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Cited by 540

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Multimodal Physics-Based Aging Model for Life Prediction of Li-Ion Batteries

Open access
Safari, M., Morcrette, M., Teyssot, A., Delacourt, C.·2009·Journal of The Electrochemical Society

Abstract

An isothermal, multimodal, physics-based aging model for life prediction of Li-ion batteries is developed, for which a solvent-decomposition reaction leading to the growth of a solid electrolyte interphase (SEI) at the carbonaceous anode material is considered as the source of capacity fade. The rate of SEI film growth depends on both solvent diffusion through the SEI film and solvent-reduction kinetics at the carbon surface. The model is able to simulate a wide variety of battery aging profiles, e.g., open-circuit and constant-voltage storage, charge/discharge cycling, etc. An analysis of capacity-fade data from the literature reveals that the same set of aging parameters may be used for predicting cycling and constant-voltage storage. The use of this set of parameters for predicting storage under open-circuit voltage points out that part of the self-discharge is reversible.

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Metadata

Authors
Safari, M., Morcrette, M., Teyssot, A., & Delacourt, C.
Venue
Journal of The Electrochemical Society
Published
2009
Volume
156
Issue
3
Pages
A145
ISSN
0013-4651 (ISSN-L)
OpenAlex
W2052648206
Cited by
540
References
3
FWCI
33.25
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Multimodal Physics-Based Aging Model for Life Prediction of Li-Ion Batteries. https://app.cochat.ai/papers/10.1149/1.3043429
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Safari, M., Morcrette, M., Teyssot, A., & Delacourt, C. (2009). Multimodal Physics-Based Aging Model for Life Prediction of Li-Ion Batteries. Journal of The Electrochemical Society. https://doi.org/10.1149/1.3043429

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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.