Battery life evaluation method

Abstract

The embodiment of the invention discloses a battery life evaluation method, and the method comprises the following steps: respectively establishing a calendar life model and a cycle life model, and determining operation simulation working condition parameters according to a user demand curve, respectively calculating a total capacity loss rate when each prediction time unit in a plurality of set prediction time units ends through a calendar life model and a cycle life model in combination with operation simulation working condition parameters, and when the total capacity loss rate of each prediction time unit is calculated, correcting the current total capacity loss rate through the total capacity retention rate of the previous prediction time unit. The service life condition of the battery is comprehensively predicted and evaluated by adopting a calendar service life and cycle life mutual coupling calculation method, so that the prediction result deviation caused by the fact that a traditional empirical model can only predict a single working condition and interaction influence is not considered when calendar attenuation and cycle attenuation are mutually superposed is avoided; and the prediction accuracy and the battery life evaluation efficiency are improved, and the method is suitable for actual application scenes.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of battery life assessment, and in particular, to a battery life assessment method. Background technique [0002] With the gradual expansion of the application field of lithium-ion batteries, the problem of its lifespan has gradually become prominent, and it has become one of the focuses of people's concern about the performance of lithium-ion batteries. Especially for power batteries, life prediction is one of the important links in the health management of power batteries. [0003] There are many factors that affect the performance degradation of lithium-ion batteries. At the same time, the internal chemical reaction mechanism of the battery is relatively complex, which makes it difficult to predict the battery life. At present, the main method used is the empirical model. Usually, it is necessary to conduct a large number of tests on the working conditions of the entire life cycle o...

AI Technical Summary

Problems solved by technology

Usually, it is necessary to conduct a large number of tests on the working conditions of the entire life cycle of the battery to obtain test data, and obtain empirical data on capacity fading by obtaining parameter values. Obtaining data, and most of the predictable range is in the evaluation of routine cycle or single calendar life, which is difficult to predict the life of batteries in actual application conditions

Images