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A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell

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  1. Deng, Zhihua & Miao, Bin & Zhang, Lan & Liu, Qinglin & Pan, Zehua & Zhang, Weike & Ding, Ovi Lian & Tong, Sirui & Liu, Hao & Chan, Siew Hwa, 2025. "Accurate long-step degradation trends prediction and remaining useful life estimation for proton exchange membrane fuel cells," Renewable Energy, Elsevier, vol. 247(C).
  2. Zhang, Chu & Hu, Haowen & Ji, Jie & Liu, Kang & Xia, Xin & Nazir, Muhammad Shahzad & Peng, Tian, 2023. "An evolutionary stacked generalization model based on deep learning and improved grasshopper optimization algorithm for predicting the remaining useful life of PEMFC," Applied Energy, Elsevier, vol. 330(PA).
  3. 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).
  4. Pei, Yaowang & Chen, Fengxiang & Zhou, Su & Huo, Haibo & Ye, Huan, 2025. "Inlet gas flow, pressure and temperature control technology for PEMFC stack testing platforms," Energy, Elsevier, vol. 333(C).
  5. Huang, Ruike & Zhang, Xuexia & Dong, Sidi & Huang, Lei & Li, Yuan, 2025. "Degradation prediction of PEM fuel cell using LSTM based on Gini gamma correlation coefficient and improved sand cat swarm optimization under dynamic operating conditions," Applied Energy, Elsevier, vol. 392(C).
  6. Xuan Meng & Jian Mei & Xingwang Tang & Jinhai Jiang & Chuanyu Sun & Kai Song, 2024. "The Degradation Prediction of Proton Exchange Membrane Fuel Cell Performance Based on a Transformer Model," Energies, MDPI, vol. 17(12), pages 1-13, June.
  7. Wang, Zifei & Tao, Jili & Liu, Zhitao & Feng, Han & Ma, Longhua & Xu, Ming & Su, Hongye, 2024. "A proton exchange membrane fuel cells degradation prediction method based on multi-scale temporal information merging network," Energy, Elsevier, vol. 313(C).
  8. Ma, Yangyang & Li, Songting & Zhou, Shulin & Wang, Xueyuan & Yuan, Hao & Chang, Guofeng & Zhu, Jiangong & Dai, Haifeng & Wei, Xuezhe, 2025. "Performance degradation prediction of proton exchange membrane fuel cells based on CNN-LSTM network with squeeze-and-excitation attention mechanism," Energy, Elsevier, vol. 335(C).
  9. Huang, Ruike & Zhang, Xuexia & Dong, Sidi & Huang, Lei & Liao, Hongbo & Li, Yuan, 2024. "A refined grey Verhulst model for accurate degradation prognostication of PEM fuel cells based on inverse hyperbolic sine function transformation," Renewable Energy, Elsevier, vol. 237(PC).
  10. 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).
  11. Tianxiang Wang & Hongliang Zhou & Chengwei Zhu, 2022. "A Short-Term and Long-Term Prognostic Method for PEM Fuel Cells Based on Gaussian Process Regression," Energies, MDPI, vol. 15(13), pages 1-17, July.
  12. 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.
  13. Chou, Jia-Hong & Wang, Fu-Kwun, 2025. "Future prediction on the remaining useful life of proton exchange membrane fuel using temporal fusion transformer model," Renewable Energy, Elsevier, vol. 247(C).
  14. Yu, Yulong & Zheng, Qiang & Zhang, Tianyi & Li, Zhengyan & Chen, Lei & Tao, Wen-Quan, 2025. "Forecasting the output performance of PEMFCs via a novel deep learning framework considering varying operating conditions and time scales," Applied Energy, Elsevier, vol. 389(C).
  15. Zhang, Xuexia & Huang, Lei & Jiang, Yu & Lin, Long & Liao, Hongbo & Liu, Wentao, 2024. "Investigation of nonlinear accelerated degradation mechanism in fuel cell stack under dynamic driving cycles from polarization processes," Applied Energy, Elsevier, vol. 355(C).
  16. Xiangdong Wang & Zerong Huang & Daxing Zhang & Haoyu Yuan & Bingzi Cai & Hanlin Liu & Chunsheng Wang & Yuan Cao & Xinyao Zhou & Yaolin Dong, 2024. "Dynamic Prediction of Proton-Exchange Membrane Fuel Cell Degradation Based on Gated Recurrent Unit and Grey Wolf Optimization," Energies, MDPI, vol. 17(23), pages 1-13, November.
