Optimal parameters estimation of lithium-ion battery in smart grid applications based on gazelle optimization algorithm DOI
Hany M. Hasanien, Ibrahim Alsaleh, Marcos Tostado‐Véliz

et al.

Energy, Journal Year: 2023, Volume and Issue: 285, P. 129509 - 129509

Published: Oct. 30, 2023

Language: Английский

Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine DOI
Te Han, Wenzhen Xie, Zhongyi Pei

et al.

Information Sciences, Journal Year: 2023, Volume and Issue: 648, P. 119496 - 119496

Published: Aug. 14, 2023

Language: Английский

Citations

124
Fault transfer diagnosis of rolling bearings across multiple working conditions via subdomain adaptation and improved vision transformer network DOI
Pengfei Liang,

Zhuoze Yu,

Bin Wang

et al.

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 57, P. 102075 - 102075

Published: June 27, 2023

Language: Английский

Citations

77
WavCapsNet: An Interpretable Intelligent Compound Fault Diagnosis Method by Backward Tracking DOI
Weihua Li, Hao Lan, Junbin Chen

et al.

IEEE Transactions on Instrumentation and Measurement, Journal Year: 2023, Volume and Issue: 72, P. 1 - 11

Published: Jan. 1, 2023

With significant advantages in feature learning, the deep learning based compound fault diagnosis method has brought many successful applications for industrial equipment. However, few studies focus on interpretability of intelligent methods, and results are hard to interpret which prevents wide application these methods practical scenarios. To solve above challenging problems, an interpretable framework, called wavelet capsule network (WavCapsNet), is proposed machinery by leveraging backward tracking technique. First, WavCapsNet constructed with a kernel convolutional layer employed learn features meaning from vibration signals, two layers endow model ability decouple intelligently. Second, trained optimized normal single samples (without samples). Finally, analysis launched coupling matrices layers, focused relationship between learned different health conditions. The experimental five-speed transmission dataset show that method, compared other not only achieves higher decoupling accuracy under scenario incomplete data but also improves transparency decision-making process diagnosis.

Language: Английский

Citations

58
Generalized open-set domain adaptation in mechanical fault diagnosis using multiple metric weighting learning network DOI
Zhuyun Chen, Jingyan Xia, Jipu Li

et al.

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 57, P. 102033 - 102033

Published: June 13, 2023

Language: Английский

Citations

52
Machine learning for battery systems applications: Progress, challenges, and opportunities DOI
Zahra Nozarijouybari, Hosam K. Fathy

Journal of Power Sources, Journal Year: 2024, Volume and Issue: 601, P. 234272 - 234272

Published: March 7, 2024

Language: Английский

Citations

31
Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems DOI
Jiachi Yao, Zhonghao Chang, Te Han

et al.

Energy, Journal Year: 2024, Volume and Issue: 294, P. 130882 - 130882

Published: March 2, 2024

Language: Английский

Citations

30
Uncertainty-aware deep learning for reliable health monitoring in safety-critical energy systems DOI
Yuantao Yao, Te Han,

Jie Yu

et al.

Energy, Journal Year: 2024, Volume and Issue: 291, P. 130419 - 130419

Published: Jan. 21, 2024

Language: Английский

Citations

27
Recent advancement of remaining useful life prediction of lithium-ion battery in electric vehicle applications: A review of modelling mechanisms, network configurations, factors, and outstanding issues DOI Creative Commons
M. S. Reza,

M. Mannan,

Muhamad Mansor

et al.

Energy Reports, Journal Year: 2024, Volume and Issue: 11, P. 4824 - 4848

Published: April 30, 2024

The remaining useful life (RUL) prediction of lithium-ion batteries (LIBs) plays a crucial role in battery management, safety assurance, and the anticipation maintenance needs for reliable electric vehicle (EV) operation. An efficient RUL can ensure its safe operation prevent both internal external failures, as well avoid any unwanted catastrophic events. However, achieving precise vehicles presents challenging task due to several issues related intricate operational characteristics dynamic shifts model parameters throughout aging process, data extraction, preprocessing, hyperparameters tuning model. This phenomenon significantly impacts advancement technology. To address these challenges, this study offers comprehensive overview various methods, presenting comparative analysis their outcomes, advantages, drawbacks, associated research constraints. Emphasis is placed on necessity management system (BMS) functioning LIBs. review delves into implementation factors, including test bench considerations, selection, feature performance evaluation indicators, hyperparameter tuning. Additionally, challenges approaches such as; thermal runaway, material cell balancing, aging, relaxation impact, training algorithms, acquisition, were outlined provide an in-depth understanding recent situations. outcome comprehensively examines methods predicting LIB EV applications, offering insights limitations, challenges. Recommendations future trends LIBs technology comprise enhancing prognostic accuracy developing robust guarantee sustainable management.

Language: Английский

Citations

22
Exploiting domain knowledge to reduce data requirements for battery health monitoring DOI
Jinpeng Tian, Liang Ma, Tieling Zhang

et al.

Energy storage materials, Journal Year: 2024, Volume and Issue: 67, P. 103270 - 103270

Published: Feb. 15, 2024

Language: Английский

Citations

17
Physics-informed machine learning for battery degradation diagnostics: A comparison of state-of-the-art methods DOI Creative Commons
Sina Navidi, Adam Thelen, Tingkai Li

et al.

Energy storage materials, Journal Year: 2024, Volume and Issue: 68, P. 103343 - 103343

Published: March 18, 2024

Language: Английский

Citations

17