A strong robust state-of-charge estimation method based on the gas-liquid dynamics model
Model-based strategies for estimating the state-of-charge (SOC) of Li-ion batteries are essential in real-time applications, such as electric vehicles and large-scale energy storage. However, based on existing models, developing estimation methods with strong robustness to initial and cumulative err...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Green Energy and Intelligent Transportation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773153724000458 |
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| _version_ | 1850110894763147264 |
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| author | Biao Chen Liang Song Haobin Jiang Zhiguo Zhao Jun Zhu Keqiang Xu |
| author_facet | Biao Chen Liang Song Haobin Jiang Zhiguo Zhao Jun Zhu Keqiang Xu |
| author_sort | Biao Chen |
| collection | DOAJ |
| description | Model-based strategies for estimating the state-of-charge (SOC) of Li-ion batteries are essential in real-time applications, such as electric vehicles and large-scale energy storage. However, based on existing models, developing estimation methods with strong robustness to initial and cumulative errors, high SOC estimation accuracy, and adaptability to sparse data remains challenging. Herein, the modeling principles of the gas-liquid dynamics model are systematically clarified, and a SOC estimation method based on this model and a dual extended Kalman filter with a watchdog function is proposed. The proposed method is comprehensively compared with general extended Kalman filter and dual extended Kalman filter methods under five working conditions. The results indicate that all three methods based on the gas-liquid dynamics model have good estimation accuracy, with a maximum SOC error of 0.016 under correct initial conditions. But the proposed method has significant advantages in robustness to large initial errors, cumulative errors, and sparse data. This study provides new insights into efficient online SOC estimation. |
| format | Article |
| id | doaj-art-5a26d901e7a64ecbb3683348ed580ffb |
| institution | OA Journals |
| issn | 2773-1537 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Green Energy and Intelligent Transportation |
| spelling | doaj-art-5a26d901e7a64ecbb3683348ed580ffb2025-08-20T02:37:45ZengElsevierGreen Energy and Intelligent Transportation2773-15372025-06-014310019310.1016/j.geits.2024.100193A strong robust state-of-charge estimation method based on the gas-liquid dynamics modelBiao Chen0Liang Song1Haobin Jiang2Zhiguo Zhao3Jun Zhu4Keqiang Xu5Jiangsu Key Laboratory of Traffic and Transportation Security, Huaiyin Institute of Technology, Huai'an 223003, China; Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, ChinaFaculty of Chemical Engineering, Huaiyin Institute of Technology, Huai'an 223003, China; Corresponding author.Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, ChinaJiangsu Key Laboratory of Traffic and Transportation Security, Huaiyin Institute of Technology, Huai'an 223003, China; Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, ChinaJiangsu Key Laboratory of Traffic and Transportation Security, Huaiyin Institute of Technology, Huai'an 223003, ChinaJiangsu Key Laboratory of Traffic and Transportation Security, Huaiyin Institute of Technology, Huai'an 223003, ChinaModel-based strategies for estimating the state-of-charge (SOC) of Li-ion batteries are essential in real-time applications, such as electric vehicles and large-scale energy storage. However, based on existing models, developing estimation methods with strong robustness to initial and cumulative errors, high SOC estimation accuracy, and adaptability to sparse data remains challenging. Herein, the modeling principles of the gas-liquid dynamics model are systematically clarified, and a SOC estimation method based on this model and a dual extended Kalman filter with a watchdog function is proposed. The proposed method is comprehensively compared with general extended Kalman filter and dual extended Kalman filter methods under five working conditions. The results indicate that all three methods based on the gas-liquid dynamics model have good estimation accuracy, with a maximum SOC error of 0.016 under correct initial conditions. But the proposed method has significant advantages in robustness to large initial errors, cumulative errors, and sparse data. This study provides new insights into efficient online SOC estimation.http://www.sciencedirect.com/science/article/pii/S2773153724000458State of charge estimationGas-liquid dynamics modelDual extended Kalman filter with a watchdog functionLi-ion battery |
| spellingShingle | Biao Chen Liang Song Haobin Jiang Zhiguo Zhao Jun Zhu Keqiang Xu A strong robust state-of-charge estimation method based on the gas-liquid dynamics model Green Energy and Intelligent Transportation State of charge estimation Gas-liquid dynamics model Dual extended Kalman filter with a watchdog function Li-ion battery |
| title | A strong robust state-of-charge estimation method based on the gas-liquid dynamics model |
| title_full | A strong robust state-of-charge estimation method based on the gas-liquid dynamics model |
| title_fullStr | A strong robust state-of-charge estimation method based on the gas-liquid dynamics model |
| title_full_unstemmed | A strong robust state-of-charge estimation method based on the gas-liquid dynamics model |
| title_short | A strong robust state-of-charge estimation method based on the gas-liquid dynamics model |
| title_sort | strong robust state of charge estimation method based on the gas liquid dynamics model |
| topic | State of charge estimation Gas-liquid dynamics model Dual extended Kalman filter with a watchdog function Li-ion battery |
| url | http://www.sciencedirect.com/science/article/pii/S2773153724000458 |
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