The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
State of Charge Estimation of Battery Energy Storage Systems Based on Adaptive Unscented Kalman Filter With a Noise Statistics Estimator
Open accessAbstract
Since the noise statistics of large-scale battery energy storage systems (BESSs) are often unknown or inaccurate in actual applications, the estimation precision of state of charge (SOC) of BESSs using extended Kalman filter (EKF) or unscented Kalman filter (UKF) is usually inaccurate or even divergent. To resolve this problem, a method based on adaptive UKF (AUKF) with a noise statistics estimator is proposed to estimate accurately SOC of BESSs. The noise statistics estimator based on the modified Sage-Husa maximum posterior is aimed to estimate adaptively the mean and error covariance of measurement and system process noises online for the AUKF when the prior noise statistics are unknown or inaccurate. The accuracy and adaptation of the proposed method is validated by the comparison with the UKF and EKF under different real-time conditions. The comparison shows that the proposed method can achieve better SOC estimation accuracy when the noise statistics of BESSs are unknown or inaccurate.
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Cites
Accurate electrical battery model capable of predicting runtime and I-V performance
Chen, M., & Rincon-Mora, G. A. (2006) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2006.874229 · Cited by 2,427
Sun, F., Hu, X., Zou, Y., & Li, S. (2011) · Energy · doi:10.1016/j.energy.2011.03.059 · Cited by 614
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
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Self-citation
Cited by
Shrivastava, P., Soon, T. K., Idris, M. Y. I. B., & Mekhilef, S. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.06.040 · Cited by 704
Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles
Espedal, I. B., Jinasena, A., Burheim, O. S., & Lamb, J. J. (2021) · Energies · doi:10.3390/en14113284 · Cited by 177
Robustness Evaluation of Extended and Unscented Kalman Filter for Battery State of Charge Estimation
Huang, C., Wang, Z., Zhao, Z., Wang, L., Lai, C. S., & Wang, D. (2018) · IEEE Access · doi:10.1109/access.2018.2833858 · Cited by 140
Peng, S., Miao, Y., Xiong, R., Bai, J., Cheng, M., & Pecht, M. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.122807 · Cited by 109
Self-citationEstimation of state of charge for lithium-ion batteries - A Review
A.M.S.M.H.S.Attanayaka, Department of Electrical Engineering, U. O. M., J.P.Karunadasa, & K.T.M.U.Hemapala (2019) · AIMS Energy · doi:10.3934/energy.2019.2.186 · Cited by 72
Metadata
- Authors
- Peng, S., Chen, C., Shi, H., & Yao, Z.
- Venue
- IEEE Access
- Published
- 2017
- Volume
- 5
- Pages
- 13202-13212
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2735663899
- Cited by
- 180
- References
- 4
- FWCI
- 12.25
- Type
- journal article
- Language
- en
- Open access
- gold
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- Peng, S., Chen, C., Shi, H., & Yao, Z. (2017). State of Charge Estimation of Battery Energy Storage Systems Based on Adaptive Unscented Kalman Filter With a Noise Statistics Estimator. IEEE Access. https://doi.org/10.1109/access.2017.2725301
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