State of Charge Estimation for Li-ion Battery Based on Extended Kalman Filter

Zhi Li; Peng Zhang; Zhifu Wang; Qiang Song; Yinan Rong

doi:10.1016/j.egypro.2017.03.806

State of Charge Estimation for Li-ion Battery Based on Extended Kalman Filter

Zhi Li
, Peng Zhang
, Zhifu Wang^*
, Qiang Song
, Yinan Rong

^*此作品的通讯作者

Beijing Institute of Technology
Huanghe Jiaotong University

科研成果: 期刊稿件 › 会议文章 › 同行评审

73 引用（Scopus）

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.

源语言	英语
页（从-至）	3515-3520
页数	6
期刊	Energy Procedia
卷	105
DOI	https://doi.org/10.1016/j.egypro.2017.03.806
出版状态	已出版 - 2017
已对外发布	是
活动	8th International Conference on Applied Energy, ICAE 2016 - Beijing, 中国期限: 8 10月 2016 → 11 10月 2016

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@article{cbe1f7b90429440da42e9f9f56f10873,

title = "State of Charge Estimation for Li-ion Battery Based on Extended Kalman Filter",

abstract = "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.",

keywords = "Battery state of charge, Extended Kalman filter algorithm, Lithium-ion battery, Thevenin model",

author = "Zhi Li and Peng Zhang and Zhifu Wang and Qiang Song and Yinan Rong",

note = "Publisher Copyright: {\textcopyright} 2017 Published by Elsevier Ltd.; 8th International Conference on Applied Energy, ICAE 2016 ; Conference date: 08-10-2016 Through 11-10-2016",

year = "2017",

doi = "10.1016/j.egypro.2017.03.806",

language = "English",

volume = "105",

pages = "3515--3520",

journal = "Energy Procedia",

issn = "1876-6102",

publisher = "Elsevier Ltd.",

}

TY - JOUR

T1 - State of Charge Estimation for Li-ion Battery Based on Extended Kalman Filter

AU - Li, Zhi

AU - Zhang, Peng

AU - Wang, Zhifu

AU - Song, Qiang

AU - Rong, Yinan

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Battery state of charge

KW - Extended Kalman filter algorithm

KW - Lithium-ion battery

KW - Thevenin model

UR - https://www.scopus.com/pages/publications/85020719126

U2 - 10.1016/j.egypro.2017.03.806

DO - 10.1016/j.egypro.2017.03.806

M3 - Conference article

AN - SCOPUS:85020719126

SN - 1876-6102

VL - 105

SP - 3515

EP - 3520

JO - Energy Procedia

JF - Energy Procedia

T2 - 8th International Conference on Applied Energy, ICAE 2016

Y2 - 8 October 2016 through 11 October 2016

ER -

State of Charge Estimation for Li-ion Battery Based on Extended Kalman Filter

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