Predictive Maximum Power Point Tracking for Proton Exchange Membrane Fuel Cell System - Jye Yun, Fam (Universiti Malaysia Sarawak (UNIMAS), 2025)
Proton exchange membrane fuel cells (PEMFC) are devices for converting renewable energy sources of hydrogen and oxygen into electricity with several benefits, including quick startup, high power density, and the ability to operate at low temperatures. However, fluctuations in the internal parameters of PEMFC can cause variations in power output. Additionally, PEMFC generates direct current (DC), while most electrical appliances rely on alternating current (AC). This project aims to design the predictive maximum power point tracking (MPPT) and voltage control system for a PEMFC. It incorporates the model predictive control algorithm for a direct current to direct current (DC-DC) boost converter and a single-phase direct current to alternating current (DC-AC) inverter to optimize the output power and convert the electrical power from DC to AC, respectively. All simulations were conducted using MATLAB software to demonstrate the PEMFC power characteristics and output power extraction. The results show that the predictive MPPT algorithms can track the maximum power point (MPP) within 19.2ms with 99.88% accuracy. The new MPP can be determined within 0.5ms when there are fast variations of PEMFC parameters. Therefore, the newly designed predictive MPPT methods exhibit fast-tracking of the MPP locus, outstanding accuracy, and robustness to environmental changes. Furthermore, the predictive voltage control technique supplies the AC power with 12V amplitude and 50Hz frequency throughout the simulations. It ensures that the DC voltage is inverted to a stable AC voltage. Keywords: DC-DC boost converter, MPPT, PEMFC, single-phase DC-AC inverter, voltage control