Yongquan Suna,b, Xueling Haoa, Michael Pechtb, and Yapeng Zhoub,c
aISRE, Harbin University of Science and Technology, Harbin, China
bCALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA
cWuhan University of Technology, Wuhan 430070, China
State of health estimation and remaining useful life prediction of lithium-ion batteries is challenging due to various health indicators characterizing battery degradation. This paper develops an integrated health indicator to predict remaining useful life by incorporating capacitance, resistance, and constant current charge time with the help of a beta distribution function, based on the correlation analysis between parameter variations and aging mechanisms. A three-order polynomial model is employed to fit the battery health degradation process, remaining useful life is predicted using a particle filter algorithm, and the probability density function for the battery remaining useful life is then provided. A case study is conducted to validate the health degradation model and battery remaining useful life prediction. The results show that the constant voltage charge time is not a good health indicator, and a threshold of 0.85 is recommended as the end-of-life criterion based on the integrated health indicator. The developed method provides a reference for battery remaining useful life prediction when sufficient energy and power are required.