Performance enhancement of segmented annular thermoelectric generator based on multi-parameter and multi-objective optimization

The excellent performance of annular thermoelectric generator (ATEG) is crucial for its commercial application, and it has been validated that the segment configuration can effectively enhance the ATEG performance. Nevertheless, owing to the limitations in conventional optimization methods which fail to simultaneously consider the co-influence of multiple parameters, further exploration is required for achieving optimal design of segmented annular thermoelectric generator (SATEG). In this study, we present a novel approach combining multi-objective genetic algorithm (MOGA) with finite element method to implement the multi-parameter and multi-objective optimization of SATEG. This approach not only reduces the computational cost but also ensures precise control on SATEG temperature below its maximum acceptable operation temperature. Furthermore, the optimization results highlight the importance of emphasizing diversity between p- and n- type TE legs as well as recognizing the different optimal geometrical structures for two types of TE legs, which have been always overlooked in previous researches. By adopting the optimal geometrical structure at a hot end temperature of 600 K, significant improvements in output power of 32.839 % and efficiency of 61.915 % are achieved for SATEG.

Automated summary

This study presents a novel approach combining multi-objective genetic algorithm with finite element method for the multi-parameter and multi-objective optimization of segmented annular thermoelectric generator. The optimization results highlight the importance of emphasizing diversity between p- and n- type TE legs as well as recognizing the different optimal geometrical structures for two types of TE legs, which have been always overlooked in previous researches.