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Online battery electric circuit model estimation on continuous-time domain using linear integral filter method
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Zhang, Cheng, Marco, James, Allafi, Walid, Dinh, Quang Truong and Widanage, Widanalage Dhammika (2017) Online battery electric circuit model estimation on continuous-time domain using linear integral filter method. In: 19th International Conference on Hybrid and Electric Vehicles, Prague, 23-24 Mar 2017. Published in: Proceedings of the World Academy of Science, Engineering and Technology, 4 (3). p. 823. ISSN 2010-376X.
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Official URL: https://www.waset.org/abstracts/67718
Abstract
Equivalent circuit models (ECMs) are widely used in battery management systems in electric vehicles and other battery energy storage systems. The battery dynamics and the model parameters vary under different working conditions, such as different temperature and state of charge (SOC) levels, and therefore online parameter identification can improve the modelling accuracy. This paper presents a novel way of online ECM parameter identification using a continuous time (CT) estimation method. The CT estimation method has several advantages over discrete time (DT) estimation methods for ECM parameter identification due to the widely separated battery dynamic modes and fast sampling. The proposed method can be used for online SOC estimation. Test data are collected using a lithium ion cell, and the experimental results show that the proposed CT method achieves better modelling accuracy compared with the conventional DT recursive least square method. The effectiveness of the proposed method for online SOC estimation is also verified on test data.
Item Type: | Conference Item (Paper) | ||||
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Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||
Library of Congress Subject Headings (LCSH): | Lithium ion batteries -- Mathematical models, Electric vehicles -- Batteries | ||||
Journal or Publication Title: | Proceedings of the World Academy of Science, Engineering and Technology | ||||
Publisher: | World Academy of Science, Engineering and Technology | ||||
ISSN: | 2010-376X | ||||
Official Date: | 1 February 2017 | ||||
Dates: |
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Volume: | 4 | ||||
Number: | 3 | ||||
Page Range: | p. 823 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access | ||||
Date of first compliant deposit: | 3 March 2017 | ||||
Date of first compliant Open Access: | 3 March 2017 | ||||
Funder: | Warwick Manufacturing Group, High Value Manufacturing Catapult, Innovate UK, Engineering and Physical Sciences Research Council (EPSRC) | ||||
Grant number: | EP/M009394/1 (EPSRC) | ||||
Adapted As: | @article{(International Science Index):http://waset.org/abstracts/67718, author = {Cheng Zhang and James Marco and Walid Allafi and Truong Q. Dinh and Dhammika Widanalage}, country = {United Kingdom}, title = {Online Battery Equivalent Circuit Model Estimation on Continuous-Time Domain Using Linear Integral Filter Method}, abstract = {Equivalent circuit models (ECMs) are widely used in battery management systems in electric vehicles and other battery energy storage systems. The battery dynamics and the model parameters vary under different working conditions, such as different temperature and state of charge (SOC) levels, and therefore online parameter identification can improve the modelling accuracy. This paper presents a novel way of online ECM parameter identification using a continuous time (CT) estimation method. The CT estimation method has several advantages over discrete time (DT) estimation methods for ECM parameter identification due to the widely separated battery dynamic modes and fast sampling. The proposed method can be used for online SOC estimation. Test data are collected using a lithium ion cell, and the experimental results show that the proposed CT method achieves better modelling accuracy compared with the conventional DT recursive least square method. The effectiveness of the proposed method for online SOC estimation is also verified on test data.}, keywords = {electric circuit model, continuous time domain estimation, linear integral filter method, parameter and SOC estimation, recursive least square}, volume = {4}, number = {3}, year = {2017}, pages = {823}, ee = {http://waset.org/abstracts/67718}, url = {http://waset.org/abstracts/Electrical-and-Computer-Engineering}, bibsource = {http://waset.org/abstracts}, conference = {ICHEV 2017: International Conference on Hybrid and Electric Vehicles, Prague, Czech Republic, (Mar 23-24, 2017)}, issn = {PISSN:2010-376X, EISSN:2010-3778}, publisher = {World Academy of Science, Engineering and Technology}, index = {International Science Index, Electrical and Computer Engineering, 4(3) 2017}, } | ||||
Conference Paper Type: | Paper | ||||
Title of Event: | 19th International Conference on Hybrid and Electric Vehicles | ||||
Type of Event: | Conference | ||||
Location of Event: | Prague | ||||
Date(s) of Event: | 23-24 Mar 2017 | ||||
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