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

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Abstract

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.

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Data availability

All authors have no relevant financial or non-financial interests to disclose. Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Funding

This work was supported by the Key Science and Technology Project of Anhui Province under Grant 202203f07020002.

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Authors and Affiliations

  1. Department of Automation, University of Science and Technology of China, Hefei, 230027, China

    Maryam Ejaz & Qiang Ling

  2. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China

    Qiang Ling

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  1. Maryam Ejaz
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  2. Qiang Ling
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by ME. The first draft of the manuscript was written by QL. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Qiang Ling.

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Ejaz, M., Ling, Q. A new metaheuristic honey badger-based maximum energy harvesting algorithm for thermoelectric generation system under dynamic operating conditions. Soft Comput 28, 4951–4966 (2024). https://doi.org/10.1007/s00500-023-09214-5

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