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Application of Electrochemical Impedance Spectroscopy to Degradation and Aging Research of Lithium-Ion Batteries
Abstract
An in-depth understanding of battery degradation and aging in- Operando not only plays a vital role in the design of battery managing systems but also helps to ensure safe use and manufacturing optimization of lithium-ion batteries (LIBs) in large-scale applications. Electrochemical impedance spectroscopy (EIS) is a nondestructive method which unravels electrode kinetic processes inside the batteries in different time domains, including charge-transfer reactions, interfacial evolutions, and mass diffusions. It has become a powerful diagnosis and pre/prognosis tool in battery aging research, as it provides important insight into the changes of internal electrochemical processes by correlating the impedance evolution to degradation mechanisms. This review gives a critical overview on rapidly developing impedance techniques for degradation and aging investigation of Li-ion batteries. The EIS variations of LIBs at different aging conditions of calendar aging and accelerated aging are systematically summarized. In addition, the working principles, data validation, and modeling methods, including equivalent circuit model (ECM), distribution of relaxation times (DRT), and transmission line model (TLM), of classical EIS and dynamic EIS are elaborately concluded. Finally, the challenges and perspectives of further application of EIS in the aging research of LIBs are presented.
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Metadata
- Authors
- Hu, W., Peng, Y., Wei, Y., & Yang, Y.
- Venue
- The Journal of Physical Chemistry C
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-03-01
- Volume
- 127
- Issue
- 9
- Pages
- 4465-4495
- ISSN
- 1932-7447 (ISSN-L)
- 1932-7455
- OpenAlex
- W4322734451
- Cited by
- 429
- References
- 153
- FWCI
- 31.16
- 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 ›
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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.
