IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v303y2021ics0306261921010230.html
   My bibliography  Save this article

Enabling real-time optimization of dynamic processes of proton exchange membrane fuel cell: Data-driven approach with semi-recurrent sliding window method

Author

Listed:
  • Wu, Kangcheng
  • Du, Qing
  • Zu, Bingfeng
  • Wang, Yupeng
  • Cai, Jun
  • Gu, Xin
  • Xuan, Jin
  • Jiao, Kui

Abstract

The reliability of proton exchange membrane fuel cell (PEMFC) tightly depends on the suitable operating conditions during dynamic operations. This study proposes an optimization framework to determine the optimal control strategy for PEMFC cold starts underpinned by a novel artificial intelligence method, to improve cold-start capacity and shorten the start-up time. The effects of constant and dynamic currents on PEMFC cold starts under various initial temperatures are studied. The numerical results from a developed PEMFC dynamic model show that the constant current slope strategy (CCSS) is more efficient than the constant current strategy (CCS) in respect of the cold-start time. In the CCSS study, a too-large current slope can lead to a voltage undershoot and then cause a failed cold start, but a too-small current slope can result in a long start-up process in the investigated range of the operating conditions. A data-driven model is developed for dynamic prediction and real-time optimization during the cold start by a semi-recurrent sliding window (SW) method coupled with artificial neural networks (NN) with the simulation data. Based on this NN-SW model, the specific safety–critical operating condition curve under the CCSS has been identified. A real-time adaptive control strategy (RACS) is further proposed to optimize the operating current during the PEMFC cold starts with various initial temperatures. Compared to the optimal CCSS, RACS proves to be more robust and efficient for PEMFC cold-start startups. Based on RACS, the start-up time for an initial temperature of −20 °C can be cut down by 26.7%. Furthermore, the ice predictions by the NN-SW model are also tested and the results are satisfying with an average R2 = 0.9773.

