Multi-population mutative moth-flame optimization algorithm for modeling and the identification of PEMFC parameters

Zhe Sun, Junlong Sun, Xiangpeng Xie, Zongquan An, Yiwei Hong and Zhixin Sun

Renewable Energy, 2025, vol. 240, issue C

Abstract: Proton Exchange Membrane Fuel Cells (PEMFCs) stand out as complex nonlinear multivariable systems, and developing a suitable model is crucial for designing the electrochemical conversion devices’ redox reaction process. To tackle the issue of parameter identification in fuel cells, this paper proposes a “Multi-population Mutative Moth–Flame Optimization” (MM-MFO) algorithm. Inspired by the diversity found in natural species, this algorithm introduces a mutation strategy based on the fitness of population segments, applying distinct mutation operations to subgroups with varying fitness levels. Consequently, it can overcome the drawbacks of single-population searches that tend to get stuck in local optima. Through testing across eight benchmark functions, MM-MFO exhibits excellent performance in convergence speed and accuracy. Leveraging its strong capabilities, the algorithm is utilized for identifying the parameters of PEMFC models, yielding more suitable parameter values. Compared to other algorithms, MM-MFO can more accurately estimate model parameters.

Keywords: PEMFCs; Parameters’ identification; MFO (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148124023061
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:240:y:2025:i:c:s0960148124023061

DOI: 10.1016/j.renene.2024.122238

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().