Optimization of multiple-module thermoelectric coolers using artificial-intelligence techniques

Genetic algorithm (GA) and simulated annealing (SA) methods were employed to optimize the current distribution of a cooler made up of a large number of thermo-electric (TE) modules. The TE modules were grouped into several clusters in the flow direction, and the electric currents supplied to different clusters were adjusted separately to achieve maximum energy efficiency or minimum refrigeration temperature for different operating conditions and cooling requirements. Optimization results based on the design parameters of a large TE cooler showed considerable improvements in energy efficiency and refrigeration temperature when compared to the results of uniform current for the parellel-flow arrangement. On the other hand, results of the counter-flow arrangement showed only slight differences between uniform- and non-uniform-current optimizations. The optimization results of GA and SA were very close to each other. SA converged faster and was more computationally economical than GA for TE system optimization.