Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 192

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Real-Time Overcharge Warning and Early Thermal Runaway Prediction of Li-Ion Battery by Online Impedance Measurement

Lyu, Nawei, Jin, Yang, Xiong, Rui, Miao, Shan, Gao, Jinfeng·2022·IEEE Transactions on Industrial Electronics

Abstract

A lithium-ion battery has advantages such as high energy density and long calendar life, but it suffers from the risk of thermal runaway. Overcharge-induced thermal runaway accidents hold a considerable percentage. This article discovers that the slope of the dynamic impedance in the frequency band of 30–90 Hz turns positive from negative when the cell just starts to overcharge and proposes the theoretical explanation. Taking 70 Hz impedance as an example, the thermal runaway accident can be successfully avoided by cutting off the charging when the slope turns positive from negative during charging. The warning time is 580 s ahead of the thermal runaway. This feature is easy to identify and requires no complex mathematical models and parameters. Besides, the prediction method based on this feature can be conducted by using an online dynamic impedance measurement device designed by us, which is suitable for large-scale applications. Thus, the overcharge-induced thermal runaway accidents can be avoided.

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/tie.2021.3062267

Cites

1 of 1 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

Metadata

Authors
Lyu, N., Jin, Y., Xiong, R., Miao, S., & Gao, J.
Venue
IEEE Transactions on Industrial Electronics
Published
2022-02
Volume
69
Issue
2
Pages
1929-1936
ISSN
  • 0278-0046 (ISSN-L)
  • 1557-9948
OpenAlex
W3134215582
Cited by
192
References
1
FWCI
7.29
Type
journal article
Language
en
Open access
closed

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Real-Time Overcharge Warning and Early Thermal Runaway Prediction of Li-Ion Battery by Online Impedance Measurement. https://app.cochat.ai/papers/10.1109/tie.2021.3062267
The paper
Lyu, N., Jin, Y., Xiong, R., Miao, S., & Gao, J. (2022). Real-Time Overcharge Warning and Early Thermal Runaway Prediction of Li-Ion Battery by Online Impedance Measurement. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2021.3062267

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.