Suggested Citation

  • Wu, Kangcheng & Du, Qing & Zu, Bingfeng & Wang, Yupeng & Cai, Jun & Gu, Xin & Xuan, Jin & Jiao, Kui, 2021. "Enabling real-time optimization of dynamic processes of proton exchange membrane fuel cell: Data-driven approach with semi-recurrent sliding window method," Applied Energy, Elsevier, vol. 303(C).
    • Handle: RePEc:eee:appene:v:303:y:2021:i:c:s0306261921010230
    DOI: 10.1016/j.apenergy.2021.117659
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921010230
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.117659?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. 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.
    2. Shao, Meng & Zhu, Xin-Jian & Cao, Hong-Fei & Shen, Hai-Feng, 2014. "An artificial neural network ensemble method for fault diagnosis of proton exchange membrane fuel cell system," Energy, Elsevier, vol. 67(C), pages 268-275.
    3. Daud, W.R.W. & Rosli, R.E. & Majlan, E.H. & Hamid, S.A.A. & Mohamed, R. & Husaini, T., 2017. "PEM fuel cell system control: A review," Renewable Energy, Elsevier, vol. 113(C), pages 620-638.
    4. Zhong, Di & Lin, Rui & Jiang, Zhenghua & Zhu, Yike & Liu, Dengchen & Cai, Xin & Chen, Liang, 2020. "Low temperature durability and consistency analysis of proton exchange membrane fuel cell stack based on comprehensive characterizations," Applied Energy, Elsevier, vol. 264(C).
    5. Lin, Rui & Zhu, Yike & Ni, Meng & Jiang, Zhenghua & Lou, Diming & Han, Lihang & Zhong, Di, 2019. "Consistency analysis of polymer electrolyte membrane fuel cell stack during cold start," Applied Energy, Elsevier, vol. 241(C), pages 420-432.
    6. Pei, Pucheng & Chen, Huicui, 2014. "Main factors affecting the lifetime of Proton Exchange Membrane fuel cells in vehicle applications: A review," Applied Energy, Elsevier, vol. 125(C), pages 60-75.
    7. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
    8. Huo, Sen & Jiao, Kui & Park, Jae Wan, 2019. "On the water transport behavior and phase transition mechanisms in cold start operation of PEM fuel cell," Applied Energy, Elsevier, vol. 233, pages 776-788.
    9. Amamou, A. & Kandidayeni, M. & Boulon, L. & Kelouwani, S., 2018. "Real time adaptive efficient cold start strategy for proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 216(C), pages 21-30.
    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. Yang, Liu & Cao, Chenxi & Gan, Quanquan & Pei, Hao & Zhang, Qi & Li, Ping, 2022. "Revealing failure modes and effect of catalyst layer properties for PEM fuel cell cold start using an agglomerate model," Applied Energy, Elsevier, vol. 312(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. Lin, Rui & Zhong, Di & Lan, Shunbo & Guo, Rong & Ma, Yunyang & Cai, Xin, 2021. "Experimental validation for enhancement of PEMFC cold start performance: Based on the optimization of micro porous layer," Applied Energy, Elsevier, vol. 300(C).
    2. Yang, Zirong & Jiao, Kui & Wu, Kangcheng & Shi, Weilong & Jiang, Shangfeng & Zhang, Longhai & Du, Qing, 2021. "Numerical investigations of assisted heating cold start strategies for proton exchange membrane fuel cell systems," Energy, Elsevier, vol. 222(C).
    3. Pengcheng Liu & Sichuan Xu, 2022. "Experimental Research on the Dynamic Characteristics and Voltage Uniformity of a PEMFC Stack under Subzero Temperatures," Energies, MDPI, vol. 15(9), pages 1-14, April.
    4. Yang, Liu & Cao, Chenxi & Gan, Quanquan & Pei, Hao & Zhang, Qi & Li, Ping, 2022. "Revealing failure modes and effect of catalyst layer properties for PEM fuel cell cold start using an agglomerate model," Applied Energy, Elsevier, vol. 312(C).
    5. Pan, Weitong & Li, Ping & Gan, Quanquan & Chen, Xueli & Wang, Fuchen & Dai, Gance, 2020. "Thermal stability analysis of cold start processes in PEM fuel cells," Applied Energy, Elsevier, vol. 261(C).
    6. Hou, Junbo & Yang, Min & Ke, Changchun & Zhang, Junliang, 2020. "Control logics and strategies for air supply in PEM fuel cell engines," Applied Energy, Elsevier, vol. 269(C).
    7. Pei, Pucheng & Meng, Yining & Chen, Dongfang & Ren, Peng & Wang, Mingkai & Wang, Xizhong, 2023. "Lifetime prediction method of proton exchange membrane fuel cells based on current degradation law," Energy, Elsevier, vol. 265(C).
    8. Won, Jinyeon & Oh, Hwanyeong & Hong, Jongsup & Kim, Minjin & Lee, Won-Yong & Choi, Yoon-Young & Han, Soo-Bin, 2021. "Hybrid diagnosis method for initial faults of air supply systems in proton exchange membrane fuel cells," Renewable Energy, Elsevier, vol. 180(C), pages 343-352.
    9. Li, Haolong & Chen, Qihong & Zhang, Liyan & Liu, Li & Xiao, Peng, 2023. "Degradation prediction of proton exchange membrane fuel cell based on the multi-inputs Bi-directional long short-term memory," Applied Energy, Elsevier, vol. 344(C).
    10. Huo, Sen & Jiao, Kui & Park, Jae Wan, 2019. "On the water transport behavior and phase transition mechanisms in cold start operation of PEM fuel cell," Applied Energy, Elsevier, vol. 233, pages 776-788.
    11. 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.
    12. Zhang, Zhuo & Wang, Qi-yao & Bai, Fan & Chen, Li & Tao, Wen-quan, 2023. "Performance simulation and key parameters in-plane distribution analysis of a commercial-size PEMFC," Energy, Elsevier, vol. 263(PC).
    13. Li, Yuehua & Pei, Pucheng & Ma, Ze & Ren, Peng & Huang, Hao, 2020. "Analysis of air compression, progress of compressor and control for optimal energy efficiency in proton exchange membrane fuel cell," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    14. Zang, Linfeng & Hao, Liang & Zhu, Xiaojing, 2023. "Effect of the pore structure of cathode catalyst layer on the PEM fuel cell cold start process," Energy, Elsevier, vol. 271(C).
    15. Antony Plait & Frédéric Dubas, 2022. "A 2D Multi-Layer Model to Study the External Magnetic Field Generated by a Polymer Exchange Membrane Fuel Cell," Mathematics, MDPI, vol. 10(20), pages 1-15, October.
    16. 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).
    17. Wang, Hanqing & Gaillard, Arnaud & Hissel, Daniel, 2019. "A review of DC/DC converter-based electrochemical impedance spectroscopy for fuel cell electric vehicles," Renewable Energy, Elsevier, vol. 141(C), pages 124-138.
    18. Li, Linjun & Wang, Shixue & Yue, Like & Wang, Guozhuo, 2019. "Cold-start method for proton-exchange membrane fuel cells based on locally heating the cathode," Applied Energy, Elsevier, vol. 254(C).
    19. Pei, Pucheng & Li, Yuehua & Xu, Huachi & Wu, Ziyao, 2016. "A review on water fault diagnosis of PEMFC associated with the pressure drop," Applied Energy, Elsevier, vol. 173(C), pages 366-385.
    20. Zuo, Jian & Lv, Hong & Zhou, Daming & Xue, Qiong & Jin, Liming & Zhou, Wei & Yang, Daijun & Zhang, Cunman, 2021. "Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application," Applied Energy, Elsevier, vol. 281(C).

    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:303:y:2021:i:c:s0306261921010230. 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.