Fuzzy logic optimization of hybrid PV-battery system for autonomous robots

Most studies on power generation systems have focused on individual components, leaving researchers without a comprehensive understanding of integrated systems. This study addresses this gap by developing a fully self-sufficient photovoltaic (PV) system for a 12 V autonomous mobile robot. We calculated the power and energy requirements of the robot to appropriately size the PV panel and designed a DC–DC buck converter. For energy storage, we used a Li-ion battery and a bidirectional current converter to manage the charging and discharging processes. To optimize performance, we implemented Maximum Power Point Tracking (MPPT) using a fuzzy logic-based control method. Simulation results demonstrate that the proposed fuzzy MPPT controller outperforms the classical P&O algorithm by achieving up to 98% MPPT efficiency, reducing power ripple by over 50% , and improving convergence speed by approximately 30% . The system maintains the output voltage regulated within ±1% of the 12 V target across all tested irradiance and load conditions, confirming both high tracking accuracy and robust stability. During optimal irradiance, the system can simultaneously power the robot and charge the battery, while under low-irradiance conditions, the battery ensures uninterrupted operation. These findings confirm that the proposed hybrid PV system enables mobile robots to operate autonomously and reliably regardless of weather conditions, providing a practical solution for continuous power supply in robotic applications.