State-of-charge estimation for lithium-ion battery using AUKF and LSSVM

Jinhao Meng; Guangzhao Luo; Fei Gao

doi:10.1109/ITEC-AP.2014.6940680

State-of-charge estimation for lithium-ion battery using AUKF and LSSVM

Jinhao Meng, Guangzhao Luo, Fei Gao

School of Automation

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

14 Scopus citations

Abstract

A new method based on adaptive unscented Kalman filter (AUKF) is proposed to improve the SOC estimation accuracy of lithium-ion battery in this paper. The noise covariance in AUKF is adaptively adjusted. To improve the accuracy of the AUKF-based method, least squares support vector machine (LSSVM) is used to establish measurement equation. A comparison with unsented Kalman filter shows that the proposed method has a better accuracy. Simulation data indicates a better SOC estimation result and a faster convergence can be obtained by using the AUKF-based method.

Original language	English
Title of host publication	IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479942398
DOIs	https://doi.org/10.1109/ITEC-AP.2014.6940680
State	Published - 30 Oct 2014
Event	2014 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Beijing, China Duration: 31 Aug 2014 → 3 Sep 2014

Publication series

Name	IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings

Conference

Conference	2014 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014
Country/Territory	China
City	Beijing
Period	31/08/14 → 3/09/14

Keywords

adaptive unscented Kalman filter (AUKF)
Battery
least squares support vector machine (LSSVM)
state of charge (SOC)

UN SDGs

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

Access to Document

10.1109/ITEC-AP.2014.6940680

Cite this

Meng, J., Luo, G., & Gao, F. (2014). State-of-charge estimation for lithium-ion battery using AUKF and LSSVM. In IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings Article 6940680 (IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITEC-AP.2014.6940680

Meng, Jinhao ; Luo, Guangzhao ; Gao, Fei. / State-of-charge estimation for lithium-ion battery using AUKF and LSSVM. IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. (IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings).

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abstract = "A new method based on adaptive unscented Kalman filter (AUKF) is proposed to improve the SOC estimation accuracy of lithium-ion battery in this paper. The noise covariance in AUKF is adaptively adjusted. To improve the accuracy of the AUKF-based method, least squares support vector machine (LSSVM) is used to establish measurement equation. A comparison with unsented Kalman filter shows that the proposed method has a better accuracy. Simulation data indicates a better SOC estimation result and a faster convergence can be obtained by using the AUKF-based method.",

keywords = "adaptive unscented Kalman filter (AUKF), Battery, least squares support vector machine (LSSVM), state of charge (SOC)",

author = "Jinhao Meng and Guangzhao Luo and Fei Gao",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 ; Conference date: 31-08-2014 Through 03-09-2014",

year = "2014",

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Meng, J, Luo, G & Gao, F 2014, State-of-charge estimation for lithium-ion battery using AUKF and LSSVM. in IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings., 6940680, IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., 2014 IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014, Beijing, China, 31/08/14. https://doi.org/10.1109/ITEC-AP.2014.6940680

State-of-charge estimation for lithium-ion battery using AUKF and LSSVM. / Meng, Jinhao; Luo, Guangzhao; Gao, Fei.
IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. 6940680 (IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings).

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

TY - GEN

T1 - State-of-charge estimation for lithium-ion battery using AUKF and LSSVM

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AU - Luo, Guangzhao

AU - Gao, Fei

PY - 2014/10/30

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N2 - A new method based on adaptive unscented Kalman filter (AUKF) is proposed to improve the SOC estimation accuracy of lithium-ion battery in this paper. The noise covariance in AUKF is adaptively adjusted. To improve the accuracy of the AUKF-based method, least squares support vector machine (LSSVM) is used to establish measurement equation. A comparison with unsented Kalman filter shows that the proposed method has a better accuracy. Simulation data indicates a better SOC estimation result and a faster convergence can be obtained by using the AUKF-based method.

AB - A new method based on adaptive unscented Kalman filter (AUKF) is proposed to improve the SOC estimation accuracy of lithium-ion battery in this paper. The noise covariance in AUKF is adaptively adjusted. To improve the accuracy of the AUKF-based method, least squares support vector machine (LSSVM) is used to establish measurement equation. A comparison with unsented Kalman filter shows that the proposed method has a better accuracy. Simulation data indicates a better SOC estimation result and a faster convergence can be obtained by using the AUKF-based method.

KW - adaptive unscented Kalman filter (AUKF)

KW - Battery

KW - least squares support vector machine (LSSVM)

KW - state of charge (SOC)

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PB - Institute of Electrical and Electronics Engineers Inc.

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Meng J, Luo G, Gao F. State-of-charge estimation for lithium-ion battery using AUKF and LSSVM. In IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2014. 6940680. (IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings). doi: 10.1109/ITEC-AP.2014.6940680

State-of-charge estimation for lithium-ion battery using AUKF and LSSVM

Abstract

Publication series

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Keywords

UN SDGs

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