Colloquia

Summary

The growing demand for batteries underscores the need for sustainable management of retired cells. This study develops efficient, non-destructive methods to assess the degradation pattern, leading to prediction State of Health (SOH) and predict the Remaining Useful Life (RUL) in future research, addressing critical gaps in current practices. Combining experimental cycling tests and data-driven analysis, the research evaluates key metrics such as capacity fade, Coulombic efficiency, and differential capacity (dQ/dV) features. Retired cells were profiled to establish baseline characteristics (SQ01), and controlled cycling tests (SOH01) were conducted to analyze SOH trends and variability in degradation patterns. Data pre-processing, including interpolation and smoothing, facilitated the identification of performance trends and degradation mechanisms. The findings contribute to basic scalable solutions towards battery health evaluation, enabling reuse, re-manufacturing, and recycling, and advancing the integration of retired batteries into the circular economy.