Structural Optimization of a Hybrid Fuzzy–Incremental Conductance MPPT Controller for Photovoltaic Systems with Battery Storage

This study presents a hybrid controller that integrates fuzzy logic control and the Incremental Conductance method. This controller optimizes maximum power point tracking in a 330 W photovoltaic system by designing a DC-DC converter. The study evaluates how the number and distribution of membership functions, specifically three-, five-, and seven-function configurations, affect system performance using the Integral Square Error (ISE) and Integral Absolute Error (IAE) indices. The empirical results demonstrate that the seven-function architecture yields optimal performance, minimizing ISE and IAE to 0.1155 and 7.365×10−4, respectively. Furthermore, this optimal configuration attains an energy efficiency of 99.7%, notably outperforming the baseline three-function configuration, which exhibited a worst-case efficiency of 98.9 %. To assess robustness against dynamic environmental variations, this study subjects the optimal configuration to fluctuating irradiance and temperature profiles. Additionally, an analysis of computational resource consumption reveals that the proposed hybrid controller incurs a lower computational load for rule evaluation than three controllers reported in the recent literature. These findings demonstrate the system’s structural efficiency and superior optimization capability, achieving maximized photovoltaic energy harvesting at a low computational cost.