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Paper Abstract and Keywords
Presentation 2026-03-18 09:10
Investigation and Evaluation of Robust Machine Learning-Based Fitting Methods for Lithium-Ion Battery Polarization Characteristics
Takashi Nishikawa, Masahiro Fukui (Ritsumeikan Univ.) CAS2025-102 CS2025-95
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, with the rapid adoption of electric vehicles (EVs), the importance of appropriately managing the degradation state of lithium-ion batteries (LIBs), which serve as their power source, and accurately evaluating their residual value has been increasing. One non-destructive approach to assessing LIB degradation is electrochemical impedance spectroscopy (EIS). In EIS, the complex impedance of a battery is represented as a Nyquist plot, and the internal state can be inferred by fitting the plot to an equivalent circuit model of the battery. The authors have been developing a deep learning–based fitting approach as an alternative to conventional least-squares-based fitting, aiming to achieve more accurate parameter estimation. This paper describes a deep learning–based fitting method that enables high-precision evaluation of internal resistance (polarization) characteristics, particularly under progressive degradation.
Keyword (in Japanese) (See Japanese page) 
(in English) Lithium-ion battery / Electrochemical Impedance Spectroscopy（EIS) / Nyquist plot / Parameter fitting / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 414, CAS2025-102, pp. 85-90, March 2026.
Paper # CAS2025-102 
Date of Issue 2026-03-10 (CAS, CS) 
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 CAS2025-102 CS2025-95

Conference Information
Committee CAS CS  
Conference Date 2026-03-17 - 2026-03-18 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network processor, Signal processing and circuits for communications, Wireless LAN / PAN, etc. 
Paper Information
Registration To CAS 
Conference Code 2026-03-CAS-CS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Investigation and Evaluation of Robust Machine Learning-Based Fitting Methods for Lithium-Ion Battery Polarization Characteristics 
Sub Title (in English)  
Keyword(1) Lithium-ion battery  
Keyword(2) Electrochemical Impedance Spectroscopy（EIS)  
Keyword(3) Nyquist plot  
Keyword(4) Parameter fitting  
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1st Author's Name Takashi Nishikawa  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
2nd Author's Name Masahiro Fukui  
2nd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
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Speaker Author-1 
Date Time 2026-03-18 09:10:00 
Presentation Time 20 minutes 
Registration for CAS 
Paper # CAS2025-102, CS2025-95 
Volume (vol) vol.125 
Number (no) no.414(CAS), no.415(CS) 
Page pp.85-90 
#Pages 6 
Date of Issue 2026-03-10 (CAS, CS) 

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