Volume 4: Innovative Solutions for Energy Transitions: Part III

Design of Segmented Thermoelectric Generator Optimized by Multiobjective Genetic Algorithm Wei-Hsin Chen*, Yi-Bin Chiou, Yu-Li Lin

Abstract

This study focuses on the design of segmented thermoelectric generator (STEG) system to achieve the system maximum output power. Multi-objective genetic algorithm (MOGA) design was applied to increase the output power. The STEG structure was optimized by altering the length of the cold side thermoelectric materials. The results showed that the optimized STEG module has the maximum output power when the lengths of p-type and n-leg cold side thermoelectric material are 0.78 mm and 0.5 mm. The cold side length which is not in the range of optimized value will reduce the maximum output power. In addition, the heat transfer rate and power generation of the STEG model was optimized. The most suitable length of the cold side thermoelectric material was found by MOGA. The output power of the optimized STEG compared with half in leg length of the segmented STEG was increased 21.94 % at ΔT= 400 K. Therefore, MOGA was an effective tool for designing STEG geometries.

Keywords Segmented; Thermoelectric generators (TEG); Impedance matching; Optimization; Multiobjective genetic algorithm (MOGA); Output power