A hybrid mutational Northern Goshawk and elite opposition learning artificial rabbits optimizer for PEMFC parameter estimation

Pradeep Jangir; Absalom E Ezugwu; Kashif Saleem; Arpita; Sunilkumar P Agrawal; Sundaram B Pandya; Anil Parmar; G Gulothungan; Laith Abualigah

doi:10.1038/s41598-024-80073-2

A hybrid mutational Northern Goshawk and elite opposition learning artificial rabbits optimizer for PEMFC parameter estimation

Sci Rep. 2024 Nov 19;14(1):28657. doi: 10.1038/s41598-024-80073-2.

Authors

Pradeep Jangir^{1

2

3

4

5}, Absalom E Ezugwu⁶, Kashif Saleem⁷, Arpita⁸, Sunilkumar P Agrawal⁹, Sundaram B Pandya¹⁰, Anil Parmar¹⁰, G Gulothungan¹¹, Laith Abualigah^{12

13}

Affiliations

¹ University Centre for Research and Development, Chandigarh University, Gharuan, 140413, Mohali, India.
² Department of CSE, Graphic Era Hill University, Dehradun, 248002, India.
³ Department of CSE, Graphic Era Deemed To Be University, Dehradun, 248002, Uttarakhand, India.
⁴ Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan.
⁵ Jadara University Research Center, Jadara University, Irbid, Jordan.
⁶ Unit for Data Science and Computing, North-West University, 11, Hofman Street, Potchefstroom, 2520, South Africa. Absalom.ezugwu@nwu.ac.za.
⁷ Department of Computer Science & Engineering, College of Applied Studies & Community Service, King Saud University, Riyadh, 11362, Saudi Arabia.
⁸ Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602 105, India.
⁹ Department of Electrical Engineering, Government Engineering College, Gandhinagar, 382028, Gujarat, India.
¹⁰ Department of Electrical Engineering, Shri K.J. Polytechnic, Bharuch, 392 001, India.
¹¹ Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chengalpattu, 603203, Tamilnadu, India.
¹² Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan.
¹³ Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India.

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.

Keywords: Adaptive rabbits optimization; MNEARO; Mutation strategy; Optimization in Electrical Engineering; Proton Exchange membrane fuel cell parameter identification.