The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
An Adaptive Unscented Kalman Filtering Approach for Online Estimation of Model Parameters and State-of-Charge of Lithium-Ion Batteries for Autonomous Mobile Robots
Abstract
In this brief, to get a more accurate and robust state of charge (SoC) estimation, the lithium-ion battery model parameters are identified using an adaptive unscented Kalman filtering method, and based on the updated model, the battery SoC is estimated consequently. An adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the unscented Kalman filter (UKF) context. The effectiveness of the proposed method is evaluated through experiments under different power duties in the laboratory environment. The obtained results are compared with that of the adaptive extended Kalman filter, extended Kalman filter, and unscented Kalman filter-based algorithms. The comparison shows that the proposed method provides better accuracy both in battery model parameters estimation and the battery SoC estimation.
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Citations
Cites
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
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Methods for state-of-charge determination and their applications
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Adaptive Kalman Filtering for INS/GPS
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State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF
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On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems
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Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering
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Cited by
Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., & Chen, Z. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110015 · Cited by 1,377
Zheng, Y., Ouyang, M., Han, X., Lu, L., & Li, J. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.11.094 · Cited by 490
SoC Estimation for Lithium-ion Batteries: Review and Future Challenges
Rivera-Barrera, J., Muñoz-Galeano, N., & Sarmiento-Maldonado, H. (2017) · Electronics · doi:10.3390/electronics6040102 · Cited by 447
Xiong, R., Yu, Q., Wang, L. Y., & Lin, C. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.05.136 · Cited by 293
Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles
Li, X., Wang, Z., & Zhang, L. (2019) · Energy · doi:10.1016/j.energy.2019.02.147 · Cited by 264
Metadata
- Authors
- Partovibakhsh, M., & Liu, G.
- Venue
- IEEE Transactions on Control Systems Technology
- Published
- 2015-01
- Volume
- 23
- Issue
- 1
- Pages
- 357-363
- ISSN
- 1063-6536 (ISSN-L)
- 1558-0865
- OpenAlex
- W2058795621
- Cited by
- 301
- References
- 25
- FWCI
- 24.10
- Type
- journal article
- Language
- en
- Open access
- closed
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- Partovibakhsh, M., & Liu, G. (2015). An Adaptive Unscented Kalman Filtering Approach for Online Estimation of Model Parameters and State-of-Charge of Lithium-Ion Batteries for Autonomous Mobile Robots. IEEE Transactions on Control Systems Technology. https://doi.org/10.1109/tcst.2014.2317781
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