Comprehensive Constraint Optimization of Thermoelectric Modules for Cooling Applications

Abstract

Thermoelectric air conditioners (TEACs) are a solid-state technology with the potential to replace conventional vapor compression-based air conditioning. This technology converts electrical power into a cooling/heating heat flux using the thermoelectric effect. TEACs are used in microelectronic, medical, and sensor industries. Their solid-state nature ensures longer lifespans and lower maintenance compared to traditional cooling systems. However, the high cost of raw materials and their lower coefficient of performance compared to other cooling technologies have limited their broader adoption to specific applications. Efforts to improve TEACs have mostly focused on optimizing individual variables on thermoelectric modules while keeping others fixed, lacking comprehensive optimization techniques. The use of nonlinear programming could address this issue but remains underutilized. In this paper, we propose using a tool that applies a three-step methodology for implementing nonlinear programming. This methodology is demonstrated through a case study of an air-to-air TEAC that operates with two thermoelectric modules. By varying multiple thermoelectric module parameters simultaneously, the optimization algorithm produced a design that delivers substantial coefficient of performance improvements across the entire operating current range compared to the reference module. The optimized configuration achieves gains of up to 83% at air flow rates of 3 g·s−1, 44% at 5 g·s−1, and 28% at 7.5 g·s−1. These results demonstrate that improving thermoelectric module performance requires optimizing the interplay among thermoelectric parameters rather than adjusting them individually.