The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives
Open accessAbstract
Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this paper, the conception of SOH is defined, and the state-of-the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction.
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Cites
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
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
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 2×
“Tomeasure internal resistance, pulse discharge and EIS are twomainstreammethods (Zhang et al., 2020b).”
BackgroundMethodSelf-citationPredicting the state of charge and health of batteries using data-driven machine learning
Ng, M.-F., Zhao, J., Yan, Q., Conduit, G. J., & Seh, Z. W. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0156-7 · Cited by 793
Yang, D., Zhang, X., Pan, R., Wang, Y., & Chen, Z. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.03.015 · Cited by 765
Self-citationLipu, M. S. H., Hannan, M. A., Hussain, A., Hoque, M. M., Ker, P. J., Saad, M. H. M., & Ayob, A. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.09.065 · Cited by 755
Gaussian process regression for forecasting battery state of health
Richardson, R. R., Osborne, M. A., & Howey, D. A. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.05.004 · Cited by 732
Random forest regression for online capacity estimation of lithium-ion batteries
Li, Y., Zou, C., Berecibar, M., Nanini-Maury, E., Chan, J. C.-W., van den Bossche, P., Van Mierlo, J., & Omar, N. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.09.182 · Cited by 727
State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network
Yang, F., Li, W., Li, C., & Miao, Q. (2019) · Energy · doi:10.1016/j.energy.2019.03.059 · Cited by 543
Shen, S., Sadoughi, M., Li, M., Wang, Z., & Hu, C. (2020) · Applied Energy · doi:10.1016/j.apenergy.2019.114296 · Cited by 537
Cited by
Hu, W., Peng, Y., Wei, Y., & Yang, Y. (2023) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.3c00033 · Cited by 429
Huang, J., He, T., Zhu, W., Liao, Y., Zeng, J., Xu, Q., & Niu, Y. (2025) · Computers and Electrical Engineering · doi:10.1016/j.compeleceng.2024.109930 · Cited by 113
Kim, S., Jung, H., Lee, M., Choi, Y. Y., & Choi, J.-I. (2023) · eTransportation · doi:10.1016/j.etran.2023.100243 · Cited by 55
State of health estimation of lithium-ion battery based on CNN–WNN–WLSTM
Yao, Q., Song, X., & Xie, W. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01300-3 · Cited by 53
Beltran, H., Sansano, E., & Pecht, M. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2022.106346 · Cited by 29
Metadata
- Authors
- Shu, X., Shen, S., Shen, J., Zhang, Y., Li, G., Chen, Z., & Liu, Y.
- Venue
- iScience
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 24
- Issue
- 11
- Pages
- 103265
- ISSN
- 2589-0042 (ISSN-L)
- OpenAlex
- W3207838049
- Cited by
- 208
- References
- 26
- FWCI
- 9.70
- Type
- journal article
- Language
- en
- Open access
- gold
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- Shu, X., Shen, S., Shen, J., Zhang, Y., Li, G., Chen, Z., & Liu, Y. (2021). State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives. iScience. https://doi.org/10.1016/j.isci.2021.103265
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