

Implementing an effective energy management system (EMS) is essential for optimizing electric vehicle (EV) charging stations (EVCSs), especially when combined with battery energy storage systems (BESSs). This study analyzes a real-world EVCS scenario and compares several EMS approaches, aiming to reduce operating costs while accounting for BESS degradation. Initially, significant savings were achieved by optimizing the EV charging schedule using genetic algorithms (GAs), even without storage. Next, different BESS-based EMSs, including rule-based and fuzzy logic systems, were optimized via GAs. Finally, in a dynamic scenario with variable electricity prices and demand, the adaptive GA-optimized fuzzy logic EMS was found to achieve the best performance, reducing annual operating costs by 15.6% compared to the baseline strategy derived from real fleet data.