NDLI: Cell seletion through two-level basis pattern recognition with low/high frequency components decomposed by DWT-based MRA

Content Provider	IEEE Xplore Digital Library
Author	Jonghoon Kim
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. Eng., Chosun Univ., Gwangju, South Korea (Jonghoon Kim)
Abstract	Differences in discharging/charging current signal (DCCS) profiles with magnitude, time interval, abrupt changes result in erroneous information for selection of Li-Ion cells that have similar electrochemical characteristics when using the conventional pattern recognition of Hamming neural network (HNN). Thus, this work gives insight to the implementation of Li-Ion cells selection by two-level basis pattern recognition with approximation $A_{n}$ and detail $D_{n}$ components decomposed by discrete wavelet transform (DWT)-based multi-resolution analysis (MRA) for stable operation of Li-Ion battery pack. In the first level, 10 current profiles with different discharging/charging sequences are applied to a Li-Ion cell for obtaining different DCCS patterns. Low/high frequency components $(A_{5}$ and $D_{5})$ decomposed by DWT-based MRA using Daubechies wavelet (dB) with 5 scale are applied as characteristic parameters in the HNN for pattern recognition of the unknown DCCS. Specifically, through simple rule, an arbitrary DCCS's current scale is little adjusted for matching with current scale of 10 representative DCCSs. In the second level, 15 representative discharging/charging voltage signal (DCVS) patterns considering previously selected DCCS in the first level, are used to the HNN for pattern recognition of an arbitrary DCVS. By proposed work, it is possible for elaborate identification of the representative DCCS and DCVS that most closely match those of an arbitrary Li-Ion cell. Consequently, this approach enables us to provide essential information for parameters estimation such as state-of-charge (SOC) and state-of-health (SOH) in addition to stable configuration of battery pack.
Starting Page	906
Ending Page	911
File Size	1112958
Page Count	6
File Format	PDF
ISBN	9781479957767
DOI	10.1109/ECCE.2014.6953494
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time-frequency analysis Microprocessors Computer architecture Discrete wavelet transforms Pattern recognition Approximation methods Standards
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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