alternativeАльтернативная энергетика и экология (ISJAEE)Alternative Energy and Ecology (ISJAEE)1608-8298Международный издательский дом научной периодики "Спейс10.15518/isjaee.2024.06.080-102alternative-2433Research ArticleIV. ВОДОРОДНАЯ ЭКОНОМИКА 12. Водородная экономикаIV. HYDROGEN ECONOMY. 12. Hydrogen EconomyОптимизация идентификации параметров топливных элементов протонообменной мембраны с использованием усовершенствованного алгоритма колибриOptimizing proton exchange membrane fuel cell parameter identification using enhanced hummingbird algorithmСинглаМ. К.SinglaM. K.

Маниш Кумар Сингла - доцент кафедры междисциплинарных инженерных курсов.

Пенджаб; 11931, Амман

Manish Kumar Singla - Assistant Professor in the Department of Interdisciplinary Courses in Engineering.

Punjab; 11931, Amman

СафаралиевМ.SafaralievM.

Сафаралиев Муродбек Холназарович - к.т.н., старший научный сотрудник кафедры «Автоматизированных электрических систем».

620002, Екатеринбург Тел.: +7 966 705-38-53

Ph. D., Senior Researcher, Department of «Automated Electrical Systems».

620002, Yekaterinburg Tel.: +7 966 705-38-53

ismoil.odinaev@urfu.ruГуптаДж.GuptaJ.

Джиоти Гупта - доцент кафедры школьного образования и инженерного дела.

122003, Харьяна, Гургаон

Jyoti Gupta - Assistant Professor in the Department of School and Engineering at K. R. Mangalam University

122003, Haryana, Gurgaon

АльджаидиМ.AljaidiM.

Мохаммад Альджаиди - доцент кафедры компьютерных наук Университета.

13110, Зарка

Mohammad Aljaidi - Assistant Professor with the Computer Science Department.

13110, Zarqa

ОдинаевИ.OdinaevI.

Исмоил Одинаев - к.т.н., научный сотрудник кафедры Автоматизированных электрических систем.

620002, Екатеринбург Тел.: +7 966 705-38-53

Ismoil Odinaev - Ph. D., Researcher, Department of «Automated Electrical Systems».

620002, Yekaterinburg Tel.: +7 966 705-38-53

ismoil.odinaev@urfu.ruКумарР.KumarR.

Рамеш Кумар - доцент кафедры междисциплинарных инженерных курсов Университета Читкара.

Раджпура

Ramesh Kumar - Assistant Professor in the Department of Interdisciplinary Courses in Engineering at Chitkara University.

Punjab

МенаемА. А.MenaemA. A.

Амир Абдель Менаем - кандидат технических наук, научный сотрудник кафедры «Автоматизированные электрические системы».

620002, Екатеринбург Тел.: +7 966 705-38-53; 35516

Amir Abdel Menaem - Ph. D., Researcher, Department of «Automated Electrical Systems».

620002, Yekaterinburg Tel.: +7 966 705-38-53; 35516

Институт инженерии и технологий Университета Читкара; Исследовательский центр прикладных наук, Частный университет прикладных наукИндияChitkara University Institute of Engineering & Technology; Applied Science Research Center, Applied Science Private UniversityIndiaУральский федеральный университетРоссияUral Federal UniversityRussian FederationШкола инженерии и технологий, Университет К.Р. МангаламаИндияSchool of Engineering and Technology, K.R. Mangalam UniversityIndiaУниверситет ЗаркаИорданияZarqa UniversityJordanИнститут инженерии и технологий Университета ЧиткараИндияChitkara University Institute of Engineering & TechnologyIndiaУральский федеральный университет; Университет МансураЕгипетUral Federal University; Electrical Engineering Department, Mansoura UniversityEgypt2024130620240680102Copyright © Международный издательский дом научной периодики "Спейс, 20242024Международный издательский дом научной периодики "СпейсМеждународный издательский дом научной периодики "Спейсhttps://www.isjaee.com/jour/about/submissions#copyrightNoticehttps://www.isjaee.com/jour/article/view/2433

Топливные элементы (ТЭ) привлекли значительный интерес из-за их универсального применения, но моделирование их нелинейного поведения является сложной задачей. В этом исследовании предлагается усовершенствованный алгоритм искусственного колибри (EAHA) для идентификации семи неизвестных параметров батарей топливных элементов с протонообменной мембраной (PEMFC) с использованием их экспериментальных данных. Цель состоит в том, чтобы точно спрогнозировать кривые тока/напряжения (I/V) путем минимизации функции стоимости, определяемой как сумма квадратов разностей между измеренными точками данных и оценками модели. EAHA сочетает в себе несколько методов территориального поиска пищи с механизмом линейного регулирования. Его производительность сравнивается с традиционным алгоритмом искусственного колибри (AHA), использующим три обычных модуля PEMFC. Кроме того, проводится сравнительный анализ с использованием ранее опубликованных методов и недавно разработанных оптимизаторов, таких как оптимизатор роя частиц (PSO), алгоритм оптимизации Grasshopper (GOA), оптимизация поиска атомов (ASO), оптимизатор Grey Wolf (GWO) и родительский алгоритм, т.е. искусственный, Алгоритм Колибри (AHA). Результаты демонстрируют эффективность предлагаемого подхода по сравнению с существующими методами и современными оптимизаторами. Две модели взяты для проверки надежности и производительности PEMFC. Результаты также сравниваются с непараметрическими тестами и делается вывод, что предложенный алгоритм намного лучше остальных сравниваемых алгоритмов в обеих моделях.

Fuel cells (FCs) have attracted significant interest due to their versatile applications, but modeling their nonlinear behavior is challenging. This research proposes an Enhanced Artificial Hummingbird Algorithm (EAHA) to identify the seven unknown parameters of proton exchange membrane fuel cell (PEMFC) stacks using their experimental data. The goal is to accurately predict the current/voltage (I/V) curves by minimizing a cost function defined as the sum of squared differences between measured data points and model estimates. The EAHA combines several territorial foraging techniques with a linear regulation mechanism. Its performance is compared to the conventional Artificial Hummingbird Algorithm (AHA) using three common PEMFC modules. Additionally, a comparative analysis is performed against previously published methods and newly developed optimizers like Particle Swarm Optimizer (PSO), Grasshopper Optimization Algorithm (GOA), Atom Search Optimization (ASO), Grey Wolf Optimizer (GWO), and parental algorithm i.e., Artificial Hummingbird Algorithm (AHA). The findings showcase the proposed approach’s efficacy relative to existing methods and state-of-the-art optimizers. The two models are taken for the checking of reliability and performance of the PEMFC. The results are also compared with the Non-Parametric tests and it is concluded that the proposed algorithm is far better than the rest of the compared algorithms in both the models.

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The authors declare that there are no conflicts of interest present.