Thermal and Electrical Contact Resistances of Thermoelectric Generator: E Xperimental Study and Artificial Neural Network Modelling

Abstract

Thermal and electrical contact resistances (TCR and ECR) of thermoelectric generator (TEG) exert essential impacts on its performance. In this article, through a series of experiments these two important properties have been estimated in a wide range of thermal and mechanical conditions, and with different interfacial materials. The magnitudes of the overall TCR were found in the range of 1.12~2.00×10 -3 m 2 K/W with air,   (0.82~1.81) ×10 -3 m 2 K/W  with graphene sheet, and with thermal grease as interfacial materials when the heat-source temperature varied from 348.15K to 599.15K and the imposed pressure loads from 266kPa to 1266kPa. The detailed TCR distributions across the thermoelectric system were also analysed, and the dominant components, which occupy more than  of the overall TCR, have been identified at the interfaces of the thermoelectric module contacting the heat source and heat sink. In our experiment, the corresponding ECRs under the same working conditions were (1.03~1.52)×10 -9 Ω∙m 2 , (0.56~9.60)×10 -10  Ω∙m 2  and (1.05~6.23)×10 -10  Ω∙m 2 , respectively. Moreover, the performance of the TEG system in terms of the open-circuit voltage, maximum power output and maximum conversion efficiency were assessed. All these results reveal that for a TEG operating at a high heat-source temperature, imposing a large pressure load and using thermal grease as interfacial material can help regulate its TCR and ECR at relatively low values, and enable it to deliver a better performance. In addition to these experimental findings, we also developed a fully-connected feed-forward artificial neural network (ANN) model. It is shown that such an ANN model can achieve a cost-effective TCR prediction in good accuracy with the mean square error 2.36×10 -9 and the correlation coefficient 99.4% . These numerical and experimental results in this work provide detailed references of TCR and ECR of a TEG system operating under broad working conditions. They will be of particular value for future TEG design and optimization.

Keywords: thermoelectric generator, thermal contact resistance, electrical contact resistance, artificial neural network