Optimization analysis of a segmented thermoelectric generator based on genetic algorithm

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

Renewable Energy, 2020, vol. 156, issue C, 710-718

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.

Keywords: Segmented thermoelectric generator; Genetic algorithm; Load following; Performance optimization (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:156:y:2020:i:c:p:710-718

DOI: 10.1016/j.renene.2020.04.120

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