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Efficient degradation prediction of PEMFCs using ELM-AE based on fuzzy extension broad learning system

Author

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  • Deng, Zhihua
  • Chan, Siew Hwa
  • Chen, Qihong
  • Liu, Hao
  • Zhang, Liyan
  • Zhou, Keliang
  • Tong, Sirui
  • Fu, Zhichao

Abstract

Degradation prediction is an important part of proton exchange membrane fuel cells (PEMFCs) prognostic research, and it is essential for prolonging the durability of PEMFCs. An accuracy and efficient degradation prediction of PEMFCs method is proposed, which combines extreme learning machine based on auto-encoder (ELM-AE) and fuzzy extension broad learning system (FEBLS). The ELM-AE inherits the fast learning ability of ELM while retaining the dimensionality reduction and feature learning ability of AE. Specifically, the proposed FEBLS consists of three parts, the Takagi–Sugeno–Kang (TSK) fuzzy subsystems, enhancement layer, and extension layer. First, a set of TSK fuzzy subsystems are utilized to replace the mapping nodes of the existing broad learning system (BLS), where the latent representation of the power time series of PEMFCs are processed. Then, the outputs of all fuzzy subsystems are sent to the next layer for nonlinear transformation to obtain enhancement nodes. After that, the enhancement nodes are further fed into the latest layer for further nonlinear transformation to obtain the extension nodes. Finally, the accuracy and efficiency of the proposed ELM-AE-FEBLS method are verified by two different aging datasets of PEMFCs. Compared with ELM, ELM based on particle swarm optimization, and BLS methods, the comparison analysis shows that ELM-AE-FEBLS of multi-input variables with power time series has more competitive in accuracy. Therefore, the proposed method can accurately predict the remaining useful life of fuel cells.

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  • 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).
    • Handle: RePEc:eee:appene:v:331:y:2023:i:c:s0306261922016427
    DOI: 10.1016/j.apenergy.2022.120385
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    1. Benaggoune, Khaled & Yue, Meiling & Jemei, Samir & Zerhouni, Noureddine, 2022. "A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 313(C).
    2. 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).
    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. Yang, Bo & Guo, Zhengxun & Yang, Yi & Chen, Yijun & Zhang, Rui & Su, Keyi & Shu, Hongchun & Yu, Tao & Zhang, Xiaoshun, 2021. "Extreme learning machine based meta-heuristic algorithms for parameter extraction of solid oxide fuel cells," Applied Energy, Elsevier, vol. 303(C).
    6. 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.
    7. Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
    8. 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.
    9. 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).
    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. 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.
    12. Zhang, Zhendong & Wang, Ya-Xiong & He, Hongwen & Sun, Fengchun, 2021. "A short- and long-term prognostic associating with remaining useful life estimation for proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 304(C).
    13. Bae, Suk Joo & Kim, Seong-Joon & Lee, Jin-Hwa & Song, Inseob & Kim, Nam-In & Seo, Yongho & Kim, Ki Buem & Lee, Naesung & Park, Jun-Young, 2014. "Degradation pattern prediction of a polymer electrolyte membrane fuel cell stack with series reliability structure via durability data of single cells," Applied Energy, Elsevier, vol. 131(C), pages 48-55.
    14. Li, Wenkai & Zhang, Qinglei & Wang, Chao & Yan, Xiaohui & Shen, Shuiyun & Xia, Guofeng & Zhu, Fengjuan & Zhang, Junliang, 2017. "Experimental and numerical analysis of a three-dimensional flow field for PEMFCs," Applied Energy, Elsevier, vol. 195(C), pages 278-288.
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    2. Han, Yongming & Du, Zilan & Hu, Xuan & Li, Yeqing & Cai, Di & Fan, Jinzhen & Geng, Zhiqiang, 2023. "Production prediction modeling of food waste anaerobic digestion for resources saving based on SMOTE-LSTM," Applied Energy, Elsevier, vol. 352(C).

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