Performance assessment of two adaptive Kalman filters for battery state-of-charge estimation

Ximing Cheng; Liguang Yao

doi:10.1109/ChiCC.2015.7260886

Performance assessment of two adaptive Kalman filters for battery state-of-charge estimation

Ximing Cheng
, Liguang Yao

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

An accurate state of charge (SOC) is required to improve the reliability, cycle life, safety, and economics of the batteries used in power applications such as electric vehicles and smart grids. The adaptive extended Kalman filter (AEKF) is an advanced technique used to determine the SOC. The first task in estimating the SOC is to choose the initial state covariance (P0) when the process noise covariance (Qk) and the measurement noise covariance (Rk) are simultaneously estimated in the AEKF. The performance of the adaptive methods is also determined by the initial states. This study evaluates the performances of two AEKF approaches, including the Bayesian adaptive estimator (BAE) and the innovation-based adaptive estimator (IAE), which are applied to simultaneously estimate Qk and Rk. These two adaptive filtering methods are implemented on the experimental data of a real lithium-ion battery pack. Their performances, including filtering stability and convergence speed, are compared, and their impact factors are discussed.

Original language	English
Title of host publication	Proceedings of the 34th Chinese Control Conference, CCC 2015
Editors	Qianchuan Zhao, Shirong Liu
Publisher	IEEE Computer Society
Pages	7843-7848
Number of pages	6
ISBN (Electronic)	9789881563897
DOIs	https://doi.org/10.1109/ChiCC.2015.7260886
Publication status	Published - 11 Sept 2015
Event	34th Chinese Control Conference, CCC 2015 - Hangzhou, China Duration: 28 Jul 2015 → 30 Jul 2015

Publication series

Name	Chinese Control Conference, CCC
Volume	2015-September
ISSN (Print)	1934-1768
ISSN (Electronic)	2161-2927

Conference

Conference	34th Chinese Control Conference, CCC 2015
Country/Territory	China
City	Hangzhou
Period	28/07/15 → 30/07/15

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Adaptive extended Kalman filter
Equivalent circuit model
Lithium-ion battery
State of charge

Access to Document

10.1109/ChiCC.2015.7260886

Cite this

@inproceedings{c4f69a4260e844f5b90d3e3bc2fa1fec,

title = "Performance assessment of two adaptive Kalman filters for battery state-of-charge estimation",

abstract = "An accurate state of charge (SOC) is required to improve the reliability, cycle life, safety, and economics of the batteries used in power applications such as electric vehicles and smart grids. The adaptive extended Kalman filter (AEKF) is an advanced technique used to determine the SOC. The first task in estimating the SOC is to choose the initial state covariance (P0) when the process noise covariance (Qk) and the measurement noise covariance (Rk) are simultaneously estimated in the AEKF. The performance of the adaptive methods is also determined by the initial states. This study evaluates the performances of two AEKF approaches, including the Bayesian adaptive estimator (BAE) and the innovation-based adaptive estimator (IAE), which are applied to simultaneously estimate Qk and Rk. These two adaptive filtering methods are implemented on the experimental data of a real lithium-ion battery pack. Their performances, including filtering stability and convergence speed, are compared, and their impact factors are discussed.",

keywords = "Adaptive extended Kalman filter, Equivalent circuit model, Lithium-ion battery, State of charge",

author = "Ximing Cheng and Liguang Yao",

note = "Publisher Copyright: {\textcopyright} 2015 Technical Committee on Control Theory, Chinese Association of Automation.; 34th Chinese Control Conference, CCC 2015 ; Conference date: 28-07-2015 Through 30-07-2015",

year = "2015",

month = sep,

day = "11",

doi = "10.1109/ChiCC.2015.7260886",

language = "English",

series = "Chinese Control Conference, CCC",

publisher = "IEEE Computer Society",

pages = "7843--7848",

editor = "Qianchuan Zhao and Shirong Liu",

booktitle = "Proceedings of the 34th Chinese Control Conference, CCC 2015",

address = "United States",

}

Cheng, X & Yao, L 2015, Performance assessment of two adaptive Kalman filters for battery state-of-charge estimation. in Q Zhao & S Liu (eds), Proceedings of the 34th Chinese Control Conference, CCC 2015., 7260886, Chinese Control Conference, CCC, vol. 2015-September, IEEE Computer Society, pp. 7843-7848, 34th Chinese Control Conference, CCC 2015, Hangzhou, China, 28/07/15. https://doi.org/10.1109/ChiCC.2015.7260886

Performance assessment of two adaptive Kalman filters for battery state-of-charge estimation. / Cheng, Ximing; Yao, Liguang.
Proceedings of the 34th Chinese Control Conference, CCC 2015. ed. / Qianchuan Zhao; Shirong Liu. IEEE Computer Society, 2015. p. 7843-7848 7260886 (Chinese Control Conference, CCC; Vol. 2015-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Performance assessment of two adaptive Kalman filters for battery state-of-charge estimation

AU - Cheng, Ximing

AU - Yao, Liguang

PY - 2015/9/11

Y1 - 2015/9/11

N2 - An accurate state of charge (SOC) is required to improve the reliability, cycle life, safety, and economics of the batteries used in power applications such as electric vehicles and smart grids. The adaptive extended Kalman filter (AEKF) is an advanced technique used to determine the SOC. The first task in estimating the SOC is to choose the initial state covariance (P0) when the process noise covariance (Qk) and the measurement noise covariance (Rk) are simultaneously estimated in the AEKF. The performance of the adaptive methods is also determined by the initial states. This study evaluates the performances of two AEKF approaches, including the Bayesian adaptive estimator (BAE) and the innovation-based adaptive estimator (IAE), which are applied to simultaneously estimate Qk and Rk. These two adaptive filtering methods are implemented on the experimental data of a real lithium-ion battery pack. Their performances, including filtering stability and convergence speed, are compared, and their impact factors are discussed.

AB - An accurate state of charge (SOC) is required to improve the reliability, cycle life, safety, and economics of the batteries used in power applications such as electric vehicles and smart grids. The adaptive extended Kalman filter (AEKF) is an advanced technique used to determine the SOC. The first task in estimating the SOC is to choose the initial state covariance (P0) when the process noise covariance (Qk) and the measurement noise covariance (Rk) are simultaneously estimated in the AEKF. The performance of the adaptive methods is also determined by the initial states. This study evaluates the performances of two AEKF approaches, including the Bayesian adaptive estimator (BAE) and the innovation-based adaptive estimator (IAE), which are applied to simultaneously estimate Qk and Rk. These two adaptive filtering methods are implemented on the experimental data of a real lithium-ion battery pack. Their performances, including filtering stability and convergence speed, are compared, and their impact factors are discussed.

KW - Adaptive extended Kalman filter

KW - Equivalent circuit model

KW - Lithium-ion battery

KW - State of charge

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M3 - Conference contribution

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BT - Proceedings of the 34th Chinese Control Conference, CCC 2015

A2 - Zhao, Qianchuan

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Y2 - 28 July 2015 through 30 July 2015

ER -

Performance assessment of two adaptive Kalman filters for battery state-of-charge estimation

Abstract

Publication series

Conference

UN SDGs

Keywords

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