A5026651455- Fuel Cells and Related Materials
- Electrocatalysts for Energy Conversion
- Advancements in Solid Oxide Fuel Cells
- Advanced Battery Technologies Research
- Advanced battery technologies research
- Membrane-based Ion Separation Techniques
- Catalytic Processes in Materials Science
- Electric and Hybrid Vehicle Technologies
- Advancements in Battery Materials
- CO2 Reduction Techniques and Catalysts
- Conducting polymers and applications
- Industrial Technology and Control Systems
- Membrane Separation Technologies
- Advanced Algorithms and Applications
- Advanced Measurement and Detection Methods
- Hybrid Renewable Energy Systems
- Molecular Junctions and Nanostructures
- Lubricants and Their Additives
- Fault Detection and Control Systems
- Catalysis and Hydrodesulfurization Studies
- Energy, Environment, and Transportation Policies
- Advanced Photocatalysis Techniques
- Electric Vehicles and Infrastructure
- Advanced Thermoelectric Materials and Devices
- Mechanical stress and fatigue analysis
Qingdao University of Science and Technology
2023
Cell Technology (China)
2017-2018
Xi'an Jiaotong University
2012
Young Invincibles
2005
Dalian Institute of Chemical Physics
2001-2004
Chinese Academy of Sciences
2002-2004
Flooding fault diagnosis is critical to the stable and efficient operation of fuel cells, while on-board embedded controller has limited computing power sensors, making it difficult incorporate complex gas-liquid two-phase flow models. Then in cell system for cars, neural network modeling usually regarded as an appropriate tool on-line water status. Traditional classifiers are not good at processing time series data, so this paper, Long Short-Term Memory (LSTM) model developed applied...
As a high efficiency hydrogen-to-power device, proton exchange membrane fuel cell (PEMFC) attracts much attention, especially for the automotive applications. Real-time prediction of output voltage and area specific resistance (ASR) via on-board model is critical to monitor health state PEMFC stack. In this study, we use transient system dynamic process simulation generate dataset, long short-term memory (LSTM) deep learning developed predict performance PEMFC. The results show that LSTM has...
Fault diagnosis is a critical process for the reliability and durability of proton exchange membrane fuel cells (PEMFCs). Due to complexity internal transport processes inside PEMFCs, developing an accurate model considering various failure mechanisms extremely difficult. In this paper, novel data-driven approach based on sensor pre-selection artificial neural network (ANN) are proposed. Firstly, features data in time-domain frequency-domain extracted sensitivity analysis. The sensors with...
Data-driven modelling methods are being developed in the quest to achieve more accurate performance prediction of protons exchange membrane fuel cell (PEMFC) systems response their complicated physicochemical phenomena. However, there is little research this field detailing pre-processing and selection balance plants (BOP) features for input layer system at different current densities. Furthermore, most previous applies neural networks based on simulation data rather than real-time bench or...
Significant performance enhancement in nanofiber-based PEMFCs featuring highly O 2 permeable ionomer films, proton-conductive fibers, and increased active sites.
Recently, the introduction of external fields (light, thermal, magnetism, etc.) during electrocatalysis reactions gradually becomes a new strategy to modulate catalytic activities. In this work, an magnetic field was innovatively employed for synthesis progress (Ni, Zn)Fe2O4 spinel oxide (M-(Ni, Zn)Fe2O4). Results indicated (≤250 mT) would affect morphology catalyst due existing Fe ions, inducing M-(Ni, nanosphere particles be uniform and coral-like nanorods. addition, electronic structure...