Design of Thermoelectric Generators and Maximum Electrical Power Using Reduced Variables and Machine Learning Approaches

Alexander Vargas-Almeida, Miguel Angel Olivares-Robles () and Andres Alfonso Andrade-Vallejo
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Alexander Vargas-Almeida: Departamento de Ingeniería en Automatización y Control Industrial, Universidad Politecnica del Golfo de Mexico, Carretera Federal Malpaso-El Bellote Km. 171, Paraíso 86600, Mexico
Miguel Angel Olivares-Robles: Instituto Politecnico Nacional, SEPI ESIME-Culhuacan, Culhuacan, Ciudad de Mexico 04430, Mexico
Andres Alfonso Andrade-Vallejo: Instituto Politecnico Nacional, SEPI ESIME-Culhuacan, Culhuacan, Ciudad de Mexico 04430, Mexico

Energies, 2023, vol. 16, issue 21, 1-27

Abstract: This work aims to contribute to studies on the geometric optimization of thermoelectric generators ( T E G s ) through a combination of the reduced variables technique and supervised machine learning. The architecture of the thermoelectric generators studied, one conventional and the other segmented, was determined by calculating the cross-sectional area and length of the legs, and applying reduced variables approximation. With the help of a supervised machine learning algorithm, the values of the thermoelectric properties were predicted, as were those of the maximum electrical power for the other temperature values. This characteristic was an advantage that allowed us to obtain approximate results for the electrical power, adjusting the design of the T E G s when experimental values were not known. The proposed method also made it possible to determine the optimal values of various parameters of the legs, which were the ratio of the cross-sectional areas ( A p / A n ) , the length of the legs ( l ) , and the space between the legs ( H ) . Aspects such as temperature-dependent thermoelectric properties (Seebeck coefficient, electrical resistivity, and thermal conductivity) and the metallic bridge that connects the legs were considered in the calculations for the design of the T E G s , obtaining more realistic models. In the training phase, the algorithm received the parameter ( H ) and an operating temperature value as input data, to predict the corresponding value of the maximum power produced. This calculation was performed for conventional and segmented systems. Recent advances have opened up the possibility of applying an algorithm for designing conventional and segmented thermocouples based on the reduced variables approach and incorporating a supervised machine learning computational technique.

Keywords: thermoelectric generator design; dimensional parameters; maximum power; supervised machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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