The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine
Abstract
An accurate algorithm for lithium polymer battery state-of-charge (SOC) estimation is proposed based on adaptive unscented Kalman filters (AUKF) and least-square support vector machines (LSSVM). A novel approach using the moving window method is applied, with AUKF and LSSVM to accurately establish the battery model with limited initial training samples. The effectiveness of the moving window modeling method is validated by both simulations and lithium polymer battery experimental results. The measurement equation of the proposed AUKF method is established by the LSSVM battery model and AUKF has the advantage of adaptively adjusting noise covariance during the estimation process. In addition, the developed LSSVM model is continuously updated online with new samples during the battery operation, in order to minimize the influence of the changes in battery internal characteristics on modeling accuracy and estimation results after a period of operation. Finally, a comparison of accuracy and performance between the AUKF and UKF is made. Simulation and experiment results indicate that the proposed algorithm is capable of predicting lithium battery SOC with a limited number of initial training samples.
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Cites
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Support Vector Machines Used to Estimate the Battery State of Charge
Álvarez Antón, J. C., García Nieto, P. J., Blanco Viejo, C., & Vilán Vilán, J. A. (2013) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2013.2243918 · Cited by 512
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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
Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries
Chemali, E., Kollmeyer, P. J., Preindl, M., Ahmed, R., & Emadi, A. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2787586 · Cited by 906
State estimation for advanced battery management: Key challenges and future trends
Hu, X., Feng, F., Liu, K., Zhang, L., Xie, J., & Liu, B. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109334 · Cited by 857
Chemali, E., Kollmeyer, P. J., Preindl, M., & Emadi, A. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.06.104 · Cited by 818
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
Metadata
- Authors
- Meng, J., Luo, G., & Gao, F.
- Venue
- IEEE Transactions on Power Electronics
- Published
- 2016-03
- Volume
- 31
- Issue
- 3
- Pages
- 2226-2238
- ISSN
- 0885-8993 (ISSN-L)
- 1941-0107
- OpenAlex
- W2113757908
- Cited by
- 349
- References
- 36
- FWCI
- 23.56
- Type
- journal article
- Language
- en
- Open access
- closed
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- Meng, J., Luo, G., & Gao, F. (2016). Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine. IEEE Transactions on Power Electronics. https://doi.org/10.1109/tpel.2015.2439578
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