Work

Measurement based data-driven modeling of a thermoelectric generator (TEG)

Year: 2026

Type: article

Abstract: Due to the complex physical behavior of thermoelectric generators (TEGs), direct physics-based modeling is often impractical. The main contribution of this research is that three different data-driven models are made for three different objectives. The first method is a classical parametric curve fitting technique using parameter identification based Bacterial Memetic Algorithm (BMA). The second is a fuzzy model with arbitrarily defined trapezoidal membership functions, where the anchor points are optimized using BMA as well. The third approach is a tensor product (TP) model transformation based polytopic model. The study provides a comparative analysis of these approaches, emphasizing their modeling flexibility and accuracy in representing the nonlinear characteristics of TEGs.

Source: Results in Engineering

DOI: 10.1016/j.rineng.2026.110860

Authors: Károly Árpád Kis, Kornél Sarvajcz, Róbert Mikuska, Péter Korondi

Institution: University of Debrecen

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $1,940