Predictive Maximum Power Point Tracking for Proton Exchange Membrane Fuel Cell System

Jye Yun, Fam and Shen Yuong, Wong and Hazrul, Mohamed Basri and Mohammad Omar, Abdullah and Kasumawati, Lias and Saad, Mekhilef (2021) Predictive Maximum Power Point Tracking for Proton Exchange Membrane Fuel Cell System. IEEE Access, 9 (2021). pp. 1-15. ISSN 2169-3536

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Official URL: https://ieeexplore.ieee.org/document/9623565

Abstract

This project aims to design a predictive maximum power point tracking (MPPT) for a proton exchange membrane fuel cell system (PEMFC). This predictive MPPT includes the predictive control algorithm of a DC-DC boost converter in the fully functional mathematical modeling of the PEMFC system. The DC-DC boost converter is controlled by the MPPT algorithm and regulates the voltage of the PEMFC to extract the maximum output power. All simulations were performed using MATLAB software to show the power characteristics extracted from the PEMFC system. As a result, the newly designed predictive MPPT algorithm has a fast-tracking of maximum power point (MPP) for different fuel cell (FC) parameters. It is confirmed that the proposed MPPT technique exhibits fast tracking of the MPP locus, outstanding accuracy, and robustness with respect to environmental changes. Furthermore, its MPP tracking time is at least five times faster than that of the particle swarm optimizer with the proportional-integral-derivative controller method.

Item Type: Article
Uncontrolled Keywords: MATLAB, FC, PEMFC, DC-DC boost converter, MPPT
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Academic Faculties, Institutes and Centres > Faculty of Engineering
Depositing User: Mohamed Basri
Date Deposited: 24 Nov 2021 02:25
Last Modified: 19 Jan 2022 04:48
URI: http://ir.unimas.my/id/eprint/36776

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