A Kepler optimization algorithm improved using a novel Lévy-Normal mechanism for optimal parameters selection of proton exchange membrane fuel cells: A comparative study DOI Creative Commons
Mohamed Abdel‐Basset, Reda Mohamed, Karam M. Sallam

et al.

Energy Reports, Journal Year: 2024, Volume and Issue: 11, P. 6109 - 6125

Published: June 1, 2024

Proton exchange membrane fuel cells (PEMFCs) are considered a promising renewable energy source and have sparked lot of interest over the last few years due to their robust efficiency, low operating temperature, longevity. The PEMFC's electrochemical model has seven unknown parameters, which not given in manufacturer's datasheets need be accurately estimated present more accurate model, leading improved efficiency performance PEMFC systems. estimation those parameters been dealt with as complex non-linear optimization problem that needs powerful algorithm solve it. existing algorithms still some disadvantages, such falling into local minima convergence speed, make them ineligible this complicated acceptable accuracy computational cost. Therefore, study presents new parameter technique for estimating accurately, thereby achieving precise modeling PEMFCs. This called IKOA is based on integrating Kepler (KOA) novel Lévy-Normal (LN) mechanism strengthen its exploration exploitation capabilities against multimodal problem. Lévy flight aims improve KOA's operator accelerate speed toward near-optimal solution, thus minimizing cost; meanwhile, normal distribution used operator, aiding escape minima. proposed KOA herein evaluated several rival using six well-known commercial stacks highlight effectiveness. Key metrics cost, fitness measures, statistical validation through Wilcoxon rank-sum test IKOA's effective enhancing predictive operational numerical findings show high superiority all optimizers solved benchmarks.

Language: Английский

Optimizing parameter extraction in proton exchange membrane fuel cell models via differential evolution with dynamic crossover strategy DOI
Driss Saadaoui, Mustapha Elyaqouti, Imade Choulli

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135397 - 135397

Published: March 1, 2025

Language: Английский

Citations

0
Remaining useful life prediction for solid-state lithium batteries based on spatial-temporal relations and neuronal ODE-assisted KAN DOI
Zhenxi Wang,

Yan Ma,

Jinwu Gao

et al.

Reliability Engineering & System Safety, Journal Year: 2025, Volume and Issue: unknown, P. 111003 - 111003

Published: March 1, 2025

Language: Английский

Citations

0
A novel ensemble network based on CNN‐AM‐BiLSTM learner for temperature prediction of distillation columns DOI Open Access
Jianji Ren,

Linpeng Fu,

Yanan Li

et al.

The Canadian Journal of Chemical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 5, 2025

Abstract In recent years, complexity has significantly increased in chemical processes where a distillation column serves as crucial unit. It is worthwhile to develop an accurate and reliable predictive model maintain the steady operation condition of column. Although data‐driven models that do not rely on any prior knowledge present promising approach, they encounter challenges associated with nonlinearity dynamic behaviour within process data. To tackle these challenges, deep learning‐based combined distilled spatiotemporal attention ensemble network (CDSAEN) proposed. The CDSAEN constructed by sequentially integrating multiple base learners, which are iteratively generated decreasing span lengths through boosting method implemented specially designed extraction evaluation function. learner, convolutional neural (CNN), mechanism (AM), bidirectional long short‐term memory (BiLSTM) utilized adaptively capture intricate features establish robust mapping relationship from inputs output. Real‐world data system plant reconstructed time series dataset subsequently fed into for training forecast temperature apparatus advance. results exhibited effectiveness reliability. Additionally, comparison six other approaches, proposed attained superior performance mean absolute error (MAE) = 0.084, root squared (RMSE) 0.108, R 2 0.974. This study can provide support maintaining stable columns processes.

