The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression
Abstract
Abstract To solve the problem of the inaccurate prediction on remaining useful life (RUL) for lithium-ion battery, we proposed an integrated algorithm which combines adaptive unscented kalman filter (AUKF) and genetic algorithm optimized support vector regression (GA-SVR). Firstly, the state space model with double exponential is established to describe the degradation of lithium battery. Then, the AUKF algorithm is introduced to update adaptively both the process noise covariance and the observation noise covariance. Next, the genetic algorithm is utilized to optimize the key parameters of SVR which realizes multi-step prediction. The effectiveness of the proposed method is verified by simulation experiments with NASA of battery dataset. Simulation results show that the proposed AUKF-GA-SVR achieves better prediction accuracy than existed methods such as unscented kalman filter, extended kalman filter, adaptive extended kalman filter (AEKF), adaptive unscented kalman filter, unscented kalman filter and relevance vector regression and AEKF-GA-SVR.
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Cites
Lithium Batteries: Status, Prospects and Future
Scrosati, B., & Garche, J. (2010) · Journal of Power Sources · doi:10.1016/j.jpowsour.2009.11.048 · Cited by 5,153
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
Lipu, M. S. H., Hannan, M. A., Hussain, A., Hoque, M. M., Ker, P. J., Saad, M. H. M., & Ayob, A. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.09.065 · Cited by 755
Wu, L., Fu, X., & Guan, Y. (2016) · Applied Sciences · doi:10.3390/app6060166 · Cited by 203
Cited by
Li, S., Fang, H., & Shi, B. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107542 · Cited by 190
An overview of data-driven battery health estimation technology for battery management system
Chen, M., Ma, G., Liu, W., Zeng, N., & Luo, X. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.02.031 · Cited by 172
Self-citationAging mechanisms, prognostics and management for lithium-ion batteries: Recent advances
Wang, Y., Xiang, H., Soo, Y.-Y., & Fan, X. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.114915 · Cited by 142
Multivariate Stacked Bidirectional Long Short Term Memory for Lithium-Ion Battery Health Management
Ardeshiri, R. R., Liu, M., & Ma, C. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108481 · Cited by 94
Early prediction of lithium-ion battery cycle life based on voltage-capacity discharge curves
Xiong, W., Xu, G., Li, Y., Zhang, F., Ye, P., & Li, B. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.106790 · Cited by 93
Metadata
- Authors
- Xue, Z., Zhang, Y., Cheng, C., & Ma, G.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 376
- Pages
- 95-102
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2980011983
- Cited by
- 440
- References
- 4
- FWCI
- 17.63
- Type
- journal article
- Language
- en
- Open access
- closed
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- Xue, Z., Zhang, Y., Cheng, C., & Ma, G. (2020). Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression. Neurocomputing. https://doi.org/10.1016/j.neucom.2019.09.074
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