IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2023i1p62-d1304085.html
   My bibliography  Save this article

Sustainable Development of a Direct Methanol Fuel Cell Using the Enhanced LSHADE Algorithm and Newton Raphson Method

Author

Listed:
  • Manish Kumar Singla

    (Department of Interdisciplinary Courses in Engineering, Chitkara University Institute of Engineering & Technology, Chitkara University, Rajpura 140401, Punjab, India
    Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan)

  • Jyoti Gupta

    (School of Engineering and Technology, K.R. Mangalam University, Gurugram 122103, Haryana, India)

  • Mohammed H. Alsharif

    (Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea)

  • Abu Jahid

    (School of Electrical Engineering and Computer Science, University of Ottawa, 25 Templeton St., Ottawa, ON K1N 6N5, Canada)

  • Khalid Yahya

    (Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Istanbul Gelisim University, Istanbul 34310, Turkey)

Abstract

This paper presents a mathematical model for stacks of direct methanol fuel cells (DMFCs) using an optimised method. In order to reduce the sum of squared errors (SSE) in calculating the polarisation profile, the suggested technique makes use of simulated experimental data. Given that DMFC is one of the viable fuel cell choices, developing an appropriate model is essential for cost reduction. However, resolving this issue has proven difficult due to its complex and highly nonlinear character, particularly when adjusting the DMFC model to various operating temperatures. By combining the algorithm and the objective function, the current work introduces a novel method called LSHADE (ELSHADE) for determining the parameters of the DMFC model. This technique seeks to accurately identify DMFCs’ characteristics. The ELSHADE method consists of two stages, the first of which is controlled by a reliable mutation process and the latter by a chaotic approach. The study also recommends an improved Newton–Raphson (INR) approach to deal with the chaotic nature of the I-V curve equation. The findings show that, when used on actual experimental data, the ELSHADE-INR technique outperforms existing algorithms in a variety of statistical metrics for accurately identifying global solutions.

