Rui Xiong1, Ju Wang1, Hongwen He1, Yongzhi Zhang2, Michael G. Pecht2, Simin Peng3
1School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China
2CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA
3Yancheng Institute of Technology, 74619 Yancheng, Jiangsu China
This paper developed an effective health indicator to indicate lithium-ion battery state of health and moving-window-based method to predict battery remaining useful life. The health indicator was extracted based on the partial charge voltage curve of cells. Battery remaining useful life was predicted using a linear aging model constructed based on the capacity data within a moving window, combined with Monte Carlo simulation to generate prediction uncertainties. Both the developed capacity estimation and remaining useful life prediction methods were implemented based on a real battery management system used in electric vehicles. Experimental data for cells tested at different current rates, including 1°C and 2°C, and different temperatures, including 25°C and 40°C, was collected and used. The implementation results show that the capacity estimation errors were within 1.5%. During the last 20% of battery lifetime, the root mean square errors of remaining useful life predictions were within 20 cycles, and the 95% confidence intervals mainly cover about 20 cycles.