SOC estimation of lithium battery based on adaptive untracked Kalman filter

doi:10.12028/j.issn.2095-4239.2019.0113

Abstract

Abstract: The accuracy of the state of charge (SOC) estimation of battery is a key issue in battery management system, which is very important to the reliability and safety of battery. In most cases, the accuracy of the battery model established is not high enough and the noise statistics of the battery system are unknown or inaccurate, which will greatly influence the SOC estimation. In this paper, the second-order RC equivalent model is adopted to reduce the error caused by the battery model. At the same time, a new SOC estimation method based on Sage-Husa filtering algorithm and untracked Kalman filtering (UKF) algorithm is proposed, an adaptive no-trace Kalman (AUKF) filtering algorithm based on noise statistical estimator can modify the system noise in real time to improve the estimation accuracy of SOC. And the accuracy and validity of the SOC estimation method are verified by comparing AUKF and UKF. The experimental results show that AUKF has a higher SOC estimation accuracy and adaptive ability, and the estimated accuracy can be kept within 4.68% under both pulse discharge and dynamic conditions, which can effectively estimate the SOC value of the battery.

Key words: state of charge (SOC), second-order RC model, UKF, Sage-Husa filter, AUKF

CLC Number:

TM912

AN Zhiguo, TIAN Maofei, ZHAO Lin, CHEN Xing, LI Yakun, SI Xin. SOC estimation of lithium battery based on adaptive untracked Kalman filter[J]. Energy Storage Science and Technology, 2019, 8(5): 856-861.

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