Analysis of The Remaining Amount Variation of A Lithium-ion Battery Using A Neural Network (뉴럴 네트워크를 이용한 리튬이온 배터리의 잔량 변화 분석)

Won-Hui Lee (이원희); JANG SUNG JIN (장성진)

doi:10.34163/jkits.2020.15.3.006

Analysis of The Remaining Amount Variation of A Lithium-ion Battery Using A Neural Network

Journal of Knowledge Information Technology and Systems
Abbr : JKITS
2020, 15(3), pp.365-372
DOI : 10.34163/jkits.2020.15.3.006
Publisher : Korea Knowledge Information Technology Society
Research Area : Interdisciplinary Studies > Interdisciplinary Research
Received : May 11, 2020
Accepted : June 11, 2020
Published : June 30, 2020

Won-Hui Lee ¹, JANG SUNG JIN ²

¹서울디지털대학교
²유한대학교

Accredited

ABSTRACT

Lithium-ion cells that are mounted on portable information terminals are almost impossible to find a matching cell due to various variables and unique characteristics. The method of calculating the remaining amount in a portable information terminal is an important item in terms of reliability of the terminal. In this paper, in order to measure the remaining amount of a specific lithium-ion cell, a measurement method is proposed that improves precision with a certain reference item for each element. First, the residual data value of the actual measured lithium-ion cell was trained using the error back propagation algorithm of the neural network. Second, computer simulation using Matlab was used as a type of residual quantity measurement method to make nonlinear numerical data relatively linear while reducing the error from the actual measured value for the residual information value. This method showed an unstable start in the initial state, but the result was relatively similar to the original data value as it went through the learning process of the actual measured data. This remaining amount measurement algorithm is an effective method that can be applied to portable information terminals. The analysis of the remaining amount variation of a lithium-ion battery using a neural network will be applicable to all IT devices as well as portable information terminals.

KEYWORDS

Portable information terminals, Lithium-ion cell, Neural network, Error back propagation algorithm, residual information value

Citation status

* References for papers published after 2023 are currently being built.

[web] / 1997 / http://www.pcim.com/articles/1997/art0006/free man1.htm / http://www.pcim.com/articles/1997/art0006/freeman1.htm

[web] / 1997 / http://www.pcim.com/articles/1997/art0006/free man2.htm / http://www.pcim.com/articles/1997/art0006/freeman2.htm

[report] LG Chemical Ltd / 1998 / Research Park / LG Chemical Company

[thesis] B. C. Lee / 1995 / Characteristic analysis of nonlinear dynamics system using chaos theory / Ph.D / Yonsei University Graduate School

[other] D. S. Kim / 1992 / Neural network theory and application(I) / Hi-TECH Information

[book] E. K. Wsel / 1998 / Wireless multimedia communications addison-wesley wireless communication series / Addison-Wesley

[other] P. Lettieri / 1999 / Advances in wireless terminals / IEEE Personal Communication Magazine Special Issues on Wireless Data

[journal] M. Langkvist / 2016 / Classification and segmentation of satellite orthoimagery using convolutional neural networks / Remote Sensing 8 : 329~350

[thesis] A. Karpathy / 2017 / Neural networks part 1: setting up the architecture, Notes for CS231n Convolutional Neural Networks for Visual Recognition / Stanford University

[journal] E. Maggiori / 2017 / Convolutional neural networks for large-scale remote-sensing image classification / IEEE Transactions on Geoscience and Remote Sensing 55(2) : 645~657

[journal] H-Y. Han / 1999 / Recognition of korean isolated digits using classification and prediction neural networks / Korea Information and Communications Society 24(12B)

[book] Amary / 1991 / Introduction to neural network / Treecaps WS

[thesis] Y. C. Kwon / 1995 / Experience sensitive binary neural network / Ph. D / Ulsan University

[confproc] D. H. Lee / 1998 / Experience sensitive binary neural network / ITC-CSCC’98 II : 1049~1052

[journal] 김성진 / 2004 / Experience Sensitive Cumulative Neural Network Using RAM / 전자공학회논문지 - CI / 대한전자공학회 41(02) : 141~148

[journal] C-S. Bae / 2012 / Direct controller for nonlinear system using a neural network / KIIECT 5(1) : 7~12

[journal] 권오성 / 2019 / Design of Convolution Neural Network Based On Solar Energy and Enhancement of Number Recognition / 한국지식정보기술학회 논문지 / 한국지식정보기술학회 14(1) : 93~101

[journal] 강부식 / 2018 / Improving Accuracy of Movie Recommender System Using Word2Vec and Deep Neural Networks / 한국지식정보기술학회 논문지 / 한국지식정보기술학회 13(5) : 561~568

KJCKorea
Journal Central

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

Analysis of The Remaining Amount Variation of A Lithium-ion Battery Using A Neural Network

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

Analysis of The Remaining Amount Variation of A Lithium-ion Battery Using A Neural Network

ABSTRACT

KEYWORDS

Statistics

Tools

Issue List

Citation status

KCI Citation Counts (1)

REFERENCES (18) * References for papers published after 2023 are currently being built.

Search PDF

Citation

* References for papers published after 2023 are currently being built.