The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error
Open accessAbstract
Remaining useful life (RUL) prediction is central to the prognostics and health management (PHM) of lithium-ion batteries. This paper proposes a novel RUL prediction method for lithium-ion batteries based on the Wiener process with measurement error (WPME). First, we use the truncated normal distribution (TND) based modeling approach for the estimated degradation state and obtain an exact and closed-form RUL distribution by simultaneously considering the measurement uncertainty and the distribution of the estimated drift parameter. Then, the traditional maximum likelihood estimation (MLE) method for population based parameters estimation is remedied to improve the estimation efficiency. Additionally, we analyze the relationship between the classic MLE method and the combination of the Bayesian updating algorithm and the expectation maximization algorithm for the real time RUL prediction. Interestingly, it is found that the result of the combination algorithm is equal to the classic MLE method. Inspired by this observation, a heuristic algorithm for the real time parameters updating is presented. Finally, numerical examples and a case study of lithium-ion batteries are provided to substantiate the superiority of the proposed RUL prediction method.
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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
Prognostics methods for battery health monitoring using a bayesian framework
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2008.2005965 · Cited by 831
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
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×
“Failure of lithium-ion batteries could lead to performance degradation, operational impairment, and even catastrophic failure [3–5].”
BackgroundLong, B., Xian, W., Jiang, L., & Liu, Z. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2013.01.006 · Cited by 244
Cited by
Hu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule · doi:10.1016/j.joule.2019.11.018 · Cited by 1,320
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
Wei, J., Dong, G., & Chen, Z. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2782224 · Cited by 643
Wu, J., Zhang, C., & Chen, Z. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.04.057 · Cited by 528
RUL Prediction of Deteriorating Products Using an Adaptive Wiener Process Model
Zhai, Q., & Ye, Z.-S. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2684821 · Cited by 363
Metadata
- Authors
- Tang, S., Yu, C., Wang, X., Guo, X., & Si, X.
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2014-01-23
- Volume
- 7
- Issue
- 2
- Pages
- 520-547
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W1981460882
- Cited by
- 277
- References
- 6
- FWCI
- 47.94
- Type
- journal article
- Language
- en
- Open access
- gold
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