4.7 Review

Future perspective and current situation of maximum power point tracking methods in thermoelectric generators

Journal

Publisher

ELSEVIER
DOI: 10.1016/j.seta.2021.101824

Keywords

Thermoelectric generators; Maximum power point tracking; Seebeck effect; Waste heat; Sustainable energy

Ask authors/readers for more resources

This study reviews over sixty-two MPPT methods used in TEGs in the last six years, aiming to provide a reference for researchers. It found that conventional MPPT algorithms have various drawbacks, leading to the emergence of metaheuristic MPPT algorithms as a solution.
One of the green technologies that can be used to increase energy efficiency by recovering a part of waste heat as electrical energy is thermoelectric generators (TEG) by using the Seebeck phenomenon. Conventional and modern maximum power point tracking (MPPT) methods used to deliver maximum power from energy sources. Conventional MPPT algorithms have disabilities such as a delay in reaching the maximum power point (MPP), certain oscillations around the MPP, being stuck at local MPP (LMPP), and not being able to find global MPP (GMPP). In order to overcome the drawbacks of conventional MPPT methods, methods using metaheuristic MPPT algorithms have come to the fore in recent years. However, the issue of determining the appropriate method among the increasing number and complexity of MPPT methods causes confusion. The aim of this study is to review more than sixty-two MPPT methods that have been used in TEGs in the last six years and have the potential to be adapted for TEGs and provide a reference for researchers. Eventually, this review will be a resource that introduces the next generation MPPT methods, presents MPPT methods with the potential to be adapted to TEGs, and will be a good reference for future studies.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available