Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter

Zheng, L; Zhu, J; Wang, G; Lu, DDC; He, T

Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter

Zheng, L Zhu, J

Wang, G

Lu, DDC

He, T

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Publication Type:: Journal Article
Citation:: Energy, 2018, 158 pp. 1028 - 1037
Issue Date:: 2018-09-01

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Field	Value	Language
dc.contributor.author	Zheng, L	en_US
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047	en_US
dc.contributor.author	Wang, G https://orcid.org/0000-0003-4295-8578	en_US
dc.contributor.author	Lu, DDC https://orcid.org/0000-0003-2145-0580	en_US
dc.contributor.author	He, T	en_US
dc.date.issued	2018-09-01	en_US
dc.identifier.citation	Energy, 2018, 158 pp. 1028 - 1037	en_US
dc.identifier.issn	0360-5442	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128625
dc.description.abstract	© 2018 Elsevier Ltd Accurate battery state of charge (SOC) estimation can contribute to safe and reliable utilization of the battery. However, commonly used battery model-based SOC estimation methods suffer from the lack of a universal battery model for cells in a battery pack since the model parameters of each cell are inevitably different from each other and variable with battery aging, leading to difficulties in promoting the model-based methods for real applications. To solve this problem, a differential voltage (DV) analysis based universal battery model and two associated SOC estimation algorithms using extended Kalman filter (EKF) and particle filter (PF), respectively, are proposed in this paper. By means of a natural cubic interpolation approach, a battery SOC-DV model is firstly derived from the SOC based DV curves of various cells at different aging levels. A novel battery model-based scheme is then proposed to incorporate the SOC-DV model for the estimation. The robustness of the proposed approaches against different cell aging levels is evaluated, and the promising SOC estimates with the maximum absolute error of 1.75% and the root mean square error of less than 1.10% can be achieved.	en_US
dc.relation.ispartof	Energy	en_US
dc.relation.isbasedon	10.1016/j.energy.2018.06.113	en_US
dc.subject.classification	Energy	en_US
dc.title	Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter	en_US
dc.type	Journal Article
utslib.citation.volume	158	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0915 Interdisciplinary Engineering	en_US
utslib.for	0914 Resources Engineering and Extractive Metallurgy	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CCET - Centre for Clean Energy Technology
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	158	en_US

Abstract:

© 2018 Elsevier Ltd Accurate battery state of charge (SOC) estimation can contribute to safe and reliable utilization of the battery. However, commonly used battery model-based SOC estimation methods suffer from the lack of a universal battery model for cells in a battery pack since the model parameters of each cell are inevitably different from each other and variable with battery aging, leading to difficulties in promoting the model-based methods for real applications. To solve this problem, a differential voltage (DV) analysis based universal battery model and two associated SOC estimation algorithms using extended Kalman filter (EKF) and particle filter (PF), respectively, are proposed in this paper. By means of a natural cubic interpolation approach, a battery SOC-DV model is firstly derived from the SOC based DV curves of various cells at different aging levels. A novel battery model-based scheme is then proposed to incorporate the SOC-DV model for the estimation. The robustness of the proposed approaches against different cell aging levels is evaluated, and the promising SOC estimates with the maximum absolute error of 1.75% and the root mean square error of less than 1.10% can be achieved.

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http://hdl.handle.net/10453/128625