Aging modes analysis and physical parameter identification based on a simplified electrochemical model for lithium-ion batteries

Abstract

Abstract Due to the limited measurements, battery internal failures are hard to quantitatively assess. This work develops a method to quantitatively analyze battery aging modes under different aging conditions and selectively extract the internal indicators to track the battery health state. Considering the changes in physical parameters can reflect the deterioration inside a battery, a simplified electrochemical model that has good identifiability of battery model parameters is introduced, and battery physical parameters are obtained via their functional relationships with the model parameters. The battery aging modes under different aging conditions are then analyzed according to the variations of physical parameters during a battery's lifetime. Lastly, analysis of the correlations between physical parameters and battery state of health is conducted, and health indicators are correspondingly selected. This work can quantificationally analyze the degree of different aging modes from a mechanism perspective, help accurately estimate battery state of health, and predict battery remaining useful life, which has important theoretical significance and practical value for improving the technical level of battery management.