The DOI matches its Crossref record; 156 of the 156 references it cites were found in a registry or an index.
Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries
Open accessAbstract
Lithium‐ion batteries (LIBs) are vital energy‐storage devices in modern society. However, the performance and cost are still not satisfactory in terms of energy density, power density, cycle life, safety, etc. To further improve the performance of batteries, traditional “trial‐and‐error” processes require a vast number of tedious experiments. Computational chemistry and artificial intelligence (AI) can significantly accelerate the research and development of novel battery systems. Herein, a heterogeneous category of AI technology for predicting and discovering battery materials and estimating the state of the battery system is reviewed. Successful examples, the challenges of deploying AI in real‐world scenarios, and an integrated framework are analyzed and outlined. The state‐of‐the‐art research about the applications of ML in the property prediction and battery discovery, including electrolyte and electrode materials, are further summarized. Meanwhile, the prediction of battery states is also provided. Finally, various existing challenges and the framework to tackle the challenges on the further development of machine learning for rechargeable LIBs are proposed.
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Issues and challenges facing rechargeable lithium batteries
Tarascon, J.-M., & Armand, M. (2001) · Nature · doi:10.1038/35104644 · Cited by 21,130
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.10045 · Cited by 11,321
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
The Cambridge Structural Database
Groom, C. R., Bruno, I. J., Lightfoot, M. P., & Ward, S. C. (2016) · Acta Crystallographica Section B Structural Science, Crystal Engineering and Materials · doi:10.1107/s2052520616003954 · Cited by 10,297
Nonaqueous Liquid Electrolytes for Lithium-Based Rechargeable Batteries
Xu, K. (2004) · Chemical Reviews · doi:10.1021/cr030203g · Cited by 7,437
Challenges in the development of advanced Li-ion batteries: a review
Etacheri, V., Marom, R., Elazari, R., Salitra, G., & Aurbach, D. (2011) · Energy & Environmental Science · doi:10.1039/c1ee01598b · Cited by 6,726
A review on the key issues for lithium-ion battery management in electric vehicles
Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.060 · Cited by 4,837
Lithium battery chemistries enabled by solid-state electrolytes
Manthiram, A., Yu, X., & Wang, S. (2017) · Nature Reviews Materials · doi:10.1038/natrevmats.2016.103 · Cited by 4,689
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
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Water Electrolysis toward Elevated Temperature: Advances, Challenges and Frontiers
Zhang, W., Liu, M., Gu, X., Shi, Y., Deng, Z., & Cai, N. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00573 · Cited by 441
Osman, A. I., Nasr, M., Eltaweil, A. S., Hosny, M., Farghali, M., Al-Fatesh, A. S., Rooney, D. W., & Abd El-Monaem, E. M. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2024.03.223 · Cited by 270
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
IoB: Internet-of-batteries for electric Vehicles–Architectures, opportunities, and challenges
Li, H., Bin Kaleem, M., Liu, Z., Wu, Y., Liu, W., & Huang, Z. (2023) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2023.100128 · Cited by 95
Li, A. G., West, A. C., & Preindl, M. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119030 · Cited by 93
Metadata
- Authors
- Lv, C., Zhou, X., Zhong, L., Yan, C., Srinivasan, M., Seh, Z. W., Liu, C., Pan, H., Li, S., Wen, Y., & Yan, Q.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2021-09-07
- Volume
- 34
- Issue
- 25
- Pages
- e2101474-e2101474
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W3196819979
- Cited by
- 412
- References
- 156
- FWCI
- 16.85
- Type
- journal article
- Language
- en
- Open access
- green
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- Lv, C., Zhou, X., Zhong, L., Yan, C., Srinivasan, M., Seh, Z. W., Liu, C., Pan, H., Li, S., Wen, Y., & Yan, Q. (2022). Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries. Advanced Materials. https://doi.org/10.1002/adma.202101474
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