The reduction of fossil fuel usage is essential for achieving sustainable energy goals, and solar photovoltaic (PV) systems offer a promising alternative. However, despite decades of development, PV panels still suffer from relatively low efficiency. To address this limitation, Maximum Power Point Tracking (MPPT) techniques are employed to continuously extract the maximum possible power under varying environmental conditions. This study compares three widely used MPPT methods—Perturb and Observe (P&O), Fuzzy Logic, and an Improved P&O algorithm—to determine the most stable, efficient, and effective approach. All simulations are performed in MATLAB/Simulink using Simscape components, with the PV panel modelled under fixed, non-shaded conditions and subjected to nearly constant irradiance with minor fluctuations. The algorithms adjust the battery charging voltage to track the maximum power point. Results show that the Improved P&O method outperforms the others, achieving an efficiency of 96.55%, faster convergence to the maximum power point, minimal oscillations, and smoother tracking performance. Conventional P&O and Fuzzy Logic controllers also perform well, with efficiencies of 93.91% and 94.39% respectively, though they exhibit more noticeable output oscillations.