Application of Ensemble Learning for Improving Failure Prediction in Lithium-Ion Batteries
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-38241-3_80
Reference16 articles.
1. Chen, Y., et al.: A review of lithium-ion battery safety concerns: the issues, strategies, and testing standards. J. Energy Chem. 59, 83–99 (2021)
2. Hu, X., Zhang, K., Liu, K., Lin, X., Dey, S., Onori, S.: Advanced fault diagnosis for lithium-ion battery systems: a review of fault mechanisms, fault features, and diagnosis procedures. IEEE Ind. Electron. Mag. 14, 65–91 (2020)
3. Li, X., Li, J., Abdollahi, A., Jones, T.: Data-driven thermal anomaly detection for batteries using unsupervised shape clustering. In: 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). IEEE (2021)
4. Wen, L., Li, X., Gao, L., Zhang, Y.: A new convolutional neural network-based data-driven fault diagnosis method. IEEE Trans. Industr. Electron. 65, 5990–5998 (2018)
5. Sagi, O., Rokach, L.: Ensemble learning: a survey. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 8, e1249 (2018)
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