The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-Ion Batteries
Open accessAbstract
This article presents the development of machine-learning-enabled data-driven models for effective capacity predictions for lithium-ion (Li-ion) batteries under different cyclic conditions. To achieve this, a model structure is first proposed with the considerations of battery aging tendency and the corresponding operational temperature and depth-of-discharge. Then based on a systematic understanding of the covariance functions within the Gaussian process regression (GPR), two related data-driven models are developed. Specifically, by modifying the isotropic squared exponential kernel with an automatic relevance determination structure, “Model A” could extract the highly relevant input features for capacity predictions. Through coupling the Arrhenius law and a polynomial equation into a compositional kernel, “Model B” is capable of considering the electrochemical and empirical knowledge of battery degradation. The developed models are validated and compared on the nickel-manganese-cobalt (NMC) oxide Li-ion batteries with various cycling patterns. The experimental results demonstrate that the modified GPR model considering the battery electrochemical and empirical aging signature outperforms other counterparts and is able to achieve satisfactory results for both one-step and multistep predictions. The proposed technique is promising for battery capacity predictions under various cycling cases.
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Cites
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
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
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×
“Deriving from the Bayesian framework, the Gaussian process regression (GPR) models have been widely applied to prognostic problems due to their advantages of being nonparametric and probabilistic [24].”
MethodSelf-citationState estimation for advanced battery management: Key challenges and future trends
Hu, X., Feng, F., Liu, K., Zhang, L., Xie, J., & Liu, B. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109334 · Cited by 857
Self-citationYang, 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
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
Self-citationWei, J., Dong, G., & Chen, Z. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2782224 · Cited by 643 · cited 1×
“In the literature, various intelligent techniques were successfully devised for cyclic capacity prediction, such as support vector machine (SVM) [19], [20] and artificial neural network (ANN) [21], [22].”
MethodA brief review on key technologies in the battery management system of electric vehicles
Liu, K., Li, K., Peng, Q., & Zhang, C. (2019) · Frontiers of Mechanical Engineering · doi:10.1007/s11465-018-0516-8 · Cited by 635
Self-citationLiu, D., Pang, J., Zhou, J., Peng, Y., & Pecht, M. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2013.03.010 · Cited by 443
Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries
Richardson, R. R., Birkl, C. R., Osborne, M. A., & Howey, D. A. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2794997 · Cited by 407
Cited by
Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., & Chen, Z. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110015 · Cited by 1,377
Liu, K., Shang, Y., Ouyang, Q., & Widanage, W. D. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.2973876 · Cited by 833
Self-citationArtificial Intelligence Applied to Battery Research: Hype or Reality?
Lombardo, T., Duquesnoy, M., El-Bouysidy, H., Årén, F., Gallo-Bueno, A., Jørgensen, P. B., Bhowmik, A., Demortière, A., Ayerbe, E., Alcaide, F., et al. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00108 · Cited by 503
Hossain Lipu, M. S., Hannan, M. A., Hussain, A., Ayob, A., Saad, M. H. M., Karim, T. F., & How, D. N. T. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.124110 · Cited by 378
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
Metadata
- Authors
- Liu, K., Hu, X., Wei, Z., Li, Y., & Jiang, Y.
- Venue
- IEEE Transactions on Transportation Electrification
- Published
- 2019-12
- Volume
- 5
- Issue
- 4
- Pages
- 1225-1236
- ISSN
- 2332-7782 (ISSN-L)
- 2372-2088
- OpenAlex
- W2978449012
- Cited by
- 358
- References
- 11
- FWCI
- 21.52
- Type
- journal article
- Language
- en
- Open access
- green
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- Liu, K., Hu, X., Wei, Z., Li, Y., & Jiang, Y. (2019). Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-Ion Batteries. IEEE Transactions on Transportation Electrification. https://doi.org/10.1109/tte.2019.2944802
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