An MPPT Controller with a Modified Four-Leg Interleaved DC/DC Boost Converter for Fuel Cell Applications

Authors

  • Arigela Satya Veerendra Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
  • Kumaran Kadirgama Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia
  • Norazlianie Sazali Faculty of Manufacturing and Mechatronic Engineering Technology, University Malaysia Pahang, 26600 Pekan, Pahang, Malaysia
  • Sivayazi Kappagantula Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka 576104, India
  • Subbarao Mopidevi Vignan’s Foundation for Science, Technology & Research, Guntur, Andhra Pradesh 522213, India

DOI:

https://doi.org/10.37934/araset.53.1.219236

Keywords:

Interleaved boost converter, Fuzzy controller, MPPT controller, Fuel cell system, Neural network

Abstract

A fuel cell system can produce electricity and water more efficiently while emitting near-zero emissions. Internal constraints and operating parameters such as hydrogen, temperature, humidity levels, and oxygen gas partial pressures trigger a nonlinear power characteristic in a typical fuel cell stack, resulting in reduced overall system efficiency. Consequently, it's critical to get the most power out of the fuel cell stack while minimizing fuel use. This study examines and proposes a radial basis function network (RBFN) based maximum power point tracking technique (MPPT) for a 6-kW proton exchange membrane fuel cell (PEMFC) system. The proposed MPPT algorithm modulates the duty cycle of the modified four-leg interleaved DC/DC boost converter (MFLIBC) to extricate the maximum power from the fuel cell system. To validate the execution of the proposed controller, the outcome is related to the various MPPT control strategies such as PID & Mamdani fuzzy inference systems. Finally, it was observed that the proposed RBFN controller has achieved an enhanced efficiency of 83.2 % relative to the PID and fuzzy logic controllers of 75.5 % and 77.4 % respectively. The efficiency of the proposed configuration is analysed using the MATLAB/Simulink platform.

Downloads

Download data is not yet available.

Author Biographies

Arigela Satya Veerendra, Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India

veerendra.babu@manipal.edu

Kumaran Kadirgama, Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia

kumaran@umpsa.edu.my

Norazlianie Sazali, Faculty of Manufacturing and Mechatronic Engineering Technology, University Malaysia Pahang, 26600 Pekan, Pahang, Malaysia

azlianie@ump.edu.my

Published

2024-10-07

Issue

Section

Articles

Most read articles by the same author(s)

1 2 > >>