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Paper Abstract and Keywords
Presentation 2018-11-13 13:50
A Study of Degradation Diagnosis of Lithium-ion Battery Using Neural Networks
Masahito Arima, Lei Lin, Masahiro Fukui (Ritsumeikan Univ.) CAS2018-73 MSS2018-49
Abstract (in Japanese) (See Japanese page) 
(in English) The battery aggregation of lithium-ion battery is studied in order to solve the problem of output fluctuation and time maldistribution of renewable battery, for example, photovoltaic energy. It is necessary for the operational economic efficiency of lithium-ion battery to predict the charge-discharge energy by degradation diagnosis. In this study, we investigated the method of degradation diagnosis using neural networks and decrease the number of the charge-discharge times which needs to be carried out in advance.
Keyword (in Japanese) (See Japanese page) 
(in English) Lithium-ion battery / Charge-discharge curve / degradation diagnosis / Econimic efficiency prediction / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 295, CAS2018-73, pp. 111-114, Nov. 2018.
Paper # CAS2018-73 
Date of Issue 2018-11-05 (CAS, MSS) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF CAS2018-73 MSS2018-49

Conference Information
Committee MSS CAS IPSJ-AL  
Conference Date 2018-11-12 - 2018-11-13 
Place (in Japanese) (See Japanese page) 
Place (in English)  
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Paper Information
Registration To CAS 
Conference Code 2018-11-MSS-CAS-AL 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study of Degradation Diagnosis of Lithium-ion Battery Using Neural Networks 
Sub Title (in English)  
Keyword(1) Lithium-ion battery  
Keyword(2) Charge-discharge curve  
Keyword(3) degradation diagnosis  
Keyword(4) Econimic efficiency prediction  
1st Author's Name Masahito Arima  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
2nd Author's Name Lei Lin  
2nd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
3rd Author's Name Masahiro Fukui  
3rd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
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Speaker Author-1 
Date Time 2018-11-13 13:50:00 
Presentation Time 25 minutes 
Registration for CAS 
Paper # CAS2018-73, MSS2018-49 
Volume (vol) vol.118 
Number (no) no.295(CAS), no.296(MSS) 
Page pp.111-114 
Date of Issue 2018-11-05 (CAS, MSS) 

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