期刊
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
卷 7, 期 2, 页码 437-451出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TTE.2020.3018143
关键词
Batteries; Integrated circuit modeling; Computational modeling; Electric vehicles; State of charge; Adaptation models; Battery management systems; big data applications; lithium batteries; performance evaluation; recursive estimation
资金
- National Natural Science Foundation of China [51805029, U1764258]
- Ministry of Science and Technology of the People's Republic of China [2019YFE0104700]
This article presents a cell inconsistency evaluation model for series-connected battery systems based on real-world EV operation data, utilizing consistency indicators and a robust regression method to effectively assess the parameter inconsistency state of the battery system.
Unmanaged cell inconsistency may cause accelerated battery degradation or even thermal runaway accidents in electric vehicles (EVs). Accurate cell inconsistency evaluation is a prerequisite for efficient battery health management to maintain safe and reliable operation and is also vital for battery second-life utilization. This article presents a cell inconsistency evaluation model for series-connected battery systems based on real-world EV operation data. The open-circuit voltage (OCV), internal resistance, and charging voltage curve are extracted as consistency indicators (CIs) from a large volume of electric taxis' operation data. The Thevenin equivalent circuit model is adopted to delineate battery dynamics, and an adaptive forgetting factor recursive least-squares method is proposed to reduce the fluctuation phenomenon in model parameter identification. With a modified robust regression method, the evolution characteristics of the three CIs are analyzed. The Mahalanobis distance in combination with the density-based spatial clustering of applications with noise is employed to comprehensively evaluate the multiparameter inconsistency state of a battery system based on the CIs. The results show that the proposed method can effectively assess cell inconsistency with high robustness and is competent for real-world applications.
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