Abstract :

This Study Presents An Advanced Energy Management Strategy For A Hybrid Electric Vehicle (EV) Charging Station Powered By Photovoltaic (PV) And Fuel Cell (FC) Systems Within A DC Microgrid. The Proposed Approach Combines A Fuzzy Logic-based Energy Management System (FL-EM) With An Artificial Neural Network (ANN) To Enhance Decision-making And Prediction Accuracy. The Fuzzy Logic Controller Dynamically Regulates The Fuel Cell Reference Power Under Both On-grid And Off-grid Operating Conditions, Considering Factors Such As Demand Power, Energy Costs, And System Uncertainties. To Further Improve System Performance, An ANN-based Forecasting Model Is Integrated To Predict Photovoltaic Power Generation And Load Demand, Enabling Proactive And Optimized Energy Distribution. This Hybrid FL-ANN Framework Effectively Addresses The Limitations Of Standalone Fuzzy Systems By Incorporating Learning Capabilities And Improving Adaptability To Dynamic Environments. Simulation Results Demonstrate That The Proposed Method Significantly Enhances System Efficiency, Reduces Hydrogen Consumption, Minimizes Grid Dependency, And Improves Overall Operational Stability. The Integration Of ANN With Fuzzy Logic Provides A Robust, Intelligent, And Scalable Solution For Nextgeneration EV Charging Infrastructure And Sustainable Microgrid Applications.

Published:

26-10-2026

Issue:

Vol. 26 No. 10 (2026)

Page Nos:

120 - 131

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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