Optimization analysis of a segmented thermoelectric generator based on genetic algorithm. (August 2020)

• Record Type: Journal Article
• Authors: Zhu, Lei; Li, Huaqi; Chen, Sen; Tian, Xiaoyan; Kang, Xiaoya; Jiang, Xinbiao; Qiu, Suizheng
• Is Part Of: Renewable energy. Volume 156(2020)
• Page Start: 710
• Page End: 718
• Publication Date: 2020-08
• Subjects: Segmented thermoelectric generator -- Genetic algorithm -- Load following -- Performance optimization; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2020.04.120
• Languages: English
• ISSNs: 0960-1481

Abstract

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 Ba0.4 ln0.4 CoSb12, Bi2 Te0.7 Se0.3, Zn4 Sb3 and Bi2 Te3 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 cm 2 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. Highlights: The optimization method based on genetic algorithm is developed for the STEG. Thermoelectric materials are selected according to the compatibility factors. An integrative optimization of the P-segmented TEG is performed by GA. Conversion efficiency and Power output of the STEG are improved significantly.