Simultaneous prediction of peak current and duration time using diffusion induced stress in lithium-ion battery active material

Abstract

The battery management system (BMS) is a system that increases the safety and efficiency of the battery by estimating the state of the battery, predicting battery life and performance, and maintaining the battery in an optimal state. Among the roles of BMS, prediction of the peak current of the battery is a factor closely related to the safety of the battery. In addition, real-time prediction of the peak current is an important factor in determining and responding to whether or not the power demand required by the device being used is met. However, the traditional methods use the peak current experimentally designed and use an equivalent circuit model(ECM) This methods don’t predict the battery’s transient characteristics and internal factors and limit the output to operate safely in most states. There is a problem in that it cannot use battery full potential. In addition, it does not consider the time that the predicted peak current can be maintained, but only deals with the instantaneous peak current. In order to solve this problem, it is necessary to predict the peak current in consideration of the transient characteristics of the battery and internal factors, but it is a difficult problem due to the complexity of the internal characteristics of the battery. This research develops a real-time electrochemical model with excellent accuracy even when high and dynamic currents are used by using the lithium-ion concentration, which is an internal factor of the battery. The mechanical stress of the electrode is derived through the relationship with the lithium-ion concentration. The mechanical stress in the electrode is derived by using the relationship between the lithium-ion concentration and the mechanical stress. The mechanical stress is used to predict the time when active material fracture, one of the causes of power and capacity loss will occur. Using the predicted value, the available peak current and duration time are simultaneously predicted. Through this research, it will play an important role in suggesting an optimal charging and discharging strategy that can maximize performance while minimizing mechanical damage to the battery