Enhanced hunger games search algorithm that incorporates the marine predator optimization algorithm for optimal extraction of parameters in PEM fuel cells

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 6, 2025

Abstract This article introduces a novel optimization approach to improve the parameter estimation of proton exchange membrane fuel cells (PEMFCs), which are critical for diverse applications but challenging model due their nonlinear behavior. The proposed method, HGS-MPA, enhances Hunger Games Search (HGS) algorithm by integrating Marine Predator Algorithm (MPA) operators, significantly boosting its exploitation capabilities and convergence rate. effectiveness HGS-MPA was validated on three commercial PEMFC datasets: 250-W stack, BCS 500-W, NedStack PS6, using Sum Squared Error (SSE) as performance metric. Experimental results highlight that achieves minimum fitness values 0.33770, 1.31620, 0.01174 respective datasets, outperforming other state-of-the-art algorithms. These findings underscore method’s potential accurate estimation, offering enhanced reliability.

Язык: Английский

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Efficient parameter extraction for accurate modeling of PEM fuel cell using Ali-Baba and forty thieves algorithm

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2025, Номер 8(4)

Опубликована: Март 15, 2025

Язык: Английский

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A method for predicting long-term degradation of fuel cells: Wavelet-linear enhanced neural network

International Journal of Hydrogen Energy, Год журнала: 2025, Номер 102, С. 594 - 608

Опубликована: Янв. 11, 2025

Язык: Английский

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A hybrid slime mold enhanced convergent particle swarm optimizer for parameter estimation of proton exchange membrane fuel cell

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 8, 2025

High efficiency and eco friendliness, proton exchange membrane fuel cells (PEMFCs) have become a good solution to cleaner energy solutions. However, due the electrochemical complexity of PEMFCs limitations existing optimization methods, accurately estimating PEMFC parameters achieve optimal performance is still challenging. In this work, we propose hybrid algorithm, SCPSO, combining Particle Swarm Optimization with Mixed Mutant Slime Mold improve precision, consistency, computational in parameter optimization. Six types, BCS 500 W, Nedstack 600 W PS6, SR-12 Horizon H-12, Ballard Mark V, STD 250 Stack were applied SCPSO compared seven state-of-the-art algorithms, FLA, HFPSO, PSOLC, ESMA, LSMA, DETDO, EGJO. all cases, consistently outperformed competitors lowest mean sum squared error (SSE) minimal standard deviation (e.g., [10−16, 10−18]), thus confirming its robustness reliability. Additionally, it demonstrated number iterations reach (less than 200 iterations) best Friedman Rank (FR = 1), signifying customer. For instance, PEMFC1, achieved SSE 0.02549 negligible variability (Std. 1.05958E−15) as HFPSO 0.001998568) DETDO 4). SCPSO's rapid convergence curves, narrow box plot spreads, precise polarization curves further validated across cells. was experimentally proved be reliable deviations between predicted experimental voltage power outputs RE 0.052587% for PEMFC1 0.016537% PEMFC2). The average runtime 3.05 s, which faster alternatives, maintains unparalleled precision. results analyses, fitting datasets confirm that adaptive tuning has significantly improved performance, resulting highest consistency accuracy fastest speed. optimization, from established algorithm strongest precision stability efficiency. extension other systems dynamic real time scenarios will investigated future research enable wider adoption sustainable management.

Язык: Английский

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A hybrid snow ablation optimized multi-strategy particle swarm optimizer for parameter estimation of proton exchange membrane fuel cell

Ionics, Год журнала: 2025, Номер unknown

Опубликована: Март 17, 2025

Язык: Английский

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Pied Kingfisher optimizer for Accurate Parameter Extraction in Proton Exchange Membrane Fuel Cell

Energy, Год журнала: 2025, Номер unknown, С. 136079 - 136079

Опубликована: Апрель 1, 2025

Язык: Английский

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Multi-strategy alpha evolution optimization for constrained parameter estimation in Proton Exchange Membrane Fuel Cells

Energy Conversion and Management, Год журнала: 2025, Номер 339, С. 119917 - 119917

Опубликована: Май 21, 2025

Язык: Английский

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