IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v271y2020ics0306261920306048.html
   My bibliography  Save this article

A least-squares support vector machine method for modeling transient voltage in polymer electrolyte fuel cells

Author

Listed:
  • Zou, Wei
  • Froning, Dieter
  • Shi, Yan
  • Lehnert, Werner

Abstract

An investigation into the credibility and suitability of a transient voltage model that characterizes the dynamic behavior of polymer electrolyte fuel cells was carried out by means of quantitative and qualitative validations. The least squares support vector machine method was then used to construct a transient voltage model of a fuel cell in the first phase, including a validation based on experimental data obtained from a test rig. In the second phase, a thorough discussion of the effect of the fuel cell’s operating conditions and the exterior load changes on the model’s performance was implemented. For this phase, the influences of the sampling interval and ramp ratio are discussed and determined following a large number of tests under a variety of operating conditions. The results show that sampling with short time intervals is an effective way to improve the model’s performance, and a smoother change to the exterior load is more likely to be approximated by the least squares support vector machine model. Moreover, the voltage model is sensitive to the ramp value by comparison to the ramp time. Suggestions for future applications of the transient voltage models are also provided. For a given combination of load changes, the sampling interval should be managed within a range to reach the demand data that satisfies the voltage accuracy. On the other hand, for a determinate sampling interval, the dynamic change of the load should be restricted within a limit to ensure that the model error is lower than the demand value.