Suggested Citation

  • Manish Kumar Singla & Jyoti Gupta & Mohammed H. Alsharif & Abu Jahid & Khalid Yahya, 2023. "Sustainable Development of a Direct Methanol Fuel Cell Using the Enhanced LSHADE Algorithm and Newton Raphson Method," Sustainability, MDPI, vol. 16(1), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:62-:d:1304085
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/1/62/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/1/62/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu, Kunjie & Qu, Boyang & Yue, Caitong & Ge, Shilei & Chen, Xu & Liang, Jing, 2019. "A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module," Applied Energy, Elsevier, vol. 237(C), pages 241-257.
    2. Ridha, Hussein Mohammed & Hizam, Hashim & Gomes, Chandima & Heidari, Ali Asghar & Chen, Huiling & Ahmadipour, Masoud & Muhsen, Dhiaa Halboot & Alghrairi, Mokhalad, 2021. "Parameters extraction of three diode photovoltaic models using boosted LSHADE algorithm and Newton Raphson method," Energy, Elsevier, vol. 224(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Isen, Evren & Duman, Serhat, 2024. "Improved stochastic fractal search algorithm involving design operators for solving parameter extraction problems in real-world engineering optimization problems," Applied Energy, Elsevier, vol. 365(C).
    2. Liu, Yun & Heidari, Ali Asghar & Ye, Xiaojia & Liang, Guoxi & Chen, Huiling & He, Caitou, 2021. "Boosting slime mould algorithm for parameter identification of photovoltaic models," Energy, Elsevier, vol. 234(C).
    3. Long, Wen & Jiao, Jianjun & Liang, Ximing & Xu, Ming & Tang, Mingzhu & Cai, Shaohong, 2022. "Parameters estimation of photovoltaic models using a novel hybrid seagull optimization algorithm," Energy, Elsevier, vol. 249(C).
    4. Papul Changmai & Sunil Deka & Shashank Kumar & Thanikanti Sudhakar Babu & Belqasem Aljafari & Benedetto Nastasi, 2022. "A Critical Review on the Estimation Techniques of the Solar PV Cell’s Unknown Parameters," Energies, MDPI, vol. 15(19), pages 1-20, September.
    5. Reem Y. Abdelghany & Salah Kamel & Hamdy M. Sultan & Ahmed Khorasy & Salah K. Elsayed & Mahrous Ahmed, 2021. "Development of an Improved Bonobo Optimizer and Its Application for Solar Cell Parameter Estimation," Sustainability, MDPI, vol. 13(7), pages 1-22, March.
    6. Jianing Li & Cheng Qin & Chen Yang & Bin Ai & Yecheng Zhou, 2023. "Extraction of Single Diode Model Parameters of Solar Cells and PV Modules by Combining an Intelligent Optimization Algorithm with Simplified Explicit Equation Based on Lambert W Function," Energies, MDPI, vol. 16(14), pages 1-23, July.
    7. Mehmet Yesilbudak, 2021. "Parameter Extraction of Photovoltaic Cells and Modules Using Grey Wolf Optimizer with Dimension Learning-Based Hunting Search Strategy," Energies, MDPI, vol. 14(18), pages 1-27, September.
    8. Ahmed Ginidi & Sherif M. Ghoneim & Abdallah Elsayed & Ragab El-Sehiemy & Abdullah Shaheen & Attia El-Fergany, 2021. "Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    9. Muhyaddin Rawa & Abdullah Abusorrah & Yusuf Al-Turki & Martin Calasan & Mihailo Micev & Ziad M. Ali & Saad Mekhilef & Hussain Bassi & Hatem Sindi & Shady H. E. Abdel Aleem, 2022. "Estimation of Parameters of Different Equivalent Circuit Models of Solar Cells and Various Photovoltaic Modules Using Hybrid Variants of Honey Badger Algorithm and Artificial Gorilla Troops Optimizer," Mathematics, MDPI, vol. 10(7), pages 1-31, March.
    10. Long, Wen & Wu, Tiebin & Xu, Ming & Tang, Mingzhu & Cai, Shaohong, 2021. "Parameters identification of photovoltaic models by using an enhanced adaptive butterfly optimization algorithm," Energy, Elsevier, vol. 229(C).
    11. Iman Ahmadianfar & Bijay Halder & Salim Heddam & Leonardo Goliatt & Mou Leong Tan & Zulfaqar Sa’adi & Zainab Al-Khafaji & Raad Z. Homod & Tarik A. Rashid & Zaher Mundher Yaseen, 2023. "An Enhanced Multioperator Runge–Kutta Algorithm for Optimizing Complex Water Engineering Problems," Sustainability, MDPI, vol. 15(3), pages 1-28, January.
    12. Fan, Yi & Wang, Pengjun & Heidari, Ali Asghar & Chen, Huiling & HamzaTurabieh, & Mafarja, Majdi, 2022. "Random reselection particle swarm optimization for optimal design of solar photovoltaic modules," Energy, Elsevier, vol. 239(PA).
    13. Mohana Alanazi & Abdulaziz Alanazi & Ahmad Almadhor & Hafiz Tayyab Rauf, 2022. "Photovoltaic Models’ Parameter Extraction Using New Artificial Parameterless Optimization Algorithm," Mathematics, MDPI, vol. 10(23), pages 1-32, December.
    14. Zhou, Junfeng & Zhang, Yanhui & Zhang, Yubo & Shang, Wen-Long & Yang, Zhile & Feng, Wei, 2022. "Parameters identification of photovoltaic models using a differential evolution algorithm based on elite and obsolete dynamic learning," Applied Energy, Elsevier, vol. 314(C).
    15. Hassan Shaban & Essam H. Houssein & Marco Pérez-Cisneros & Diego Oliva & Amir Y. Hassan & Alaa A. K. Ismaeel & Diaa Salama AbdElminaam & Sanchari Deb & Mokhtar Said, 2021. "Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer," Mathematics, MDPI, vol. 9(18), pages 1-22, September.
    16. Słowik, Adam & Cpałka, Krzysztof & Xue, Yu & Hapka, Aneta, 2024. "An efficient approach to parameter extraction of photovoltaic cell models using a new population-based algorithm," Applied Energy, Elsevier, vol. 364(C).
    17. Edwidge Raissa Mache Kengne & Alain Soup Tewa Kammogne & Thomas Tatietse Tamo & Ahmad Taher Azar & Ahmed Redha Mahlous & Saim Ahmed, 2023. "Photovoltaic Systems Based on Average Current Mode Control: Dynamical Analysis and Chaos Suppression by Using a Non-Adaptive Feedback Outer Loop Controller," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    18. Choulli, Imade & Elyaqouti, Mustapha & Arjdal, El hanafi & Ben hmamou, Dris & Saadaoui, Driss & Lidaighbi, Souad & Elhammoudy, Abdelfattah & Abazine, Ismail, 2023. "Hybrid optimization based on the analytical approach and the particle swarm optimization algorithm (Ana-PSO) for the extraction of single and double diode models parameters," Energy, Elsevier, vol. 283(C).
    19. Husheng Wu & Qiang Peng & Meimei Shi & Lining Xing & Shi Cheng, 2022. "Drunkard Adaptive Walking Chaos Wolf Pack Algorithm in Parameter Identification of Photovoltaic Module Model," Energies, MDPI, vol. 15(17), pages 1-24, August.
    20. Xiaobing Yu & Xuejing Wu & Wenguan Luo, 2022. "Parameter Identification of Photovoltaic Models by Hybrid Adaptive JAYA Algorithm," Mathematics, MDPI, vol. 10(2), pages 1-28, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:62-:d:1304085. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.