The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Robust Adaptive Sliding-Mode Observer Using RBF Neural Network for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles
Abstract
This paper presents a robust sliding-mode observer (RSMO) for state-of-charge (SOC) estimation of a lithium-polymer battery (LiPB) in electric vehicles (EVs). A radial basis function (RBF) neural network (NN) is employed to adaptively learn an upper bound of system uncertainty. The switching gain of the RSMO is adjusted based on the learned upper bound to achieve asymptotic error convergence of the SOC estimation. A battery equivalent circuit model (BECM) is constructed for battery modeling, and its BECM is identified in real time by using a forgetting-factor recursive least squares (FFRLS) algorithm. The experiments under the discharge current profiles based on EV driving cycles are conducted on the LiPB to validate the effectiveness and accuracy of the proposed framework for the SOC estimation.
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Cites
Lithium Batteries: Status, Prospects and Future
Scrosati, B., & Garche, J. (2010) · Journal of Power Sources · doi:10.1016/j.jpowsour.2009.11.048 · Cited by 5,153
A review on the key issues for lithium-ion battery management in electric vehicles
Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.060 · Cited by 4,837
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
A comparative study of equivalent circuit models for Li-ion batteries
Hu, X., Li, S., & Peng, H. (2012) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.10.013 · Cited by 2,179
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
The novel state of charge estimation method for lithium battery using sliding mode observer
Kim, I.-S. (2006) · Journal of Power Sources · doi:10.1016/j.jpowsour.2006.09.006 · Cited by 359
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
Vidal, C., Malysz, P., Kollmeyer, P., & Emadi, A. (2020) · IEEE Access · doi:10.1109/access.2020.2980961 · Cited by 512
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
Hossain Lipu, M. S., Hannan, M. A., Hussain, A., Ayob, A., Saad, M. H. M., Karim, T. F., & How, D. N. T. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.124110 · Cited by 378
Selvaraj, V., & Vairavasundaram, I. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108777 · Cited by 220
Metadata
- Authors
- Chen, X., Shen, W., Dai, M., Cao, Z., Jin, J., & Kapoor, A.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2016-04
- Volume
- 65
- Issue
- 4
- Pages
- 1936-1947
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2341585352
- Cited by
- 207
- References
- 6
- FWCI
- 11.95
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chen, X., Shen, W., Dai, M., Cao, Z., Jin, J., & Kapoor, A. (2016). Robust Adaptive Sliding-Mode Observer Using RBF Neural Network for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/tvt.2015.2427659
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