New AI Model Predicts Fuel Cell Degradation Earlier, Cutting Maintenance Costs
Researchers have developed an artificial intelligence system that predicts when hydrogen fuel cells will fail—days or weeks in advance—by analyzing voltage patterns that conventional methods miss. The breakthrough could slash maintenance spending for truck makers and transit agencies by catching problems before they cause breakdowns.
Originaltitel: Low-frequency Consensus knowledge Transfer in PEM Fuel Cells for Cross-Domain Online Voltage Degradation Prediction
Traditional time–frequency domain methods face critical limitations in predicting voltage degradation of proton exchange membrane fuel cells (PEMFCs). Time-domain models struggle to robustly separate long-term degradation-related low-frequency trends from contaminated voltage signals under highly dynamic and non-stationary conditions, while conventional frequency-domain analysis loses essential time-localized information during feature extraction. Both approaches exhibit significantly degraded prediction performance under limited data conditions. To overcome these challenges, this paper proposes a time–frequency fusion algorithm that integrates TimesNet with long short-term memory (LSTM), effectively combining 2D frequency-domain representations with 1D temporal memory to enhance voltage degradation prediction under dynamic conditions. Based on the capability of TimesNet-LSTM to extract low-frequency voltage features, a transfer learning technique grounded in low-frequency consensus knowledge (LCK-TL) is further developed. By selectively transferring low-frequency voltage features that robustly reflect aging patterns, LCK-TL considerably reduces distribution discrepancy between source and target domains, achieving joint optimization of predictive modeling and transfer mechanisms. Leveraging the inherently low computational cost of transfer learning, LCK-TL enables rapid multi-step predictions while maintaining accuracy, providing effective guidance for cross-device and cross-condition fuel cell health management. • 2D frequency analysis integrated with 1D temporal memory for PEMFC prognostics. • Co-optimization framework integrating prognostics and transfer learning. • Domain discrepancy reduction via low-frequency voltage characteristics. • Online cross-domain knowledge transfer for accurate PEMFC degradation prediction.