Rui Xiong 1, Yongzhi Zhang 1, Ju Wang 1, Hongwen He 1, Simin Peng 2, and Michael Pecht 3
1 National Engineering Laboratory for Electric Vehicles Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China
2 School of Electrical Engineering, Yancheng Institute of Technology, Yancheng, China
3 CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA
This paper developed an effective health indicator to indicate lithium-ion battery state of health and moving-windowbased 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 and 2 C, and different temperatures, including 25 and 40 °C, were 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.