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 step-down DC-DC converter. The study evaluates the impact of the number of membership functions (three, five, and seven) and their distribution using the integral performance indices: Integral Square Error (ISE) and Integral Absolute Error (IAE). The results demonstrate that the seven-function configuration achieved optimal values of 0.1155 and 7.365 ×10−4, for ISE and IAE respectively. In addition, this configuration achieved an energy efficiency of 99.7%, which is superior to the 98.9% efficiency obtained with three functions in the worst-case scenario. The optimal configuration was subjected to changes in irradiance and temperature to assess its dynamic response robustness. Additionally, an analysis of computational resource consumption reveals that the proposed hybrid controller requires a lower computational load for rule evaluation than three controllers from recent literature. These findings demonstrate the structural efficiency and optimization capability of the proposed system to maximize energy harvesting in photovoltaic panels at a low computational cost.