Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/37123
Title: Analysis and prediction of the discharge characteristics of the lithium-ion battery based on the Grey system theory
Authors: Chen, L.
Tian, B.
Lin, W.
Ji, Bing
Li, J.
Pan, H.
First Published: 1-Dec-2015
Publisher: Institution of Engineering and Technology (IET)
Citation: IET Power Electronics, 2015, 8 (12), pp. 2361-2369 (9)
Abstract: The capacity/state-of-charge (SoC) and voltage of lithium–ion batteries are of prime importance in electric vehicles (EVs), so their condition-monitoring techniques are extensively studied. This study focuses on the application of the grey system theory to the parameters analysing and predicting behaviour during the discharge/charge cycles of the battery. First, Grey relation analysis is applied to study and analyse the relationship between capacity/SoC and various influencing factors. Second, the segment Grey prediction model is proposed in order to test and improve the accuracy of the capacity/SoC prediction. Finally, based on the ageing data from the National Aeronautics and Space Administration Prognostics Data Repository, the effects of different Grey theory models, such as the GM(1,1), the Verhulst model and the segment Grey prediction model, are investigated. The results show that: (i) the GRA is efficient in figuring out the relationship between the capacity/SoC and various influencing factors; (ii) the segment Grey prediction model is an effective mode of prediction for EV batteries, because its accuracy is more reliable than other two Grey models; and (iii) the segment Grey prediction model is suitable for predicting the capacity/SoC of batteries under various loading conditions.
DOI Link: 10.1049/iet-pel.2015.0182
ISSN: 1755-4535
Links: http://digital-library.theiet.org/content/journals/10.1049/iet-pel.2015.0182
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7364319
http://hdl.handle.net/2381/37123
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2015,The Institution of Engineering and Technology. This paper is a postprint of a paper submitted to and accepted for publication in [journal] and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library. Deposited with reference to the publisher’s archiving policy available on the SHERPA/RoMEO website.
Appears in Collections:Published Articles, Dept. of Engineering

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