The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries
Open accessAbstract
Battery calendar aging prediction is of extreme importance for developing durable electric vehicles. This article derives machine learning-enabled calendar aging prediction for lithium-ion batteries. Specifically, the Gaussian process regression (GPR) technique is employed to capture the underlying mapping among capacity, storage temperature, and state-of-charge. By modifying the isotropic kernel function with an automatic relevance determination (ARD) structure, high relevant input features can be effectively extracted to improve prediction accuracy and robustness. Experimental battery calendar aging data from nine storage cases are utilized for model training, validation, and comparison, which is more meaningful and practical than using the data from a single condition. Illustrative results demonstrate that the proposed GPR model with ARD Matern32 (M32) kernel outperforms other counterparts and can achieve reliable prediction results for all storage cases. Even for the partial-data training test, multistep prediction test, and accelerated aging training test, the proposed ARD-based GPR model is still capable of excavating the useful features, therefore offering good generalization ability and accurate prediction results for calendar aging under various storage conditions. This is the first-known data-driven application that utilizes the GPR with ARD kernel to perform battery calendar aging prognosis.
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Cites
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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
A Review on Electric Vehicles: Technologies and Challenges
Sanguesa, J. A., Torres-Sanz, V., Garrido, P., Martinez, F. J., & Marquez-Barja, J. M. (2021) · Smart Cities · doi:10.3390/smartcities4010022 · Cited by 1,367
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
Wang, S., Takyi-Aninakwa, P., Jin, S., Yu, C., Fernandez, C., & Stroe, D.-I. (2022) · Energy · doi:10.1016/j.energy.2022.124224 · Cited by 355
Li, Y., Li, K., Liu, X., Wang, Y., & Zhang, L. (2021) · Applied Energy · doi:10.1016/j.apenergy.2020.116410 · Cited by 247
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Metadata
- Authors
- Liu, K., Li, Y., Hu, X., Lucu, M., & Widanage, W. D.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2020-06
- Volume
- 16
- Issue
- 6
- Pages
- 3767-3777
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2978202285
- Cited by
- 384
- References
- 35
- FWCI
- 22.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Liu, K., Li, Y., Hu, X., Lucu, M., & Widanage, W. D. (2020). Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2019.2941747
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