Capacity multistage degradation analysis and knee point prediction method of Lithium-ion battery

Analyzing capacity degradation characteristics and accurately predicting the knee point of capacity are crucial for the safety management of lithium-ion batteries (LIBs). However, the degradation mechanism of LIBs is complex. A key but challenging problem is how to clarify the degradation mechanism and predict the knee point. According to the external characteristics such as capacity decline rate and the peak value of increment capacity curve (IC curve), the capacity degradation was divided into four stages, including initial decline stage, slow decline stage, transition stage and high-speed decline stage. Moreover, a method based on neural network was proposed to predict the knee point. Two features were used to predict the capacity and cycle of the knee point, which were the slope of the capacity degradation curve and the difference of the IC curve with the maximum correlation. Hence, the experimental results showed that the position of the knee point could be accurately predicted using only the first 100 cycles of the battery, which leaded to the accurately prediction of the knee point.