[HTML][HTML] Deep learning-based state-of-health estimation of proton-exchange membrane fuel cells under dynamic operation conditions
Y Zhang, X Tang, S Xu, C Sun - Sensors, 2024 - mdpi.com
Proton-exchange membrane fuel cells (PEMFCs) play a crucial role in the transition to
sustainable energy systems. Accurately estimating the state of health (SOH) of PEMFCs …
sustainable energy systems. Accurately estimating the state of health (SOH) of PEMFCs …
Generative adversarial networks for stack voltage degradation and RUL estimation in PEMFCs under static and dynamic loads
Accurately predicting the degradation and remaining useful life (RUL) of Proton Exchange
Membrane Fuel Cells (PEMFCs) is crucial for enhancing their reliability, particularly in …
Membrane Fuel Cells (PEMFCs) is crucial for enhancing their reliability, particularly in …
Consistency prediction and analysis of fuel cells based on relative deviation
J Qin, Y Hou, R Gu, D Jiao, Q Yang - International Journal of Hydrogen …, 2024 - Elsevier
This paper presents a predictive analysis of individual cell consistency within a fuel cell
stack, grounded in the concept of relative deviation. Relative deviation and its corresponding …
stack, grounded in the concept of relative deviation. Relative deviation and its corresponding …
Empirical model, capacity recovery-identification correction and machine learning co-driven Li-ion battery remaining useful life prediction
Z Lv, Z Chen, P Wang, C Wang, R Di, X Li… - Journal of Energy …, 2024 - Elsevier
Li-ion battery is the most important energy storage and conversion device. RUL prediction,
as an important part of the battery health management system, provides important …
as an important part of the battery health management system, provides important …
An overview of artificial intelligence-based techniques for PEMFC system diagnosis
Proton Exchange Membrane Fuel Cell (PEMFC) systems represent a crucial clean energy
component, offering a sustainable alternative to traditional power sources. However …
component, offering a sustainable alternative to traditional power sources. However …
DDTCN: Decomposed dimension time-domain convolutional neural network along spatial dimensions for multiple long-term series forecasting
K Zheng, J Wang, Y Chen, R Jiang, W Wang - Applied Intelligence, 2024 - Springer
Time series analysis is widely applied in action recognition, anomaly detection, and weather
forecasting. Time series forecasting remains a key challenge due to the complexity of …
forecasting. Time series forecasting remains a key challenge due to the complexity of …
Comprehensive sensitivity and mechanism analysis of fuel cell performance under varying operating conditions using RF–Sobol–DRT approach
B Liang, H Wei, M Shen, Y Gao, T Zhang… - Energy Conversion and …, 2025 - Elsevier
Fuel cell performance is significantly influenced by operating conditions. Optimizing these
conditions can enhance efficiency and extend lifespan. This study introduces a novel …
conditions can enhance efficiency and extend lifespan. This study introduces a novel …
Multi-step fusion model for predicting indoor temperature in residential buildings based on attention mechanism and neural network
G Zheng, R Jia, W Yi, X Yue - Journal of Building Engineering, 2025 - Elsevier
Indoor temperature prediction is vital in HVAC system control, ensuring thermal comfort and
energy efficiency. This study aims to propose a multi-step indoor temperature prediction …
energy efficiency. This study aims to propose a multi-step indoor temperature prediction …
A Reversible Degradation Adaptive Avoidance-Driven Echo State Network: Application to Fuel Cell Prognostics
S Zhang, C Wang, Z Chen, Z Lv, X Li… - 2024 IEEE 7th …, 2024 - ieeexplore.ieee.org
Inadequate stack durability limits the wide commercialization of fuel cell vehicles. A crucial
prerequisite in enhancing fuel cell stack durability is the accurate prediction of their …
prerequisite in enhancing fuel cell stack durability is the accurate prediction of their …