An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error
Accurate state of charge (SOC) plays a vital role in battery management systems (BMSs). Among several developed SOC estimation methods, the extended Kalman filter (EKF) has been extensively applied. However, EKF cannot achieve valid estimation when the model accuracy is inadequate, the noise covaria...
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MDPI AG
2022-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/10/3499 |
| _version_ | 1827669340922576896 |
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| author | Deng Ma Kai Gao Yutao Mu Ziqi Wei Ronghua Du |
| author_facet | Deng Ma Kai Gao Yutao Mu Ziqi Wei Ronghua Du |
| author_sort | Deng Ma |
| collection | DOAJ |
| description | Accurate state of charge (SOC) plays a vital role in battery management systems (BMSs). Among several developed SOC estimation methods, the extended Kalman filter (EKF) has been extensively applied. However, EKF cannot achieve valid estimation when the model accuracy is inadequate, the noise covariance matrix is uncertain, and the sensor has large errors. This paper makes two contributions to overcome these drawbacks: (1) A variable forgetting factor recursive least squares (VFFRLS) is proposed to accomplish parameters identification. This method updates the forgetting factor according to the innovation sequence, which accuracy is superior to the forgetting factor recursive least squares (FFRLS); (2) an adaptive tracking EKF (ATEKF) is proposed to estimate the SOC of the battery. In ATEKF, the error covariance matrix is adaptively corrected according to the innovation sequence and correction factor. The value of the correction factor is related to the actual error. Proposed algorithms are validated with a publicly available dataset from the University of Maryland. The experimental results indicate that the identification error of VFFRLS can be reduced from 0.05% to 0.018%. Additionally, ATEKF has better accuracy and robustness than EKF when having large sensor errors and uncertainty of the error covariance matrix, in which case it can reduce SOC estimation error from 1.09% to 0.15%. |
| first_indexed | 2024-03-10T03:59:00Z |
| format | Article |
| id | doaj.art-64342fe0528a46f09ab079d92d951c21 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T03:59:00Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
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| series | Energies |
| spelling | doaj.art-64342fe0528a46f09ab079d92d951c212023-11-23T10:48:56ZengMDPI AGEnergies1996-10732022-05-011510349910.3390/en15103499An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor ErrorDeng Ma0Kai Gao1Yutao Mu2Ziqi Wei3Ronghua Du4International College of Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaCollege of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaInternational College of Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaInternational College of Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaCollege of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaAccurate state of charge (SOC) plays a vital role in battery management systems (BMSs). Among several developed SOC estimation methods, the extended Kalman filter (EKF) has been extensively applied. However, EKF cannot achieve valid estimation when the model accuracy is inadequate, the noise covariance matrix is uncertain, and the sensor has large errors. This paper makes two contributions to overcome these drawbacks: (1) A variable forgetting factor recursive least squares (VFFRLS) is proposed to accomplish parameters identification. This method updates the forgetting factor according to the innovation sequence, which accuracy is superior to the forgetting factor recursive least squares (FFRLS); (2) an adaptive tracking EKF (ATEKF) is proposed to estimate the SOC of the battery. In ATEKF, the error covariance matrix is adaptively corrected according to the innovation sequence and correction factor. The value of the correction factor is related to the actual error. Proposed algorithms are validated with a publicly available dataset from the University of Maryland. The experimental results indicate that the identification error of VFFRLS can be reduced from 0.05% to 0.018%. Additionally, ATEKF has better accuracy and robustness than EKF when having large sensor errors and uncertainty of the error covariance matrix, in which case it can reduce SOC estimation error from 1.09% to 0.15%.https://www.mdpi.com/1996-1073/15/10/3499SOCEKFmodel uncertaintysensor error |
| spellingShingle | Deng Ma Kai Gao Yutao Mu Ziqi Wei Ronghua Du An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error Energies SOC EKF model uncertainty sensor error |
| title | An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error |
| title_full | An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error |
| title_fullStr | An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error |
| title_full_unstemmed | An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error |
| title_short | An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error |
| title_sort | adaptive tracking extended kalman filter for soc estimation of batteries with model uncertainty and sensor error |
| topic | SOC EKF model uncertainty sensor error |
| url | https://www.mdpi.com/1996-1073/15/10/3499 |
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