The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
State of Charge Estimation of Lithium-Ion Batteries in Electric Drive Vehicles Using Extended Kalman Filtering
Abstract
A more accurate battery state of charge (SOC) estimation method for electric drive vehicles is developed based on a nonlinear battery model and an extended Kalman filter (EKF) supported by experimental data. A nonlinear battery model is constructed by separating the model into a nonlinear open circuit voltage and a two-order resistance-capacitance model. EKF is used to eliminate the measurement and process noise and remove the need of prior knowledge of initial SOC. A hardware-in-the-loop test bench was built to validate the method. The experimental results show that the proposed method can estimate the battery SOC with high accuracy.
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Citations
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
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
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
Cited by
Hannan, M. A., Lipu, M. S. H., Hussain, A., & Mohamed, A. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.001 · Cited by 2,005
Waag, W., Fleischer, C., & Sauer, D. U. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.02.064 · Cited by 1,059
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
State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks
Chaoui, H., & Ibe-Ekeocha, C. C. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2715333 · Cited by 555
Towards the future of smart electric vehicles: Digital twin technology
Bhatti, G., Mohan, H., & Raja Singh, R. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.110801 · Cited by 533
Metadata
- Authors
- Chen, Z., Fu, Y., & Mi, C. C.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2013-03
- Volume
- 62
- Issue
- 3
- Pages
- 1020-1030
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2103480424
- Cited by
- 442
- References
- 3
- FWCI
- 28.30
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chen, Z., Fu, Y., & Mi, C. C. (2013). State of Charge Estimation of Lithium-Ion Batteries in Electric Drive Vehicles Using Extended Kalman Filtering. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/tvt.2012.2235474
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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