The DOI matches its Crossref record; 86 of the 86 references it cites were found in a registry or an index.
Review and Performance Comparison of Mechanical-Chemical Degradation Models for Lithium-Ion Batteries
Open accessAbstract
The maximum energy that lithium-ion batteries can store decreases as they are used because of various irreversible degradation mechanisms. Many models of degradation have been proposed in the literature, sometimes with a small experimental data set for validation. However, a comprehensive comparison between different model predictions is lacking, making it difficult to select modelling approaches which can explain the degradation trends actually observed from data. Here, various degradation models from literature are implemented within a single particle model framework and their behavior is compared. It is shown that many different models can be fitted to a small experimental data set. The interactions between different models are simulated, showing how some of the models accelerate degradation in other models, altering the overall degradation trend. The effects of operating conditions on the various degradation models is simulated. This identifies which models are enhanced by which operating conditions and might therefore explain specific degradation trends observed in data. Finally, it is shown how a combination of different models is needed to capture different degradation trends observed in a large experimental data set. Vice versa, only a large data set enables to properly select the models which best explain the observed degradation.
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Cites
Practical Bayesian Optimization of Machine Learning Algorithms
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691
Ageing mechanisms in lithium-ion batteries
Vetter, J., Novák, P., Wagner, M. R., Veit, C., Möller, K.-C., Besenhard, J. O., Winter, M., Wohlfahrt-Mehrens, M., Vogler, C., & Hammouche, A. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2005.01.006 · Cited by 3,987
A review of the features and analyses of the solid electrolyte interphase in Li-ion batteries
Verma, P., Maire, P., & Novák, P. (2010) · Electrochimica Acta · doi:10.1016/j.electacta.2010.05.072 · Cited by 2,903
Simulation and Optimization of the Dual Lithium Ion Insertion Cell
Fuller, T. F., Doyle, M., & Newman, J. (1994) · Journal of The Electrochemical Society · doi:10.1149/1.2054684 · Cited by 2,057
A review on lithium-ion battery ageing mechanisms and estimations for automotive applications
Barré, A., Deguilhem, B., Grolleau, S., Gérard, M., Suard, F., & Riu, D. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.05.040 · Cited by 1,758
Electrode–Electrolyte Interface in Li-Ion Batteries: Current Understanding and New Insights
Gauthier, M., Carney, T. J., Grimaud, A., Giordano, L., Pour, N., Chang, H.-H., Fenning, D. P., Lux, S. F., Paschos, O., Bauer, C., et al. (2015) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5b01727 · Cited by 1,028
Yang, X.-G., Leng, Y., Zhang, G., Ge, S., & Wang, C.-Y. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.05.110 · Cited by 948
Pinson, M. B., & Bazant, M. Z. (2013) · Journal of The Electrochemical Society · doi:10.1149/2.044302jes · Cited by 946
Lithium Diffusion in Graphitic Carbon
Persson, K., Sethuraman, V. A., Hardwick, L. J., Hinuma, Y., Meng, Y. S., van der Ven, A., Srinivasan, V., Kostecki, R., & Ceder, G. (2010) · The Journal of Physical Chemistry Letters · doi:10.1021/jz100188d · Cited by 852
Development of First Principles Capacity Fade Model for Li-Ion Cells
Ramadass, P., Haran, B., Gomadam, P. M., White, R., & Popov, B. N. (2004) · Journal of The Electrochemical Society · doi:10.1149/1.1634273 · Cited by 794
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Lithium-ion battery degradation: how to model it
O'Kane, S. E. J., Ai, W., Madabattula, G., Alonso-Alvarez, D., Timms, R., Sulzer, V., Edge, J. S., Wu, B., Offer, G. J., & Marinescu, M. (2022) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp00417h · Cited by 400
Wu, B., Widanage, W. D., Yang, S., & Liu, X. (2020) · Energy and AI · doi:10.1016/j.egyai.2020.100016 · Cited by 396
Mechanical properties of cathode materials for lithium-ion batteries
Stallard, J. C., Wheatcroft, L., Booth, S. G., Boston, R., Corr, S. A., De Volder, M. F. L., Inkson, B. J., & Fleck, N. A. (2022) · Joule · doi:10.1016/j.joule.2022.04.001 · Cited by 336
Prognostics of battery capacity based on charging data and data-driven methods for on-road vehicles
Deng, Z., Xu, L., Liu, H., Hu, X., Duan, Z., & Xu, Y. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120954 · Cited by 268
Solid–Electrolyte Interphase During Battery Cycling: Theory of Growth Regimes
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Metadata
- Authors
- Reniers, J. M., Mulder, G., & Howey, D. A.
- Venue
- Journal of The Electrochemical Society
- Publisher
- The Electrochemical Society
- Published
- 2019
- Volume
- 166
- Issue
- 14
- Pages
- A3189-A3200
- ISSN
- 0013-4651 (ISSN-L)
- 1945-7111
- OpenAlex
- W2974904579
- Cited by
- 462
- References
- 86
- FWCI
- 19.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
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