Abstract: In order to solve the difficulty in measuring capacity directly and capacity regeneration during online remaining useful life (RUL) prediction for lithium-ion batteries, a new method is proposed based on time interval of equal charging voltage difference and optimized Gaussian process regression (GPR) model. Firstly, the GPR model with uncertainty expression is established and optimized by using combined kernel functions and particle swarm optimization. The time interval of equal charging voltage difference is extracted during the constant current charging process of lithium-ion batteries. The relationship between the time interval of equal charging voltage difference and capacity is analyzed by a generalized linear regression model. The time interval of equal charging voltage difference can act as a health indicator for RUL prediction of lithium-ion batteries. According to the data sets of charge/discharge tests of lithium-ion batteries, the verification experiments are carried out. The results show that the proposed method can predict nonlinear degradation of capacity well and have high prediction accuracy and online RUL prediction ability for lithium-ion batteries.

Key words: lithium-ion battery; life prediction; Gaussian process regression; time interval of equal charging voltage difference