On-Line Parameter Identification and SOC Estimation for Lithium-Ion Batteries Based on Improved Sage–Husa Adaptive EKF
For the Battery Management System (BMS) to manage and control the battery, State of Charge (SOC) is an important battery performance indicator. In order to identify the parameters of the LiFePO<sub>4</sub> battery, this paper employs the forgetting factor recursive least squares (FFRLS)...
| 出版年: | Energies |
|---|---|
| 主要な著者: | , , , , , , |
| フォーマット: | 論文 |
| 言語: | 英語 |
| 出版事項: |
MDPI AG
2024-11-01
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| 主題: | |
| オンライン･アクセス: | https://www.mdpi.com/1996-1073/17/22/5722 |
| _version_ | 1849691481864929280 |
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| author | Xuan Tang Hai Huang Xiongwu Zhong Kunjun Wang Fang Li Youhang Zhou Haifeng Dai |
| author_facet | Xuan Tang Hai Huang Xiongwu Zhong Kunjun Wang Fang Li Youhang Zhou Haifeng Dai |
| author_sort | Xuan Tang |
| collection | DOAJ |
| container_title | Energies |
| description | For the Battery Management System (BMS) to manage and control the battery, State of Charge (SOC) is an important battery performance indicator. In order to identify the parameters of the LiFePO<sub>4</sub> battery, this paper employs the forgetting factor recursive least squares (FFRLS) method, which considers the computational volume and model correctness, to determine the parameters of the LiFePO<sub>4</sub> battery. On this basis, the two resistor-capacitor equivalent circuit model is selected for estimating the SOC of the Li-ion battery by combining the extended Kalman filter (EKF) with the Sage–Husa adaptive algorithm. The positivity is improved by modifying the system noise estimation matrix. The paper concludes with a MATLAB 2016B simulation, which serves to validate the SOC estimation algorithm. The results demonstrate that, in comparison to the conventional EKF, the enhanced EKF exhibits superior estimation precision and resilience to interference, along with enhanced convergence during the estimation process. |
| format | Article |
| id | doaj-art-2208aad201014d24a7c3ee1e55bd5975 |
| institution | Directory of Open Access Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| spelling | doaj-art-2208aad201014d24a7c3ee1e55bd59752025-08-20T02:08:00ZengMDPI AGEnergies1996-10732024-11-011722572210.3390/en17225722On-Line Parameter Identification and SOC Estimation for Lithium-Ion Batteries Based on Improved Sage–Husa Adaptive EKFXuan Tang0Hai Huang1Xiongwu Zhong2Kunjun Wang3Fang Li4Youhang Zhou5Haifeng Dai6School of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, ChinaSchool of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, ChinaCRRC Times Electric Vehicle Co., Ltd., Zhuzhou 412007, ChinaCRRC Times Electric Vehicle Co., Ltd., Zhuzhou 412007, ChinaSchool of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, ChinaSchool of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, ChinaClean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, ChinaFor the Battery Management System (BMS) to manage and control the battery, State of Charge (SOC) is an important battery performance indicator. In order to identify the parameters of the LiFePO<sub>4</sub> battery, this paper employs the forgetting factor recursive least squares (FFRLS) method, which considers the computational volume and model correctness, to determine the parameters of the LiFePO<sub>4</sub> battery. On this basis, the two resistor-capacitor equivalent circuit model is selected for estimating the SOC of the Li-ion battery by combining the extended Kalman filter (EKF) with the Sage–Husa adaptive algorithm. The positivity is improved by modifying the system noise estimation matrix. The paper concludes with a MATLAB 2016B simulation, which serves to validate the SOC estimation algorithm. The results demonstrate that, in comparison to the conventional EKF, the enhanced EKF exhibits superior estimation precision and resilience to interference, along with enhanced convergence during the estimation process.https://www.mdpi.com/1996-1073/17/22/5722state of chargethe Sage–Husa adaptive methodextended Kalman filterequivalent circuit model |
| spellingShingle | Xuan Tang Hai Huang Xiongwu Zhong Kunjun Wang Fang Li Youhang Zhou Haifeng Dai On-Line Parameter Identification and SOC Estimation for Lithium-Ion Batteries Based on Improved Sage–Husa Adaptive EKF state of charge the Sage–Husa adaptive method extended Kalman filter equivalent circuit model |
| title | On-Line Parameter Identification and SOC Estimation for Lithium-Ion Batteries Based on Improved Sage–Husa Adaptive EKF |
| title_full | On-Line Parameter Identification and SOC Estimation for Lithium-Ion Batteries Based on Improved Sage–Husa Adaptive EKF |
| title_fullStr | On-Line Parameter Identification and SOC Estimation for Lithium-Ion Batteries Based on Improved Sage–Husa Adaptive EKF |
| title_full_unstemmed | On-Line Parameter Identification and SOC Estimation for Lithium-Ion Batteries Based on Improved Sage–Husa Adaptive EKF |
| title_short | On-Line Parameter Identification and SOC Estimation for Lithium-Ion Batteries Based on Improved Sage–Husa Adaptive EKF |
| title_sort | on line parameter identification and soc estimation for lithium ion batteries based on improved sage husa adaptive ekf |
| topic | state of charge the Sage–Husa adaptive method extended Kalman filter equivalent circuit model |
| url | https://www.mdpi.com/1996-1073/17/22/5722 |
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