An improved parameter identification method considering multi-timescale characteristics of lithium-ion batteries
To monitor and predict battery states, a battery model with accurate model parameters is important to battery management systems (BMS). However, for multi-timescale dynamic characteristics, the precision and adaptability of parameter identification of the Li-ion battery model is unsatisfactory up to...
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| Vydáno v: | Journal of energy storage Ročník 59; s. 106462 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.03.2023
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| ISSN: | 2352-152X, 2352-1538 |
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| Abstract | To monitor and predict battery states, a battery model with accurate model parameters is important to battery management systems (BMS). However, for multi-timescale dynamic characteristics, the precision and adaptability of parameter identification of the Li-ion battery model is unsatisfactory up to now. In this paper, an improved parameter identification algorithm is proposed combining fixed memory recursive least squares (FMRLS) and fading extended Kalman filter (FEKF) which are used to obtain the fast dynamic (FD) and slow dynamic (SD) parameters of equivalent circuit model (ECM) respectively. Open-circuit voltage (OCV) is identified as a component of the SD part because of its slow dynamic nature in this algorithm. Federal urban driving schedule (FUDS) and dynamic stress test (DST) tests with different initial state of charge (SOC) and temperatures were employed for verifications, and the results show that the algorithm can track the battery terminal voltage in time and the root mean square error (RMSE) is as low as 1 mV. Meanwhile, the results reveal that the advanced SOC-OCV tests can be avoided indeed, and model parameters identified by this algorithm have good robustness in different temperatures and high consistency in different operating conditions which are significantly better than conventional algorithms.
•Parameter identification based on the multi-timescale characteristics of Li-ion batteries.•Synchronous OCV identification instead of OCV-SOC tests.•More reasonable ciruit model and coupling mode to improve the identification accuracy.•The proposed method offers high consistency and strong robustness at different temperatures and operation conditions. |
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| AbstractList | To monitor and predict battery states, a battery model with accurate model parameters is important to battery management systems (BMS). However, for multi-timescale dynamic characteristics, the precision and adaptability of parameter identification of the Li-ion battery model is unsatisfactory up to now. In this paper, an improved parameter identification algorithm is proposed combining fixed memory recursive least squares (FMRLS) and fading extended Kalman filter (FEKF) which are used to obtain the fast dynamic (FD) and slow dynamic (SD) parameters of equivalent circuit model (ECM) respectively. Open-circuit voltage (OCV) is identified as a component of the SD part because of its slow dynamic nature in this algorithm. Federal urban driving schedule (FUDS) and dynamic stress test (DST) tests with different initial state of charge (SOC) and temperatures were employed for verifications, and the results show that the algorithm can track the battery terminal voltage in time and the root mean square error (RMSE) is as low as 1 mV. Meanwhile, the results reveal that the advanced SOC-OCV tests can be avoided indeed, and model parameters identified by this algorithm have good robustness in different temperatures and high consistency in different operating conditions which are significantly better than conventional algorithms.
•Parameter identification based on the multi-timescale characteristics of Li-ion batteries.•Synchronous OCV identification instead of OCV-SOC tests.•More reasonable ciruit model and coupling mode to improve the identification accuracy.•The proposed method offers high consistency and strong robustness at different temperatures and operation conditions. |
| ArticleNumber | 106462 |
| Author | Wang, Xuemei Yang, Zhao |
| Author_xml | – sequence: 1 givenname: Zhao surname: Yang fullname: Yang, Zhao – sequence: 2 givenname: Xuemei surname: Wang fullname: Wang, Xuemei email: epxmwang@scut.edu.cn |
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| Keywords | FEKF Equivalent circuit model Parameter identification FMRLS Multi-timescale characteristics |
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| Title | An improved parameter identification method considering multi-timescale characteristics of lithium-ion batteries |
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