Abstract

For the purpose of simulating, controlling, evaluating, managing and optimizing PEMFCs it is necessary to develop accurate mathematical models. The present study develops a mathematical model which uses empirical or semi-empirical equations to estimate unknown model parameters through optimization techniques. This thesis calculates, analyzes and discusses the sum of squares error (SSE) between measured and estimated current and voltage values using parameters derived from multiple optimization techniques for six commercially available PEMFCs: BCS 500 W-PEMFC, 500 W SR-12 PEMFC, Nedstack PS6 PEMFC, H-12 PEMFC, HORIZON 500 W PEMFC and a 250 W-stack PEMFC. To minimize the SSE between measured and estimated current values under these new models we employ an advanced version of Artificial Rabbits Optimization called Mutational Northern goshawk and Elite opposition learning-based Artificial Rabbits Optimizer (MNEARO). Additionally SSE, Absolute Error (AE), and Mean Bias Error (MBE) are computed for different recent methods according to literature on voltage measurement. Other optimization algorithms including ARO, TLBO, DE and SSA are used for comparative analysis purposes. On top of that MNEARO outperforms others in terms of both computational cost as well as solution quality while experiments carried out using benchmark problems indicate its superiority over other meta-heuristics approaches.

Details

Title
A hybrid mutational Northern Goshawk and elite opposition learning artificial rabbits optimizer for PEMFC parameter estimation
Author
Jangir, Pradeep 1 ; Ezugwu, Absalom E. 2 ; Saleem, Kashif 3 ; Arpita 4 ; Agrawal, Sunilkumar P. 5 ; Pandya, Sundaram B. 6 ; Parmar, Anil 6 ; Gulothungan, G. 7 ; Abualigah, Laith 8 

 Chandigarh University, University Centre for Research and Development, Gharuan, India (GRID:grid.448792.4) (ISNI:0000 0004 4678 9721); Graphic Era Hill University, Department of CSE, Dehradun, India (GRID:grid.448792.4) (ISNI:0000 0004 5894 758X); Graphic Era Deemed To Be University, Department of CSE, Dehradun, India (GRID:grid.449504.8) (ISNI:0000 0004 1766 2457); Applied Science Private University, Applied Science Research Center, Amman, Jordan (GRID:grid.411423.1) (ISNI:0000 0004 0622 534X); Jadara University Research Center, Jadara University, Irbid, Jordan (GRID:grid.449338.1) (ISNI:0000 0004 0645 5794) 
 North-West University, Unit for Data Science and Computing, Potchefstroom, South Africa (GRID:grid.25881.36) (ISNI:0000 0000 9769 2525) 
 King Saud University, Department of Computer Science & Engineering, College of Applied Studies & Community Service, Riyadh, Saudi Arabia (GRID:grid.56302.32) (ISNI:0000 0004 1773 5396) 
 Saveetha Institute of Medical and Technical Sciences, Department of Biosciences, Saveetha School of Engineering, Chennai, India (GRID:grid.412431.1) (ISNI:0000 0004 0444 045X) 
 Government Engineering College, Department of Electrical Engineering, Gandhinagar, India (GRID:grid.412084.b) (ISNI:0000 0001 0700 1709) 
 Shri K.J. Polytechnic, Department of Electrical Engineering, Bharuch, India (GRID:grid.412084.b) 
 SRM Institute of Science and Technology, Department of Electronics and Communication Engineering, Kattankulathur, Chengalpattu, India (GRID:grid.412742.6) (ISNI:0000 0004 0635 5080) 
 Al al-Bayt University, Computer Science Department, Mafraq, Jordan (GRID:grid.411300.7) (ISNI:0000 0001 0679 2502); Chitkara University, Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Rajpura, 140401, Punjab, India (GRID:grid.428245.d) (ISNI:0000 0004 1765 3753) 
Pages
28657
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3130576157
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.