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Paper Abstract and Keywords
Presentation 2024-09-06 13:50
Study and evaluation of lithium-ion battery SOC estimation using LSTM
Yamato Kohara, Takaki Ojima, Masahiro Fukui (Ritsumeikan Univ.), Tomohisa Ohmoto (UPDATE ENERGY) NLP2024-55
Abstract (in Japanese) (See Japanese page) 
(in English) This paper investigates how to improve the accuracy of charging rate estimation for lithium-ion storage batteries using LSTM, which is effective as deep learning for time-series data. The contents of the study include training data preparation, LSTM parameter settings and optimality evaluation, practicality, and future development. Training data is constructed using the battery simulator. The battery simulator can be used to generate input and output waveforms in a wide variety of states. We verified the optimality of the time step, which is an important factor for learning time series information in LSTM. We found that more accurate SOC estimation is possible by increasing the time step.
Keyword (in Japanese) (See Japanese page) 
(in English) LSTM / Charge Rate Estimation / Lithium-ion battery / / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 178, NLP2024-55, pp. 71-76, Sept. 2024.
Paper # NLP2024-55 
Date of Issue 2024-08-29 (NLP) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 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 NLP2024-55

Conference Information
Committee NLP  
Conference Date 2024-09-05 - 2024-09-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Takayama City Library 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Nonlinear Problems, etc. 
Paper Information
Registration To NLP 
Conference Code 2024-09-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study and evaluation of lithium-ion battery SOC estimation using LSTM 
Sub Title (in English)  
Keyword(1) LSTM  
Keyword(2) Charge Rate Estimation  
Keyword(3) Lithium-ion battery  
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1st Author's Name Yamato Kohara  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
2nd Author's Name Takaki Ojima  
2nd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
3rd Author's Name Masahiro Fukui  
3rd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
4th Author's Name Tomohisa Ohmoto  
4th Author's Affiliation UPDATE ENERGY Co. Ltd. (UPDATE ENERGY)
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Speaker Author-1 
Date Time 2024-09-06 13:50:00 
Presentation Time 25 minutes 
Registration for NLP 
Paper # NLP2024-55 
Volume (vol) vol.124 
Number (no) no.178 
Page pp.71-76 
#Pages 6 
Date of Issue 2024-08-29 (NLP) 

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