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URN to cite this document: urn:nbn:de:bvb:703-epub-8879-6
Title data
Ramasubramanian, Srivatsan ; Plank, Christian ; Danzer, Michael A. ; Röder, Fridolin:
Degradation path indicators for lithium-ion batteries.
In: Journal of Energy Storage.
Vol. 140, Part B
(2025)
.
- 119113.
ISSN 2352-1538
DOI der Verlagsversion: https://doi.org/10.1016/j.est.2025.119113
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Project information
| Project title: |
Project's official title Project's id Untersuchung der Interaktion von Degradationsprozessen bei der Alterung von Lithium-Ionen-Batterien mit variierenden Sequenzen 527466263 Open Access Publizieren No information |
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| Project financing: |
Deutsche Forschungsgemeinschaft |
Abstract
Battery aging can follow multiple degradation pathways, which influence future aging due to self-amplification, self-limitation, and interactions among mechanisms. This phenomenon is known as path-dependent aging. Understanding path dependency is crucial for reliable lifetime prediction and requires identifying distinct degradation pathways. In this study, k-means clustering is applied to aging data from 48 commercial lithium-ion batteries (LIB), cycled under 24 combinations of temperature and C-rate. Key degradation metrics, including capacity fade, pulse resistance, and degradation modes, are used to construct path-indicator spaces. Clustering with degradation modes reveals three distinct degradation regimes, characterized by proximity in both path-indicator and stress-factor space. These regimes are further validated using microscopic analysis of the negative electrode and distribution of relaxation times analysis. Based on the findings, general guidelines are proposed for designing dynamic usage schedules to test path dependency in LIB aging. Therefore, the methodology presented in this study provides a generalizable framework for characterizing battery degradation with a multi-dimensional feature space and introduces an unsupervised approach for identifying distinct degradation pathways. Additionally, the proposed method can help in building dynamic test protocols that trigger distinct degradation pathways and aid in the development and validation of lifetime prediction models.
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