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

被引：1

作者：

Liu F. ^{[1
]}

Liu Y.-P. ^{[1
]}

Li J.-D. ^{[1
]}

Bu F.-T. ^{[2
]}

机构：

[1] Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University, Tianjin

[2] Neusoft Reach Automotive Technology, Co., Ltd, Shenyang

来源：

Dongbei Daxue Xuebao/Journal of Northeastern University | 2022年 / 43卷 / 11期

关键词：

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

D O I：

10.12068/j.issn.1005-3026.2022.11.004

中图分类号：

学科分类号：

摘要：

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. © 2022 Northeastern University. All rights reserved.

引用

页码：1544 / 1551

页数：7

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