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

Yu Fujitaki; Hiroyuki Kobayashi

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Paper

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

Topics: Energy Efficiency and Green Manufacturing

In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, 418-422, 2024 , Porto, Portugal

Authors: Yu Fujitaki and Hiroyuki Kobayashi

Affiliation: Osaka Institute of Technology, Osaka, Japan

Keyword(s): Lithium-Ion Batteries, Deep Learning, Cnn, Prediction.

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.

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Paper citation in several formats:

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; ISSN 2184-2809, SciTePress, pages 418-422. DOI: 10.5220/0013072100003822

@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},
issn={2184-2809},
}

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
IS - 2184-2809
AU - Fujitaki, Y.
AU - Kobayashi, H.
PY - 2024
SP - 418
EP - 422
DO - 10.5220/0013072100003822
PB - SciTePress