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Novel extraction method of working condition spectrum for the lifetime prediction and energy management strategy evaluation of automotive fuel cells

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  • Chen, Dongfang
  • Pei, Pucheng
  • Meng, Yining
  • Ren, Peng
  • Li, Yuehua
  • Wang, Mingkai
  • Wang, Xizhong

Abstract

Lifetime is a major bottleneck for the commercialization of fuel cells. A quick evaluating method for fuel cell lifetime can assist in immediately assessing technology progress, predicting lifetime in real-time, and extending durability. Among the existing methods, the lifetime prediction method based on working conditions is a cost-effective, time-saving, and realistic method. However, there are no methods and clear standards for extracting the time and frequency of working conditions in test protocols used in this method. In this study, the working condition spectrum extraction method is proposed and used to extract the working condition spectra of commonly used durability test protocols. The lifetime prediction of fuel cells operating under different specific test protocols based on the working condition spectrum extraction method is performed and verified to demonstrate its reliability. Furthermore, the proposed method is used to extract the working condition spectra and predict the lifetime of fuel cells in hybrid power systems with different energy management strategies (EMSs). The results show that the extraction method can provide an evaluation method for developing EMSs of fuel cell hybrid power systems from the lifetime perspective, which is useful for fuel cell durability research.

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  • Chen, Dongfang & Pei, Pucheng & Meng, Yining & Ren, Peng & Li, Yuehua & Wang, Mingkai & Wang, Xizhong, 2022. "Novel extraction method of working condition spectrum for the lifetime prediction and energy management strategy evaluation of automotive fuel cells," Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:energy:v:255:y:2022:i:c:s0360544222014268
    DOI: 10.1016/j.energy.2022.124523
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    Cited by:

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    7. 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).
    8. Zhao, Lei & Yuan, Hao & Xie, Jiaping & Jiang, Shangfeng & Wei, Xuezhe & Tang, Wei & Ming, Pingwen & Dai, Haifeng, 2023. "Inconsistency evaluation of vehicle-oriented fuel cell stacks based on electrochemical impedance under dynamic operating conditions," Energy, Elsevier, vol. 265(C).
    9. Zhou, Yu & Chen, Ben & Chen, Wenshang & Deng, Qihao & Shen, Jun & Tu, Zhengkai, 2022. "A novel opposite sinusoidal wave flow channel for performance enhancement of proton exchange membrane fuel cell," Energy, Elsevier, vol. 261(PB).
    10. Fan Yang & Xiaoming Xu & Yuehua Li & Dongfang Chen & Song Hu & Ziwen He & Yi Du, 2023. "A Review on Mass Transfer in Multiscale Porous Media in Proton Exchange Membrane Fuel Cells: Mechanism, Modeling, and Parameter Identification," Energies, MDPI, vol. 16(8), pages 1-24, April.
    11. Mousavi, Shadi Bashiri & Ahmadi, Pouria & Raeesi, Mehrdad, 2024. "Performance evaluation of a hybrid hydrogen fuel cell/battery bus with fuel cell degradation and battery aging," Renewable Energy, Elsevier, vol. 227(C).
    12. Quan, Shengwei & He, Hongwen & Chen, Jinzhou & Zhang, Zhendong & Han, Ruoyan & Wang, Ya-Xiong, 2023. "Health-aware model predictive energy management for fuel cell electric vehicle based on hybrid modeling method," Energy, Elsevier, vol. 278(PA).
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    17. Chen, Dongfang & Pei, Pucheng & Ren, Peng & Song, Xin & Wang, He & Zhang, Lu & Wang, Mingkai, 2022. "Analytical methods for the effect of anode nitrogen concentration on performance and voltage consistency of proton exchange membrane fuel cell stack," Energy, Elsevier, vol. 258(C).

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