The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles
Abstract
This paper proposes a co-estimation scheme of state of charge (SOC), state of health (SOH), and state of function (SOF) for lithium-ion batteries in electric vehicles. The co-estimation denotes that the SOC, SOH, and SOF are estimated simultaneously in real-time application. The model-based SOC estimation is fulfilled by the extended Kalman filter. The battery parameters related with the battery SOH and SOF are online identified using the recursive least square algorithm with a forgetting factor. The capacity and the maximum available output power are then estimated based on the identified parameters. The online update of the capacity and correlated parameters help improve the accuracy of the state estimation but with limited increase in the computation load, by making good use of the correlations among the states. The co-estimation scheme is validated in a real battery management system with good real-time performance and convincible estimation accuracy.
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Cited by
State estimation for advanced battery management: Key challenges and future trends
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Metadata
- Authors
- Shen, P., Ouyang, M., Lu, L., Li, J., & Feng, X.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2018-01
- Volume
- 67
- Issue
- 1
- Pages
- 92-103
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2755729630
- Cited by
- 534
- References
- 7
- FWCI
- 22.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Funders
- National Natural Science Foundation of China (U1564205)
- Ministry of Science and Technology of China (2016YFE0102200)
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- Shen, P., Ouyang, M., Lu, L., Li, J., & Feng, X. (2018). The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/tvt.2017.2751613
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
