Lithium battery health state estimation and residual life prediction method under time-varying temperature

Abstract

The invention discloses a lithium battery health state estimation and residual life prediction method under a time-varying temperature, and the method comprises the following steps: S1, building a lithium battery performance degradation model under a time-varying temperature condition based on a Wiener process, a Power Rule stress model and an Arrhenius temperature stress model; s2, estimating prior parameters of the lithium battery performance degradation model based on a maximum likelihood estimation method, and then updating posterior distribution of a drift coefficient on line according to detected field degradation data of the lithium battery; s3, obtaining an expectation, variance and probability density distribution expression according to the detected on-site degradation data of the lithium battery in combination with the degradation process of the lithium battery; and S4, according to the detected on-site degradation data of the lithium battery, in combination with the degradation process of the lithium battery and the relationship between the residual life and the first arrival time, obtaining a probability density distribution function of the residual life of the lithium battery under the time-varying temperature working condition.

Description

technical field [0001] The invention belongs to the technical field of reliability engineering, and in particular relates to a method for estimating the state of health of a lithium battery and predicting the remaining life of a lithium battery under a time-varying temperature. Background technique [0002] Lithium batteries have been widely used in civilian and military fields due to their advantages of environmental protection, high energy density, light weight and long life. However, once the lithium battery fails, it will cause inconvenience, shutdown or even catastrophic accident of the application system, so it is necessary to take measures to ensure the safety and reliability of the lithium battery. Engineering practice shows that accurate health state estimation and remaining life prediction are important methods to improve the reliability of lithium batteries and reduce the risk of failure. Most of the existing methods are based on ideal working conditions under la...

AI Technical Summary

Problems solved by technology

In actual use, time-varying temperature conditions will cause irregular degradation of lithium battery performance, which will increase the complexity and difficulty of health state estimation and remaining life prediction

Images