Residual capacity estimation for ultracapacitors in electric vehicles using artificial neural network

Lei, Z; Wang, Z; Hu, X; Dorrell, DG

Residual capacity estimation for ultracapacitors in electric vehicles using artificial neural network

Lei, Z Wang, Z Hu, X Dorrell, DG

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Publication Type:: Conference Proceeding
Citation:: IFAC Proceedings Volumes (IFAC-PapersOnline), 2014, 19 pp. 3899 - 3904
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Lei, Z	en_US
dc.contributor.author	Wang, Z	en_US
dc.contributor.author	Hu, X	en_US
dc.contributor.author	Dorrell, DG https://orcid.org/0000-0001-8060-3386	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IFAC Proceedings Volumes (IFAC-PapersOnline), 2014, 19 pp. 3899 - 3904	en_US
dc.identifier.isbn	9783902823625	en_US
dc.identifier.issn	1474-6670	en_US
dc.identifier.uri	http://hdl.handle.net/10453/41655
dc.description.abstract	© IFAC. The energy storage system (ESS) plays a significant role in fulfilling the driving performance requirements and ensuring operational safety in an electric vehicle. Ultracapacitors (UCs) are being actively studied and used in parallel with batteries or fuel cells forming hybrid energy storage systems in electric vehicles. They show excellent potential in terms of the sourcing and sinking of power, particularly for the peak-power demand encountered in aggressive regenerative braking. Since there are an increasing number of ultracapacitor applications, which now includes commercial automotive applications, establishing a good model to represent their dynamics, especially the residual capacity estimation (RCE), is vital; but this is challenging. This paper presents a residual capacity estimation model which is based on an artificial neural network (ANN). This takes both charging and discharging current and temperature into consideration. The proposed ANN model comprises of three inputs and one output: the inputs are temperature, current and voltage, and the output is the residual charge. The model is trained and validated by feeding a test database which is extracted from experimental testing of ultracapacitors at different currents and temperatures on a well-established test rig. The training data should span the whole prediction scope, therefore the test currents and temperatures both vary over a wide range and cover all the possible operational conditions of the on-board ultracapacitors. The Back-Propagation (BP) algorithm, together with an early stopping strategy, is adopted to train the proposed ANN model to assure adequately accurate prediction while avoiding overfitting risks. The model performance is validated with experimental results over a set of test data randomly selected.	en_US
dc.relation.ispartof	IFAC Proceedings Volumes (IFAC-PapersOnline)	en_US
dc.title	Residual capacity estimation for ultracapacitors in electric vehicles using artificial neural network	en_US
dc.type	Conference Proceeding
utslib.citation.volume	19	en_US
utslib.for	090699 Electrical and Electronic Engineering not elsewhere classified	en_US
utslib.for	090607 Power and Energy Systems Engineering (excl. Renewable Power)	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/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

© IFAC. The energy storage system (ESS) plays a significant role in fulfilling the driving performance requirements and ensuring operational safety in an electric vehicle. Ultracapacitors (UCs) are being actively studied and used in parallel with batteries or fuel cells forming hybrid energy storage systems in electric vehicles. They show excellent potential in terms of the sourcing and sinking of power, particularly for the peak-power demand encountered in aggressive regenerative braking. Since there are an increasing number of ultracapacitor applications, which now includes commercial automotive applications, establishing a good model to represent their dynamics, especially the residual capacity estimation (RCE), is vital; but this is challenging. This paper presents a residual capacity estimation model which is based on an artificial neural network (ANN). This takes both charging and discharging current and temperature into consideration. The proposed ANN model comprises of three inputs and one output: the inputs are temperature, current and voltage, and the output is the residual charge. The model is trained and validated by feeding a test database which is extracted from experimental testing of ultracapacitors at different currents and temperatures on a well-established test rig. The training data should span the whole prediction scope, therefore the test currents and temperatures both vary over a wide range and cover all the possible operational conditions of the on-board ultracapacitors. The Back-Propagation (BP) algorithm, together with an early stopping strategy, is adopted to train the proposed ANN model to assure adequately accurate prediction while avoiding overfitting risks. The model performance is validated with experimental results over a set of test data randomly selected.

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http://hdl.handle.net/10453/41655