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An embedding layer-based quantum long short-term memory model with transfer learning for proton exchange membrane fuel stack remaining useful life prediction

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  • Wang, Fu-Kwun
  • Kebede, Getnet Awoke
  • Lo, Shih-Che
  • Woldegiorgis, Bereket Haile

Abstract

The battery management system (BMS) plays a critical role in electric vehicles (EVs), with the prediction of remaining useful life (RUL) being of utmost importance. This functionality enables real-time monitoring and enhances driver assistance through intelligent driving mechanisms. To improve the precision of stack voltage degradation and RUL prognostication for proton exchange membrane fuel cell (PEMFC) aging, especially with limited real-time training data, we propose a novel approach using a layer-enhanced quantum long short-term memory model with transfer learning. The proposed model is trained offline using historical stack voltage degradation data from a source stack, which is then fine-tuned and transferred to a target stack. Comparative evaluations demonstrate the model's robustness, with consistently low root mean square errors in stack voltage predictions, even with increased training data. The proposed model significantly improves RUL prediction accuracy, achieving a maximum relative accuracy enhancement of 1.33 % when initiating predictions at 712 h. These results affirm the effectiveness of our approach in enhancing the accuracy of PEMFC aging prognosis, thereby offering valuable insights for practical integration within the BMS framework for EVs.

Suggested Citation

  • Wang, Fu-Kwun & Kebede, Getnet Awoke & Lo, Shih-Che & Woldegiorgis, Bereket Haile, 2024. "An embedding layer-based quantum long short-term memory model with transfer learning for proton exchange membrane fuel stack remaining useful life prediction," Energy, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:energy:v:308:y:2024:i:c:s0360544224028299
    DOI: 10.1016/j.energy.2024.133054
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    References listed on IDEAS

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    2. Xiao, Yutang & Zhu, Xiaoyong & Wu, Jiqi & Luo, Jun & Quan, Li & Xiong, Rui & Chen, Wenhua, 2025. "A multi-stage augmentative generalization learning prediction model for lithium-ion battery remaining useful life under uncertain working conditions," Energy, Elsevier, vol. 335(C).
    3. Wu, Hangyu & Fan, Fulin & Song, Kai & Sun, Chuanyu & Li, Yang & Xie, Changjun & Meng, Xuan & Mei, Jian & Chan, Siew Hwa, 2026. "Reliable cross-domain lifetime prediction for fuel cells: A time-frequency fusion transfer learning architecture," Energy, Elsevier, vol. 344(C).
    4. Feng, Ruizhe & Zhu, Shuzhao & Jiang, Ruixin & Cai, Xin & Lin, Rui, 2026. "Degradation prediction of the low-Pt loading proton exchange membrane fuel cell based on spatio-temporal Transformer network," Energy, Elsevier, vol. 342(C).
    5. Huang, Kai & Zhang, XinYu & Guo, Yongfang & Li, MengShi, 2026. "Source domain selection with early-cycle features for transfer learning-based prediction of lithium-ion battery degradation trajectories," Energy, Elsevier, vol. 344(C).

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