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Cited by 431

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

Battery State Estimation for a Single Particle Model With Electrolyte Dynamics

Open access
Moura, Scott J., Argomedo, Federico Bribiesca, Klein, Reinhardt, Mirtabatabaei, Anahita, Krstic, Miroslav·2017·IEEE Transactions on Control Systems Technology
DOI 10.1109/tcst.2016.2571663
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Abstract

This paper studies a state estimation scheme for a reduced electrochemical battery model, using voltage and current measurements. Real-time electrochemical state information enables high-fidelity monitoring and high-performance operation in advanced battery management systems, for applications such as consumer electronics, electrified vehicles, and grid energy storage. This paper derives a single particle model (SPM) with electrolyte that achieves higher predictive accuracy than the SPM. Next, we propose an estimation scheme and prove estimation error system stability, assuming that the total amount of lithium in the cell is known. The state estimation scheme exploits the dynamical properties, such as marginal stability, local invertibility, and conservation of lithium. Simulations demonstrate the algorithm's performance and limitations.

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Metadata

Authors
Moura, S. J., Argomedo, F. B., Klein, R., Mirtabatabaei, A., & Krstic, M.
Venue
IEEE Transactions on Control Systems Technology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2017-03
Volume
25
Issue
2
Pages
453-468
ISSN
  • 1063-6536 (ISSN-L)
  • 1558-0865
DOI
10.1109/tcst.2016.2571663
OpenAlex
W1916811336
Cited by
431
References
45
FWCI
15.10
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Battery State Estimation for a Single Particle Model With Electrolyte Dynamics. https://app.cochat.ai/papers/10.1109/tcst.2016.2571663
The paper
Moura, S. J., Argomedo, F. B., Klein, R., Mirtabatabaei, A., & Krstic, M. (2017). Battery State Estimation for a Single Particle Model With Electrolyte Dynamics. IEEE Transactions on Control Systems Technology. https://doi.org/10.1109/tcst.2016.2571663

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.