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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Veröffentlicht in:Journal of energy storage Jg. 59; S. 106462
Hauptverfasser: Yang, Zhao, Wang, Xuemei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.03.2023
Schlagworte:
Equivalent circuit model
FEKF
FMRLS
Multi-timescale characteristics
Parameter identification
FEKF
Equivalent circuit model
Parameter identification
FMRLS
Multi-timescale characteristics
ISSN:2352-152X, 2352-1538
Online-Zugang:Volltext
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Zusammenfassung: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.
ISSN:2352-152X
2352-1538
DOI:10.1016/j.est.2022.106462