Optimization of a unileg thermoelectric generator by the combination of Taguchi method and evolutionary neural network for green power generation

Abstract

The performance optimization of thermoelectric generators (TEGs) is crucial for efficiently converting waste heat to green energy. This study analyzes the performance and thermal stress of a unileg thermoelectric module by combining the Taguchi method, analysis of variance (ANOVA), and artificial neural network. The results of the Taguchi method indicate that the leg geometry exerts a negligible effect on output power (OP) and conversion efficiency but significantly affects the thermal stress (TS). The analysis of the objective function (maximum OP-to-maximum TS ratio, denoted by P/S) reveals the optimal configuration results in a P/S value of 0.01255 W‧MPa−1, accompanied by a maximum output power (2.9 W) and thermal stress (232.32 MPa). The maximum thermal stress of the rectangle TE leg (182.29 MPa) is less than that of the triangle (279.85 MPa) by 34.86 %. The Taguchi method, analysis of variance, and artificial neural network have the same sensitivity order on P/S with leg height > leg geometry > copper thickness > ceramic thickness. Furthermore, an evolutionary neural network (ENN) method with five-step optimization from 4000 datasets has been developed to improve machine learning prediction. The average error of P/S value and conversion efficiency from ENN is 1.61 % and 0.35 %, respectively. However, for the ENN predictions, the relative errors of P/S value and efficiency between predictions and simulations are 1.28 % and 0.19 %, respectively. In conclusion, this work has demonstrated the numerical optimization of the performance of unileg TEM for the first time. The use of ENN can predict the performance of TEM precisely, saving running time and markedly reducing computational costs. Moreover, the results are conducive to designing high-performance TEGs with long lifespans.