Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
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
Open this paper in CoChat — free account

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tcst.2016.2571663

Cites

45 of 45 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 45 references

Cited by

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

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.