A novel maximum power point tracking with hybrid control algorithm for automotive thermoelectric generator system

Abstract

To dynamically track the maximum power of an automotive thermoelectric generator (ATEG) system in real-time, this study introduces a novel maximum power point tracking (MPPT) algorithm that integrates Kalman filtering and fuzzy control. Employing a two-phase interleaved parallel DC–DC boost converter in the MPPT controller effectively reduces current ripple and switch loss. Results demonstrated a significant improvement in tracking time compared to the traditional incremental conductance algorithm, attributed to the elimination of high-frequency components in output power by the Kalman filter. The novel algorithm exhibits enhanced tracking stability through the application of fuzzy control. Ultimately, the tracking accuracy of the novel algorithm surpasses that of the incremental conductance algorithm by 5.2%, achieving an impressive 94.9%. This study, therefore, presents a valuable contribution to a novel MPPT algorithm for precisely and rapidly tracking the global maximum power points of the ATEG system throughout the entire vehicle driving cycle.