Suggested Citation:
Zhao, Jingyuan, Di Li, Yuqi Li, Dapai Shi, Jinrui Nan, Andrew Burke (2025)

Battery State of Health Estimation Under Fast Charging via Deep Transfer Learning

Accurate state of health (SOH) estimation is essential for effective lithium-ion battery management, particularly under fast-charging conditions with a constrained voltage window. This study proposes a hybrid deep neural network (DNN) learning model to improve SOH prediction. With approximately 22,000 parameters, the model effectively estimates battery capacity by combining local feature extraction (CNNs) and global dependency analysis (self-attention). The model was validated on 222 lithium iron phosphate (LFP) batteries, encompassing 146,074 cycles, with limited data availability in an SOC range of 80%–97%. Trained on fast-charging protocols (3.6C–8C charge, 4C discharge), it demonstrates high predictive accuracy, achieving a mean absolute percentage error (MAPE) of 3.89 mAh, a root mean square error (RMSE) of 4.79 mAh, and a coefficient of determination (R²) of 0.97. By integrating local and global analysis, this approach significantly enhances battery aging detection under fast-charging conditions, demonstrating strong potential for battery health management systems.