IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v252y2019ic47.html
   My bibliography  Save this article

Degradation model of proton exchange membrane fuel cell based on a novel hybrid method

Author

Listed:
  • Chen, Kui
  • Laghrouche, Salah
  • Djerdir, Abdesslem

Abstract

This paper proposes a new hybrid degradation model of Proton Exchange Membrane Fuel Cell (PEMFC) used in Fuel Cell Electric Vehicle (FCEV) operating under real conditions. This hybrid method is based on the wavelet analysis, Extreme Learning Machine (ELM) and Genetic Algorithm (GA). This new degradation model considers the influence of PEMFC load current, relative humidity, temperature, and hydrogen pressure. First, the wavelet analysis is used to decompose the voltage aging waveform of PEMFC into multiple sub-waveforms. Second, the ELM is applied to build the degradation model of each sub-waveform. Then, the overall degradation model of PEMFC is obtained by combining the degradation model of each sub-waveform. Finally, GA is adopted to improve the global optimization of the proposed degradation model. This model is experimentally validated by using actual data from 3 PEMFCs in 3 FCEVs carrying out postal mail delivery missions in the field. The results show that the proposed hybrid method can get higher accuracy than several traditional approaches.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:252:y:2019:i:c:47
    DOI: 10.1016/j.apenergy.2019.113439
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919311134
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.113439?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. Zaccaria, V. & Tucker, D. & Traverso, A., 2017. "Operating strategies to minimize degradation in fuel cell gas turbine hybrids," Applied Energy, Elsevier, vol. 192(C), pages 437-445.
    2. Zou, Dexuan & Li, Steven & Kong, Xiangyong & Ouyang, Haibin & Li, Zongyan, 2019. "Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy," Applied Energy, Elsevier, vol. 237(C), pages 646-670.
    3. 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.
    4. 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.
    5. Sulaiman, N. & Hannan, M.A. & Mohamed, A. & Ker, P.J. & Majlan, E.H. & Wan Daud, W.R., 2018. "Optimization of energy management system for fuel-cell hybrid electric vehicles: Issues and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 2061-2079.
    6. Matraji, Imad & Laghrouche, Salah & Jemei, Samir & Wack, Maxime, 2013. "Robust control of the PEM fuel cell air-feed system via sub-optimal second order sliding mode," Applied Energy, Elsevier, vol. 104(C), pages 945-957.
    7. Ettihir, K. & Boulon, L. & Agbossou, K., 2016. "Optimization-based energy management strategy for a fuel cell/battery hybrid power system," Applied Energy, Elsevier, vol. 163(C), pages 142-153.
    8. Cao, Tao-Feng & Lin, Hong & Chen, Li & He, Ya-Ling & Tao, Wen-Quan, 2013. "Numerical investigation of the coupled water and thermal management in PEM fuel cell," Applied Energy, Elsevier, vol. 112(C), pages 1115-1125.
    9. Zhang, Tong & Wang, Peiqi & Chen, Huicui & Pei, Pucheng, 2018. "A review of automotive proton exchange membrane fuel cell degradation under start-stop operating condition," Applied Energy, Elsevier, vol. 223(C), pages 249-262.
    10. Blal, Mohamed & Benatiallah, Ali & NeÇaibia, Ammar & Lachtar, Salah & Sahouane, Nordine & Belasri, Ahmed, 2019. "Contribution and investigation to compare models parameters of (PEMFC), comprehensives review of fuel cell models and their degradation," Energy, Elsevier, vol. 168(C), pages 182-199.
    11. Cuneo, A. & Zaccaria, V. & Tucker, D. & Traverso, A., 2017. "Probabilistic analysis of a fuel cell degradation model for solid oxide fuel cell and gas turbine hybrid systems," Energy, Elsevier, vol. 141(C), pages 2277-2287.
    12. Jouin, Marine & Bressel, Mathieu & Morando, Simon & Gouriveau, Rafael & Hissel, Daniel & Péra, Marie-Cécile & Zerhouni, Noureddine & Jemei, Samir & Hilairet, Mickael & Ould Bouamama, Belkacem, 2016. "Estimating the end-of-life of PEM fuel cells: Guidelines and metrics," Applied Energy, Elsevier, vol. 177(C), pages 87-97.
    13. Xu, Liangfei & Mueller, Clemens David & Li, Jianqiu & Ouyang, Minggao & Hu, Zunyan, 2015. "Multi-objective component sizing based on optimal energy management strategy of fuel cell electric vehicles," Applied Energy, Elsevier, vol. 157(C), pages 664-674.
    14. Kwan, Trevor Hocksun & Wu, Xiaofeng & Yao, Qinghe, 2018. "Multi-objective genetic optimization of the thermoelectric system for thermal management of proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 217(C), pages 314-327.
