Extract features from lithium-ion battery electrochemical impedance spectra and estimate state of health based on improved convolutional autoencoder-temporal convolutional network

Predicting the state of health (SOH) of batteries is crucial for understanding their remaining lifespan and formulating more effective maintenance and management strategies. Utilizing electrochemical impedance spectroscopy (EIS) to assess the health status of batteries offers advantages such as high...

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Vydáno v:	Ionics Ročník 31; číslo 5; s. 4261 - 4279
Hlavní autoři:	Lou, Cheng, Wang, Shi, Li, Zhaoting, Wang, Kai
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2025 Springer Nature B.V
Témata:	Aging Alternative energy Artificial neural networks Batteries Chemistry Chemistry and Materials Science Condensed Matter Physics Deep learning Electric vehicles Electrochemical impedance spectroscopy Electrochemistry Energy Storage Feature extraction Life span Lithium-ion batteries Machine learning Neural networks Optical and Electronic Materials Real time Renewable and Green Energy Spectrum analysis Time dependence Temporal convolutional network State of health Unsupervised feature extraction Electrochemical impedance spectroscopy
ISSN:	0947-7047, 1862-0760
On-line přístup:	Získat plný text
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