Maximizing the cooling capacity and COP of two-stage thermoelectric coolers through genetic algorithm

This study presented a method based on genetic algorithm (GA) to maximize the cooling capacity and the coefficient of performance (COP) for two-stage thermoelectric coolers (TECs). Focusing on the two-stage TECs arranged in cascade, parameters, including the applied electrical current and the pair number of thermocouples of each stage, were optimized. A new mathematical modelling was proposed to deal with the temperature-dependent material properties and to include the effects of contact and spreading thermal resistances. The optimal parameters obtained from GA were firstly verified by being compared with the data obtained from analytical methods. After the verification, the optimization was executed to generate the optimal parameters for a desired temperature difference of 90 °C. Including the effects of thermal resistances, the cooling capacity and the COP of two-stage TECs can be maximized and improved through slightly tuning the parameters. Throughout the calculation in GA, the search processes converged quickly and the optimal parameters were found without difficulty.