Open Access

ARTICLE

A Novel Method for Thermoelectric Generator Based on Neural Network

Mohammad Saraireh^1,*, A. M. Maqableh², Manar Jaradat³, Omar A. Saraereh⁴

1 Mechanical Engineering Department, Faculty of Engineering, Mutah University, Karak, 61710, Jordan
2 Electromechanical Engineering Department, Luminus Technical University College, Amman, 11118, Jordan
3 Department of Computer Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, Jordan
4 Department of Electrical Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, Jordan

* Corresponding Author: Mohammad Saraireh. Email:

Computers, Materials & Continua 2022, 73(1), 2115-2133. https://doi.org/10.32604/cmc.2022.029978

Received 15 March 2022; Accepted 14 April 2022; Issue published 18 May 2022

Abstract

The growing need for renewable energy and zero carbon dioxide emissions has fueled the development of thermoelectric generators with improved power generating capability. Along with the endeavor to develop thermoelectric materials with greater figures of merit, the geometrical and structural optimization of thermoelectric generators is equally critical for maximum power output and efficiency. Green energy strategies that are constantly updated are a viable option for addressing the global energy issue while also protecting the environment. There have been significant focuses on the development of thermoelectric modules for a range of solar, automotive, military, and aerospace applications in recent years due to various advantages including as low vibration, great reliability and durability, and the absence of moving components. In order to enhance the system performance of the thermoelectric generator, an artificial neural network (ANN) based algorithm is proposed. Furthermore, to achieve high efficiency and system stability, a buck converter is designed and deployed. Simulation and experimental findings demonstrate that the suggested method is viable and available, and that it is almost similar to the real value in the steady state with the least power losses, making it ideal for vehicle exhaust thermoelectric generator applications. Furthermore, the proposed hybrid algorithm has a high reference value for the development of a dependable and efficient car exhaust thermoelectric generating system.

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