State of Health Estimation for Calendar-Aged Lithium-Ion Batteries Based on Diffusion Characteristics

Lithium-ion batteries experience prolonged calendar aging during their service life, making <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{S o H}$</tex> assessment critical for operational safety. Current research on calendar-aged batteries mainly focuses on SEI growth kinetics, leading to evaluation models with strong dependencies on temperature and aging duration. This study investigates the impact of calendar aging on battery diffusion characteristics, achieving high-precision SoH estimation through a Random Forest-based ensemble learning algorithm. The model demonstrates MAE and RMSE below 0.55 % and 0.9 % respectively, effectively reducing temperature-duration dependencies and broadening the model's applicability across diverse operational scenarios.