IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v219y2023ip2s0960148123014404.html
   My bibliography  Save this article

Degradation prediction of proton exchange membrane fuel cells with model uncertainty quantification

Author

Listed:
  • Yang, Yang
  • Yu, Xiaoran
  • Zhu, Wenchao
  • Xie, Changjun
  • Zhao, Bo
  • Zhang, Leiqi
  • Shi, Ying
  • Huang, Liang
  • Zhang, Ruiming

Abstract

This study presents a new approach for state of health (SOH) estimation and degradation trend prediction of proton exchange membrane fuel cells (PEMFCs) considering model uncertainty quantification. The model uncertainty is considered as completely unknown in this paper, and the Bayesian framework-based model uncertainty and state simultaneous estimation (Bayes-MUSE) method is proposed to quantify the model uncertainty from the perspective of the form of its effect on the system state transfer, and this method of quantifying model uncertainty is first applied to PEMFC long-term aging prediction and RUL estimation. The proposed Bayes-MUSE method is compared with extended Kalman filter (EKF), Unscented Kalman filter (UKF) and Unscented Particle filter (UPF) methods in terms of SOH estimation, noise immunity, aging prediction performance and RUL estimation using two aging test datasets, and the method is verified to have excellent noise immunity and the highest long-term aging prediction and RUL estimation accuracy. Compared with EKF, UKF and UPF methods, the proposed Bayes-MUSE method has a maximum improvement of 93.95% and 52.2% in RUL estimation accuracy of the two data sets under quasi-dynamic and static conditions, respectively.

