The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems
Open accessAbstract
Effective management of lithium-ion batteries is a key enabler for a low carbon future, with applications including electric vehicles and grid scale energy storage. The lifetime of these devices depends greatly on the materials used, the system design and the operating conditions. This complexity has therefore made real-world control of battery systems challenging. However, with the recent advances in understanding battery degradation, modelling tools and diagnostics, there is an opportunity to fuse this knowledge with emerging machine learning techniques towards creating a battery digital twin. In this cyber-physical system, there is a close interaction between a physical and digital embodiment of a battery, which enables smarter control and longer lifetime. This perspectives paper thus presents the state-of-the-art in battery modelling, in-vehicle diagnostic tools, data driven modelling approaches, and how these elements can be combined in a framework for creating a battery digital twin. The challenges, emerging techniques and perspective comments provided here, will enable scientists and engineers from industry and academia with a framework towards more intelligent and interconnected battery management in the future.
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Citations
Cites
Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell
Doyle, M., Fuller, T. F., & Newman, J. (1993) · Journal of The Electrochemical Society · doi:10.1149/1.2221597 · Cited by 4,328 · cited 1×
“A more comprehensive approach to describing the performance of LIBs is through the use of continuum level physics models which were pi-oneered by Doyle, Fuller and Newman [15] .”
MethodThermal 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
Digital twin-driven product design, manufacturing and service with big data
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0233-1 · Cited by 2,920
Digital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952
Degradation diagnostics for lithium ion cells
Birkl, C. R., Roberts, M. R., McTurk, E., Bruce, P. G., & Howey, D. A. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.12.011 · Cited by 1,945 · cited 2×
“Maximising battery lifetime is a particular hallenge due to the strong coupling between LIB longevity and operatng conditions such as: temperature, load-profile and operating state-ofharge (SOC) range [1] .”
BackgroundExtended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.032 · Cited by 1,923
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.033 · Cited by 1,768
Lithium-ion battery fast charging: A review
Tomaszewska, A., Chu, Z., Feng, X., O'Kane, S., Liu, X., Chen, J., Ji, C., Endler, E., Li, R., Liu, L., et al. (2019) · eTransportation · doi:10.1016/j.etran.2019.100011 · Cited by 1,592
Self-citationExtended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.031 · Cited by 1,385
Cited by
Digital Twin Technology Challenges and Applications: A Comprehensive Review
Botín-Sanabria, D. M., Mihaita, A.-S., Peimbert-García, R. E., Ramírez-Moreno, M. A., Ramírez-Mendoza, R. A., & Lozoya-Santos, J. D. J. (2022) · Remote Sensing · doi:10.3390/rs14061335 · Cited by 878
Towards the future of smart electric vehicles: Digital twin technology
Bhatti, G., Mohan, H., & Raja Singh, R. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.110801 · Cited by 533
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
Lithium-ion battery data and where to find it
dos Reis, G., Strange, C., Yadav, M., & Li, S. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100081 · Cited by 377
Implementation for a cloud battery management system based on the CHAIN framework
Yang, S., Zhang, Z., Cao, R., Wang, M., Cheng, H., Zhang, L., Jiang, Y., Li, Y., Chen, B., Ling, H., et al. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100088 · Cited by 178
Self-citation
Metadata
- Authors
- Wu, B., Widanage, W. D., Yang, S., & Liu, X.
- Venue
- Energy and AI
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 1
- Pages
- 100016
- ISSN
- 2666-5468 (ISSN-L)
- OpenAlex
- W3041101137
- Cited by
- 396
- References
- 67
- FWCI
- 16.34
- Type
- journal article
- Language
- en
- Open access
- gold
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