The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach
Open accessAbstract
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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Cites
He, W., Williard, N., Osterman, M., & Pecht, M. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.08.040 · Cited by 1,105
A review on prognostics and health monitoring of Li-ion battery
Zhang, J., & Lee, J. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.03.101 · Cited by 792
Importance Weighted Autoencoders
Burda, Y., Grosse, R., & Salakhutdinov, R. (2015) · arXiv · doi:10.48550/arxiv.1509.00519 · Cited by 644
Prognostics in Battery Health Management
Goebel, K., Saha, B., Saxena, A., Celaya, J., & Christophersen, J. (2008) · IEEE Instrumentation & Measurement Magazine · doi:10.1109/mim.2008.4579269 · Cited by 594
Battery management systems in electric and hybrid vehicles
Xing, Y., Ma, E. W. M., Tsui, K. L., & Pecht, M. (2011) · Energies · doi:10.3390/en4111840 · Cited by 540 · cited 1×
“As a key power source for a variety of industrial systems, lithium-ion battery defects often lead to fatal system failures [8].”
BackgroundAn Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries
Liu, J., Saxena, A., Goebel, K., Saha, B., & Wang, W. (2010) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2010.v2i1.1896 · Cited by 274
Hu, X., Cao, D., & Egardt, B. (2018) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2017.2675920 · Cited by 205
Cited by
Predicting the state of charge and health of batteries using data-driven machine learning
Ng, M.-F., Zhao, J., Yan, Q., Conduit, G. J., & Seh, Z. W. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0156-7 · Cited by 793
A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life
Ren, L., Dong, J., Wang, X., Meng, Z., Zhao, L., & Deen, M. J. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3008223 · Cited by 690
Self-citationArtificial Intelligence Applied to Battery Research: Hype or Reality?
Lombardo, T., Duquesnoy, M., El-Bouysidy, H., Årén, F., Gallo-Bueno, A., Jørgensen, P. B., Bhowmik, A., Demortière, A., Ayerbe, E., Alcaide, F., et al. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00108 · Cited by 503
Ferreira, C., & Gonçalves, G. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.05.010 · Cited by 417
Lv, C., Zhou, X., Zhong, L., Yan, C., Srinivasan, M., Seh, Z. W., Liu, C., Pan, H., Li, S., Wen, Y., et al. (2022) · Advanced Materials · doi:10.1002/adma.202101474 · Cited by 412
Metadata
- Authors
- Ren, L., Zhao, L., Hong, S., Zhao, S., Wang, H., & Zhang, L.
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 50587-50598
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2883525675
- Cited by
- 433
- References
- 7
- FWCI
- 16.69
- Type
- journal article
- Language
- en
- Open access
- gold
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