Machine learning-based optimization of segmented thermoelectric power generators using temperature-dependent performance properties

Segmented thermoelectric generators (STEGs) provide an excellent platform for thermal energy harvesting devices because they improve power generation performance across a broad range of operating temperatures. Despite the benefit of direct thermal energy-to-electricity conversion, conventional STEG optimization approaches are unable to provide a systematic method for selecting the optimal multiple stacks of p- and-n-type thermoelectric materials (TEs) legs from a set of numerous TE materials. In this study, we propose a systematic optimization method based on machine learning to find the optimal STEG for performance maximization. A deep neural network (DNN) is trained using an initial dataset generated via the Finite Element Method (FEM), with inputs including the temperature-dependent properties of p- and n-type TE materials, the lengths of each segment, and external loads, as well as the corresponding performance outputs. The trained DNN captures the inherent nonlinear relationship between these inputs and outputs. The combination of a trained DNN and a genetic algorithm (GA) efficiently navigates a vast design space using 88 p-type and 70 n-type TE materials along with device factors. It formulates four stacked segment pairs in p- and n-leg TEGs, targeting new superior designs with enhanced power, efficiency, or both. The DNN is iteratively refined via active learning (AL) by incorporating new superior STEG designs to enhance prediction accuracy. The optimized STEGs exhibit power and efficiency that are 1.91 and 1.5 times higher, respectively, than the top designs from the initial training dataset composed of 157.916 STEGs. Furthermore, compared to conventional TEG designs without segmentation, our method discovered numerous high-performing STEG designs.