DNN‐based temperature prediction of large‐scale battery pack
Abstract Temperature monitoring is critical for estimating the available capacity of Lithium‐ion batteries. In electric vehicle applications using large‐scale battery packs, monitoring individual cell temperature is challenging due to difficulties in sensor management. To address this issue, a senso...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-08-01
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| Series: | Electronics Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ell2.12917 |
| _version_ | 1797733776773611520 |
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| author | Jiwon Kim Rhan Ha |
| author_facet | Jiwon Kim Rhan Ha |
| author_sort | Jiwon Kim |
| collection | DOAJ |
| description | Abstract Temperature monitoring is critical for estimating the available capacity of Lithium‐ion batteries. In electric vehicle applications using large‐scale battery packs, monitoring individual cell temperature is challenging due to difficulties in sensor management. To address this issue, a sensor‐less battery temperature prediction technique is proposed that ensures both accuracy and rapid runtime execution using deep learning. A deep neural network‐based temperature prediction model is introduced that utilizes short sequences of battery voltage and discharge current. An adaptive sequence length strategy is then devised to ensure high accuracy and responsiveness, covering the non‐identically distributed nature of the data. The proposed technique is experimentally validated with commercial batteries, verifying its accuracy and rapid execution. |
| first_indexed | 2024-03-12T12:33:12Z |
| format | Article |
| id | doaj.art-a99b019fb37342109bd8ce39b49f15f3 |
| institution | Directory Open Access Journal |
| issn | 0013-5194 1350-911X |
| language | English |
| last_indexed | 2024-03-12T12:33:12Z |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Electronics Letters |
| spelling | doaj.art-a99b019fb37342109bd8ce39b49f15f32023-08-29T07:22:28ZengWileyElectronics Letters0013-51941350-911X2023-08-015916n/an/a10.1049/ell2.12917DNN‐based temperature prediction of large‐scale battery packJiwon Kim0Rhan Ha1Department of Computer Science Yonsei University Seoul South KoreaDepartment of Computer Engineering Hongik University Seoul South KoreaAbstract Temperature monitoring is critical for estimating the available capacity of Lithium‐ion batteries. In electric vehicle applications using large‐scale battery packs, monitoring individual cell temperature is challenging due to difficulties in sensor management. To address this issue, a sensor‐less battery temperature prediction technique is proposed that ensures both accuracy and rapid runtime execution using deep learning. A deep neural network‐based temperature prediction model is introduced that utilizes short sequences of battery voltage and discharge current. An adaptive sequence length strategy is then devised to ensure high accuracy and responsiveness, covering the non‐identically distributed nature of the data. The proposed technique is experimentally validated with commercial batteries, verifying its accuracy and rapid execution.https://doi.org/10.1049/ell2.12917artificial intelligencebattery powered vehiclestemperature measurement |
| spellingShingle | Jiwon Kim Rhan Ha DNN‐based temperature prediction of large‐scale battery pack Electronics Letters artificial intelligence battery powered vehicles temperature measurement |
| title | DNN‐based temperature prediction of large‐scale battery pack |
| title_full | DNN‐based temperature prediction of large‐scale battery pack |
| title_fullStr | DNN‐based temperature prediction of large‐scale battery pack |
| title_full_unstemmed | DNN‐based temperature prediction of large‐scale battery pack |
| title_short | DNN‐based temperature prediction of large‐scale battery pack |
| title_sort | dnn based temperature prediction of large scale battery pack |
| topic | artificial intelligence battery powered vehicles temperature measurement |
| url | https://doi.org/10.1049/ell2.12917 |
| work_keys_str_mv | AT jiwonkim dnnbasedtemperaturepredictionoflargescalebatterypack AT rhanha dnnbasedtemperaturepredictionoflargescalebatterypack |