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 for Lithium-Ion Batteries Using Neural Networks and EKF
Abstract
This paper presents a method for modeling and estimation of the state of charge (SOC) of lithium-ion (Li-Ion) batteries using neural networks (NNs) and the extended Kalman filter (EKF). The NN is trained offline using the data collected from the battery-charging process. This network finds the model needed in the state-space equations of the EKF, where the state variables are the battery terminal voltage at the previous sample and the SOC at the present sample. Furthermore, the covariance matrix for the process noise in the EKF is estimated adaptively. The proposed method is implemented on a Li-Ion battery to estimate online the actual SOC of the battery. Experimental results show a good estimation of the SOC and fast convergence of the EKF state variables.
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Citations
Cites
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
Huet, F. (1998) · Journal of Power Sources · doi:10.1016/s0378-7753(97)02665-7 · Cited by 593
Fuzzy logic modelling of state-of-charge and available capacity of nickel/metal hydride batteries
Singh, P., Fennie, C., & Reisner, D. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.03.035 · Cited by 117
Cited by
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
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
A review on lithium-ion battery ageing mechanisms and estimations for automotive applications
Barré, A., Deguilhem, B., Grolleau, S., Gérard, M., Suard, F., & Riu, D. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.05.040 · Cited by 1,758
Materials discovery and design using machine learning
Liu, Y., Zhao, T., Ju, W., & Shi, S. (2017) · Journal of Materiomics · doi:10.1016/j.jmat.2017.08.002 · Cited by 1,276
Jaguemont, J., Boulon, L., & Dubé, Y. (2016) · Applied Energy · doi:10.1016/j.apenergy.2015.11.034 · Cited by 1,188
Metadata
- Authors
- Charkhgard, M., & Farrokhi, M.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2010-12
- Volume
- 57
- Issue
- 12
- Pages
- 4178-4187
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2129746134
- Cited by
- 779
- References
- 3
- FWCI
- 73.62
- Type
- journal article
- Language
- en
- Open access
- closed
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- Charkhgard, M., & Farrokhi, M. (2010). State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2010.2043035
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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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