期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 66, 期 4, 页码 3227-3236出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2018.2842782
关键词
Extended Kalman filter (EKF); Lebesgue sampling (LS); lithium-ion battery; state-of-charge (SOC); state-of-health (SOH)
资金
- Advanced Support for Innovative Research Excellence (ASPIRE) Grant Program at the University of South Carolina
The estimation and prediction of state-ofhealth (SOH) and state-of- charge (SOC) of Lithium-ion batteries are two main functions of the battery management system (BMS). In order to reduce the computation cost and enable deployment of the BMS on the low-cost hardware, a Lebesgue-sampling-based extended Kalman filter (LS-EKF) is developed to estimate the SOH and SOC. An LS-EKF is able to eliminate unnecessary computations, especially when the states change slowly. In this paper, the SOH is first estimated and the remaining useful life is predicted by the LS-EKF. Then, the estimated SOH is used as the initial battery capacity for SOC estimation and prediction. The SOH and SOC estimation and prediction are calculated repeatedly in the whole battery service life. The proposed method is verified with the application to the capacity degradation of the Lithium-ion battery. The results show that the LSEKF-based algorithm has a good performance in SOH and SOC estimation and prediction in terms of accuracy and computation cost.
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