Short Term Charging Data Based Battery State of Health and State of Charge Estimation Using Feature Pyramid
Bowen Dou, Shujuan Hou, Li Hai, Haosen Chen, Zhongbao Wei, Лэй Сун
Abstract
Accurate battery states estimation is critical to the safe and stable operation of Li-ion batteries, and it is one of the fundamental functions of a battery management system (BMS). This paper proposes a novel deep learning framework called multi-timescale dual feature-based state estimation network (MFN) using extremely limited charging data to estimate three critical states simultaneously end-to-end: maximum capacity, the capacity at the beginning of charging, and the capacity at the end of charging. The core of the framework is the dual feature extraction module (DFM) and the multi-timescale information extraction module (MTM). First, DFM extracts independent and coupling features of external signals (voltage, current, temperature) in the time dimension, respectively, and then merges them. Based on this, MTM explores the health features of the external signals on different time scales and merges them. Finally, the three internal states are estimated using the mined depth features. Experimental results in the Oxford battery data show that the root mean square error (RMSE) for three internal states is less than 9.71 mAh, corresponding to 1.31% of the nominal capacity.