Suggested Citation

  • Yang, Yang & Yu, Xiaoran & Zhu, Wenchao & Xie, Changjun & Zhao, Bo & Zhang, Leiqi & Shi, Ying & Huang, Liang & Zhang, Ruiming, 2023. "Degradation prediction of proton exchange membrane fuel cells with model uncertainty quantification," Renewable Energy, Elsevier, vol. 219(P2).
    • Handle: RePEc:eee:renene:v:219:y:2023:i:p2:s0960148123014404
    DOI: 10.1016/j.renene.2023.119525
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123014404
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.119525?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. Sutharssan, Thamo & Montalvao, Diogo & Chen, Yong Kang & Wang, Wen-Chung & Pisac, Claudia & Elemara, Hakim, 2017. "A review on prognostics and health monitoring of proton exchange membrane fuel cell," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 440-450.
    2. Yue, Meiling & Jemei, Samir & Zerhouni, Noureddine & Gouriveau, Rafael, 2021. "Proton exchange membrane fuel cell system prognostics and decision-making: Current status and perspectives," Renewable Energy, Elsevier, vol. 179(C), pages 2277-2294.
    3. 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.
    4. 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).
    5. Wang, Yujie & Chen, Zonghai, 2020. "A framework for state-of-charge and remaining discharge time prediction using unscented particle filter," Applied Energy, Elsevier, vol. 260(C).
    6. Xu, Zhijie & Xu, Wei & Stephens, Elizabeth & Koeppel, Brian, 2017. "Mechanical reliability and life prediction of coated metallic interconnects within solid oxide fuel cells," Renewable Energy, Elsevier, vol. 113(C), pages 1472-1479.
    7. Zhou, Daming & Gao, Fei & Breaz, Elena & Ravey, Alexandre & Miraoui, Abdellatif, 2017. "Degradation prediction of PEM fuel cell using a moving window based hybrid prognostic approach," Energy, Elsevier, vol. 138(C), pages 1175-1186.
    8. Bressel, Mathieu & Hilairet, Mickael & Hissel, Daniel & Ould Bouamama, Belkacem, 2016. "Extended Kalman Filter for prognostic of Proton Exchange Membrane Fuel Cell," Applied Energy, Elsevier, vol. 164(C), pages 220-227.
    9. 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).
    10. Liu, Hao & Chen, Jian & Hissel, Daniel & Su, Hongye, 2019. "Remaining useful life estimation for proton exchange membrane fuel cells using a hybrid method," Applied Energy, Elsevier, vol. 237(C), pages 910-919.
    11. Xuexia Zhang & Zixuan Yu & Weirong Chen, 2019. "Life Prediction Based on D-S ELM for PEMFC," Energies, MDPI, vol. 12(19), pages 1-15, September.
    12. Žnidarič, Luka & Nusev, Gjorgji & Morel, Bertrand & Mougin, Julie & Juričić, Đani & Boškoski, Pavle, 2021. "Evaluating uncertainties in electrochemical impedance spectra of solid oxide fuel cells," Applied Energy, Elsevier, vol. 298(C).
    13. Olabi, A.G. & Wilberforce, Tabbi & Abdelkareem, Mohammad Ali, 2021. "Fuel cell application in the automotive industry and future perspective," Energy, Elsevier, vol. 214(C).
    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. Lv, Jianfeng & Shen, Xiaoning & Gao, Yabin & Liu, Jianxing & Sun, Guanghui, 2024. "The seasonal-trend disentangle based prognostic framework for PEM fuel cells," Renewable Energy, Elsevier, vol. 228(C).
    2. Zuo, Jian & Steiner, Nadia Yousfi & Li, Zhongliang & Hissel, Daniel, 2024. "Health management review for fuel cells: Focus on action phase," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(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. Song, Ke & Huang, Xing & Huang, Pengyu & Sun, Hui & Chen, Yuhui & Huang, Dongya, 2024. "Data-driven health state estimation and remaining useful life prediction of fuel cells," Renewable Energy, Elsevier, vol. 227(C).
    2. Chen, Kui & Badji, Abderrezak & Laghrouche, Salah & Djerdir, Abdesslem, 2022. "Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm," Applied Energy, Elsevier, vol. 318(C).
    3. Aihua Tang & Yuanhang Yang & Quanqing Yu & Zhigang Zhang & Lin Yang, 2022. "A Review of Life Prediction Methods for PEMFCs in Electric Vehicles," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    4. 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).
    5. He, Wenbin & Liu, Ting & Ming, Wuyi & Li, Zongze & Du, Jinguang & Li, Xiaoke & Guo, Xudong & Sun, Peiyan, 2024. "Progress in prediction of remaining useful life of hydrogen fuel cells based on deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    6. Zuo, Jian & Steiner, Nadia Yousfi & Li, Zhongliang & Hissel, Daniel, 2024. "Health management review for fuel cells: Focus on action phase," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(C).
    7. Yue, Meiling & Jemei, Samir & Zerhouni, Noureddine & Gouriveau, Rafael, 2021. "Proton exchange membrane fuel cell system prognostics and decision-making: Current status and perspectives," Renewable Energy, Elsevier, vol. 179(C), pages 2277-2294.
    8. Tianxiang Wang & Hongliang Zhou & Chengwei Zhu, 2022. "A Short-Term and Long-Term Prognostic Method for PEM Fuel Cells Based on Gaussian Process Regression," Energies, MDPI, vol. 15(13), pages 1-17, July.
    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. Deng, Huiwen & Hu, Weihao & Cao, Di & Chen, Weirong & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2022. "Degradation trajectories prognosis for PEM fuel cell systems based on Gaussian process regression," Energy, Elsevier, vol. 244(PA).
    11. Chen, Hong & Zhan, Zhigang & Jiang, Panxing & Sun, Yahao & Liao, Liwen & Wan, Xiongbiao & Du, Qing & Chen, Xiaosong & Song, Hao & Zhu, Ruijie & Shu, Zhanhong & Li, Shang & Pan, Mu, 2022. "Whole life cycle performance degradation test and RUL prediction research of fuel cell MEA," Applied Energy, Elsevier, vol. 310(C).
    12. Zhang, Caizhi & Zhang, Yuqi & Wang, Lei & Deng, Xiaozhi & Liu, Yang & Zhang, Jiujun, 2023. "A health management review of proton exchange membrane fuel cell for electric vehicles: Failure mechanisms, diagnosis techniques and mitigation measures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    13. 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.
    14. Huu-Linh Nguyen & Sang-Min Lee & Sangseok Yu, 2023. "A Comprehensive Review of Degradation Prediction Methods for an Automotive Proton Exchange Membrane Fuel Cell," Energies, MDPI, vol. 16(12), pages 1-32, June.
    15. Deng, Zhihua & Chan, Siew Hwa & Chen, Qihong & Liu, Hao & Zhang, Liyan & Zhou, Keliang & Tong, Sirui & Fu, Zhichao, 2023. "Efficient degradation prediction of PEMFCs using ELM-AE based on fuzzy extension broad learning system," Applied Energy, Elsevier, vol. 331(C).
    16. Zhang, Zhendong & He, Hongwen & Wang, Yaxiong & Quan, Shengwei & Chen, Jinzhou & Han, Ruoyan, 2024. "A novel generalized prognostic method of proton exchange membrane fuel cell using multi-point estimation under various operating conditions," Applied Energy, Elsevier, vol. 357(C).
    17. Liu, Ze & Xu, Sichuan & Zhao, Honghui & Wang, Yupeng, 2022. "Durability estimation and short-term voltage degradation forecasting of vehicle PEMFC system: Development and evaluation of machine learning models," Applied Energy, Elsevier, vol. 326(C).
    18. Pei, Pucheng & Chen, Dongfang & Wu, Ziyao & Ren, Peng, 2019. "Nonlinear methods for evaluating and online predicting the lifetime of fuel cells," Applied Energy, Elsevier, vol. 254(C).
    19. Chen, Kui & Laghrouche, Salah & Djerdir, Abdesslem, 2021. "Prognosis of fuel cell degradation under different applications using wavelet analysis and nonlinear autoregressive exogenous neural network," Renewable Energy, Elsevier, vol. 179(C), pages 802-814.
    20. Wang, Chu & Li, Zhongliang & Outbib, Rachid & Dou, Manfeng & Zhao, Dongdong, 2022. "Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 305(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:renene:v:219:y:2023:i:p2:s0960148123014404. 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.journals.elsevier.com/renewable-energy .

    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.