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Material Property Characterization and Parameter Estimation of Thermoelectric Generator by Using a Master–Slave Strategy Based on Metaheuristics Techniques

Author

Listed:
  • Daniel Sanin-Villa

    (Departamento de Mecatrónica y Electromecánica, Instituto Tecnológico Metropolitano, Medellín 050036, Colombia)

  • Oscar Danilo Montoya

    (Grupo de Compatibilidad e Interferencia Electromagnética (GCEM), Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia)

  • Luis Fernando Grisales-Noreña

    (Department of Electrical Engineering, Faculty of Engineering, Universidad de Talca, Curicó 3340000, Chile)

Abstract

Thermoelectric generators (TEGs) have gained significant interest as a sustainable energy source, due to their ability to convert thermal energy into electrical energy through the Seebeck effect. However, the power output of TEGs is highly dependent on the thermoelectric material properties and operational conditions. Accurate modeling and parameter estimation are essential for optimizing and designing TEGs, as well as for integrating them into smart grids to meet fluctuating energy demands. This work examines the challenges of accurate modeling and parameter estimation of TEGs and explores various optimization metaheuristics techniques to find TEGs parameters in real applications from experimental conditions. The paper stresses the importance of determining the properties of TEGs with precision and using parameter estimation as a technique for determining the optimal values for parameters in a TEG mathematical model that represent the actual behavior of a thermoelectric module. This methodological approach can improve TEG performance and aid in efficient energy supply and demand management, thus reducing the reliance on traditional fossil fuel-based power generation.

Suggested Citation

  • Daniel Sanin-Villa & Oscar Danilo Montoya & Luis Fernando Grisales-Noreña, 2023. "Material Property Characterization and Parameter Estimation of Thermoelectric Generator by Using a Master–Slave Strategy Based on Metaheuristics Techniques," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1326-:d:1092306
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    References listed on IDEAS

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    Cited by:

    1. Daniel Sanin-Villa & Miguel Angel Rodriguez-Cabal & Luis Fernando Grisales-Noreña & Mario Ramirez-Neria & Juan C. Tejada, 2024. "A Comparative Analysis of Metaheuristic Algorithms for Enhanced Parameter Estimation on Inverted Pendulum System Dynamics," Mathematics, MDPI, vol. 12(11), pages 1-17, May.
    2. Daniel Sanin-Villa & Oscar D. Monsalve-Cifuentes, 2023. "A Methodological Approach of Predicting the Performance of Thermoelectric Generators with Temperature-Dependent Properties and Convection Heat Losses," Energies, MDPI, vol. 16(20), pages 1-24, October.
    3. Santiago Bustamante-Mesa & Jorge W. Gonzalez-Sanchez & Sergio D. Saldarriaga-Zuluaga & Jesús M. López-Lezama & Nicolás Muñoz-Galeano, 2024. "Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection," Energies, MDPI, vol. 17(2), pages 1-20, January.
    4. Daniel Sanin-Villa & Oscar Danilo Montoya & Walter Gil-González & Luis Fernando Grisales-Noreña & Alberto-Jesus Perea-Moreno, 2023. "Parameter Estimation of a Thermoelectric Generator by Using Salps Search Algorithm," Energies, MDPI, vol. 16(11), pages 1-16, May.

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