Suggested Citation

  • Zou, Wei & Froning, Dieter & Shi, Yan & Lehnert, Werner, 2020. "A least-squares support vector machine method for modeling transient voltage in polymer electrolyte fuel cells," Applied Energy, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:appene:v:271:y:2020:i:c:s0306261920306048
    DOI: 10.1016/j.apenergy.2020.115092
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920306048
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.115092?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chavan, Sudarshan L. & Talange, Dhananjay B., 2017. "Modeling and performance evaluation of PEM fuel cell by controlling its input parameters," Energy, Elsevier, vol. 138(C), pages 437-445.
    2. Myo-Eun Kim & Young-Jun Sohn, 2020. "Study on Polymer Electrolyte Fuel Cells with Nonhumidification Using Metal Foam in Dead-Ended Operation," Energies, MDPI, vol. 13(5), pages 1-12, March.
    3. Yan Shi & Holger Janßen & Werner Lehnert, 2019. "A Transient Behavior Study of Polymer Electrolyte Fuel Cells with Cyclic Current Profiles," Energies, MDPI, vol. 12(12), pages 1-13, June.
    4. Pratt, Joseph W. & Klebanoff, Leonard E. & Munoz-Ramos, Karina & Akhil, Abbas A. & Curgus, Dita B. & Schenkman, Benjamin L., 2013. "Proton exchange membrane fuel cells for electrical power generation on-board commercial airplanes," Applied Energy, Elsevier, vol. 101(C), pages 776-796.
    5. Yancai Xiao & Ruolan Dai & Guangjian Zhang & Weijia Chen, 2017. "The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT," Energies, MDPI, vol. 10(11), pages 1-13, November.
    6. Ma, Rui & Yang, Tao & Breaz, Elena & Li, Zhongliang & Briois, Pascal & Gao, Fei, 2018. "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method," Applied Energy, Elsevier, vol. 231(C), pages 102-115.
    7. Lv, You & Hong, Feng & Yang, Tingting & Fang, Fang & Liu, Jizhen, 2017. "A dynamic model for the bed temperature prediction of circulating fluidized bed boilers based on least squares support vector machine with real operational data," Energy, Elsevier, vol. 124(C), pages 284-294.
    8. Wang, Yun & Chen, Ken S. & Mishler, Jeffrey & Cho, Sung Chan & Adroher, Xavier Cordobes, 2011. "A review of polymer electrolyte membrane fuel cells: Technology, applications, and needs on fundamental research," Applied Energy, Elsevier, vol. 88(4), pages 981-1007, April.
    9. Kheirandish, Azadeh & Motlagh, Farid & Shafiabady, Niusha & Dahari, Mahidzal & Khairi Abdul Wahab, Ahmad, 2017. "Dynamic fuzzy cognitive network approach for modelling and control of PEM fuel cell for power electric bicycle system," Applied Energy, Elsevier, vol. 202(C), pages 20-31.
    10. Kim, Bosung & Cha, Dowon & Kim, Yongchan, 2015. "The effects of air stoichiometry and air excess ratio on the transient response of a PEMFC under load change conditions," Applied Energy, Elsevier, vol. 138(C), pages 143-149.
    11. Li, Zhongliang & Outbib, Rachid & Giurgea, Stefan & Hissel, Daniel & Jemei, Samir & Giraud, Alain & Rosini, Sebastien, 2016. "Online implementation of SVM based fault diagnosis strategy for PEMFC systems," Applied Energy, Elsevier, vol. 164(C), pages 284-293.
    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. Zou, Wei & Froning, Dieter & Shi, Yan & Lehnert, Werner, 2021. "An online adaptive model for the nonlinear dynamics of fuel cell voltage," Applied Energy, Elsevier, vol. 288(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. Mezzi, Rania & Yousfi-Steiner, Nadia & Péra, Marie Cécile & Hissel, Daniel & Larger, Laurent, 2021. "An Echo State Network for fuel cell lifetime prediction under a dynamic micro-cogeneration load profile," Applied Energy, Elsevier, vol. 283(C).
    2. Mo, Jingke & Kang, Zhenye & Yang, Gaoqiang & Retterer, Scott T. & Cullen, David A. & Toops, Todd J. & Green, Johney B. & Zhang, Feng-Yuan, 2016. "Thin liquid/gas diffusion layers for high-efficiency hydrogen production from water splitting," Applied Energy, Elsevier, vol. 177(C), pages 817-822.
    3. Zou, Wei & Froning, Dieter & Shi, Yan & Lehnert, Werner, 2021. "Working zone for a least-squares support vector machine for modeling polymer electrolyte fuel cell voltage," Applied Energy, Elsevier, vol. 283(C).
    4. Liu, Ze & Zhang, Baitao & Xu, Sichuan, 2022. "Research on air mass flow-pressure combined control and dynamic performance of fuel cell system for vehicles application," Applied Energy, Elsevier, vol. 309(C).
    5. Guida, D. & Minutillo, M., 2017. "Design methodology for a PEM fuel cell power system in a more electrical aircraft," Applied Energy, Elsevier, vol. 192(C), pages 446-456.
    6. Niu, Zhiqiang & Bao, Zhiming & Wu, Jingtian & Wang, Yun & Jiao, Kui, 2018. "Two-phase flow in the mixed-wettability gas diffusion layer of proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 232(C), pages 443-450.
    7. Pandu Ranga Tirumalasetti & Fang-Bor Weng & Mangaliso Menzi Dlamini & Chia-Hung Chen, 2024. "Numerical Simulation of Double Layered Wire Mesh Integration on the Cathode for a Proton Exchange Membrane Fuel Cell (PEMFC)," Energies, MDPI, vol. 17(2), pages 1-15, January.
    8. Behzad Najafi & Paolo Bonomi & Andrea Casalegno & Fabio Rinaldi & Andrea Baricci, 2020. "Rapid Fault Diagnosis of PEM Fuel Cells through Optimal Electrochemical Impedance Spectroscopy Tests," Energies, MDPI, vol. 13(14), pages 1-19, July.
    9. Chen, Kui & Laghrouche, Salah & Djerdir, Abdesslem, 2019. "Degradation model of proton exchange membrane fuel cell based on a novel hybrid method," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    10. Chen, Yong-Song & Yang, Chih-Wei & Lee, Jiunn-Yih, 2014. "Implementation and evaluation for anode purging of a fuel cell based on nitrogen concentration," Applied Energy, Elsevier, vol. 113(C), pages 1519-1524.
    11. Chen, Huicui & Zhao, Xin & Qu, Bingwang & Zhang, Tong & Pei, Pucheng & Li, Congxin, 2018. "An evaluation method of gas distribution quality in dynamic process of proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 232(C), pages 26-35.
    12. Park, Jaeman & Oh, Hwanyeong & Lee, Yoo Il & Min, Kyoungdoug & Lee, Eunsook & Jyoung, Jy-Young, 2016. "Effect of the pore size variation in the substrate of the gas diffusion layer on water management and fuel cell performance," Applied Energy, Elsevier, vol. 171(C), pages 200-212.
    13. Oh, Hwanyeong & Lee, Won-Yong & Won, Jinyeon & Kim, Minjin & Choi, Yoon-Young & Han, Soo-Bin, 2020. "Residual-based fault diagnosis for thermal management systems of proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 277(C).
    14. Danqi Su & Jiayang Zheng & Junjie Ma & Zizhe Dong & Zhangjie Chen & Yanzhou Qin, 2023. "Application of Machine Learning in Fuel Cell Research," Energies, MDPI, vol. 16(11), pages 1-32, May.
    15. Hua, Zhiguang & Zheng, Zhixue & Péra, Marie-Cécile & Gao, Fei, 2020. "Remaining useful life prediction of PEMFC systems based on the multi-input echo state network," Applied Energy, Elsevier, vol. 265(C).
    16. Mohammed, Hanin & Al-Othman, Amani & Nancarrow, Paul & Tawalbeh, Muhammad & El Haj Assad, Mamdouh, 2019. "Direct hydrocarbon fuel cells: A promising technology for improving energy efficiency," Energy, Elsevier, vol. 172(C), pages 207-219.
    17. Liu, Hao & Chen, Jian & Hissel, Daniel & Lu, Jianguo & Hou, Ming & Shao, Zhigang, 2020. "Prognostics methods and degradation indexes of proton exchange membrane fuel cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    18. Wang, Xuechao & Chen, Jinzhou & Quan, Shengwei & Wang, Ya-Xiong & He, Hongwen, 2020. "Hierarchical model predictive control via deep learning vehicle speed predictions for oxygen stoichiometry regulation of fuel cells," Applied Energy, Elsevier, vol. 276(C).
    19. Wan, Zhongmin & Liu, Jing & Luo, Zhiping & Tu, Zhengkai & Liu, Zhichun & Liu, Wei, 2013. "Evaluation of self-water-removal in a dead-ended proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 104(C), pages 751-757.
    20. Kang, Sanggyu & Zhao, Li & Brouwer, Jacob, 2019. "Dynamic modeling and verification of a proton exchange membrane fuel cell-battery hybrid system to power servers in data centers," Renewable Energy, Elsevier, vol. 143(C), pages 313-327.

    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:eee:appene:v:271:y:2020:i:c:s0306261920306048. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.