Multi-objective genetic optimization of the thermoelectric system for thermal management of proton exchange membrane fuel cells

Abstract

As a clean power system with a narrow temperature range of typically 60–95 °C, the low temperature (LT) proton exchange membrane fuel cell (PEMFC) requires an effective thermal management system to enhance its efficiency and durability. This paper focuses on a genetic algorithm based optimization of the thermoelectric generator (TEG) as applied to the PEMFC system. The genetic algorithm approach is advantageous over similar previous research in that it enables multi-objective optimization where the various TEG module parameters can be configured towards critical objectives such as maximum output power, minimal mass and maintaining the PEMFC within its operating temperature range. A second case study is also studied where the combined efficiency of the PEMFC and TEG is selected as an objective in replacement of the maximum TEG output power. Optimization results suggest that, in both cases, there is a trade-off situation between maximum output TEG power or maximum system efficiency with respect to system mass. It is also shown that the most important benefit of increasing the cooling convection coefficient is that it increases the system’s specific power where the heat sink areas can be smaller to achieve the same cooling rate.