RBF network-aided adaptive unscented kalman filter for lithium-ion battery SOC estimation in electric vehicles

RBF network-aided adaptive unscented kalman filter for lithium-ion battery SOC estimation in electric vehicles

An accurate battery State of Charge (SOC) estimation is very important for electric vehicles. In this paper, a method is proposed to estimate the SOC of the lithium-ion batteries using radial basis function (RBF) networks and the adaptive unscented Kalman filter (AUKF). The RBF networks are to model...

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Main Authors: Liu, Zhitao, Wang, Youyi, Du, Jiani, Chen, Can
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2013
Online Access:https://hdl.handle.net/10356/99709
http://hdl.handle.net/10220/12820
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-997092020-03-07T13:24:49Z RBF network-aided adaptive unscented kalman filter for lithium-ion battery SOC estimation in electric vehicles Liu, Zhitao Wang, Youyi Du, Jiani Chen, Can School of Electrical and Electronic Engineering IEEE Conference on Industrial Electronics and Applications (7th : 2012 : Singapore) An accurate battery State of Charge (SOC) estimation is very important for electric vehicles. In this paper, a method is proposed to estimate the SOC of the lithium-ion batteries using radial basis function (RBF) networks and the adaptive unscented Kalman filter (AUKF). The RBF networks are to model the battery-discharging process, then the AUKF is applied to estimate the SOC of the battery. Simulation results show that the proposed method has good performance in battery modeling and SOC estimation. 2013-08-02T02:28:59Z 2019-12-06T20:10:37Z 2013-08-02T02:28:59Z 2019-12-06T20:10:37Z 2012 2012 Conference Paper https://hdl.handle.net/10356/99709 http://hdl.handle.net/10220/12820 10.1109/ICIEA.2012.6360994 en
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
description An accurate battery State of Charge (SOC) estimation is very important for electric vehicles. In this paper, a method is proposed to estimate the SOC of the lithium-ion batteries using radial basis function (RBF) networks and the adaptive unscented Kalman filter (AUKF). The RBF networks are to model the battery-discharging process, then the AUKF is applied to estimate the SOC of the battery. Simulation results show that the proposed method has good performance in battery modeling and SOC estimation.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Liu, Zhitao
Wang, Youyi
Du, Jiani
Chen, Can
format Conference or Workshop Item
author Liu, Zhitao
Wang, Youyi
Du, Jiani
Chen, Can
spellingShingle Liu, Zhitao
Wang, Youyi
Du, Jiani
Chen, Can
RBF network-aided adaptive unscented kalman filter for lithium-ion battery SOC estimation in electric vehicles
author_sort Liu, Zhitao
title RBF network-aided adaptive unscented kalman filter for lithium-ion battery SOC estimation in electric vehicles
title_short RBF network-aided adaptive unscented kalman filter for lithium-ion battery SOC estimation in electric vehicles
title_full RBF network-aided adaptive unscented kalman filter for lithium-ion battery SOC estimation in electric vehicles
title_fullStr RBF network-aided adaptive unscented kalman filter for lithium-ion battery SOC estimation in electric vehicles
title_full_unstemmed RBF network-aided adaptive unscented kalman filter for lithium-ion battery SOC estimation in electric vehicles
title_sort rbf network-aided adaptive unscented kalman filter for lithium-ion battery soc estimation in electric vehicles
publishDate 2013
url https://hdl.handle.net/10356/99709
http://hdl.handle.net/10220/12820
_version_ 1681047720766210048

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