Online Estimation of State of Health During Discharging of Vehicle Power Battery

doi:10.12068/j.issn.1005-3026.2022.11.004

Abstract

Abstract: According to the characteristics of irregular random charge and discharge of electric vehicles and the requirements of on-line detection， it is difficult to ensure the accuracy of off-line experimental data analysis methods due to battery consistency problems. In this paper， an on-line closed-loop correction SOH (state of health) estimation architecture based on the idea of unscented Kalman filter(UKF)is proposed， which is based on the off-line SOH external indicator function. The advantage of this method is that it can quickly estimate the high-precision SOH value in the random discharge process and the algorithm complexity is relatively reduced. It is easy to implement in practical engineering and the proposed method has better robustness. Through verification， it can be proved that the SOH estimation method proposed in this paper has better practicability and higher estimation accuracy.

Key words: state of health (SOH); unscented Kalman filter; auto regression model; electric vehicle; lithium ion battery

CLC Number:

TP20

LIU Fang， LIU Yan-peng， LI Jing-dong， BU Fan-tao. Online Estimation of State of Health During Discharging of Vehicle Power Battery[J]. Journal of Northeastern University(Natural Science), 2022, 43(11): 1544-1551.

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