Adaptive Fuzzy Logic–Based Energy Management for PV– Grid–Battery Integrated Electric Vehicle Charging Stations

DOI

https://www.doi.org/10.59256/ijsreat.20260603001

Published In

Volume 6, Issue 3 (2026)

Published Online

May-June 2026

Pages

05-18

Article Type

Original Article

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The rapid proliferation of electric vehicles (EVs) poses significant challenges to conventional power systems, including increased peak demand, voltage instability, and transformer overloading. Integrating renewable energy sources, particularly photovoltaic (PV) systems, with EV charging infrastructure offers environmental and operational benefits, but introduces variability and uncertainty in power supply. To address these challenges, this paper proposes an adaptive fuzzy logic–based energy management framework for a PV–grid–battery integrated EV charging station. The system employs a common DC bus architecture to coordinate power flow among PV generation, battery energy storage, the utility grid, and EV charging loads. Incremental conductance–based maximum power point tracking (MPPT) is implemented for optimal solar energy extraction, while a bidirectional DC–DC converter regulates battery charging and discharging. The fuzzy logic controller supervises real-time power management, enabling seamless transitions between grid-connected and grid-outage scenarios, ensuring uninterrupted EV charging. Simulation results demonstrate stable DC bus voltage, effective battery state-of-charge regulation, improved renewable energy utilization, and reduced grid dependency. Comparative analysis with conventional PI, droop, ANN, and MPC-based methods indicates superior performance in efficiency (96%), fault ride-through capability, and response time (18 ms). The proposed methodology provides a scalable, intelligent, and grid-friendly solution for sustainable EV charging infrastructure.