期刊
JOURNAL OF ENERGY STORAGE
卷 41, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.est.2021.102843
关键词
State of charge (SOC) estimation; Lagrange multiplier; Sigma point Kalman filter (SPKF); Open-circuit voltage (OCV); Model identification
This paper proposes a fused online approach consisting of the Lagrange multiplier technique and sigma point Kalman filter for lithium-ion battery model identification and state of charge estimation. Experimental results show that the proposed method outperforms other techniques in terms of accuracy.
In this paper, a fused online approach consisting of the Lagrange multiplier technique and sigma point Kalman filter (SPKF) is proposed for the lithium-ion battery model identification and state of charge (SOC) estimation, respectively. The Lagrange multiplier technique minimized the error between the reference and estimated SOC by estimating the accurate battery parameters, whereas SPKF helps to calculate non-linear system dynamics more precisely. The effectiveness of the proposed technique is evaluated using different publicly available experimental profiles such as the Beijing dynamic stress test, dynamic stress test, and hybrid pulse power characteristics. The effect of sensor accuracy on the SOC estimation is also analyzed. The comparative analysis reveals that the proposed methodology yields better performance than recursive least squares (RLS)-SPKF and forgetting factor RLS-SPKF. The maximum noted errors for the proposed technique were 0.2%, 0.4%, and 0.9% for hybrid pulse power characteristics, dynamic stress test, and Beijing dynamic stress test, respectively. The improvement in SOC estimation accuracy shows the effectiveness, superiority, and distinctiveness of the proposed approach.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据