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Novel model and maximum power tracking algorithm for thermoelectric generators operated under constant heat flux

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  • Compadre Torrecilla, Marcos
  • Montecucco, Andrea
  • Siviter, Jonathan
  • Knox, Andrew R.
  • Strain, Andrew

Abstract

Thermoelectric generators (TEGs) are solid-state devices used to convert heat into electricity. The use of TEGs in waste heat recovery systems offers a source of sustainable electricity, which helps to reduce emissions to the environment. Optimization of the electrical operating point of TEGs is important to improve the overall efficiency of TEG systems. Previous literature focused mostly on characterizing the maximum power point (MPP) of TEGs when operating at constant temperature difference. However, in most practical applications TEGs operate under constant or limited heat conditions. In fact, in waste heat recovery systems the amount of thermal energy is limited. This work presents a new simplified TEG model that simulates the dynamic response of the TEG systems with a good degree of accuracy. With the aid of this TEG model, power and control electronics have been designed to operate a TEG system, with limited input heat flux, at its optimum load. The control architecture is based on the perturb and observe maximum power point tracking (MPPT) technique and modified totake into consideration the thermal transient response of the TEG. A boost dc-dc converter is used to step-up the TEG voltage to 28 V for connection to an eight-cell Lithium-Ion battery. A microcontroller implements the control algorithm that drives the power converter. Experimental results show that the proposed algorithm outperforms two state-of-the-art algorithms (standard perturb and observe and fractional open-circuit) by 1.14% and 2.08%, respectively, when the TEG operates under constant heat flux.

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  • Compadre Torrecilla, Marcos & Montecucco, Andrea & Siviter, Jonathan & Knox, Andrew R. & Strain, Andrew, 2019. "Novel model and maximum power tracking algorithm for thermoelectric generators operated under constant heat flux," Applied Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:appene:v:256:y:2019:i:c:s0306261919316174
    DOI: 10.1016/j.apenergy.2019.113930
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    References listed on IDEAS

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    Cited by:

    1. Ahmed Fathy & Hegazy Rezk & Dalia Yousri & Essam H. Houssein & Rania M. Ghoniem, 2021. "Parameter Identification of Optimized Fractional Maximum Power Point Tracking for Thermoelectric Generation Systems Using Manta Ray Foraging Optimization," Mathematics, MDPI, vol. 9(22), pages 1-18, November.
    2. Fathy, Ahmed, 2023. "Efficient energy valley optimization approach for reconfiguring thermoelectric generator system under non-uniform heat distribution," Renewable Energy, Elsevier, vol. 217(C).
    3. Yang, Bo & Wu, Shaocong & Li, Qiang & Yan, Yingjie & Li, Danyang & Luo, Enbo & Zeng, Chunyuan & Chen, Yijun & Guo, Zhengxun & Shu, Hongchun & Li, Zilin & Wang, Jingbo, 2023. "Jellyfish search algorithm based optimal thermoelectric generation array reconfiguration under non-uniform temperature distribution condition," Renewable Energy, Elsevier, vol. 204(C), pages 197-217.
    4. He, Min & Wang, Enhua & Zhang, Yuanyin & Zhang, Wen & Zhang, Fujun & Zhao, Changlu, 2020. "Performance analysis of a multilayer thermoelectric generator for exhaust heat recovery of a heavy-duty diesel engine," Applied Energy, Elsevier, vol. 274(C).
    5. Samir Ezzitouni & Pablo Fernández-Yáñez & Luis Sánchez Rodríguez & Octavio Armas & Javier de las Morenas & Eduard Massaguer & Albert Massaguer, 2021. "Electrical Modelling and Mismatch Effects of Thermoelectric Modules on Performance of a Thermoelectric Generator for Energy Recovery in Diesel Exhaust Systems," Energies, MDPI, vol. 14(11), pages 1-15, May.
    6. Buchalik, Ryszard & Nowak, Grzegorz & Nowak, Iwona, 2021. "Mathematical model of a thermoelectric system based on steady- and rapid-state measurements," Applied Energy, Elsevier, vol. 293(C).

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