Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model
Accurate estimation of the state of charge (SOC) of batteries is one of the key problems in a battery management system. This paper proposes an adaptive SOC estimation method based on unscented Kalman filter algorithms for lithium (Li)-ion batteries. First, an enhanced battery model is proposed to i...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-08-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/6/8/4134 |
| _version_ | 1811307194135609344 |
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| author | Yuanyuan Liu Leyi Wang Mingyu Gao Caisheng Wang Zhiwei He |
| author_facet | Yuanyuan Liu Leyi Wang Mingyu Gao Caisheng Wang Zhiwei He |
| author_sort | Yuanyuan Liu |
| collection | DOAJ |
| description | Accurate estimation of the state of charge (SOC) of batteries is one of the key problems in a battery management system. This paper proposes an adaptive SOC estimation method based on unscented Kalman filter algorithms for lithium (Li)-ion batteries. First, an enhanced battery model is proposed to include the impacts due to different discharge rates and temperatures. An adaptive joint estimation of the battery SOC and battery internal resistance is then presented to enhance system robustness with battery aging. The SOC estimation algorithm has been developed and verified through experiments on different types of Li-ion batteries. The results indicate that the proposed method provides an accurate SOC estimation and is computationally efficient, making it suitable for embedded system implementation. |
| first_indexed | 2024-04-13T09:00:12Z |
| format | Article |
| id | doaj.art-b36448acfb83423ca765adb099eb0e6e |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T09:00:12Z |
| publishDate | 2013-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-b36448acfb83423ca765adb099eb0e6e2022-12-22T02:53:10ZengMDPI AGEnergies1996-10732013-08-01684134415110.3390/en6084134Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery ModelYuanyuan LiuLeyi WangMingyu GaoCaisheng WangZhiwei HeAccurate estimation of the state of charge (SOC) of batteries is one of the key problems in a battery management system. This paper proposes an adaptive SOC estimation method based on unscented Kalman filter algorithms for lithium (Li)-ion batteries. First, an enhanced battery model is proposed to include the impacts due to different discharge rates and temperatures. An adaptive joint estimation of the battery SOC and battery internal resistance is then presented to enhance system robustness with battery aging. The SOC estimation algorithm has been developed and verified through experiments on different types of Li-ion batteries. The results indicate that the proposed method provides an accurate SOC estimation and is computationally efficient, making it suitable for embedded system implementation.http://www.mdpi.com/1996-1073/6/8/4134batterystate of chargeonline estimationunscented Kalman filter |
| spellingShingle | Yuanyuan Liu Leyi Wang Mingyu Gao Caisheng Wang Zhiwei He Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model Energies battery state of charge online estimation unscented Kalman filter |
| title | Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model |
| title_full | Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model |
| title_fullStr | Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model |
| title_full_unstemmed | Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model |
| title_short | Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model |
| title_sort | adaptive state of charge estimation for li ion batteries based on an unscented kalman filter with an enhanced battery model |
| topic | battery state of charge online estimation unscented Kalman filter |
| url | http://www.mdpi.com/1996-1073/6/8/4134 |
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