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Fu‐Kwun Wang, Alebachew Mengistu Worku, Jia‐Hong Chou, William Gomez
Abstract
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Self-adaptive digital twin of fuel cell for remaining useful lifetime prediction
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datacite
Confidence 0%
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Application of machine learning in fuel cell research
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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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Cross-domain electrochemical impedance spectroscopy reconstruction for PEMFC aging characterization: a time-frequency PINN framework with ECM priors
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A non-stationary transformer-based remaining useful life prediction method for proton exchange membrane fuel cells
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The degradation prediction of proton exchange membrane fuel cell performance based on a transformer model
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A parallel LTCN-PHA network for remaining useful life prediction of lithium-ion batteries
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Data-driven modeling of polymer electrolyte fuel cells: Towards predictive analytics with explainable artificial intelligence
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BenchXAI: Comprehensive benchmarking of post-hoc explainable AI methods on multi-modal biomedical data
10.1016/j.compbiomed.2025.110124 · 2025
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Transfer learning-based deep learning models for proton exchange membrane fuel remaining useful life prediction
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Degradation prediction of proton exchange membrane fuel cell based on the multi-inputs Bi-directional long short-term memory
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Energy forecasting model based on CNN-LSTM-AE for many time series with unequal lengths
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Short-term smart grid energy forecasting using a hybrid deep learning method on univariate and multivariate data sets
10.1016/j.energy.2025.138081 · doi-reference
Future prediction on the remaining useful life of proton exchange membrane fuel using temporal fusion transformer model
10.1016/j.renene.2025.123019 · doi-reference
Li-ion battery capacity prediction using improved temporal fusion transformer model
10.1016/j.energy.2024.131114 · doi-reference
10.52202/085713-0410
10.52202/085713-0410 · doi-reference
BenchXAI: Comprehensive benchmarking of post-hoc explainable AI methods on multi-modal biomedical data
10.1016/j.compbiomed.2025.110124 · doi-reference
Data-driven modeling of polymer electrolyte fuel cells: Towards predictive analytics with explainable artificial intelligence
10.1016/j.egyai.2025.100577 · doi-reference
A parallel LTCN-PHA network for remaining useful life prediction of lithium-ion batteries
10.1016/j.energy.2025.138436 · doi-reference
10.1103/4t7t-v19l
10.1103/4t7t-v19l · doi-reference
PEMFC RUL Prediction for non-stationary time series based on crossformer model
10.3390/app15052515 · doi-reference
The degradation prediction of proton exchange membrane fuel cell performance based on a transformer model
10.3390/en17123050 · doi-reference
A non-stationary transformer-based remaining useful life prediction method for proton exchange membrane fuel cells
10.1016/j.ijhydene.2024.02.150 · doi-reference
Cross-domain electrochemical impedance spectroscopy reconstruction for PEMFC aging characterization: a time-frequency PINN framework with ECM priors
10.1016/j.geits.2026.100438 · doi-reference
Metaheuristic optimization of flight control and power conversion in fuel cell-powered quadrotors
10.1016/j.jpowsour.2026.239708 · doi-reference
Remaining useful life prediction of vehicle-oriented PEMFCs based on seasonal trends and hybrid data-driven models under real-world traffic conditions
10.1016/j.renene.2025.123193 · doi-reference
Remaining useful life prediction of PEMFCs based on mode decomposition and hybrid method under real-world traffic conditions
10.1016/j.energy.2024.134279 · doi-reference
PEMFC performance degradation prediction model based on temporal convolutional network coupling bi-directional long short-term memory and sparrow search optimization algorithm
10.1016/j.ijhydene.2025.153348 · doi-reference
Degradation prediction based on physics-constrained data-driven framework for hydrogen fuel cell lifetime
10.1016/j.ijhydene.2026.153438 · doi-reference
An LSTM and ANN fusion dynamic model of a proton exchange membrane fuel cell
10.1109/tii.2022.3196621 · doi-reference
Investigation of fuel cell stack performance degradation based on 1000 h durability experiments and long short-term memory prediction frameworks under dynamic load conditions
10.1016/j.egyai.2025.100628 · doi-reference
A deep learning method based on CNN-BiGRU and attention mechanism for proton exchange membrane fuel cell performance degradation prediction
10.1016/j.ijhydene.2024.11.127 · doi-reference
Modeling of microbial fuel cell power generation using machine learning-based super learner algorithms
10.1016/j.fuel.2023.128646 · doi-reference
Application of machine learning in fuel cell research
10.3390/en16114390 · doi-reference
Machine learning and bayesian optimization for performance prediction of proton-exchange membrane fuel cells
10.1016/j.egyai.2024.100380 · doi-reference
AI-enhanced RUL prediction of PEMFCs under dynamic operating conditions using XGBoost-based HI extraction and hybrid transformer-GRU model
10.1016/j.ress.2025.112042 · doi-reference
Short and long-term prognostics of the remaining useful life of a proton exchange membrane fuel cell using deep learning and transformer model
10.1016/j.ijhydene.2024.12.512 · doi-reference
Enhanced performance prediction for proton exchange membrane fuel cells: a comprehensive study with different load profiles
10.1016/j.ijhydene.2024.12.348 · doi-reference
A prediction method with uncertainty-aware for the remaining useful life and state of health of the proton exchange membrane fuel stack
10.1016/j.jpowsour.2025.238844 · doi-reference
A review on lifetime prediction of proton exchange membrane fuel cells system
10.1016/j.jpowsour.2022.231256 · doi-reference
Self-adaptive digital twin of fuel cell for remaining useful lifetime prediction
10.1016/j.ijhydene.2024.09.266 · doi-reference