Remaining useful life prediction of vehicle-level fuel cells based on self-attention gated recurrent unit modeling
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1. Joint prediction of polarization losses and internal states in fuel cell via time–frequency feature fusion and machine learning10.1016/j.etran.2026.100548 ↗
2. Integrated study of prediction and optimization performance of PBI-HTPEM fuel cell using deep learning, machine learning and statistical correlation10.1016/j.renene.2024.121295 ↗
3. Durability degradation mechanism and consistency analysis for proton exchange membrane fuel cell stack10.1016/j.apenergy.2022.119020 ↗
4. Main degradation mechanisms of polymer electrolyte membrane fuel cell stacks – Mechanisms, influencing factors, consequences, and mitigation strategies10.1016/j.ijhydene.2023.06.215 ↗
5. A health management review of proton exchange membrane fuel cell for electric vehicles: Failure mechanisms, diagnosis techniques and mitigation measures10.1016/j.rser.2023.113369 ↗
6. A health index-based approach for fuel cell lifetime estimation10.1016/j.isci.2024.110979 ↗
7. Performance degradation prediction method of PEM fuel cells using bidirectional long short-term memory neural network based on Bayesian optimization10.1016/j.energy.2023.129469 ↗
8. Short-term performance degradation prediction of a commercial vehicle fuel cell system based on CNN and LSTM hybrid neural network10.1016/j.ijhydene.2022.12.005 ↗
9. Lifetime prediction for proton exchange membrane fuel cell under real driving cycles based on platinum particle dissolve model10.1016/j.ijhydene.2020.08.188 ↗
10. Model predictive control for electrochemical impedance spectroscopy measurement of fuel cells based on neural network optimization10.1109/TTE.2019.2909687 ↗
11. Prediction of fuel cell performance degradation using a combined approach of machine learning and impedance spectroscopy10.1016/j.jechem.2023.08.028 ↗
12. Adaptive prognostic of fuel cells by implementing ensemble echo state networks in time-varying model space10.1109/TIE.2019.2893827 ↗
13. Multi-timescale lifespan prediction for PEMFC systems under dynamic operating conditions10.1109/TTE.2021.3103921 ↗
14. Remaining useful life prediction of vehicle-oriented PEMFC systems based on IGWO-BP neural network under real-world traffic conditions10.1016/j.energy.2024.130334 ↗
15. A fusion prognostics strategy for fuel cells operating under dynamic conditions10.1016/j.etran.2022.100166 ↗
16. Degradation prediction of 65 kW proton exchange membrane fuel cells on city buses using a hybrid approach with the advantage actor-critic method10.1016/j.ijhydene.2023.08.191 ↗
17. Hybrid physics-based and data-driven prognostic for PEM fuel cells considering voltage recovery10.1109/TEC.2023.3311460 ↗
18. A multi-scale hybrid degradation index for proton exchange membrane fuel cells10.1016/j.jpowsour.2019.226916 ↗
19. Lifetime prediction method of proton exchange membrane fuel cells based on current degradation law10.1016/j.energy.2022.126341 ↗
20. A novel approach based on semi-empirical model for degradation prediction of fuel cells10.1016/j.jpowsour.2020.229435 ↗
21. A degradation prediction method for PEM fuel cell based on deep temporal feature extraction and transfer learning10.1109/TTE.2023.3262588 ↗
22. Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection10.1016/j.enconman.2021.114367 ↗
23. An LSTM and ANN fusion dynamic model of a proton exchange membrane fuel cell10.1109/TII.2022.3196621 ↗
24. A hybrid health prognostics method for proton exchange membrane fuel cells with internal health recovery10.1109/TTE.2023.3243788 ↗
25. Performance degradation prediction of proton exchange membrane fuel cell using a hybrid prognostic approach10.1016/j.ijhydene.2020.08.082 ↗
26. Accurate long-term prognostics of proton exchange membrane fuel cells using recurrent and convolutional neural networks10.1016/j.ijhydene.2023.04.143 ↗
27. Evolutionary gate recurrent unit coupling convolutional neural network and improved manta ray foraging optimization algorithm for performance degradation prediction of PEMFC10.1016/j.apenergy.2023.120821 ↗
28. An interpretable uncertainty quantification framework for proton exchange membrane fuel cell degradation prediction10.1016/j.renene.2025.123137 ↗
29. Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application10.1016/j.apenergy.2020.115937 ↗
30. Deep learning with dual-stage attention mechanism for interpretable prediction of proton exchange membrane fuel cell performance degradation10.1016/j.ijhydene.2024.01.308 ↗
31. Lifespan prediction model for proton exchange membrane fuel cell vehicle based on time series information feature extraction and optimization10.1016/j.renene.2025.122558 ↗
32. Dynamic programming improved online fuzzy power distribution in a demonstration fuel cell hybrid bus10.1016/j.energy.2023.128549 ↗
33. Collaborative optimization of velocity planning and energy management for fuel cell hybrid buses at multiple intersections and bus stations10.1016/j.energy.2025.135912 ↗
34. Optimal online energy management strategy of a fuel cell hybrid bus via reinforcement learning10.1016/j.enconman.2023.117921 ↗
35. Remaining useful life prediction for proton exchange membrane fuel cells using combined convolutional neural network and recurrent neural network10.1016/j.ijhydene.2022.09.207 ↗
36. Degradation and polarization curve prediction of proton exchange membrane fuel cells: An interpretable model perspective10.1016/j.apenergy.2024.123289 ↗
37. Experimentally investigation on current density distribution characteristics of hydrogen-oxygen proton exchange membrane fuel cells under dynamic loading10.1016/j.jclepro.2023.136315 ↗
38. Fuel cell life prediction considering the recovery phenomenon of reversible voltage loss10.1016/j.jpowsour.2024.235634 ↗
39. A predictive framework for PEMFC dynamic load performance degradation based on feature parameter analysis10.1016/j.ijhydene.2024.05.338 ↗
40. Adaptive state-of-health temperature sensitivity characteristics for durability improvement of PEM fuel cells10.1016/j.cej.2024.151951 ↗
41. A review on lifetime prediction of proton exchange membrane fuel cells system10.1016/j.jpowsour.2022.231256 ↗
42. Uncertainty quantification of proton-exchange-membrane fuel cells degradation prediction based on Bayesian-Gated Recurrent Unit10.1016/j.etran.2023.100230 ↗
43. An explainable data-driven framework for fuel cell aging prediction under dynamic condition10.1109/TIE.2022.3199935 ↗
44. Transfer learning in demand response: A review of algorithms for data-efficient modelling and control10.1016/j.egyai.2021.100126 ↗