Enabling future inline quality management: early battery cell quality forecasting using machine learning with electrochemical impedance spectroscopy and incremental capacity analysis

Publication date
2026-05-07
Document type
Konferenzbeitrag
Author
Li, Rui Yan
Deng, Sicong
Sayin, Alisan
Kidai, Asmaa
Lingohr, Paul
Born, Henrik
Heimes, Hans Heiner
Kampker, Achim
Organisational unit
RWTH Aachen University
DOI
URI
Conference
Publisher
Universitätsbibliothek der HSU/UniBw H
Book title
Machine learning for cyber physical systems : proceedings of the conference ML4CPS 2026
First page
60
Last page
72
Is part of
Peer-reviewed
✅
Part of the university bibliography
Nein
File(s)
Language
English
Keyword
Cell finishing
Machine learning
Quality prediction
Incremental capacity analysis
Electrochemical impedance spectroscopy
Abstract
Cell finishing represents a time-critical bottleneck in lithium-ion battery cell manufacturing, yet decisive quality information is typically confirmed only after extended aging and end-of-line testing. This delay ties up production resources and limits early defect detection. To address this limitation, this study proposes a multimodal feature fusion approach combining incremental capacity analysis (ICA) and electrochemical impedance spectroscopy (EIS) for early-stage capacity prediction. Physically interpretable features, including ICA peak characteristics and EIS-derived impedance parameters, are extracted from formation data acquired under production-representative conditions. Ensemble-based regression models are trained and evaluated, with XGBoost yielding the lowest prediction error. A complementarity analysis demonstrates that combining ICA and EIS features improves prediction accuracy compared to unimodal approaches. Feature importance analysis confirms that both modalities contribute substantially, with ohmic resistance and ICA peak height among the most influential predictors. The results validate the complementary nature of ICA and EIS for early quality assessment and support inline decision-making for cell grading and scrap reduction in battery cell manufacturing.
Description
This contribution is part of the conference proceedings, which are licensed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/)
Version
Published version
Access right on openHSU
Open access