Investigation of RUL prediction of lithium-ion battery equivalent cycle battery pack - Lian-bing LI; Liang JI; Ya-zun ZHU; Zhi-jiang WANG; Lei JI (工程科学学报, 2020)
The depletion and environmental pollution associated with traditional fossil energy sources has generated great interest in the development of new energy. Among the kinds of new-energy batteries, lithium-ion batteries have the advantages of small size, high energy density, a long life cycle, zero emissions, and no pollution. These batteries are widely used in many industries and fields, including vehicles. Currently, assessments of the health status of lithium-ion batteries have become a hot research topic. The lithium-ion battery has complex electrochemical characteristics and its capacity tends to degrade with cyclic charges and discharges. When its capacity degrades to the failure threshold (usually 70%–80% of rated capacity), the life of lithium ion battery is considered to have reached an end. Therefore, investigations to better predict the remaining useful life of a lithium-ion battery can help to improve system reliability and prevent accidents. Battery-system health evaluations have important research and application value. In this study, the voltage change curves of the lithium-ion battery were investigated with discharge time under equivalent cycle conditions and different cycle times. By analyzing the slope change rule of the derivative function at an equivalent characteristic point for different cycle times, the life degradation curves of the lithium-ion battery under equivalent cycle conditions were established. Using the NASA and self-test JZ equivalent cycle batteries, the intersection point of the specific-slope straight line and curve at early and late stages of discharge was taken as the equivalent feature points for predicting the equivalent cycle life. Based on these two groups of feature points, Mini and Mlat degradation models were established, respectively. To verify this method, other batteries in the equivalent-cycle battery pack were tested. The results of the test data set validate the prediction accuracy and stability, which has strong application value.