This paper presents a detailed model of a photovoltaic (PV) system integrated with a bidirectional DC-DC converter, employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) for intelligent control. The model considers two key input variables: time and ambient temperature. By incorporating ANFIS, the system adapts dynamically to environmental fluctuations, enhancing its overall performance. Key performance indicators such as voltage stability, current fluctuation mitigation, and battery charge optimization are analyzed to assess system effectiveness. Simulations are carried out in the MATLAB/Simulink environment, providing a robust framework for evaluating system behavior under varying operating conditions. Results indicate that the integration of ANFIS significantly improves energy flow management, enhances stability, and ensures a higher quality of power output. Furthermore, the model demonstrates adaptability to changing external conditions, making it a viable solution for real-world renewable energy applications and intelligent PV power management.

photovoltaic panel, fuzzy logic, fuzzy controller, ANFIS, MATLAB/Simulink modeling, bidirectional converter, MPPT, renewable energy.

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