Enhancing Electric Vehicle Charging Station Performance Through Automatic Mode Transition-Based Model Predictive Control

The rapid expansion of Electric Vehicles (EVs) has sharply increased the demand for Electric Vehicle Charging Stations (EVCSs), which presents serious obstacles to system design, energy coordination, and operational efficiency. To address these challenges, this study introduces an advanced Energy Management System (EMS) incorporating Automatic Mode Transitions (AMTs) governed by Model Predictive Control (MPC). The proposed MPC-based framework optimizes energy distribution across multiple operating modes while ensuring seamless transitions under varying system constraints, load variations such as the number and timing of Battery Electric Vehicle (BEV) charging events, and fluctuations in irradiance levels. Furthermore, by preventing battery overcharging and deep discharging, the proposed system effectively extends battery lifespan and maintains the overall power balance within the EVCS. The proposed work is strongly aligned with the principles of sustainability, focusing on renewable-energy-based electric vehicle charging systems integrated with battery energy storage. It aims to improve renewable energy utilization, enhance energy efficiency, reduce environmental impact, and support the development of more sustainable and resilient energy infrastructure. This functionality is achieved through localized control of battery voltage and current, where each battery employs a Proportional–Integral (PI) controller implementing a Constant Current–Constant Voltage (CCCV) charging scheme. Consequently, the MPC functions as a supervisory control layer, dynamically coordinating the actions of the local controllers according to the selected optimal operating mode. In the same context, the effectiveness of the proposed approach is validated through comprehensive MATLAB (2023A/Simulink simulations). The results demonstrate that the AMT-based MPC substantially enhances the reliability, responsiveness, and robustness of the EVCS under dynamic operating conditions, including BEV load variations, fluctuations in photovoltaic (PV) irradiance, and parameter uncertainties.