Journal
IEEE ACCESS
Volume 6, Issue -, Pages 50587-50598Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2858856
Keywords
Lithium-ion battery; remaining useful life; RUL prediction model; deep learning; deep neural network
Categories
Funding
- National Natural Science Foundation of China [61572057, 61773001]
- National Key Research and Development Program of China [2018YFB1004001]
- Beijing Natural Science Foundation-Rail Traffic Control Science and Technology Joint Fund [16L10010]
- Beijing Natural Science Foundation [172023]
- Fundamental Research Funds for the Central Universities [YWF-17-BJ-Y-83]
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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.
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