Spectral Analysis of Approximated Capacity Fade Curvature for Lithium-Ion Batteries

TL;DR

This work addresses degradation diagnosis in lithium-ion batteries by identifying knee points in capacity fade curves using a curvature-based metric that relies solely on cyclic capacity data. It extends and validates a previously proposed knee identification method on a new aging dataset with a realistic driving profile and conducts spectral analysis of the approximated curvature to link degradation phases with electrode phase transitions. The results show knee onset correlates with estimated degradation modes (LLI, LAM_NE, LAM_PE) and phase-transition shifts, while the curvature in phase 2 exhibits steady, multi-frequency fluctuations without dominant spectral peaks. The method is data-efficient and applicable to battery management systems for runtime degradation diagnosis in both vehicular and grid storage contexts, with future work aimed at field data validation and physics-based parametric analyses.

Abstract

The techno-economic benefits of incorporating battery degradation into advanced control strategies necessitate the development of degradation diagnosis as an advanced function in battery management systems (BMSs). To address this, a curvature-based knee identification method was proposed in our previous work [1]. Here, we further validate its effectiveness on a new battery aging dataset under a realistic driving profile and conduct spectral analysis of the approximated capacity fade curvature. The curvature-based method shows consistent knee identification performance on this dataset and the approximated curvature is found to correlate with underlying degradation modes and a shift of electrode material phase transition points. The method uses capacity data as the only input, which is easy to acquire in the lab and it is applicable in battery energy storage systems for grid applications.

Figures (7)

• Figure 1: One full charge-discharge cycle of a sample cell in experiment 4 of ICL dataset.
• Figure 2: Cyclic capacity data of cell B and C in experiment 4 of ICL dataset.
• Figure 3: The approximated curvature (left) and internal degradation modes (right) for cell B. The knee-onset and knee points are identified at the 190th and 452rd cycle, respectively.
• Figure 4: The approximated curvature (left) and internal degradation modes (right) for cell C. The knee-onset and knee points are identified at the 177th and 423rd cycle, respectively.
• Figure 5: The IC curves with largest peaks marked with solid red squares (left) and amplitudes of largest peaks (right) from 10 RPTs for cell B.