Optimal parameters of PEM fuel cells using chaotic binary shark smell optimizer

ABSTRACT

Proton Exchange Membrane Fuel Cells (PEMFCs) are regarded as a promising system for power generation and energy sources in recent years. PEMFCs exhibit various advantages in comparison to other traditional energy sources especially due to their requirement for Hydrogen and air for operation while the final product is only water. Thus, the PEMFCs have received great attention for investigation of the operation under various conditions. In recent years, many researchers have done lots of mathematical modeling, but because of exact modeling absence, evolutionary optimization methods are used to adopt for parameter estimation of PEMFCs. This paper suggested a novel optimization scheme, namely, chaotic binary shark smell optimization (CBSSO), to optimize unknown parameters of PEMFC model. The CBSSO is a Meta-Heuristic algorithm which is introduced newly and has shown an appropriate ability in parameter identification and optimization cases. Moreover, the results of this proposed method are compared with some other results including typical SSO, Hybrid Artificial Bee Colony, Adaptive RNA Genetic Algorithm, Real-coded GA, Modified Particle Swarm Optimization, and Artificial Immune System. Based on the obtained results, this proposed method is very efficient in convergence and accuracy aspects compared to other famous methods.

KEYWORDS:

• Proton exchange membrane fuel cells (PEMFCs)
• CBSSO optimization method
• parameter estimation

Acknowledgments

This work is supported by the National Science Foundation China (U1766210).

Additional information

Notes on contributors

Wenqi Han

Wenqi Han received the bachelor degree in electrical engineering from the Northeast Electric Power University, Jilin, China in 2018. Since 2018, she has been a postgraduate in the School of Electrical Engineering, Northeast Electric Power University. Her current research interest is the integrated energy system optimization.

Dezhi Li

Dezhi Li has worked for 5 years in the department of power consumption, China Electric Power Research Institute, Beijing, China. His current research interests include multi-energy system analysis, demand response and power system optimization.

Dongmin Yu

Dongmin Yu received the BEng degree in electrical engineering from the North China Electric Power University, Baoding, China in 2013. In 2016, he received the master and doctor degree in electrical engineering from the University of Bath, Bath, UK. Since 2017, he has been an associate professor with the School of Electrical Engineering, Northeast Electric Power University. His current research interests include power system configuration and optimization, power market and multi-energy system analysis. He has published 1 patent, and authored and co-authored over 20 journal and conference papers.

Homayoun Ebrahimian

Homayoun Ebrahimian received the master and doctor degree in biomedical engineering from the Islamic Azad University Science and Research Branch, Iran. His current research interests include biomedical systems energy system analysis. He has authored and co-authored over 20 journal and conference papers.