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Remaining useful life prediction of PEMFCs based on mode decomposition and hybrid method under real-world traffic conditions

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  • Chen, Li
  • Yang, Jibin
  • Wu, Xiaohua
  • Deng, Pengyi
  • Xu, Xiaohui
  • Peng, Yiqiang

Abstract

Accurately predicting the remaining useful life (RUL) of proton-exchange membrane fuel cells (PEMFCs) is essential for the health management of vehicle-oriented PEMFCs. Utilizing operational data of a demonstration PEMFC city bus in Chengdu, China, an RUL prediction method for PEMFCs that combines mode decomposition and a hybrid prediction model is developed. The improved complete ensemble empirical mode decomposition with adaptive noise is used to decompose and reconstruct the data into high- and low-frequency components to cope with the voltage recovery phenomenon. The hybrid prediction model comprises two phases. In the data-driven phase, a bidirectional long short-term memory (BiLSTM) neural network is used to predict the low-frequency components, while a mixed model comprising a convolutional neural network, BiLSTM, and an attention mechanism is used to predict the high-frequency components. The dung beetle optimization algorithm is used to optimize the parameters of the mixed model. The prediction results of the data-driven phase are then used as observed values in the model-driven phase, which involves applying the adaptive unscented Kalman filter to a voltage degradation model and obtaining the final prediction. The results show that the mean absolute percentage error of the proposed method is less than 1 % under real-world traffic conditions.

Suggested Citation

  • Chen, Li & Yang, Jibin & Wu, Xiaohua & Deng, Pengyi & Xu, Xiaohui & Peng, Yiqiang, 2025. "Remaining useful life prediction of PEMFCs based on mode decomposition and hybrid method under real-world traffic conditions," Energy, Elsevier, vol. 314(C).
    • Handle: RePEc:eee:energy:v:314:y:2025:i:c:s036054422404057x
    DOI: 10.1016/j.energy.2024.134279
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    References listed on IDEAS

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