Prediction method for life cycle of battery

Abstract

The invention discloses a prediction method for a life cycle of a battery. The prediction method comprises the following steps: drawing a curve of the number of discharge and capacity fading rate; determining a function model of the capacity fading rate Qloss of the battery and the number of discharge N of the battery; substituting data of a certain impact factor impacting the life of the battery into the function model, wherein Qloss is 50-80 percent of the nominal capacity of the battery; calculating to obtain the expected cycle period N of the battery. According to the prediction method disclosed by the invention, as long as little cyclic test is performed on the battery, the residual life of the battery can be judged, and therefore, the test time is greatly shortened and the test cost is greatly reduced; the objective condition of capacity fading during the actual use process of the battery can be truly and comprehensively described, the life and the residual life of the battery can be obtained, the performance of the battery can be helped to be determined, and the life of the battery is prolonged. As the established model is universal, the prediction method disclosed by the invention is suitable for establishing life fading models of various storage batteries.

Description

technical field [0001] The invention relates to a battery life cycle prediction method, in particular to the secondary utilization of chargeable and dischargeable waste lithium power batteries. Background technique [0002] With the gradual development and promotion of electric vehicles in the world, there are already a considerable number of decommissioned lithium power batteries to be used, and a large number of decommissioned lithium power batteries will be produced in the future. The basic principles for the reuse of decommissioned power batteries are: high specific energy or high output power, good cycle performance, especially when multiple decommissioned battery cells are combined and reused, they also have good consistency. However, before decommissioning, the degradation of each single battery during use is not the same, so it is necessary to predict the remaining life of each single battery. In this way, not only can the decommissioned batteries be quickly and rea...

AI Technical Summary

Problems solved by technology

[0003] On the other hand, the factors that affect battery life are coupled with each other and affect battery life together, which leads to the complexity of life prediction; in order to predict battery life more accurately, a reasonable cycle life model for decommissioned lithium-ion batteries is established, which is sufficient for It is of great significance to play the energy storage characteristics of decommissioned batteries

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