The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety
Open accessAbstract
Summary Enabling accurate prediction of battery failure will lead to safer battery systems, as well as accelerating cell design and manufacturing processes for increased consistency and reliability. Data-driven prediction methods have shown promise for accurately predicting cell behaviors with low computational cost, but they are expensive to train. Furthermore, given that the risk of battery failure is already very low, gathering enough relevant data to facilitate data-driven predictions is extremely challenging. Here, a perspective for designing experiments to facilitate a relatively low number of tests, handling the data, applying data-driven methods, and improving our understanding of behavior-dictating physics is outlined. This perspective starts with effective strategies for experimentally replicating rare failure scenarios and thus reducing the number of experiments, and proceeds to describe means to acquire high-quality datasets, apply data-driven prediction techniques, and to extract physical insights into the events that lead to failure by incorporating physics into data-driven approaches.
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Cites
Thermal runaway mechanism of lithium ion battery for electric vehicles: A review
Feng, X., Ouyang, M., Liu, X., Lu, L., Xia, Y., & He, X. (2018) · Energy Storage Materials · doi:10.1016/j.ensm.2017.05.013 · Cited by 4,182
Self-citationData-driven prediction of battery cycle life before capacity degradation
Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., et al. (2019) · Nature Energy · doi:10.1038/s41560-019-0356-8 · Cited by 3,228
Self-citationDeepXDE: A Deep Learning Library for Solving Differential Equations
Lu, L., Meng, X., Mao, Z., & Karniadakis, G. E. (2021) · SIAM Review · doi:10.1137/19m1274067 · Cited by 2,512
DGM: A deep learning algorithm for solving partial differential equations
Sirignano, J., & Spiliopoulos, K. (2018) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.08.029 · Cited by 2,208
Solving high-dimensional partial differential equations using deep learning
Han, J., Jentzen, A., & E, W. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1718942115 · Cited by 1,722
Hu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule · doi:10.1016/j.joule.2019.11.018 · Cited by 1,320
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109254 · Cited by 1,193 · cited 1×
“learning, statistical analysis, and signal processing have been systematically summarized in several review articles.(4,6,54) Another example related to battery safety is identifying the most critical condition of battery cells under which a mechanical failure or short circuit is most likely to happen.”
BackgroundClosed-loop optimization of fast-charging protocols for batteries with machine learning
Attia, P. M., Grover, A., Jin, N., Severson, K. A., Markov, T. M., Liao, Y.-H., Chen, M. H., Cheong, B., Perkins, N., Yang, Z., et al. (2020) · Nature · doi:10.1038/s41586-020-1994-5 · Cited by 1,180
Raissi, M., Perdikaris, P. G., & Karniadakis, G. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.10561 · Cited by 925
Zhang, Y., Tang, Q., Zhang, Y., Wang, J., Stimming, U., & Lee, A. A. (2020) · Nature Communications · doi:10.1038/s41467-020-15235-7 · Cited by 889 · cited 1×
“A typical example of such extrapolative validations in battery research is the prediction of RUL of battery cells by using measured data during the service life.(11,53) Various data-driven approaches based on machine Figure 3.”
Method
Cited by
The timescale identification decoupling complicated kinetic processes in lithium batteries
Lu, Y., Zhao, C.-Z., Huang, J.-Q., & Zhang, Q. (2022) · Joule · doi:10.1016/j.joule.2022.05.005 · Cited by 2,378
Lv, C., Zhou, X., Zhong, L., Yan, C., Srinivasan, M., Seh, Z. W., Liu, C., Pan, H., Li, S., Wen, Y., et al. (2022) · Advanced Materials · doi:10.1002/adma.202101474 · Cited by 412
End-of-life or second-life options for retired electric vehicle batteries
Zhu, J., Mathews, I., Ren, D., Li, W., Cogswell, D., Xing, B., Sedlatschek, T., Kantareddy, S. N. R., Yi, M., Gao, T., et al. (2021) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2021.100537 · Cited by 306
Self-citationStrielkowski, W., Vlasov, A., Selivanov, K., Muraviev, K., & Shakhnov, V. (2023) · Energies · doi:10.3390/en16104025 · Cited by 237
Battery Safety: Machine Learning-Based Prognostics
Zhao, J., Feng, X., Pang, Q., Fowler, M., Lian, Y., Ouyang, M., & Burke, A. F. (2024) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2023.101142 · Cited by 216
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Metadata
- Authors
- Finegan, D. P., Zhu, J., Feng, X., Keyser, M., Ulmefors, M., Li, W., Bazant, M. Z., & Cooper, S. J.
- Venue
- Joule
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 5
- Issue
- 2
- Pages
- 316-329
- ISSN
- 2542-4351 (ISSN-L)
- OpenAlex
- W3117254276
- Cited by
- 303
- References
- 70
- FWCI
- 11.29
- Type
- journal article
- Language
- en
- Open access
- bronze
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