Application of Anomaly Detection Algorithms in Lithium-Ion Battery Packs - A Case Study-Reference-Cited by-同舟云学术

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Application of Anomaly Detection Algorithms in Lithium-Ion Battery Packs - A Case Study

Published:2023 Issue: Volume: Page:753-760
ISSN:2195-4356
Container-title:Lecture Notes in Mechanical Engineering
language:
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Author:

Gotz Joelton Deonei,Guerrero Gabriel Carrico,Espolador João Felipe Raffs,Werlich Samuel Henrique,Borsato Milton,Corrêa Fernanda Cristina

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-17629-6_79

Reference13 articles.

1. 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)

2. Jiang, J., Cong, X., Li, S., Zhang, C., Zhang, W., Jiang, Y.: A hybrid signal-based fault diagnosis method for lithium-ion batteries in electric vehicles. IEEE Access 9, 19175–19186 (2021)

3. Li, X., Li, J., Abdollahi, A., Jones, T., Habeebullah, A.: Data-driven thermal anomaly detection for batteries using unsupervised shape clustering. In: 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). IEEE, Kyoto (2021)

4. Diao, W., Naqvi, I.H., Pecht, M.: Early detection of anomalous degradation behavior in lithium-ion batteries. J. Energy Storage 32, 101710 (2020)

5. Saxena, S., Kang, M., Xing, Y., Pecht, M.: Anomaly Detection during lithium-ion battery qualification testing. In: 2018 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 1–6. IEEE, Seatle (2018)

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Application of Ensemble Learning for Improving Failure Prediction in Lithium-Ion Batteries;Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems;2023-08-24

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