A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented Kalman filter
The state of lithium-ion battery is a key indicator for the battery management system (BMS) of electric vehicles (EVs). State of charge (SOC) and state of health (SOH) of the power cell are the main parameters of the BMS during operation. In this paper, an adaptive unscented Kalman filter algorithm...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722018170 |
| _version_ | 1828061804173983744 |
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| author | Shulin Liu Xia Dong Xiaodong Yu Xiaoqing Ren Jinfeng Zhang Rui Zhu |
| author_facet | Shulin Liu Xia Dong Xiaodong Yu Xiaoqing Ren Jinfeng Zhang Rui Zhu |
| author_sort | Shulin Liu |
| collection | DOAJ |
| description | The state of lithium-ion battery is a key indicator for the battery management system (BMS) of electric vehicles (EVs). State of charge (SOC) and state of health (SOH) of the power cell are the main parameters of the BMS during operation. In this paper, an adaptive unscented Kalman filter algorithm (AUKF) is presented for the joint estimation of SOC and SOH of lithium-ion batteries. Firstly, this paper develops a 2-RC equivalent circuit model and identifies the model parameters using recursive least squares algorithm with forgetting factor. Then, the SOC and SOH of the battery are estimated simultaneously by AUKF. Finally, the accuracy of the proposed method is verified under different operating conditions. The experiment results show that the maximum SOC estimation error is under 0.08% by the proposed method. Compared with the unscented Kalman filtering (UKF), it is shown that the proposed method is more accurate and reliable. An effective method is provided for state estimation for battery management system. |
| first_indexed | 2024-04-10T22:19:33Z |
| format | Article |
| id | doaj.art-2ad6cbbe49944b80ae3a00c072a1df06 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T22:19:33Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-2ad6cbbe49944b80ae3a00c072a1df062023-01-18T04:31:30ZengElsevierEnergy Reports2352-48472022-11-018426436A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented Kalman filterShulin Liu0Xia Dong1Xiaodong Yu2Xiaoqing Ren3Jinfeng Zhang4Rui Zhu5School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China; Corresponding author.Department of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaDepartment of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaDepartment of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaDepartment of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan 250358, ChinaThe state of lithium-ion battery is a key indicator for the battery management system (BMS) of electric vehicles (EVs). State of charge (SOC) and state of health (SOH) of the power cell are the main parameters of the BMS during operation. In this paper, an adaptive unscented Kalman filter algorithm (AUKF) is presented for the joint estimation of SOC and SOH of lithium-ion batteries. Firstly, this paper develops a 2-RC equivalent circuit model and identifies the model parameters using recursive least squares algorithm with forgetting factor. Then, the SOC and SOH of the battery are estimated simultaneously by AUKF. Finally, the accuracy of the proposed method is verified under different operating conditions. The experiment results show that the maximum SOC estimation error is under 0.08% by the proposed method. Compared with the unscented Kalman filtering (UKF), it is shown that the proposed method is more accurate and reliable. An effective method is provided for state estimation for battery management system.http://www.sciencedirect.com/science/article/pii/S2352484722018170Lithium-ion batterySOCSOHAUKF |
| spellingShingle | Shulin Liu Xia Dong Xiaodong Yu Xiaoqing Ren Jinfeng Zhang Rui Zhu A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented Kalman filter Energy Reports Lithium-ion battery SOC SOH AUKF |
| title | A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented Kalman filter |
| title_full | A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented Kalman filter |
| title_fullStr | A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented Kalman filter |
| title_full_unstemmed | A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented Kalman filter |
| title_short | A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented Kalman filter |
| title_sort | method for state of charge and state of health estimation of lithium ion battery based on adaptive unscented kalman filter |
| topic | Lithium-ion battery SOC SOH AUKF |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722018170 |
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