A State-of-Health Estimation Method of a Lithium-Ion Power Battery for Swapping Stations Based on a Transformer Framework
Against the backdrop of automobile electrification, an increasing number of battery-swapping stations for electric vehicles have been launched to address the issue of slow battery charging under cold temperature conditions. However, due to the separation of the discharging and charging processes for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/11/1/22 |
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| _version_ | 1832589094389022720 |
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| author | Yu Shi Haicheng Xie Xinhong Wang Xiaoming Lu Jing Wang Xin Xu Dingheng Wang Siyan Chen |
| author_facet | Yu Shi Haicheng Xie Xinhong Wang Xiaoming Lu Jing Wang Xin Xu Dingheng Wang Siyan Chen |
| author_sort | Yu Shi |
| collection | DOAJ |
| description | Against the backdrop of automobile electrification, an increasing number of battery-swapping stations for electric vehicles have been launched to address the issue of slow battery charging under cold temperature conditions. However, due to the separation of the discharging and charging processes for lithium-ion batteries (LIBs) at swapping stations, and the circulation of batteries across different vehicles and stations, the operating data become fragmented, making it difficult to accurately identify the battery state-of-health (SOH). This study proposes a BiLSTM-Transformer framework that extracts the Constant Voltage Time (CVT) feature using only charging data, enabling the precise estimation of battery capacity degradation. Validation experiments conducted on battery samples under different operating temperatures showed that the model achieved a normalized RMSE of less than 1.6%. In ideal conditions, the normalized RMSE of the estimation reached as low as 0.11%. This model enables SOH estimation without relying on discharge data, contributing to the efficient and safe operation of battery swapping stations. |
| format | Article |
| id | doaj-art-08c14b6845154d60930231a7e216b7cf |
| institution | Kabale University |
| issn | 2313-0105 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-08c14b6845154d60930231a7e216b7cf2025-01-24T13:22:26ZengMDPI AGBatteries2313-01052025-01-011112210.3390/batteries11010022A State-of-Health Estimation Method of a Lithium-Ion Power Battery for Swapping Stations Based on a Transformer FrameworkYu Shi0Haicheng Xie1Xinhong Wang2Xiaoming Lu3Jing Wang4Xin Xu5Dingheng Wang6Siyan Chen7Jilin State Power Economic and Technical Research Institute, Changchun 130022, ChinaCollege of Automotive Engineering, Jilin University, Changchun 130022, ChinaJilin State Power Economic and Technical Research Institute, Changchun 130022, ChinaJilin State Power Economic and Technical Research Institute, Changchun 130022, ChinaJilin State Power Economic and Technical Research Institute, Changchun 130022, ChinaJilin State Power Economic and Technical Research Institute, Changchun 130022, ChinaJilin State Power Economic and Technical Research Institute, Changchun 130022, ChinaCollege of Automotive Engineering, Jilin University, Changchun 130022, ChinaAgainst the backdrop of automobile electrification, an increasing number of battery-swapping stations for electric vehicles have been launched to address the issue of slow battery charging under cold temperature conditions. However, due to the separation of the discharging and charging processes for lithium-ion batteries (LIBs) at swapping stations, and the circulation of batteries across different vehicles and stations, the operating data become fragmented, making it difficult to accurately identify the battery state-of-health (SOH). This study proposes a BiLSTM-Transformer framework that extracts the Constant Voltage Time (CVT) feature using only charging data, enabling the precise estimation of battery capacity degradation. Validation experiments conducted on battery samples under different operating temperatures showed that the model achieved a normalized RMSE of less than 1.6%. In ideal conditions, the normalized RMSE of the estimation reached as low as 0.11%. This model enables SOH estimation without relying on discharge data, contributing to the efficient and safe operation of battery swapping stations.https://www.mdpi.com/2313-0105/11/1/22lithium-ion batterycapacity estimationtransformer frameworkswapping stationsmulti-feature analysis |
| spellingShingle | Yu Shi Haicheng Xie Xinhong Wang Xiaoming Lu Jing Wang Xin Xu Dingheng Wang Siyan Chen A State-of-Health Estimation Method of a Lithium-Ion Power Battery for Swapping Stations Based on a Transformer Framework Batteries lithium-ion battery capacity estimation transformer framework swapping stations multi-feature analysis |
| title | A State-of-Health Estimation Method of a Lithium-Ion Power Battery for Swapping Stations Based on a Transformer Framework |
| title_full | A State-of-Health Estimation Method of a Lithium-Ion Power Battery for Swapping Stations Based on a Transformer Framework |
| title_fullStr | A State-of-Health Estimation Method of a Lithium-Ion Power Battery for Swapping Stations Based on a Transformer Framework |
| title_full_unstemmed | A State-of-Health Estimation Method of a Lithium-Ion Power Battery for Swapping Stations Based on a Transformer Framework |
| title_short | A State-of-Health Estimation Method of a Lithium-Ion Power Battery for Swapping Stations Based on a Transformer Framework |
| title_sort | state of health estimation method of a lithium ion power battery for swapping stations based on a transformer framework |
| topic | lithium-ion battery capacity estimation transformer framework swapping stations multi-feature analysis |
| url | https://www.mdpi.com/2313-0105/11/1/22 |
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