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
- Electric and Hybrid Vehicle Technologies
- Catalytic Processes in Materials Science
- Advancements in Battery Materials
- Industrial Technology and Control Systems
- Conducting polymers and applications
- Membrane Separation Technologies
- CO2 Reduction Techniques and Catalysts
- Catalysis and Hydrodesulfurization Studies
- Energy, Environment, and Transportation Policies
- Engineering Applied Research
- Advanced Photocatalysis Techniques
- Molecular Junctions and Nanostructures
- Nanomaterials for catalytic reactions
- Mechanical stress and fatigue analysis
- Advanced Algorithms and Applications
- Advanced Measurement and Detection Methods
- Advanced Thermoelectric Materials and Devices
- Hybrid Renewable Energy Systems
- Analog and Mixed-Signal Circuit Design
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...
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...
Significant performance enhancement in nanofiber-based PEMFCs featuring highly O 2 permeable ionomer films, proton-conductive fibers, and increased active sites.