Battery Degradation and Performance
Page 606 of 768Sources and links on page 606 (3)
Links embedded in the original report PDF, including citations and other references. These destinations have not been independently reviewed. Links open in a new tab.
Text on page 606
Stanford 11-Year Calendar Aging Study Researchers at Stanford published a dataset (available open-source) with 8 commercial cells from 5 manufacturers calendar aged over 13 years, showing significant time-varying nonlinearity, cell-to-cell variability, and cell-type dissimilarity. They highlighted the shortcomings of widely-accepted storage time and temperature dependencies on calendar aging, i.e., power-law exponents and the Arrhenius law,, respectively. They also compared 3 different models to predict the EOL and capacity trajectories of unseen cells, unseen temperatures & unseen cell types: semi-empirical (SE), symbolic regression and data-driven (DD) models.
For long storage times, capacity fade predictions require more sophisticated models than the Arrhenius law and power-law exponents. In unforeseen conditions, the SE model excels in predicting EOL, whereas for unseen cell types the DD model was effective in predicting the degradation trajectory.
Source: [1] X Cui et al., Journal of the Electrochemical Society (2025), [2] V. Lam et al., Joule (2025)
Extracted from the PDF; charts and images may read imperfectly.
More in Academia (12)
Page 606 · 2/13