Highly robust co‐estimation of state of charge and state of health using recursive total least squares and unscented Kalman filter for lithium‐ion battery
Abstract State of charge (SOC) and state of health (SOH) constitute pivotal factors in the efficient and secure management of lithium‐ion batteries, particularly within the context of electric vehicles. A highly‐robust co‐estimation method is proposed in this paper to accurately assess the SOC and S...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.12965 |
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| author | Xiaohui Li Weidong Liu Bin Liang Qian Li Yue Zhao Jian Hu |
| author_facet | Xiaohui Li Weidong Liu Bin Liang Qian Li Yue Zhao Jian Hu |
| author_sort | Xiaohui Li |
| collection | DOAJ |
| description | Abstract State of charge (SOC) and state of health (SOH) constitute pivotal factors in the efficient and secure management of lithium‐ion batteries, particularly within the context of electric vehicles. A highly‐robust co‐estimation method is proposed in this paper to accurately assess the SOC and SOH under strong electromagnetic interference environment. First, the 1‐RC equivalent circuit model is adopted and the model parameters are identified in a real‐time manner using the recursive total least‐square method to improve the accuracy and adaptivity of the battery model. Subsequently, the SOH estimation is reframed as capacity estimation and an unscented Kalman filter is designed to co‐estimate the SOC and capacity based on the battery model. The results suggest that the proposed method has strong robustness against the measurement noises on current and voltage. The average estimation errors of SOC and capacity are 1.57% and 0.11 Ahr, respectively. |
| format | Article |
| id | doaj-art-794ad8e059b14f29be161d59698efc2b |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-794ad8e059b14f29be161d59698efc2b2025-01-30T12:15:53ZengWileyIET Renewable Power Generation1752-14161752-14242024-12-0118163574358110.1049/rpg2.12965Highly robust co‐estimation of state of charge and state of health using recursive total least squares and unscented Kalman filter for lithium‐ion batteryXiaohui Li0Weidong Liu1Bin Liang2Qian Li3Yue Zhao4Jian Hu5State Grid Tianjin Electric Power Company Marketing Service Center Tianjin ChinaState Grid Tianjin Electric Power Company Marketing Service Center Tianjin ChinaState Grid Tianjin Electric Power Company Marketing Service Center Tianjin ChinaElectric Power Research Institute of Tianjin Power System Tianjin ChinaElectric Power Research Institute of Tianjin Power System Tianjin ChinaNational Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology Beijing ChinaAbstract State of charge (SOC) and state of health (SOH) constitute pivotal factors in the efficient and secure management of lithium‐ion batteries, particularly within the context of electric vehicles. A highly‐robust co‐estimation method is proposed in this paper to accurately assess the SOC and SOH under strong electromagnetic interference environment. First, the 1‐RC equivalent circuit model is adopted and the model parameters are identified in a real‐time manner using the recursive total least‐square method to improve the accuracy and adaptivity of the battery model. Subsequently, the SOH estimation is reframed as capacity estimation and an unscented Kalman filter is designed to co‐estimate the SOC and capacity based on the battery model. The results suggest that the proposed method has strong robustness against the measurement noises on current and voltage. The average estimation errors of SOC and capacity are 1.57% and 0.11 Ahr, respectively.https://doi.org/10.1049/rpg2.12965battery management systemsenergy storage |
| spellingShingle | Xiaohui Li Weidong Liu Bin Liang Qian Li Yue Zhao Jian Hu Highly robust co‐estimation of state of charge and state of health using recursive total least squares and unscented Kalman filter for lithium‐ion battery IET Renewable Power Generation battery management systems energy storage |
| title | Highly robust co‐estimation of state of charge and state of health using recursive total least squares and unscented Kalman filter for lithium‐ion battery |
| title_full | Highly robust co‐estimation of state of charge and state of health using recursive total least squares and unscented Kalman filter for lithium‐ion battery |
| title_fullStr | Highly robust co‐estimation of state of charge and state of health using recursive total least squares and unscented Kalman filter for lithium‐ion battery |
| title_full_unstemmed | Highly robust co‐estimation of state of charge and state of health using recursive total least squares and unscented Kalman filter for lithium‐ion battery |
| title_short | Highly robust co‐estimation of state of charge and state of health using recursive total least squares and unscented Kalman filter for lithium‐ion battery |
| title_sort | highly robust co estimation of state of charge and state of health using recursive total least squares and unscented kalman filter for lithium ion battery |
| topic | battery management systems energy storage |
| url | https://doi.org/10.1049/rpg2.12965 |
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