The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Model-based thermal runaway prediction of lithium-ion batteries from kinetics analysis of cell components
Abstract
Thermal runaway (TR) is a major safety concern in lithium-ion batteries. Model-based TR prediction is critically needed to optimize safety designs of cells. This paper presents a novel scheme for developing reliable battery TR model from kinetics analysis of cell components. First, differential scanning calorimetry (DSC) tests on the individual cell components and their mixtures are conducted to reveal the TR mechanism and characterize the exothermic reactions, of which the major six (such as the decomposition of solid electrolyte interface (SEI) film) are determined as the dominant heat sources. The kinetics parameters of each exothermic reactions are identified from the DSC tests results at variant heating rates using Kissinger’s method and nonlinear fitting method. A predictive battery TR model is established by superimposing the chemical kinetics equations of the six exothermic reactions. The model fits well with the adiabatic TR test results and the oven tests results of a 24 Ah lithium-ion battery, indicating that the model can well reflect the battery TR mechanism and be trusted to predict battery safety performance without assembling a real battery.
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Cites
A review on the key issues for lithium-ion battery management in electric vehicles
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Self-citationThermal runaway mechanism of lithium ion battery for electric vehicles: A review
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Self-citationVariation of peak temperature with heating rate in differential thermal analysis
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Abuse behavior of high-power, lithium-ion cells
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Self-citationA three-dimensional thermal abuse model for lithium-ion cells
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Lithium-ion battery fast charging: A review
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Self-citationLi, J., Fleetwood, J., Hawley, W. B., & Kays, W. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00565 · Cited by 895
Self-citationFeng, X., Zheng, S., Ren, D., He, X., Wang, L., Cui, H., Liu, X., Jin, C., Zhang, F., Xu, C., et al. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.04.009 · Cited by 771
Self-citationRen, D., Feng, X., Liu, L., Hsu, H., Lu, L., Wang, L., He, X., & Ouyang, M. (2021) · Energy Storage Materials · doi:10.1016/j.ensm.2020.10.020 · Cited by 698
Self-citationLai, X., Jin, C., Yi, W., Han, X., Feng, X., Zheng, Y., & Ouyang, M. (2021) · Energy Storage Materials · doi:10.1016/j.ensm.2020.11.026 · Cited by 438
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Metadata
- Authors
- Ren, D., Liu, X., Feng, X., Lu, L., Ouyang, M., Li, J., & He, X.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 228
- Pages
- 633-644
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2877955991
- Cited by
- 438
- References
- 46
- FWCI
- 15.11
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ren, D., Liu, X., Feng, X., Lu, L., Ouyang, M., Li, J., & He, X. (2018). Model-based thermal runaway prediction of lithium-ion batteries from kinetics analysis of cell components. Applied Energy. https://doi.org/10.1016/j.apenergy.2018.06.126
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