A meta-learning based multimodal neural network for multistep ahead battery thermal runaway forecasting
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...
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/160292 |
| _version_ | 1824457006223720448 |
|---|---|
| author | Ding, Shuya Dong, Chaoyu Zhao, Tianyang Koh, Liang Mong Bai, Xiaoyin Luo, Jun |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Ding, Shuya Dong, Chaoyu Zhao, Tianyang Koh, Liang Mong Bai, Xiaoyin Luo, Jun |
| author_sort | Ding, Shuya |
| collection | NTU |
| description | 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. |
| first_indexed | 2025-02-19T04:03:08Z |
| format | Journal Article |
| id | ntu-10356/160292 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T04:03:08Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1602922022-07-19T01:40:44Z A meta-learning based multimodal neural network for multistep ahead battery thermal runaway forecasting Ding, Shuya Dong, Chaoyu Zhao, Tianyang Koh, Liang Mong Bai, Xiaoyin Luo, Jun School of Computer Science and Engineering Energy Research Institute @ NTU (ERI@N) Engineering::Computer science and engineering Feature Extraction Batteries 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. Nanyang Technological University National Research Foundation (NRF) This work was supported in part by the Energy Program Energy Storage System Test Bed Project under Grant NRF2016ENC-ESS001-022, and in part by the Energy Research Institute at Nanyang Technological University (Singapore). 2022-07-19T01:40:43Z 2022-07-19T01:40:43Z 2020 Journal Article 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. https://dx.doi.org/10.1109/TII.2020.3015555 1551-3203 https://hdl.handle.net/10356/160292 10.1109/TII.2020.3015555 2-s2.0-85104196858 7 17 4503 4511 en NRF2016ENC-ESS001-022 IEEE Transactions on Industrial Informatics © 2020 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Feature Extraction Batteries Ding, Shuya Dong, Chaoyu Zhao, Tianyang Koh, Liang Mong Bai, Xiaoyin Luo, Jun A meta-learning based multimodal neural network for multistep ahead battery thermal runaway forecasting |
| title | A meta-learning based multimodal neural network for multistep ahead battery thermal runaway forecasting |
| title_full | A meta-learning based multimodal neural network for multistep ahead battery thermal runaway forecasting |
| title_fullStr | A meta-learning based multimodal neural network for multistep ahead battery thermal runaway forecasting |
| title_full_unstemmed | A meta-learning based multimodal neural network for multistep ahead battery thermal runaway forecasting |
| title_short | A meta-learning based multimodal neural network for multistep ahead battery thermal runaway forecasting |
| title_sort | meta learning based multimodal neural network for multistep ahead battery thermal runaway forecasting |
| topic | Engineering::Computer science and engineering Feature Extraction Batteries |
| url | https://hdl.handle.net/10356/160292 |
| work_keys_str_mv | AT dingshuya ametalearningbasedmultimodalneuralnetworkformultistepaheadbatterythermalrunawayforecasting AT dongchaoyu ametalearningbasedmultimodalneuralnetworkformultistepaheadbatterythermalrunawayforecasting AT zhaotianyang ametalearningbasedmultimodalneuralnetworkformultistepaheadbatterythermalrunawayforecasting AT kohliangmong ametalearningbasedmultimodalneuralnetworkformultistepaheadbatterythermalrunawayforecasting AT baixiaoyin ametalearningbasedmultimodalneuralnetworkformultistepaheadbatterythermalrunawayforecasting AT luojun ametalearningbasedmultimodalneuralnetworkformultistepaheadbatterythermalrunawayforecasting AT dingshuya metalearningbasedmultimodalneuralnetworkformultistepaheadbatterythermalrunawayforecasting AT dongchaoyu metalearningbasedmultimodalneuralnetworkformultistepaheadbatterythermalrunawayforecasting AT zhaotianyang metalearningbasedmultimodalneuralnetworkformultistepaheadbatterythermalrunawayforecasting AT kohliangmong metalearningbasedmultimodalneuralnetworkformultistepaheadbatterythermalrunawayforecasting AT baixiaoyin metalearningbasedmultimodalneuralnetworkformultistepaheadbatterythermalrunawayforecasting AT luojun metalearningbasedmultimodalneuralnetworkformultistepaheadbatterythermalrunawayforecasting |