Overall flowchart of the proposed methodology.

<div><p>Lithium-ion batteries are high-performance energy storage devices that have been widely used in a variety of applications. Accurate early-stage prediction of their remaining useful life is essential for preventing failures and mitigating safety risks. This study proposes a novel...

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Bibliographic Details
Main Author:	Peijian Jin (22265437) (author)
Other Authors:	Shuo Yang (209399) (author), Xinwan Xu (22331819) (author), Chaoqun Li (104301) (author), Shihao Guo (5136650) (author), Wei Yan (4327) (author), Hui Miao (143177) (author), Shimei Sun (1427905) (author)
Published:	2025
Subjects:	Neuroscience Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified remaining useful life mitigating safety risks fully connected layer frequency image fusion channel color images capture spatial dependencies novel multiview approach model temporal dependencies frequency images using extract spatial features xlink "> lithium time &# 8211 final soh estimate convolutional neural network raw temporal features stage attention mechanism three representative features frequency features spatial correlations grayscale images stage prediction widely used wavelet transform term memory study proposes preventing failures multiview state meaningful contribution long short ion batteries integrating time component makes comparative experiments accurate early
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Overall flowchart of the proposed methodology.

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