A5103243092- Fuel Cells and Related Materials
- Advanced Battery Technologies Research
- Dengue and Mosquito Control Research
- Data Stream Mining Techniques
- Smart Agriculture and AI
- Electrocatalysts for Energy Conversion
- Characterization and Applications of Magnetic Nanoparticles
- Reliability and Maintenance Optimization
- Power Transformer Diagnostics and Insulation
- Advanced Combustion Engine Technologies
- Hydrogels: synthesis, properties, applications
- Micro and Nano Robotics
- Risk and Safety Analysis
Wuhan University of Technology
2021-2024
Predicting the degradation behaviors is challenging and essential for prognostics health management proton exchange membrane fuel cells (PEMFCs). However, existing methods based on data-driven or model-based can face problem of significant performance inconsistencies in different prediction stages. We investigate cause attribute it to ignorance voltage recovery phenomena PEMFCs observed during frequent start-stop processes practical applications. A novel prognostic method proposed provide a...
Proton exchange membrane fuel cells (PEMFCs) are essential modern sustainable energy generation devices that have received extensive research attention in recent years. Since such an electrochemical system has a limited lifetime, accurately estimating its performance degradation is critical for practical applications. When large amount of measurement data available, many nonlinear forecasting methods can be used to predict the PEMFC system, and prediction accuracy improved by optimizing...
Accurately predicting the degradation trends of Proton Exchange Membrane Fuel Cells (PEMFC) can provide a solid basis for optimizing control vehicles and stations based on PEMFC. However, most prediction methods do not consider factors like measurement errors from experimental environments inherent cognitive uncertainty model. They only offer point estimates, lacking credibility. This paper introduces deep learning framework that combines an RNN model with Truncated Bayes by Backprop Through...