Optimal charging for lithium-ion batteries to avoid lithium plating based on ultrasound-assisted diagnosis and model predictive control

Abstract

Lithium plating in lithium-ion batteries for electric vehicles, occurring due to low-temperature or high-rate charging, is a significant factor impacting safety and service life. To address this issue, a novel adaptive charging approach is proposed, combining ultrasound-assisted diagnosis and model predictive control (MPC). In the method, a discrete state-space electrochemical model is used to describe the dynamic characteristics of the battery, and a model predictive controller (MPC) is utilized to optimize the charging current to avoid lithium plating. Considering that factors such as battery performance degradation and variable working temperature affect the battery model's judgment of lithium plating, an ultrasound-assisted diagnosis method is used to determine the critical point of lithium plating. The effectiveness of the method is validated through low-temperature charging and cycle aging experiments. The results indicate that without complex model parameter calibration in different temperatures, the new charging method not only has a higher charging speed than constant current charging, but also can effectively suppress the occurrence of lithium plating on the negative electrode of the battery. The method is expected to be applied in electrochemical energy storage systems to enhance safety and service life.