Optimization of Thermoelectric Generators Using Genetic Algorithms

This research studies optimizing segmented thermoelectric and two-stage thermoelectric generators and using Matlab programming language and Real Number Genetic Algorithm to perform optimization. With different segment lengths and different cross-sectional areas, the material properties of the cold and hot sections can be obtained by calculating the temperature of the material contact surface. The thermal resistivity, electrical resistivity, Seebeck coefficient, and thermoelectric generator output power can also be calculated. After optimization by a genetic algorithm, the output power of the two-stage thermoelectric generator increases by 17.06%, and the output power of the segmented thermoelectric generator increases by 159.32%. Finally, observe the thermoelectric material distribution and temperature changes to make inferences.