Robust remaining useful life prediction of lithium-ion battery with convolutional denoising autoencoder

Robust remaining useful life prediction of lithium-ion battery with convolutional denoising autoencoder

Using lithium-ion (Li-ion) batteries exceeding their useful lifetime may be dangerous for users, and hence, developing an accurate prediction system for batteries that remain useful for life is necessary. Many deep learning models, such as gated recurrent units and long short-term memory (LSTM), hav...

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Bibliographic Details
Main Authors: Asri Rizki Yuliani, Hilman Ferdinandus Pardede, Ade Ramdan, Vicky Zilvan, Raden Sandra Yuwana, M Faizal Amri, R. Budiarianto Suryo Kusumo, Subrata Pramanik
Format: Article
Language:English
Published: Indonesian Institute of Sciences 2024-07-01
Series:Journal of Mechatronics, Electrical Power, and Vehicular Technology
Subjects:
denoising autoencoder (dae)
lithium-ion (li-ion) battery
neural network
remaining useful life (rul)
system robustness.
Online Access:https://mev.brin.go.id/mev/article/view/905
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