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Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach

Ren, Lei; Zhao, Li; Hong, Sheng; Zhao, Shiqiang; Wang, Hao; Zhang, Lin
Journal article, Peer reviewed
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Ren.pdf (8.741Mb)
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http://hdl.handle.net/11250/2616945
Utgivelsesdato
2018
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  • Publikasjoner fra CRIStin - NTNU [41946]
Originalversjon
IEEE Access. 2018, 6 50587-50598.   10.1109/ACCESS.2018.2858856
Sammendrag
Accurate prediction of remaining useful life (RUL) of lithium-ion battery plays an increasingly crucial role in the intelligent battery health management systems. The advances in deep learning introduce new data-driven approaches to this problem. This paper proposes an integrated deep learning approach for RUL prediction of lithium-ion battery by integrating autoencoder with deep neural network (DNN). First, we present a multi-dimensional feature extraction method with autoencoder model to represent battery health degradation. Then, the RUL prediction model-based DNN is trained for multi-battery remaining cycle life estimation. The proposed approach is applied to the real data set of lithium-ion battery cycle life from NASA, and the experiment results show that the proposed approach can improve the accuracy of RUL prediction.
Utgiver
Institute of Electrical and Electronics Engineers (IEEE)
Tidsskrift
IEEE Access

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