@inproceedings{303ee0934afc41f997273c9e024d0834,
title = "Degradation Prognostics of Lithium-ion Batteries Based on Partial Features and Long Short-term Memory Network",
abstract = "The accurate degradation prediction of Lithium-ion batteries is beneficial to the reliability and safety of battery-driven systems. In this paper, a long short-term memory network (LSTM) model is utilized to predict the capacity degradation trend using partial charge and discharge features of Lithium-ion batteries. Firstly, significant features are extracted from the original charge and discharge data. Then the Pearson correlation coefficient is adopted to filter the features with high correlation coefficients. Selected features are subsequently treated as the input of the prediction model. Finally, a LSTM model is developed and associated hyperparameters are established by Adam algorithm. The proposed method is validated by experimental results on the NASA battery dataset.",
keywords = "LSTM, Lithium-ion batteries, degradation, feature extraction",
author = "Mengyao Geng and Huixing Meng and Xu An and Jinduo Xing",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022 ; Conference date: 21-08-2022 Through 24-08-2022",
year = "2022",
doi = "10.1109/ICRMS55680.2022.9944563",
language = "English",
series = "13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "265--269",
booktitle = "13th International Conference on Reliability, Maintainability, and Safety",
address = "United States",
}