    15. Petrone, Giovanni & Zamboni, Walter & Spagnuolo, Giovanni, 2019. "An interval arithmetic-based method for parametric identification of a fuel cell equivalent circuit model," Applied Energy, Elsevier, vol. 242(C), pages 1226-1236.
    16. Qiu, Diankai & Peng, Linfa & Liang, Peng & Yi, Peiyun & Lai, Xinmin, 2018. "Mechanical degradation of proton exchange membrane along the MEA frame in proton exchange membrane fuel cells," Energy, Elsevier, vol. 165(PB), pages 210-222.
    17. Tribioli, Laura & Cozzolino, Raffaello & Chiappini, Daniele & Iora, Paolo, 2016. "Energy management of a plug-in fuel cell/battery hybrid vehicle with on-board fuel processing," Applied Energy, Elsevier, vol. 184(C), pages 140-154.
    18. Zhu, Li & Chen, Junghui, 2018. "Prognostics of PEM fuel cells based on Gaussian process state space models," Energy, Elsevier, vol. 149(C), pages 63-73.
    19. 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.
    20. Morando, S. & Jemei, S. & Hissel, D. & Gouriveau, R. & Zerhouni, N., 2017. "ANOVA method applied to proton exchange membrane fuel cell ageing forecasting using an echo state network," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 283-294.
    21. Feroldi, Diego & Carignano, Mauro, 2016. "Sizing for fuel cell/supercapacitor hybrid vehicles based on stochastic driving cycles," Applied Energy, Elsevier, vol. 183(C), pages 645-658.
    22. 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.
    23. Pei, Pucheng & Jia, Xiaoning & Xu, Huachi & Li, Pengcheng & Wu, Ziyao & Li, Yuehua & Ren, Peng & Chen, Dongfang & Huang, Shangwei, 2018. "The recovery mechanism of proton exchange membrane fuel cell in micro-current operation," Applied Energy, Elsevier, vol. 226(C), pages 1-9.
    24. Zhang, Hongtao & Li, Xianguo & Liu, Xinzhi & Yan, Jinyue, 2019. "Enhancing fuel cell durability for fuel cell plug-in hybrid electric vehicles through strategic power management," Applied Energy, Elsevier, vol. 241(C), pages 483-490.
    25. 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.
    26. 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.
    27. Hosseinzadeh, Elham & Rokni, Masoud & Rabbani, Abid & Mortensen, Henrik Hilleke, 2013. "Thermal and water management of low temperature Proton Exchange Membrane Fuel Cell in fork-lift truck power system," Applied Energy, Elsevier, vol. 104(C), pages 434-444.
    28. 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.
    29. 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.
    30. 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.
    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. 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).
    2. 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).
    3. 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.
    4. 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).
    5. 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).
    6. 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).
    7. 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).
    8. 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).
    9. 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.
    10. 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.
    11. 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).
    12. Ke Song & Yimin Wang & Xiao Hu & Jing Cao, 2020. "Online Prediction of Vehicular Fuel Cell Residual Lifetime Based on Adaptive Extended Kalman Filter," Energies, MDPI, vol. 13(23), pages 1-21, November.
    13. 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).
    14. Lingling Lv & Pucheng Pei & Peng Ren & He Wang & Geng Wang, 2025. "Exploring Performance Degradation of Proton Exchange Membrane Fuel Cells Based on Diffusion Transformer Model," Energies, MDPI, vol. 18(5), pages 1-22, February.
    15. 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).
    16. 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).
    17. Nicu Bizon & Phatiphat Thounthong, 2021. "A Simple and Safe Strategy for Improving the Fuel Economy of a Fuel Cell Vehicle," Mathematics, MDPI, vol. 9(6), pages 1-29, March.
    18. 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).
    19. Zhang, Xiaojie & Zhang, Tong & Chen, Huicui & Cao, Yinliang, 2021. "A review of online electrochemical diagnostic methods of on-board proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 286(C).
    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:appene:v:252:y:2019:i:c:47. 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.