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

^*Corresponding author for this work

School of Mechanical Engineering

Research output: Contribution to journal › Conference article › peer-review

62 Citations (Scopus)

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.

Original language	English
Pages (from-to)	3515-3520
Number of pages	6
Journal	Energy Procedia
Volume	105
DOIs	https://doi.org/10.1016/j.egypro.2017.03.806
Publication status	Published - 2017
Event	8th International Conference on Applied Energy, ICAE 2016 - Beijing, China Duration: 8 Oct 2016 → 11 Oct 2016

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.egypro.2017.03.806

Cite this

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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",

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journal = "Energy Procedia",

issn = "1876-6102",

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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 - http://www.scopus.com/inward/record.url?scp=85020719126&partnerID=8YFLogxK

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

Abstract

Keywords

UN SDGs

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