A Conceptual Framework for Lithium-ion Battery RUL Prediction Using Deep Learning

DOI

10.2991/icmse-18.2018.30How to use a DOI?

Keywords

Lithium-ion Battery, RUL prediction, Deep learning, Deep neural network, Autoencoder.

Abstract

In this paper, a conceptual framework for Remaining Useful Life (RUL) prediction of lithium-ion battery integrating deep learning is presented. The main processing stages, i.e., feature extraction, redundant information removal, data preprocessing, DNN model training, RUL prediction and evaluation, are discussed. Finally, a feature extraction method is presented by analyzing a lithium-ion battery data set from NASA AMES Center.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shiqiang Zhao
PY  - 2018/05
DA  - 2018/05
TI  - A Conceptual Framework for Lithium-ion Battery RUL Prediction Using Deep Learning
BT  - Proceedings of the 2018 8th International Conference on Manufacturing Science and Engineering (ICMSE 2018)
PB  - Atlantis Press
SP  - 149
EP  - 153
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmse-18.2018.30
DO  - 10.2991/icmse-18.2018.30
ID  - Zhao2018/05
ER  -