Real-time Optimization of Piezoelectric Energy Harvesting Using ANN-based Maximum Power Point Tracking

Piezoelectric Energy Harvesting (PEH) plays a pivotal role in powering low-power electronic systems by leveraging ambient vibrations. However, the nonlinear and fluctuating nature of mechanical inputs leads to continuous variations in electrical output, making Maximum Power Point Tracking (MPPT) essential for efficient energy extraction. Traditional control methods based on fixed-duty cycle or pulses struggle to adapt to these dynamic conditions, resulting in significant energy loss. To address this challenge, this study proposes an innovative solution by developing an intelligent MPPT controller based on Artificial Neural Network (ANN). This controller is capable of adaptively determining the optimal duty cycle for a DC-DC Buck converter in real time. To generate the necessary training data for the network, an integrated simulation model was constructed-comprising a piezoelectric bender, a full-wave rectifier, and a lithium-ion battery-and evaluated under various vibration amplitudes and frequencies. The results demonstrated that the trained ANN achieved a remarkably high correlation coefficient (R = 0.99332), confirming its high accuracy and excellent generalization capability. Furthermore, simulation results proved the efficiency of the proposed system in significantly stabilizing the rectifier voltage, enhancing impedance matching, and improving battery charging performance. These findings demonstrate that utilizing an ANN-based MPPT provides a robust and effective solution for optimizing real-time energy harvesting and generation from piezoelectric vibration systems.