Online lithium-ion battery intelligent perception for thermal fault detection and localization
Equipping lithium-ion batteries with a reasonable thermal fault diagnosis can avoid thermal runaway and ensure the safe and reliable operation of the batteries. This research built a lithium-ion battery thermal fault diagnosis model that optimized the original mask region-based convolutional neural...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/178490 |
| _version_ | 1811689560431656960 |
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| author | Tian, Luyu Dong, Chaoyu Mu, Yunfei Yu, Xiaodan Jia, Hongjie |
| author2 | Energy Research Institute @ NTU (ERI@N) |
| author_facet | Energy Research Institute @ NTU (ERI@N) Tian, Luyu Dong, Chaoyu Mu, Yunfei Yu, Xiaodan Jia, Hongjie |
| author_sort | Tian, Luyu |
| collection | NTU |
| description | Equipping lithium-ion batteries with a reasonable thermal fault diagnosis can avoid thermal runaway and ensure the safe and reliable operation of the batteries. This research built a lithium-ion battery thermal fault diagnosis model that optimized the original mask region-based convolutional neural network based on the battery dataset in both parameters and structure. The model processes the thermal images of the battery surface, identifies problematic batteries, and locates the problematic regions. A backbone network is used to process the battery thermal images and extract feature information. Through the RPN network, the thermal feature is classified and regressed, and the Mask branch is used to ultimately determine the faulty battery's location. Additionally, we have optimized the original mask region-based convolutional neural network based on the battery dataset in both parameters and structure. The improved LBIP-V2 performs better than LBIP-V1 in most cases. We tested the performance of LBIP on the single-cell battery dataset, the 1P3S battery pack dataset, and the flattened 1P3S battery pack dataset. The results show that the recognition accuracy of LBIP exceeded 95 %. At the same time, we simulated the failure of the 1P3S battery pack within 0-15 min and tested the effectiveness of LBIP in real-time battery fault diagnosis. The results indicate that LBIP can effectively respond to online faults with a confidence level of over 98 %. |
| first_indexed | 2024-10-01T05:50:03Z |
| format | Journal Article |
| id | ntu-10356/178490 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:50:03Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1784902024-06-25T15:37:35Z Online lithium-ion battery intelligent perception for thermal fault detection and localization Tian, Luyu Dong, Chaoyu Mu, Yunfei Yu, Xiaodan Jia, Hongjie Energy Research Institute @ NTU (ERI@N) Engineering Lithium-ion battery Thermal diagnosis Equipping lithium-ion batteries with a reasonable thermal fault diagnosis can avoid thermal runaway and ensure the safe and reliable operation of the batteries. This research built a lithium-ion battery thermal fault diagnosis model that optimized the original mask region-based convolutional neural network based on the battery dataset in both parameters and structure. The model processes the thermal images of the battery surface, identifies problematic batteries, and locates the problematic regions. A backbone network is used to process the battery thermal images and extract feature information. Through the RPN network, the thermal feature is classified and regressed, and the Mask branch is used to ultimately determine the faulty battery's location. Additionally, we have optimized the original mask region-based convolutional neural network based on the battery dataset in both parameters and structure. The improved LBIP-V2 performs better than LBIP-V1 in most cases. We tested the performance of LBIP on the single-cell battery dataset, the 1P3S battery pack dataset, and the flattened 1P3S battery pack dataset. The results show that the recognition accuracy of LBIP exceeded 95 %. At the same time, we simulated the failure of the 1P3S battery pack within 0-15 min and tested the effectiveness of LBIP in real-time battery fault diagnosis. The results indicate that LBIP can effectively respond to online faults with a confidence level of over 98 %. Published version This research was supported by the project of National Natural Science Foundation of China (U23B6006, 52277116). 2024-06-24T05:37:18Z 2024-06-24T05:37:18Z 2024 Journal Article Tian, L., Dong, C., Mu, Y., Yu, X. & Jia, H. (2024). Online lithium-ion battery intelligent perception for thermal fault detection and localization. Heliyon, 10(4), e25298-. https://dx.doi.org/10.1016/j.heliyon.2024.e25298 2405-8440 https://hdl.handle.net/10356/178490 10.1016/j.heliyon.2024.e25298 38370222 2-s2.0-85184596757 4 10 e25298 en Heliyon © 2024 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). application/pdf |
| spellingShingle | Engineering Lithium-ion battery Thermal diagnosis Tian, Luyu Dong, Chaoyu Mu, Yunfei Yu, Xiaodan Jia, Hongjie Online lithium-ion battery intelligent perception for thermal fault detection and localization |
| title | Online lithium-ion battery intelligent perception for thermal fault detection and localization |
| title_full | Online lithium-ion battery intelligent perception for thermal fault detection and localization |
| title_fullStr | Online lithium-ion battery intelligent perception for thermal fault detection and localization |
| title_full_unstemmed | Online lithium-ion battery intelligent perception for thermal fault detection and localization |
| title_short | Online lithium-ion battery intelligent perception for thermal fault detection and localization |
| title_sort | online lithium ion battery intelligent perception for thermal fault detection and localization |
| topic | Engineering Lithium-ion battery Thermal diagnosis |
| url | https://hdl.handle.net/10356/178490 |
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