Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online Identification
The state-of-charge (SOC) is a fundamental indicator representing the remaining capacity of lithium-ion batteries, which plays an important role in the battery’s optimized operation. In this paper, the model-based SOC estimation strategy is studied for batteries. However, the battery’s model paramet...
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| Format: | Article |
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MDPI AG
2020-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/18/4968 |
| _version_ | 1797552992525746176 |
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| author | Quan Ouyang Rui Ma Zhaoxiang Wu Guotuan Xu Zhisheng Wang |
| author_facet | Quan Ouyang Rui Ma Zhaoxiang Wu Guotuan Xu Zhisheng Wang |
| author_sort | Quan Ouyang |
| collection | DOAJ |
| description | The state-of-charge (SOC) is a fundamental indicator representing the remaining capacity of lithium-ion batteries, which plays an important role in the battery’s optimized operation. In this paper, the model-based SOC estimation strategy is studied for batteries. However, the battery’s model parameters need to be extracted through cumbersome prior experiments. To remedy such deficiency, a recursive least squares (RLS) algorithm is utilized for model parameter online identification, and an adaptive square-root unscented Kalman filter (SRUKF) is designed to estimate the battery’s SOC. As demonstrated in extensive experimental results, the designed adaptive SRUKF combined with RLS-based model identification is a promising SOC estimation approach. Compared with other commonly used Kalman filter-based methods, the proposed algorithm has higher precision in the SOC estimation. |
| first_indexed | 2024-03-10T16:09:05Z |
| format | Article |
| id | doaj.art-cd27f70f3a4541ceb013620f52db7f5b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T16:09:05Z |
| publishDate | 2020-09-01 |
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| record_format | Article |
| series | Energies |
| spelling | doaj.art-cd27f70f3a4541ceb013620f52db7f5b2023-11-20T14:39:28ZengMDPI AGEnergies1996-10732020-09-011318496810.3390/en13184968Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online IdentificationQuan Ouyang0Rui Ma1Zhaoxiang Wu2Guotuan Xu3Zhisheng Wang4College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaThe state-of-charge (SOC) is a fundamental indicator representing the remaining capacity of lithium-ion batteries, which plays an important role in the battery’s optimized operation. In this paper, the model-based SOC estimation strategy is studied for batteries. However, the battery’s model parameters need to be extracted through cumbersome prior experiments. To remedy such deficiency, a recursive least squares (RLS) algorithm is utilized for model parameter online identification, and an adaptive square-root unscented Kalman filter (SRUKF) is designed to estimate the battery’s SOC. As demonstrated in extensive experimental results, the designed adaptive SRUKF combined with RLS-based model identification is a promising SOC estimation approach. Compared with other commonly used Kalman filter-based methods, the proposed algorithm has higher precision in the SOC estimation.https://www.mdpi.com/1996-1073/13/18/4968lithium-ion batteriesstate-of-charge estimationadaptive square-root unscented Kalman filterrecursive least squares |
| spellingShingle | Quan Ouyang Rui Ma Zhaoxiang Wu Guotuan Xu Zhisheng Wang Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online Identification Energies lithium-ion batteries state-of-charge estimation adaptive square-root unscented Kalman filter recursive least squares |
| title | Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online Identification |
| title_full | Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online Identification |
| title_fullStr | Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online Identification |
| title_full_unstemmed | Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online Identification |
| title_short | Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online Identification |
| title_sort | adaptive square root unscented kalman filter based state of charge estimation for lithium ion batteries with model parameter online identification |
| topic | lithium-ion batteries state-of-charge estimation adaptive square-root unscented Kalman filter recursive least squares |
| url | https://www.mdpi.com/1996-1073/13/18/4968 |
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