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The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles

Open access
Xiong, Rui, Cao, Jiayi, Yu, Quanqing, He, Hongwen, Sun, Fengchun·2018·IEEE Access
DOI 10.1109/access.2017.2780258
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Abstract

Battery technology is the bottleneck of the electric vehicles (EVs). It is important, both in theory and practical application, to do research on the modeling and state estimation of batteries, which is essential to optimizing energy management, extending the life cycle, reducing cost, and safeguarding the safe application of batteries in EVs. However, the batteries, with strong time-variables and nonlinear characteristics, are further influenced by such random factors such as driving loads, operational conditions, in the application of EVs. The real-time, accurate estimation of their state is challenging. The classification of the estimation methodologies for estimating state-of-charge (SoC) of battery focusing with the estimation method/algorithm, advantages, drawbacks, and estimation error are systematically and separately discussed. Especially for the battery packs existing of the inevitable inconsistency in cell capacity, resistance and voltage, the advanced characterizing monomer selection, and bias correction-based method has been described and discussed. The review also presents the key feedback factors that are indispensable for accurate estimation of battery SoC, it will be helpful for ensuring the SoC estimation accuracy. It will be very helpful for choosing an appropriate method to develop a reliable and safe battery management system and energy management strategy of the EVs. Finally, the paper also highlights a number of key factors and challenges, and presents the possible recommendations for the development of next generation of smart SoC estimation and battery management systems for electric vehicles and battery energy storage system.

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Metadata

Authors
Xiong, R., Cao, J., Yu, Q., He, H., & Sun, F.
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2018
Volume
6
Pages
1832-1843
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2017.2780258
OpenAlex
W2774327138
Cited by
1,004
References
13
FWCI
48.14
Type
journal article
Language
en
Open access
gold
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
Funders
  • National Natural Science Foundation of China (51507012)
  • Beijing Nova Program (Z171100001117063)

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CoChat. (2026). Citation check: Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles. https://app.cochat.ai/papers/10.1109/access.2017.2780258
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Xiong, R., Cao, J., Yu, Q., He, H., & Sun, F. (2018). Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles. IEEE Access. https://doi.org/10.1109/access.2017.2780258

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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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