Battery state of health estimation under fast charging via deep transfer learning

Jingyuan Zhao; Di Li; Yuqi Li; Dapai Shi; Jinrui Nan; Andrew F Burke

doi:10.1016/j.isci.2025.112235

Battery state of health estimation under fast charging via deep transfer learning

iScience. 2025 Mar 18;28(5):112235. doi: 10.1016/j.isci.2025.112235. eCollection 2025 May 16.

Authors

Jingyuan Zhao¹, Di Li², Yuqi Li³, Dapai Shi², Jinrui Nan⁴, Andrew F Burke¹

Affiliations

¹ Institute of Transportation Studies, University of California, Davis, Davis, CA 95616, USA.
² Hubei Longzhong Laboratory, Hubei University of Arts and Science, Xiangyang 441000, China.
³ Key Laboratory for Renewable Energy, Beijing Key Laboratory for New Energy Materials and Devices, Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China.
⁴ Shenzhen Automotive Research Institute, Beijing Institute of Technology, Shenzhen 518000, China.

Abstract

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 (convolutional neural networks [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 a state of charge (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.

Keywords: Electrochemical energy storage; Energy storage; Energy systems;.