Impacts of thermal and electric contact resistance on the material design in segmented thermoelectric generators

Abstract

Segmented thermoelectric generators (STEGs) can exhibit present superior performance than those of the conventional thermoelectric generators. Thermal and electrical contact resistances exist between the thermoelectric material interfaces in each thermoelectric leg. This may significantly hinder performance improvement. In this study, a five-layer STEG with three pairs of thermoelectric (TE) materials was investigated considering the thermal and electrical contact resistances on the material contact surface. The STEG performance under different contact resistances with various combinations of TE materials were analyzed. The relationship between the material sequence and performance indicators under different contact resistances is established by machine learning. Based on the genetic algorithm, for each contact resistance combination, the optimal material sequences were identified by maximizing the electric power and energy conversion efficiency. To reveal the underlying mechanism that determines the heat-to-electrical performance, the total electrical resistance, output voltage, ZT value, and temperature distribution under each optimized scenario were analyzed. The STEG can augment the heat-to-electricity performance only at small contact resistances. A large contact resistance significantly reduces the performance. At an electrical contact resistance of RE = 10–3 K·m2·W−1 and thermal contact resistance of RT = 10–8 Ω·m2, the maximum electric power was reduced to 5.71 mW (90.86 mW without considering the contact resistance). And the maximum energy conversion efficiency is lowered to 2.54% (12.59% without considering the contact resistance).

Keywords

• Segmented thermoelectric generator
• Contact resistance
• Material design
• Machine learning