Optimal design of a segmented thermoelectric generator based on three-dimensional numerical simulation and multi-objective genetic algorithm

Ya Ge, Zhichun Liu, Henan Sun and Wei Liu

Energy, 2018, vol. 147, issue C, 1060-1069

Abstract: This paper proposes a general method to optimize the structure and load current for a segmented thermoelectric generator (TEG) module, where the bismuth telluride is selected as the cold side material, and the skutterudite is selected as the hot side material, respectively. Two objectives, minimum semiconductor volume V′ and maximum output power P, are simultaneously considered to assess the performance of the TEG module. All the simulation models to be optimized by the multi-objective genetic algorithm are established and solved by finite element method, where the Thomson effect, in conjunction with Peltier effect, Joule heating, and Fourier heat conduction are simultaneously considered. In order to achieve the ultimate optimal design, TOPSIS (technique for order preference by similarity to an ideal solution) is employed to determine the best compromise solution. The results of Pareto solutions show that V′ varies from 432 mm3 to 3868 mm3, while P varies from 5.523 W to 56.293 W, respectively. Meanwhile, optimal design variables are investigated to provide practical guidance for the industrial applications. The mechanism of performance improvement has also been explained in this work by comparing the optimal segmented TEG and the skutterudite TEG.

Keywords: Thermoelectric generator; Numerical simulation; Multi-objective optimization; Genetic algorithm (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (37)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:147:y:2018:i:c:p:1060-1069

DOI: 10.1016/j.energy.2018.01.099

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