State of charge estimation for Li-ion battery based on extended Kalman filter

被引：48

作者：

Li Zhi ^{[1
]}

Zhang Peng ^{[2
]}

Wang Zhifu ^{[1
]}

Song Qiang ^{[1
]}

Rong Yinan ^{[1
]}

机构：

[1] Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing 100081, Peoples R China

[2] Huanghe Jiaotong Univ, Coll Vehicle Engn, Wuzhi 454950, Henan Province, Peoples R China

来源：

8TH INTERNATIONAL CONFERENCE ON APPLIED ENERGY (ICAE2016) | 2017年 / 105卷

基金：

对外科技合作项目（国际科技项目）;

关键词：

Lithium-ion battery; Thevenin model; Extended Kalman filter algorithm; Battery state of charge;

D O I：

10.1016/j.egypro.2017.03.806

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：

It is difficult to estimate Lithium-ion battery state of charge (SOC) accurately. By using extended Kalman filter (EKF). the interference of system noise can be effectively reduced, which improved the estimation accuracy. First, the battery model was studied and a Thevenin model was established. Then the appropriate battery charge-and-discharge experiments were performed to identify the parameters of the model. Finally EKF applied to the model experiments show that EKF has high precision. (C) 2017 Published by Elsevier Ltd.

引用

页码：3515 / 3520

页数：6

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