Enhancing PV Advantages for Electric Vehicle Charging Stations Through the Application of a Fuzzy Inference System Method

The rapid increase of electric vehicles (EVs) amplifies power demands, putting additional pressure on the public grid. This heightened demand creates load fluctuations, posing a challenge to the widespread adoption of EVs. Introducing a charging station that uses photovoltaic (PV) technology, known as a PV charging station (PVCS), integrates PV modules and stationary storage connected to the public grid. This integration provides cost-effective and reliable charging solutions for EVs. This paper concentrates on developing a power management algorithm and precise system modeling to optimize energy flow, considering the physical limitations of PVCS and the public grid, and the EVs characteristics. The primary goal is to enhance fuzzy logic-based power management for EVs, aiming for more efficient energy resource utilization and seamless integration of solar charging infrastructure into the existing grid. The outcomes of this research have significant potential to influence policy frameworks and advocate for sustainable progress in electric mobility, contributing to a reduced environmental impact in the transportation sector. The study explores two distinct energy management scenarios: slow EV charging with knowledge of parking time in the first scenario and fast EV charging with knowledge of parking time in the second scenario. This analysis assesses the impact of various charging modes and the knowledge of parking time on the utilization of PV production, serving as a metric for evaluating the effectiveness of the proposed fuzzy logic approach in modulating of the charging profile of the EVs.