The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer
Open accessAbstract
In order to safely and efficiently use the power as well as to extend the lifetime of the traction battery pack, accurate estimation of State of Charge (SoC) is very important and necessary. This paper presents an adaptive observer-based technique for estimating SoC of a lithium-ion battery pack used in an electric vehicle (EV). The RC equivalent circuit model in ADVISOR is applied to simulate the lithium-ion battery pack. The parameters of the battery model as a function of SoC, are identified and optimized using the numerically nonlinear least squares algorithm, based on an experimental data set. By means of the optimized model, an adaptive Luenberger observer is built to estimate online the SoC of the lithium-ion battery pack. The observer gain is adaptively adjusted using a stochastic gradient approach so as to reduce the error between the estimated battery output voltage and the filtered battery terminal voltage measurement. Validation results show that the proposed technique can accurately estimate SoC of the lithium-ion battery pack without a heavy computational load.
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Citations
Cites
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.033 · Cited by 1,768
Observers for multivariable systems
Luenberger, D. (1966) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1966.1098323 · Cited by 1,763
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.031 · Cited by 1,385
ADAPTIVE SIGNAL PROCESSING FOR ADAPTIVE CONTROL
Widrow, B., & Walach, E. (1984) · Adaptive Systems in Control and Signal Processing 1983 · doi:10.1016/b978-0-08-030565-3.50007-1 · Cited by 76
Cited by
Waag, W., Fleischer, C., & Sauer, D. U. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.02.064 · Cited by 1,059
Zheng, F., Xing, Y., Jiang, J., Sun, B., Kim, J., & Pecht, M. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.09.010 · Cited by 574
Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles
Hu, X., Li, S. E., & Yang, Y. (2016) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2015.2512237 · Cited by 375
Self-citationThe State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer
Xu, J., Mi, C. C., Cao, B., Deng, J., Chen, Z., & Li, S. (2014) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2013.2287375 · Cited by 367
Selvaraj, V., & Vairavasundaram, I. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108777 · Cited by 220
Metadata
- Authors
- Hu, X., Sun, F., & Zou, Y.
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2010-09-09
- Volume
- 3
- Issue
- 9
- Pages
- 1586-1603
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W2080478703
- Cited by
- 281
- References
- 4
- FWCI
- 18.97
- Type
- journal article
- Language
- en
- Open access
- gold
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- Hu, X., Sun, F., & Zou, Y. (2010). Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer. Energies. https://doi.org/10.3390/en3091586
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