@inproceedings{baf6295ff8d14c1cbf9ef092df7eb7a2,
title = "State of Health Estimation for Calendar-Aged Lithium-Ion Batteries Based on Diffusion Characteristics",
abstract = "Lithium-ion batteries experience prolonged calendar aging during their service life, making SoH 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.",
keywords = "Ensemble Learning, GITT, Ion diffusion, Random Forest, SoH",
author = "Wenzhong Cong and Cheng Fan",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2nd International Symposium on New Energy Technologies and Power Systems, NETPS 2025 ; Conference date: 23-05-2025 Through 25-05-2025",
year = "2025",
doi = "10.1109/NETPS65392.2025.11102002",
language = "English",
series = "2025 2nd International Symposium on New Energy Technologies and Power Systems, NETPS 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "202--207",
booktitle = "2025 2nd International Symposium on New Energy Technologies and Power Systems, NETPS 2025",
address = "United States",
}