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Open AccessArticle
Implementation of Accurate Parameter Identification for Proton Exchange Membrane Fuel Cells and Photovoltaic Cells Based on Improved Honey Badger Algorithm
by
Wei-Lun Yu
Wei-Lun Yu 1,2,
Chen-Kai Wen
Chen-Kai Wen 3,
En-Jui Liu
En-Jui Liu 3,*
and
Jen-Yuan Chang
Jen-Yuan Chang 1,*
1
Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
2
Mechanical and Mechatronics Systems Research Laboratories, Industrial Technology Research Institute Hsinchu 310401, Taiwan
3
Department of Green Energy and Information Technology, National Taitung University, Taitung 95092, Taiwan
*
Authors to whom correspondence should be addressed.
Micromachines 2024, 15(8), 998; https://doi.org/10.3390/mi15080998 (registering DOI)
Submission received: 24 June 2024
/
Revised: 23 July 2024
/
Accepted: 29 July 2024
/
Published: 31 July 2024
Abstract
Predicting the system efficiency of green energy and developing forward-looking power technologies are key points to accelerating the global energy transition. This research focuses on optimizing the parameters of proton exchange membrane fuel cells (PEMFCs) and photovoltaic (PV) cells using the honey badger algorithm (HBA), a swarm intelligence algorithm, to accurately present the performance characteristics and efficiency of the systems. Although the HBA has a fast search speed, it was found that the algorithm’s search stability is relatively low. Therefore, this study also enhances the HBA’s global search capability through the rapid iterative characteristics of spiral search. This method will effectively expand the algorithm’s functional search range in a multidimensional and complex solution space. Additionally, the introduction of a sigmoid function will smoothen the algorithm’s exploration and exploitation mechanisms. To test the robustness of the proposed methodology, an extensive test was conducted using the CEC’17 benchmark functions set and real-life applications of PEMFC and PV cells. The results of the aforementioned test proved that with regard to the optimization of PEMFC and PV cell parameters, the improved HBA is significantly advantageous to the original in terms of both solving capability and speed. The results of this research study not only make definite progress in the field of bio-inspired computing but, more importantly, provide a rapid and accurate method for predicting the maximum power point for fuel cells and photovoltaic cells, offering a more efficient and intelligent solution for green energy.
Share and Cite
MDPI and ACS Style
Yu, W.-L.; Wen, C.-K.; Liu, E.-J.; Chang, J.-Y.
Implementation of Accurate Parameter Identification for Proton Exchange Membrane Fuel Cells and Photovoltaic Cells Based on Improved Honey Badger Algorithm. Micromachines 2024, 15, 998.
https://doi.org/10.3390/mi15080998
AMA Style
Yu W-L, Wen C-K, Liu E-J, Chang J-Y.
Implementation of Accurate Parameter Identification for Proton Exchange Membrane Fuel Cells and Photovoltaic Cells Based on Improved Honey Badger Algorithm. Micromachines. 2024; 15(8):998.
https://doi.org/10.3390/mi15080998
Chicago/Turabian Style
Yu, Wei-Lun, Chen-Kai Wen, En-Jui Liu, and Jen-Yuan Chang.
2024. "Implementation of Accurate Parameter Identification for Proton Exchange Membrane Fuel Cells and Photovoltaic Cells Based on Improved Honey Badger Algorithm" Micromachines 15, no. 8: 998.
https://doi.org/10.3390/mi15080998
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