Enhanced hybrid MPPT controller integrating Type-2 Fuzzy Logic and P&O methods for photovoltaic battery charging systems
摘要
As the world turns increasingly to clean energy alternatives, photovoltaic (PV) systems gain popularity and power various applications, including solar battery chargers. However, these systems still require improvements to maximize efficiency. A critical area for enhancement is the application of Maximum Power Point Tracking (MPPT) techniques. One commonly used technique is the Perturb and Observe (P&O) strategy, known for its simplicity. However, the performance of this method depends on the chosen step size, creating a trade-off between accuracy and tracking speed. To address this issue, we propose an enhanced hybrid MPPT method that combines Type-2 Fuzzy Logic with the P&O method, referred to as AP&O-FT2. This method dynamically optimizes the step size using the Type-2 Fuzzy Logic framework. Our work aims to prove the effectiveness of the AP&O-FT2 method under various temperature and irradiance conditions by comparing it with traditional P&O, standard adaptive P&O, and the hybrid Type-1 Fuzzy Logic-based P&O method. The proposed system includes the following components: a 305W PV panel, a buck converter controlled via PWM signals from an MPPT controller, and a lithium-ion battery. We use MATLAB/Simulink software to simulate the system modeling. Performance analysis shows that the developed hybrid regulator decreases ripples at the MPP (for example, a power ripple of 0.560 W under an irradiance of 1000 W/m2 and a temperature of 45 °C), achieves higher efficiency (e.g., between 99.82 and 99.99% across irradiances of 300–1000 W/m2 and 25 °C), and improves battery voltage regulation.