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Optimization analysis of a segmented thermoelectric generator based on genetic algorithm

Author

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  • Zhu, Lei
  • Li, Huaqi
  • Chen, Sen
  • Tian, Xiaoyan
  • Kang, Xiaoya
  • Jiang, Xinbiao
  • Qiu, Suizheng

Abstract

Optimizing the geometry structures and operating conditions is an effective way to improve the performance of the segmented thermoelectric generator (STEG). A one-dimensional numerical model combined with genetic algorithm (GA) is presented for performance analysis and design optimization of the STEG. The model’s predictions being in good agreement with experimental data in the published literature confirms the accuracy of the model. According to the compatibility factors, the Ba0.4ln0.4CoSb12, Bi2Te0.7Se0.3, Zn4Sb3 and Bi2Te3 are selected as materials for segments of N1, N2, P1, and P2, respectively. The p-segmented TEG is recommended through performance comparison between STEGs with four different structures. After that, the load following region and rated operating point are given, through load following characteristic analysis. At last, the effect of contact resistance on the performance of the STEG is analyzed. The analysis results show that, by reducing the contact resistance to 50 μΩ cm2 per leg, the peak conversion efficiency of the p-segmented TEG proposed in this paper can reach 9.83% at a temperature difference of 350 K, which is 25.4% higher than that of traditional thermoelectric generator.

Suggested Citation

  • Zhu, Lei & Li, Huaqi & Chen, Sen & Tian, Xiaoyan & Kang, Xiaoya & Jiang, Xinbiao & Qiu, Suizheng, 2020. "Optimization analysis of a segmented thermoelectric generator based on genetic algorithm," Renewable Energy, Elsevier, vol. 156(C), pages 710-718.
    • Handle: RePEc:eee:renene:v:156:y:2020:i:c:p:710-718
    DOI: 10.1016/j.renene.2020.04.120
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    References listed on IDEAS

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    Cited by:

    1. Ge, Ya & He, Kui & Xiao, Liehui & Yuan, Wuzhi & Huang, Si-Min, 2022. "Geometric optimization for the thermoelectric generator with variable cross-section legs by coupling finite element method and optimization algorithm," Renewable Energy, Elsevier, vol. 183(C), pages 294-303.
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    6. Maduabuchi, Chika & Eneh, Chibuoke & Alrobaian, Abdulrahman Abdullah & Alkhedher, Mohammad, 2023. "Deep neural networks for quick and precise geometry optimization of segmented thermoelectric generators," Energy, Elsevier, vol. 263(PC).
    7. Ye-Qi Zhang & Jiao Sun & Guang-Xu Wang & Tian-Hu Wang, 2022. "Advantage of a Thermoelectric Generator with Hybridization of Segmented Materials and Irregularly Variable Cross-Section Design," Energies, MDPI, vol. 15(8), pages 1-18, April.
    8. 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.
    9. Sourav Bhakta & Balaram Kundu, 2024. "A Review of Thermoelectric Generators in Automobile Waste Heat Recovery Systems for Improving Energy Utilization," Energies, MDPI, vol. 17(5), pages 1-46, February.
    10. Chen, Zhiwei & Zhao, Weicheng & Lin, Xiaoyong & Han, Yongming & Hu, Xuan & Yuan, Kui & Geng, Zhiqiang, 2024. "Load prediction of integrated energy systems for energy saving and carbon emission based on novel multi-scale fusion convolutional neural network," Energy, Elsevier, vol. 290(C).
    11. Yang, Huizhu & Li, Mingxuan & Wang, Zehui & Ren, Fengsheng & Yang, Yue & Ma, Bijian & Zhu, Yonggang, 2023. "Performance optimization for a novel two-stage thermoelectric generator with different PCMs embedding modes," Energy, Elsevier, vol. 281(C).
    12. Zhu, Yuxiao & Newbrook, Daniel W. & Dai, Peng & de Groot, C.H. Kees & Huang, Ruomeng, 2022. "Artificial neural network enabled accurate geometrical design and optimisation of thermoelectric generator," Applied Energy, Elsevier, vol. 305(C).
    13. Ge, Ya & Xiao, Qiyin & Wang, Wenhao & Lin, Yousheng & Huang, Si-Min, 2022. "Design of high-performance photovoltaic-thermoelectric hybrid systems using multi-objective genetic algorithm," Renewable Energy, Elsevier, vol. 200(C), pages 136-145.
    14. Yan, Weichao & Cui, Xin & Meng, Xiangzhao & Yang, Chuanjun & Zhang, Yu & Liu, Yilin & An, Hui & Jin, Liwen, 2024. "Multi-objective optimization of hollow fiber membrane-based water cooler for enhanced cooling performance and energy efficiency," Renewable Energy, Elsevier, vol. 222(C).
    15. Luo, Yang & Li, Linlin & Chen, Yiping & Kim, Chang Nyung, 2022. "Influence of geometric parameter and contact resistances on the thermal-electric behavior of a segmented TEG," Energy, Elsevier, vol. 254(PC).
    16. Sun, Jinxiang & Zhang, Ruibo & Wang, Mingjun & Zhang, Jing & Qiu, Suizheng & Tian, Wenxi & Su, G.H., 2022. "Multi-objective optimization of helical coil steam generator in high temperature gas reactors with genetic algorithm and response surface method," Energy, Elsevier, vol. 259(C).
    17. Harb, Abd El-Moneim A. & Elsayed, Khairy & Sedrak, Momtaz & Ahmed, Mahmoud & Abdo, Ahmed, 2024. "Enhancing the performance of thermoelectric generators using novel segmental arrangement of multi-functional gradient materials," Renewable Energy, Elsevier, vol. 225(C).
    18. Demeke, Wabi & Ryu, Byungki & Ryu, Seunghwa, 2024. "Machine learning-based optimization of segmented thermoelectric power generators using temperature-dependent performance properties," Applied Energy, Elsevier, vol. 355(C).

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