Language: Английский

Citations

0
A two phase differential evolution algorithm with perturbation and covariance matrix for PEMFC parameter estimation challenges DOI Creative Commons
Mohammad Aljaidi, Pradeep Jangir,

Arpita

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 13, 2025

Parameter identification of Proton Exchange Membrane Fuel Cells (PEMFCs) is a key factor in improving the performance fuel cell and assuring operational reliability. In this study, novel algorithm PCM-DE, based on Differential Evolution framework, proposed. A perturbation mechanism along with stagnation indicator Covariance Matrix incorporated into algorithm. Three innovations are introduced PCM-DE two phase approach fitness values used to develop parameter adaptation strategy, firstly. The idea here move evolutionary process more promising areas search space different occasions. Second, that targets archived population. This utilizes weight coefficient, which determined positional attributes individuals, improve exploration efficiency. Lastly, leveraging covariance matrix analysis employed evaluate diversity within identifies stagnant individuals applies perturbations them, promoting preventing premature convergence. effectiveness validated against nine state-of-the-art algorithms, including TDE, PSO-sono, CS-DE, jSO, EDO, LSHADE, HSES, E-QUATRE, EA4eig, through estimation six PEMFC stacks—BCS 500 W, Nedstack 600 W PS6, SR-12 Horizon H-12, Ballard Mark V, STD 250 W. Across all test cases, consistently achieved lowest minimum SSE values, 0.025493 for BCS 0.275211 0.242284 0.102915 0.148632 0.283774 also demonstrated rapid convergence, superior robustness standard deviations (e.g., 3.54E−16 PS6), highest computational efficiency, runtimes as low 0.191303 s. These numerical results emphasize PCM-DE's ability outperform existing algorithms accuracy, convergence speed, consistency, showcasing its potential advancing modeling optimization. Future research will explore applicability dynamic operating conditions adaptability other energy systems, paving way efficient sustainable technologies.

Language: Английский

Citations

0
A parrot optimizer for solving multiobjective design sensor placement in helicopter main rotor blade DOI Creative Commons
Hamad Aldawsari

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 27, 2025

Various sectors and applications, including machine learning, data mining, operations research, economical problem, science, can be structured as multi-objective optimization problems. This study introduces a novel algorithm based on the recently developed parrot optimizer (PO) called MOPO. An external repository matrix i.e. "archive" is incorporated with PO so that maintain Pareto optimal solutions achieved. The MOPO utilizes elitist non-dominated sorting, to diversity among set of solutions, further mutate-leaders strategy proposed strengthen obtained mitigates risk local minima. efficacy assessed through optimizing two categories multi-objective, include twenty benchmark test suite from IEEE CEC'20, real-world design challenge, sensor placement in helicopter main rotor blade. compared against nine well-known, recent robust algorithms. quantative qualitative metrics are employed conduct comprehensive examination results; Friedman Wilcoxon applied results four performance PSP, HV, IGDf IDGX, it demonstrates performed comparably other algorithms most methods, achieved first rank competitors. exhibit significant variance rather competitors p-value = 0.05. takes average execution time less than MOSMA, SPEA2, MOPSO by 20% rate.

Language: Английский

Citations

0
Accurate long-step degradation trends prediction and remaining useful life estimation for proton exchange membrane fuel cells DOI
Zhihua Deng, Bin Miao, Lan Zhang

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122924 - 122924

Published: April 1, 2025

Language: Английский

Citations

0
Osprey Algorithm-Based Optimization of Selective Laser Melting Parameters for Enhanced Hardness and Wear Resistance in AlSi10Mg Alloy DOI Creative Commons

Nagareddy Gadlegaonkar,

Premendra J. Bansod,

Avinash Lakshmikanthan

et al.

Journal of Materials Research and Technology, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Maximizing Electric Vehicle Efficiency: Integrating Direct Torque Control for Two-Wheel Drive System using Performance Enhancing Approach DOI
N.V. Uma Maheswari,

Jessi Sahaya,

P. Rama Mohan

et al.

Energy, Journal Year: 2025, Volume and Issue: 324, P. 135237 - 135237

Published: March 5, 2025

Language: Английский

Citations

0
Pied Kingfisher optimizer for Accurate Parameter Extraction in Proton Exchange Membrane Fuel Cell DOI
Badreddine Kanouni, Abdelbaset Laib,

Salah Necaibia

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 136079 - 136079

Published: April 1, 2025

Language: Английский

Citations

0
Hybrid Beluga whale and jellyfish search optimizer for optimizing proton exchange membrane fuel cell parameter estimation DOI
Mohammad Aljaidi, Pradeep Jangir,

Arpita

et al.

Ionics, Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Language: Английский

Citations

0