Development of a Lithium-Ion Battery Lifetime Prediction Model Using Deep Learning for Short-Term Learning

Yu Fujitaki, Hiroyuki Kobayashi

2024

Abstract

We will use the open data utilized in Severson’s research. This data consists of cycle data obtained from repeated charging and discharging of lithium-ion batteries, which will be analyzed.One issue is that the amount of cycle data is limited, which could lead to inadequate training. To address this problem, we have adopted a method that extracts multiple data points from a single battery dataset, thereby improving prediction accuracy. In this experiment, we compared data from 100 charge-discharge cycles with data from just 1 charge-discharge cycle.

Paper Citation

in Harvard Style

Fujitaki Y. and Kobayashi H. (2024). Development of a Lithium-Ion Battery Lifetime Prediction Model Using Deep Learning for Short-Term Learning. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-717-7, SciTePress, pages 418-422. DOI: 10.5220/0013072100003822

in Bibtex Style

@conference{icinco24,
author={Yu Fujitaki and Hiroyuki Kobayashi},
title={Development of a Lithium-Ion Battery Lifetime Prediction Model Using Deep Learning for Short-Term Learning},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2024},
pages={418-422},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013072100003822},
isbn={978-989-758-717-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - Development of a Lithium-Ion Battery Lifetime Prediction Model Using Deep Learning for Short-Term Learning
SN - 978-989-758-717-7
AU - Fujitaki Y.
AU - Kobayashi H.
PY - 2024
SP - 418
EP - 422
DO - 10.5220/0013072100003822
PB - SciTePress