Digital Twin Model for Lithium-Ion Battery SOC Estimation in Battery Swapping Station
As electric vehicles rapidly gain popularity, battery swapping stations have emerged as key infrastructure to enhance the convenience of electric vehicles. Accurately estimating the State of Charge (SOC) of batteries during the battery swapping process is critical for ensuring efficient operation an...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10741523/ |
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| author | Ruopeng Wu Ying Shi Qin Hu Changjun Xie Jicheng Yu Boyang Ma Shengzhong Liu |
| author_facet | Ruopeng Wu Ying Shi Qin Hu Changjun Xie Jicheng Yu Boyang Ma Shengzhong Liu |
| author_sort | Ruopeng Wu |
| collection | DOAJ |
| description | As electric vehicles rapidly gain popularity, battery swapping stations have emerged as key infrastructure to enhance the convenience of electric vehicles. Accurately estimating the State of Charge (SOC) of batteries during the battery swapping process is critical for ensuring efficient operation and optimizing battery management. This paper proposes a digital twin framework and establishes a data-driven model for SOC estimation. The model, Ensemble Weighted Network (EWN), is embedded in the digital twin framework. It has three remarkable processes: multi-model ensemble, accuracy weight scaling, and time weight scaling. The multi-model ensemble combines five popular SOC estimating learners, to avoid the accuracy limitations of a single estimation model. The accuracy weight scaling and time weight scaling strategies are put forward to assign appropriate weights to each model, addressing significant fluctuations in estimation capability among different models across various periods. The results show the root mean square error, mean absolute error, and mean absolute percentage error of the model are less than 1.10%, 0.96%, and 1.92% respectively. Furthermore, we conduct experiments on public datasets to prove the proposed model has high estimation accuracy and robustness for different temperatures and battery types. Finally, the visualization of the digital twin is built, allowing operators to monitor and manage the batteries. |
| format | Article |
| id | doaj-art-13d72cb92c0f41048be42e4d8da9c33d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-13d72cb92c0f41048be42e4d8da9c33d2024-11-19T00:03:05ZengIEEEIEEE Access2169-35362024-01-011216566316567710.1109/ACCESS.2024.349065510741523Digital Twin Model for Lithium-Ion Battery SOC Estimation in Battery Swapping StationRuopeng Wu0Ying Shi1https://orcid.org/0000-0003-3519-2308Qin Hu2https://orcid.org/0000-0002-1571-292XChangjun Xie3https://orcid.org/0000-0002-9626-0813Jicheng Yu4https://orcid.org/0000-0002-4525-2987Boyang Ma5Shengzhong Liu6School of Automation, Wuhan University of Technology, Wuhan, ChinaSchool of Automation, Wuhan University of Technology, Wuhan, ChinaSchool of Automation, Wuhan University of Technology, Wuhan, ChinaSchool of Automation, Wuhan University of Technology, Wuhan, ChinaChina Electric Power Research Institute, Wuhan, ChinaState Grid Tianjin Electric Power Company, Tianjin, ChinaState Grid Tianjin Electric Power Company, Tianjin, ChinaAs electric vehicles rapidly gain popularity, battery swapping stations have emerged as key infrastructure to enhance the convenience of electric vehicles. Accurately estimating the State of Charge (SOC) of batteries during the battery swapping process is critical for ensuring efficient operation and optimizing battery management. This paper proposes a digital twin framework and establishes a data-driven model for SOC estimation. The model, Ensemble Weighted Network (EWN), is embedded in the digital twin framework. It has three remarkable processes: multi-model ensemble, accuracy weight scaling, and time weight scaling. The multi-model ensemble combines five popular SOC estimating learners, to avoid the accuracy limitations of a single estimation model. The accuracy weight scaling and time weight scaling strategies are put forward to assign appropriate weights to each model, addressing significant fluctuations in estimation capability among different models across various periods. The results show the root mean square error, mean absolute error, and mean absolute percentage error of the model are less than 1.10%, 0.96%, and 1.92% respectively. Furthermore, we conduct experiments on public datasets to prove the proposed model has high estimation accuracy and robustness for different temperatures and battery types. Finally, the visualization of the digital twin is built, allowing operators to monitor and manage the batteries.https://ieeexplore.ieee.org/document/10741523/Battery swappinglithium-ion batterystate of chargedigital twinensemble model |
| spellingShingle | Ruopeng Wu Ying Shi Qin Hu Changjun Xie Jicheng Yu Boyang Ma Shengzhong Liu Digital Twin Model for Lithium-Ion Battery SOC Estimation in Battery Swapping Station IEEE Access Battery swapping lithium-ion battery state of charge digital twin ensemble model |
| title | Digital Twin Model for Lithium-Ion Battery SOC Estimation in Battery Swapping Station |
| title_full | Digital Twin Model for Lithium-Ion Battery SOC Estimation in Battery Swapping Station |
| title_fullStr | Digital Twin Model for Lithium-Ion Battery SOC Estimation in Battery Swapping Station |
| title_full_unstemmed | Digital Twin Model for Lithium-Ion Battery SOC Estimation in Battery Swapping Station |
| title_short | Digital Twin Model for Lithium-Ion Battery SOC Estimation in Battery Swapping Station |
| title_sort | digital twin model for lithium ion battery soc estimation in battery swapping station |
| topic | Battery swapping lithium-ion battery state of charge digital twin ensemble model |
| url | https://ieeexplore.ieee.org/document/10741523/ |
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