A Novel Online SOC Estimation Method for the Power Lithium Battery Pack Based on the Unscented Kalman Filter

被引：0

作者：

Wang, Shun-Li ^{[1
]}

Shang, Li-Ping ^{[1
]}

Li, Zhan-Feng ^{[1
]}

Xie, Wei ^{[2
]}

Yuan, Hui-Fang ^{[1
]}

机构：

[1] Southwest Univ Sci & Technol, Mianyang 621010, Peoples R China

[2] Sichuan Huatai Elect Co Ltd, Suining 629000, Peoples R China

来源：

INTERNATIONAL CONFERENCE ON ENERGY DEVELOPMENT AND ENVIRONMENTAL PROTECTION (EDEP 2017) | 2017年 / 168卷

关键词：

lithium battery pack; SOC estimation; unscented Kalman filter; equivalent circuit model; state of balance; CHARGE ESTIMATION; STATE; TRACKING; ALGORITHM;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The SOC (State Of Charge) estimation is a core aspect for the associated BMS (Battery Management System) equipment of the lithium battery pack, in which the KF (Kalman Filter)-based methods have been extensively used but suffers from the linearization accuracy drawbacks. A novel UKF (Unscented Kalman Filter) estimation method battery model is proposed for the SOC estimation of the lithium battery pack, in which the linearization treatment is not required and fewer Sigma data points are used, reducing the computational requirement of the SOC estimation. The UKF method improves the SOC covariance properties for the lithium battery pack, the estimation performance of which has been validated by the experimental results. The proposed SOC estimation method has a RMSE (Root Mean Square Error) value of 1.42%, playing an important role in the popularization and application of the lithium battery pack.

引用

页码：98 / 105

页数：8

共 13 条

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