  17. González-Morán, Laura & Suárez, Christian & Iranzo, Alfredo & Han, Lei & Rosa, Felipe, 2024. "A numerical study on heat transfer for serpentine-type cooling channels in a PEM fuel cell stack," Energy, Elsevier, vol. 307(C).
  18. Ong, Samuel & Al-Othman, Amani & Tawalbeh, Muhammad, 2023. "Emerging technologies in prognostics for fuel cells including direct hydrocarbon fuel cells," Energy, Elsevier, vol. 277(C).
  19. Aihua Tang & Yuanhang Yang & Quanqing Yu & Zhigang Zhang & Lin Yang, 2022. "A Review of Life Prediction Methods for PEMFCs in Electric Vehicles," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
  20. Gong, Zhichao & Wang, Bowen & Xu, Yifan & Ni, Meng & Gao, Qingchen & Hou, Zhongjun & Cai, Jun & Gu, Xin & Yuan, Xinjie & Jiao, Kui, 2022. "Adaptive optimization strategy of air supply for automotive polymer electrolyte membrane fuel cell in life cycle," Applied Energy, Elsevier, vol. 325(C).
  21. Nasser, Bshaer & Tawalbeh, Muhammad & Al-Othman, Amani & Olabi, Abdul Ghani, 2026. "Advances in machine learning for the prediction of fuel cell membrane degradation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PA).
  22. Chen, Dongfang & Wu, Wenlong & Chang, Kuanyu & Li, Yuehua & Pei, Pucheng & Xu, Xiaoming, 2023. "Performance degradation prediction method of PEM fuel cells using bidirectional long short-term memory neural network based on Bayesian optimization," Energy, Elsevier, vol. 285(C).
  23. Hong, Jun-Tao & Han, Shuang & Yan, Jie & Liu, Yong-Qian, 2025. "Dual-path frequency Mamba-Transformer model for wind power forecasting," Energy, Elsevier, vol. 332(C).
  24. Zhang, Tian & Hou, Zhengmeng & Li, Xiaoqin & Chen, Qianjun & Wang, Qichen & Lüddeke, Christian & Wu, Lin & Wu, Xuning & Sun, Wei, 2025. "A novel multivariable prognostic approach for PEMFC degradation and remaining useful life prediction using random forest and temporal convolutional network," Applied Energy, Elsevier, vol. 385(C).
  25. Hou, Yanzhu & Yin, Cong & Sheng, Xia & Xu, Dechao & Chen, Junxiong & Tang, Hao, 2025. "Automotive fuel cell performance degradation prediction using Multi-Agent Cooperative Advantage Actor-Critic model," Energy, Elsevier, vol. 318(C).
  26. Lv, Jianfeng & Shen, Xiaoning & Gao, Yabin & Liu, Jianxing & Sun, Guanghui, 2024. "The seasonal-trend disentangle based prognostic framework for PEM fuel cells," Renewable Energy, Elsevier, vol. 228(C).
  27. Segura, F. & Vivas, F.J. & Andújar, J.M. & Martínez, M., 2023. "Hydrogen-powered refrigeration system for environmentally friendly transport and delivery in the food supply chain," Applied Energy, Elsevier, vol. 338(C).
  28. Wang, Renkang & Li, Kai & Cao, Jishen & Yang, Haiyu & Tang, Hao, 2024. "Air supply subsystem efficiency optimization for fuel cell power system with layered control method," Renewable Energy, Elsevier, vol. 235(C).
  29. Yang, Yijun & Han, Qingyang & Zhang, Zhizheng & Zhang, Hailun & Xue, Haoyuan & Sun, Wenxu & Jia, Lei, 2026. "Multi-scale degradation forecasting of PEMFCs under non-stationary operating conditions: A novel spatio-temporal deep learning framework," Renewable Energy, Elsevier, vol. 256(PD).
  30. Zuo, Jian & Steiner, Nadia Yousfi & Li, Zhongliang & Hissel, Daniel, 2024. "Health management review for fuel cells: Focus on action phase," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(C).
  31. Song, Houde & Liu, Xiaojing & Song, Meiqi, 2023. "Comparative study of data-driven and model-driven approaches in prediction of nuclear power plants operating parameters," Applied Energy, Elsevier, vol. 341(C).
  32. Ko, Taehwan & Kim, Dukyong & Park, Jaewoong & Lee, Seung Hwan, 2025. "Physics-informed neural network for long-term prognostics of proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 382(C).
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