A novel classification method of commercial lithium-ion battery cells based on fast and economic detection of self-discharge rate

Abstract Packs with high self-discharge accelerate the capacity decline and even cause the safe issues. It is important to keep the self-discharge rate at a uniform and small level for all the cells in a pack. The traditional clustering methods are either costly or time-consuming. In this paper, a novel classification method is invented to realize the fast estimation of the relative self-discharge rate. Firstly, the balancing technology for large-scale cells is proposed to ensure the voltage equalization. Small batches of equalized cells are then clamped in detection circuits to realize the external mapping of the internal current originated from the self-discharge. The relative self-discharge rates of the cells are estimated by the equivalent parallel circuit model with self-discharge. The results show that the method can detect the cell with a relative self-discharge rate of 3%/month for three cells at a time within 1 h. To further classify the cells according to the relative self-discharge rate, microammeters are connected in each branch of the detection circuits. The results show that the method can identify the relative self-discharge rate in less than 2 h, and the errors can be kept within 0.1%/month.