The DOI matches its Crossref record; 274 of the 274 references it cites were found in a registry or an index.
Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles
Open accessAbstract
Abstract Sate of charge (SOC) estimation is generally acknowledged as one of the most important functions in battery management system for lithium-ion batteries in new energy vehicles. Though every effort is made for various online SOC estimation methods to reliably increase the estimation accuracy as much as possible within the limited on-chip resources, little literature discusses the error sources for those SOC estimation methods. This paper firstly reviews the commonly studied SOC estimation methods from a conventional classification. A novel perspective focusing on the error analysis of the SOC estimation methods is proposed. SOC estimation methods are analyzed from the views of the measured values, models, algorithms and state parameters. Subsequently, the error flow charts are proposed to analyze the error sources from the signal measurement to the models and algorithms for the widely used online SOC estimation methods in new energy vehicles. Finally, with the consideration of the working conditions, choosing more reliable and applicable SOC estimation methods is discussed, and the future development of the promising online SOC estimation methods is suggested.
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Predicting the state of charge and health of batteries using data-driven machine learning
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Metadata
- Authors
- Zheng, Y., Ouyang, M., Han, X., Lu, L., & Li, J.
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2018-02
- Volume
- 377
- Pages
- 161-188
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W2780369086
- Cited by
- 490
- References
- 274
- FWCI
- 23.64
- Type
- journal article
- Language
- en
- Open access
- green
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- Zheng, Y., Ouyang, M., Han, X., Lu, L., & Li, J. (2018). Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles. Journal of Power Sources. https://doi.org/10.1016/j.jpowsour.2017.11.094
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