A new metaheuristic honey badger-based maximum energy harvesting algorithm for thermoelectric generation system under dynamic operating conditions

Thermoelectric generators (TEGs) are widely employed in microscale applications to generate power from waste-heat energy emitted by nonrenewable energy sources. The energy extracted from TEG is dependent on the temperature gradient across the hot- and cold-ends along with the electrical load applied. Therefore, a maximum power point tracking (MPPT) control is strongly preferred to constantly track the optimal point of TEG under diverse operational conditions. In this study, a new MPPT-based metaheuristic optimizer so-called Honey badger algorithm (HBA) is implemented to maximize the energy efficiency of TEG systems. The proposed technique seeks to optimize the dynamic response and eradicate oscillations near MPP. Various dynamic operating conditions are used to investigate the reliability of the optimized HBA-based MPPT method. The obtained results are compared with other energy harvesting approaches including incremental conductance (INC), hybrid gray wolf optimizer-sine cosine algorithm (HGWOSCA), and marine predators algorithm (MPA). The major findings verify that the employed optimized HBA method achieved highest power tracking efficiency more than 99% with smallest tracking time up to 120 ms and maintaining a steady-state output at the different studied scenarios.

Automated summary

Thermoelectric generators (TEGs) are commonly used in microscale applications to generate power from waste-heat energy. In this study, a new MPPT-based metaheuristic optimizer called Honey badger algorithm (HBA) is implemented to maximize the energy efficiency of TEG systems. The results show that the optimized HBA method achieves efficient power tracking under various dynamic operating conditions.