A method for binning power batteries

Abstract

The invention discloses a method for classifying power batteries, comprising the steps of: firstly, drawing the relationship curve between voltage and SOC, and the dV / dQ curve; secondly, drawing the curve of the relationship between battery storage temperature and voltage ; The third step is to perform a preset formation operation on the battery, and then calculate the actual capacity of each battery; the fourth step is to use the battery steady-state voltage of each battery after the third step of the formation operation as the sorting voltage, Combined with the actual capacity of each battery obtained in the third step, each battery is classified according to the preset sorting voltage binning numerical interval and the actual capacity binning numerical interval of batteries of different grades, and finally the binning operation is completed. The invention discloses a method for classifying power batteries, which can optimize the formation process of square power batteries, so that the battery can ensure better voltage consistency, and at the same time, realize the accuracy and consistency of binning, and improve the efficiency of the battery. The matching rate is of great significance.

Description

technical field [0001] The invention relates to the technical field of batteries, in particular to a method for classifying power batteries. Background technique [0002] At present, for power batteries, in the production process, the pole pieces are wound into pole groups, and the pole groups are connected to the shell and cover, and the electrolyte is injected into the semi-finished battery cells after exhausting and secondary liquid injection. And the sorting process is to fully activate the cells while characterizing the cells of different performance levels, and accurately classify and screen them. [0003] The original formation process is to charge the battery to the same capacity after one charge and discharge, and finally use the relationship between voltage and capacity to classify. Taking LP2714897AK ternary power battery as an example, the original formation process classification principle is as follows: [0004] First, assuming that the actual capacity of the ...

AI Technical Summary

Problems solved by technology

[0011] However, the aging time of different batteries and the ambient temperature are different, resulting in deviations in the voltage tested before sorting, which ultimately affects the accuracy of battery capacity classification, and additional classification levels (or sorting levels) are added. On-site management and battery matching rate are unfavorable

[0012] In addition, due to the large pressure difference (both the voltage and the voltage drop of the same support are relatively discrete), the sorter cannot realize automatic screening and self-discharge, requiring staff to manually process data every day, which increases labor costs

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