IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i15p5587-d877734.html
   My bibliography  Save this article

Power and Voltage Modelling of a Proton-Exchange Membrane Fuel Cell Using Artificial Neural Networks

Author

Listed:
  • Tabbi Wilberforce

    (Mechanical Engineering and Design, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, UK)

  • Mohammad Biswas

    (Department of Mechanical Engineering, The University of Texas at Tyler, 3900 University Blvd, Tyler, TX 75799, USA)

  • Abdelnasir Omran

    (Mechanical Engineering and Design, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, UK)

Abstract

A proton exchange membrane fuel cell (PEMFC) is a more environmentally friendly alternative to deliver electric power in various applications, including in the transportation industry. As PEMFC performance characteristics are inherently nonlinear and involved, the prediction of the performance in a given application for different operating conditions is important in order to optimize the efficiency of the system. Thus, modelling using artificial neural networks (ANNs) to predict its performance can significantly improve the capabilities of handling the multi-variable nonlinear performance of the PEMFC. However, further investigation is needed to develop a dynamic model using ANNs to predict the transient behavior of a PEMFC. This paper predicts the dynamic electrical and thermal performance of a PEMFC stack under various operating conditions. The input variables of the PEMFC stack for the analysis consist of the cathode inlet temperature, anode inlet pressure, anode and cathode inlet flow rates, and stack current. The performances of the ANN models using three different learning algorithms are determined based on the stack voltage and temperature, which have been shown to be consistently predicted by most of these models. Almost all models with varying hidden neurons have coefficients of determination of 0.9 or higher and mean squared errors of less than 5. Thus, the results show promise for dynamic modelling approaches using ANNs for the development of optimal operation of a PEMFC in various system applications.

Suggested Citation

  • Tabbi Wilberforce & Mohammad Biswas & Abdelnasir Omran, 2022. "Power and Voltage Modelling of a Proton-Exchange Membrane Fuel Cell Using Artificial Neural Networks," Energies, MDPI, vol. 15(15), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5587-:d:877734
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/15/5587/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/15/5587/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gurong Shen & Jing Liu & Hao Bin Wu & Pengcheng Xu & Fang Liu & Chasen Tongsh & Kui Jiao & Jinlai Li & Meilin Liu & Mei Cai & John P. Lemmon & Grigorii Soloveichik & Hexing Li & Jian Zhu & Yunfeng Lu, 2020. "Multi-functional anodes boost the transient power and durability of proton exchange membrane fuel cells," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    2. Ogungbemi, Emmanuel & Ijaodola, Oluwatosin & Khatib, F.N. & Wilberforce, Tabbi & El Hassan, Zaki & Thompson, James & Ramadan, Mohamad & Olabi, A.G., 2019. "Fuel cell membranes – Pros and cons," Energy, Elsevier, vol. 172(C), pages 155-172.
    3. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Javaid, Usman & Mehmood, Adeel & Iqbal, Jamshed & Uppal, Ali Arshad, 2023. "Neural network and URED observer based fast terminal integral sliding mode control for energy efficient polymer electrolyte membrane fuel cell used in vehicular technologies," Energy, Elsevier, vol. 269(C).

    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. Cha, Dowon & Yang, Wonseok & Kim, Yongchan, 2019. "Performance improvement of self-humidifying PEM fuel cells using water injection at various start-up conditions," Energy, Elsevier, vol. 183(C), pages 514-524.
    2. Ahmed Mohmed Dafalla & Lin Wei & Bereket Tsegai Habte & Jian Guo & Fangming Jiang, 2022. "Membrane Electrode Assembly Degradation Modeling of Proton Exchange Membrane Fuel Cells: A Review," Energies, MDPI, vol. 15(23), pages 1-26, December.
    3. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    4. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    5. Wang, Chenfang & Li, Qingshan & Wang, Chunmei & Zhang, Yangjun & Zhuge, Weilin, 2021. "Thermodynamic analysis of a hydrogen fuel cell waste heat recovery system based on a zeotropic organic Rankine cycle," Energy, Elsevier, vol. 232(C).
    6. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    7. Ahmad, Tanveer & Chen, Huanxin, 2018. "Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment," Energy, Elsevier, vol. 160(C), pages 1008-1020.
    8. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
    9. Teixeira, Fátima C. & Teixeira, António P.S. & Rangel, C.M., 2022. "New proton conductive membranes of indazole- and condensed pyrazolebisphosphonic acid-Nafion membranes for PEMFC," Renewable Energy, Elsevier, vol. 196(C), pages 1187-1196.
    10. Olabi, A.G. & Wilberforce, Tabbi & Abdelkareem, Mohammad Ali, 2021. "Fuel cell application in the automotive industry and future perspective," Energy, Elsevier, vol. 214(C).
    11. Tabbi Wilberforce & Abdul Ghani Olabi, 2020. "Performance Prediction of Proton Exchange Membrane Fuel Cells (PEMFC) Using Adaptive Neuro Inference System (ANFIS)," Sustainability, MDPI, vol. 12(12), pages 1-16, June.
    12. Fan, Cheng & Xiao, Fu & Yan, Chengchu & Liu, Chengliang & Li, Zhengdao & Wang, Jiayuan, 2019. "A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning," Applied Energy, Elsevier, vol. 235(C), pages 1551-1560.
    13. Qingwen, Wang & XiaoHui, Chu & Chao, Yu, 2024. "Modeling of heat gain through green roofs utilizing artificial intelligence techniques," Energy, Elsevier, vol. 303(C).
    14. Anubhav Kumar Pandey & Vinay Kumar Jadoun & Jayalakshmi N. Sabhahit, 2022. "Real-Time Peak Valley Pricing Based Multi-Objective Optimal Scheduling of a Virtual Power Plant Considering Renewable Resources," Energies, MDPI, vol. 15(16), pages 1-30, August.
    15. Yuzer, B. & Selcuk, H. & Chehade, G. & Demir, M.E. & Dincer, I., 2020. "Evaluation of hydrogen production via electrolysis with ion exchange membranes," Energy, Elsevier, vol. 190(C).
    16. R. Rueda & M. P. Cuéllar & M. Molina-Solana & Y. Guo & M. C. Pegalajar, 2019. "Generalised Regression Hypothesis Induction for Energy Consumption Forecasting," Energies, MDPI, vol. 12(6), pages 1-22, March.
    17. Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.
    18. Shailendra Singh & Abdulsalam Yassine, 2018. "Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting," Energies, MDPI, vol. 11(2), pages 1-26, February.
    19. Julian Schiele & Thomas Koperna & Jens O. Brunner, 2021. "Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 65-88, February.
    20. Seyed Azad Nabavi & Alireza Aslani & Martha A. Zaidan & Majid Zandi & Sahar Mohammadi & Naser Hossein Motlagh, 2020. "Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors," Energies, MDPI, vol. 13(19), pages 1-22, October.

    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:jeners:v:15:y:2022:i:15:p:5587-:d:877734. 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.