Photovoltaic (PV) battery chargers utilize solar energy to charge batteries. They are generally used in off-grid applications where access to grid electricity is limited. They are also used in portable electronic devices. PV battery charging systems play an important role in harnessing renewable energy and reducing dependence on fossil fuels. To improve the efficiency of photovoltaic systems and optimize their performance, the utilization of Maximum Power Point Tracking (MPPT) techniques is crucial.

This work aims to simulate and compare the performance of three MPPT methods under various environmental conditions when applied to a PV battery charger application: the perturbation and observation (P&O) method, the Fuzzy Logic Controller (FLC), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) strategy. The proposed system comprises the following components: a 305W-rated power solar panel, a buck converter, and a lithium-Ion battery. The system aims to extract the maximum power point and charge the battery optimally. It is implemented and simulated using MATLAB/SIMULINK software.

The simulation results of the output power of the PV panel and buck converter using different controllers are compared. Performance analysis reveals that the controller using an ANFIS regulator has reduced gap oscillation around the maximum power point, enhanced rapidity characterized by a shorter convergence time, better voltage regulation of the battery, and higher efficiency.