Online modelling and state-of-charge estimation for lithium-titanate battery

Superior safety, is a promising energy storage element for electric vehicles. Its features can be fully utilised by using a fast charger and a high performance battery management system. Battery model is vital to a battery charger design for characterising the charging behaviours of a battery. Addit...

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Main Author:	Low, Wen Yao
Format:	Thesis
Language:	English
Published:	2016
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/84073/1/LowWenYaoPFKE2016.pdf
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spelling	my-utm-ep.840732019-12-16T01:53:21Z Online modelling and state-of-charge estimation for lithium-titanate battery 2016 Low, Wen Yao TK Electrical engineering. Electronics Nuclear engineering Superior safety, is a promising energy storage element for electric vehicles. Its features can be fully utilised by using a fast charger and a high performance battery management system. Battery model is vital to a battery charger design for characterising the charging behaviours of a battery. Additionally, a robust state-ofcharge (SoC) estimation should be realised for a reliable battery management. This thesis develops a battery model for charger design and a robust method for SoC estimation by using MATLAB. The thesis proposed a transfer function-based battery model which is applicable for small-signal analysis and large-signal simulation of battery charger design, in order to capture the charging profiles of LTO battery. Busse’s adaptive rule, which has simple computations, is applied to improve the accuracy of Kalman filter-based SoC estimation. Busse’s adaptive Kalman filters are also applied for SoC estimation with online battery modelling to eliminate the complicated process of battery modelling. This study was conducted by using 2.4 V, 15 Ah LTO batteries. The batteries were tested with continuous current test and pulsed current test at several ambient temperatures (-25 ºC, 0 ºC, 25 ºC and 50 ºC) and charge/discharge currents (0.5 C, 1 C, 2 C). Additionally, modified dynamic stress tests at several temperatures (-15 ºC, 0 ºC, 15 ºC, 25 ºC, 35 ºC and 50 ºC) were also performed to test the battery under real EV environment. Results of the battery modelling showed that the developed transfer function-based battery model is accurate where the root-mean-square modelling error is less than 30 mV. The results also revealed that the Busse’s adaptive rule has effectively improved the Kalman filter-based SoC estimation for the case of offline battery model by giving a higher accuracy and shorter convergence time. Additionally, Busse’s adaptive Extended Kalman Filter gave a better accuracy in SoC estimation with online battery modelling. The proposed transfer function-based battery model provides a helpful solution for the battery charger design while the proposed Busse’s adaptive Kalman filter offers an accurate and robust SoC estimation for both offline and online battery models. 2016 Thesis http://eprints.utm.my/id/eprint/84073/ http://eprints.utm.my/id/eprint/84073/1/LowWenYaoPFKE2016.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:126334 phd doctoral Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Low, Wen Yao Online modelling and state-of-charge estimation for lithium-titanate battery
description	Superior safety, is a promising energy storage element for electric vehicles. Its features can be fully utilised by using a fast charger and a high performance battery management system. Battery model is vital to a battery charger design for characterising the charging behaviours of a battery. Additionally, a robust state-ofcharge (SoC) estimation should be realised for a reliable battery management. This thesis develops a battery model for charger design and a robust method for SoC estimation by using MATLAB. The thesis proposed a transfer function-based battery model which is applicable for small-signal analysis and large-signal simulation of battery charger design, in order to capture the charging profiles of LTO battery. Busse’s adaptive rule, which has simple computations, is applied to improve the accuracy of Kalman filter-based SoC estimation. Busse’s adaptive Kalman filters are also applied for SoC estimation with online battery modelling to eliminate the complicated process of battery modelling. This study was conducted by using 2.4 V, 15 Ah LTO batteries. The batteries were tested with continuous current test and pulsed current test at several ambient temperatures (-25 ºC, 0 ºC, 25 ºC and 50 ºC) and charge/discharge currents (0.5 C, 1 C, 2 C). Additionally, modified dynamic stress tests at several temperatures (-15 ºC, 0 ºC, 15 ºC, 25 ºC, 35 ºC and 50 ºC) were also performed to test the battery under real EV environment. Results of the battery modelling showed that the developed transfer function-based battery model is accurate where the root-mean-square modelling error is less than 30 mV. The results also revealed that the Busse’s adaptive rule has effectively improved the Kalman filter-based SoC estimation for the case of offline battery model by giving a higher accuracy and shorter convergence time. Additionally, Busse’s adaptive Extended Kalman Filter gave a better accuracy in SoC estimation with online battery modelling. The proposed transfer function-based battery model provides a helpful solution for the battery charger design while the proposed Busse’s adaptive Kalman filter offers an accurate and robust SoC estimation for both offline and online battery models.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Low, Wen Yao
author_facet	Low, Wen Yao
author_sort	Low, Wen Yao
title	Online modelling and state-of-charge estimation for lithium-titanate battery
title_short	Online modelling and state-of-charge estimation for lithium-titanate battery
title_full	Online modelling and state-of-charge estimation for lithium-titanate battery
title_fullStr	Online modelling and state-of-charge estimation for lithium-titanate battery
title_full_unstemmed	Online modelling and state-of-charge estimation for lithium-titanate battery
title_sort	online modelling and state-of-charge estimation for lithium-titanate battery
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2016
url	http://eprints.utm.my/id/eprint/84073/1/LowWenYaoPFKE2016.pdf
_version_	1747818438335660032

Online modelling and state-of-charge estimation for lithium-titanate battery

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