A remaining useful life prediction method based on CNN-LSTM

Lijuan Wang; Lijin Cai; Jian Cheng

doi:10.1117/12.3025506

28 February 2024 A remaining useful life prediction method based on CNN-LSTM

Lijuan Wang, Lijin Cai, Jian Cheng

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130710V (2024) https://doi.org/10.1117/12.3025506
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

Currently, lithium batteries are widely used in industry such as electric vehicles. It’s important to know the RUL (Remaining useful life). In this paper, we apply a frame of deep learning technology in order to predict the RUL of batteries. CNN method is provided to exact features, while LSTM method is to do the prediction. The NASA lithium batteries data are used for verifying the proposed algorithm. The result shows that the RUL prediction method of lithium batteries is accurate.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lijuan Wang, Lijin Cai, and Jian Cheng "A remaining useful life prediction method based on CNN-LSTM", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130710V (28 February 2024); https://doi.org/10.1117/12.3025506

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