Ultracapacitor modelling and parameter identification using the Extended Kalman Filter

Zhang, L; Wang, Z; Sun, F; Dorrell, D

Ultracapacitor modelling and parameter identification using the Extended Kalman Filter

Zhang, L Wang, Z Sun, F Dorrell, D

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Publication Type:: Conference Proceeding
Citation:: IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings, 2014
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Wang, Z	en_US
dc.contributor.author	Sun, F	en_US
dc.contributor.author	Dorrell, D https://orcid.org/0000-0001-8060-3386	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings, 2014	en_US
dc.identifier.isbn	9781479942398	en_US
dc.identifier.uri	http://hdl.handle.net/10453/41659
dc.description.abstract	© 2014 IEEE. Energy storage systems (ESSs) play an important role in sinking and sourcing of power in an electric vehicle and ensuring operational safety. Ultracapacitors (UCs) are a recent addition to the types of energy storage unit that can be used in an electric vehicle as an ESS because of their high power density, fast charging or discharging, and low internal loss. They can be used in parallel with batteries or fuel cells to form a hybrid energy storage system that makes better use of merits of each component and offsets their drawbacks. Establishing a good model with properly identified parameters to precisely represent the UC dynamics is vital for energy management and optimal power control; but this is challenging. This paper firstly presents the classic circuit equivalent model that consists of a series resistance, a parallel resistance and a main capacitor. The model dynamics are described with the state space equations. The Extended Kalman Filter is then used to simultaneously estimate the state and the model parameters through a simple constant-current charging test. Finally, the obtained model is validated through a dynamic test. The model output shows a good agreement with the experimental results. They verify that the model is sufficiently precise to represent the dynamics of an UC.	en_US
dc.relation.ispartof	IEEE Transportation Electrification Conference and Expo, ITEC Asia-Pacific 2014 - Conference Proceedings	en_US
dc.relation.isbasedon	10.1109/ITEC-AP.2014.6940626	en_US
dc.title	Ultracapacitor modelling and parameter identification using the Extended Kalman Filter	en_US
dc.type	Conference Proceeding
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/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. Energy storage systems (ESSs) play an important role in sinking and sourcing of power in an electric vehicle and ensuring operational safety. Ultracapacitors (UCs) are a recent addition to the types of energy storage unit that can be used in an electric vehicle as an ESS because of their high power density, fast charging or discharging, and low internal loss. They can be used in parallel with batteries or fuel cells to form a hybrid energy storage system that makes better use of merits of each component and offsets their drawbacks. Establishing a good model with properly identified parameters to precisely represent the UC dynamics is vital for energy management and optimal power control; but this is challenging. This paper firstly presents the classic circuit equivalent model that consists of a series resistance, a parallel resistance and a main capacitor. The model dynamics are described with the state space equations. The Extended Kalman Filter is then used to simultaneously estimate the state and the model parameters through a simple constant-current charging test. Finally, the obtained model is validated through a dynamic test. The model output shows a good agreement with the experimental results. They verify that the model is sufficiently precise to represent the dynamics of an UC.

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http://hdl.handle.net/10453/41659