Fuzzy Logic Controller-Based Control for Fast EV Charging Stations in DC Microgrid Systems

N. Sree Ramula Reddy; Nagamohan Reddy Kodathala; Sinivasulu A; Sivaganesh Reddy B; Suresh M; Sharath Kumar B

doi:10.5935/jetia.v12i59.3427

Autores

N. Sree Ramula Reddy Departmentof Electrical and Electronics Engineering, Annamacharya University, 516 126, Rajampet, India
Nagamohan Reddy Kodathala Departmentof Electrical and Electronics Engineering, Annamacharya Institute of Technology and Sciences, 516 126, Rajampet, India
Sinivasulu A Departmentof Electrical and Electronics Engineering, Annamacharya Institute of Technology and Sciences, 516 126, Rajampet, India
Sivaganesh Reddy B Departmentof Electrical and Electronics Engineering, Annamacharya Institute of Technology and Sciences, 516 126, Rajampet, India
Suresh M Departmentof Electrical and Electronics Engineering, Annamacharya Institute of Technology and Sciences, 516 126, Rajampet, India
Sharath Kumar B Departmentof Electrical and Electronics Engineering, Annamacharya Institute of Technology and Sciences, 516 126, Rajampet, India

DOI:

https://doi.org/10.5935/jetia.v12i59.3427

Resumo

This project proposes an advanced fuzzy logic controller (FLC) for fast EV charging stations integrated with DC microgrids. Unlike conventional PI controllers, which struggle with dynamic operating conditions, the FLC adapts in real time using parameters such as battery state of charge, grid conditions, and battery health. In Grid-to-Vehicle (G2V), Grid + Photovoltaic to Vehicle (G+PV2V) modes, the controller ensures efficient fast charging by optimizing current flow, reducing voltage ripples, and maintaining DC-link stability. In Vehicle-to-Grid (V2G) mode, it dynamically regulates battery discharge to enhance grid support while protecting battery life. Simulation results show that the proposed FLC significantly reduces charging time and improves system efficiency, achieving a runtime of 213.3 seconds compared to 317 seconds using a PI controller. Overall, the FLC-based strategy enhances system stability, responsiveness, and adaptability, making it a suitable solution for smart EV charging in modern DC microgrids.