<![CDATA[The transition to renewable energy is a crucial step in achieving sustainable development. However, the efficiency of Photovoltaic (PV) systems remains a challenge due to fluctuations in solar irradiation, which affect power generation. This study aimed to enhance Maximum Power Point Tracking (MPPT) performance by integrating Fuzzy logic Control (FLC) into the charging system of a solar power plant. The research employed an experimental approach involving the testing of a 100 Wp PV module and a 44 Ah battery, where data collection was conducted at 10-minute intervals from 10:30 AM to 3:00 PM. The proposed FLC-based MPPT system was compared with a conventional MPPT system to evaluate charging efficiency, power stability, and response time. The findings indicated that the FLC-based MPPT exhibited superior stability, maintaining output voltage within 12V to 12.5V, whereas the non-Fuzzy MPPT showed wider voltage variations. Additionally, the FLC-based system achieved an average charging current of 2.05 A, reducing the full battery charging time to 21 hours 46 minutes, compared to 46 hours 31 minutes for the conventional MPPT system. These results confirm that FLC enhances MPPT performance, particularly in optimizing power output and reducing charging time. However, efficiency trade-offs were observed due to step-down losses in the buck converter. Future research should focus on hybrid MPPT approaches, parameter optimization, and large-scale implementation, potentially integrating Artificial Intelligence (AI) techniques to further improve efficiency. This study contributes to advancing intelligent MPPT systems for renewable energy applications.]]>
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