Cycle Life Prediction of Lithium-Ion Batteries Using Deep Learning

Yu Fujitaki, Hiroyuki Kobayashi

2023

Abstract

To improve the accuracy of lithium-ion battery life prediction, we decided to train multiple LSTMs separately, as each battery may have its own unique characteristics. When verifying the results, we found similarities between the verification and training batteries and used LSTMs to predict the verification battery, but we show that the results were not successful.

Paper Citation

in Harvard Style

Fujitaki Y. and Kobayashi H. (2023). Cycle Life Prediction of Lithium-Ion Batteries Using Deep Learning. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-670-5, SciTePress, pages 307-310. DOI: 10.5220/0012233500003543

in Bibtex Style

@conference{icinco23,
author={Yu Fujitaki and Hiroyuki Kobayashi},
title={Cycle Life Prediction of Lithium-Ion Batteries Using Deep Learning},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2023},
pages={307-310},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012233500003543},
isbn={978-989-758-670-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - Cycle Life Prediction of Lithium-Ion Batteries Using Deep Learning
SN - 978-989-758-670-5
AU - Fujitaki Y.
AU - Kobayashi H.
PY - 2023
SP - 307
EP - 310
DO - 10.5220/0012233500003543
PB - SciTePress