Please use this identifier to cite or link to this item:
Title: A meta-learning based multimodal neural network for multistep ahead battery thermal runaway forecasting
Authors: Ding, Shuya
Dong, Chaoyu
Zhao, Tianyang
Koh, Liang Mong
Bai, Xiaoyin
Luo, Jun
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Ding, S., Dong, C., Zhao, T., Koh, L. M., Bai, X. & Luo, J. (2020). A meta-learning based multimodal neural network for multistep ahead battery thermal runaway forecasting. IEEE Transactions On Industrial Informatics, 17(7), 4503-4511.
Project: NRF2016ENC-ESS001-022
Journal: IEEE Transactions on Industrial Informatics
Abstract: An effective forecast method to trigger Thermal Runaway (TR) warning in an early stage is essential for monitoring battery safety. In this article, we propose a novel data-driven approach to perform multistep ahead forecast accurately for battery TR state at cell-level. We formulate this forecasting task as an imbalance data classification task and propose meta thermal runaway forecasting neural network (Meta-TRFNN) to solve it. Essentially, we exploit high-dimensional thermal images along with low-dimensional temperature and voltage data to capture a more representative thermal profile. Moreover, we adapt a meta-learning framework to handle the data deficiency problem. We evaluate Meta-TRFNN on simulated samples and also explore its applicability in the real world with real samples. Although this classification task is highly imbalanced, Meta-TRFNN is still proven effective with limited historical information. Our further comparison experiments not only demonstrate the forecasting ability of Meta-TRFNN, but also validate the benefit of involving high-dimensional thermal images and the efficacy of meta-learning framework.
ISSN: 1551-3203
DOI: 10.1109/TII.2020.3015555
Schools: School of Computer Science and Engineering 
Research Centres: Energy Research Institute @ NTU (ERI@N) 
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:ERI@N Journal Articles
SCSE Journal Articles

Citations 20

Updated on Dec 5, 2023

Web of ScienceTM
Citations 20

Updated on Oct 29, 2023

Page view(s)

Updated on Dec 7, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.