/MNQ_LSTM

Code for the paper: "T-shape data and probabilistic remaining useful life prediction for Li-ion batteries using multiple non-crossing quantile long short-term memory".

Primary LanguageJupyter Notebook

MNQ_LSTM

Code for training multiple non-crossing quantile LSTM. If you find that the code and contents are helpful, please cite the paper:

Sel Ly, Jiahang Xie, Franz-Erich Wolter, Hung D. Nguyen, Yu Weng (2023), T-shape data and probabilistic remaining useful life prediction for Li-ion batteries using multiple non-crossing quantile long short-term memory, Applied Energy, Volume 349, 2023, pp. 121355, https://doi.org/10.1016/j.apenergy.2023.121355.