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

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.

Automated summary

This project focuses on developing a predictive maximum power point tracking (MPPT) for a proton exchange membrane fuel cell system, incorporating a predictive control algorithm for a DC-DC boost converter in the mathematical modeling. Simulation results in MATLAB demonstrate that the newly designed MPPT algorithm offers fast tracking, accuracy, and robustness for different fuel cell parameters, with a tracking time at least five times faster than traditional methods like the particle swarm optimizer with the proportional-integral-derivative controller.