Lithium ion battery remaining life prediction method based on grey particle filter

Abstract

The invention relates to a lithium ion battery remaining life prediction method based on grey particle filter, which can use a small data amount for aging modeling, accurately estimate the battery capacity state and improve the battery life prediction accuracy. The method comprises steps: battery capacity data which are needed for prediction are firstly extracted as sample data to be inputted to agrey prediction model, the grey evolution coefficient a for capacity degradation of the lithium ion battery is calculated, and thus, a state transfer equation and an observation equation for the lithium ion battery capacity during the aging process are built; a particle filter algorithm is then used to track and update the battery capacity state changes; and finally, when prediction begins, according to the grey evolution coefficient, the change value of the capacity of each particle along with the cycle number is extrapolated, and according to the weight of each particle, the remaining lifeof the battery is predicted, and probability density distribution is given.

Description

Technical field: [0001] The invention belongs to the technical field of lithium-ion batteries, and more specifically relates to a method for predicting the remaining life of lithium-ion batteries. Background technique: [0002] In the face of global energy and environmental crises, lithium-ion batteries have been widely used as the main energy storage equipment in the automotive industry, aerospace, and grid energy storage. The normal operation and status monitoring of lithium-ion batteries are the guarantee of system stability, and the correct prediction of battery remaining life is a key factor to realize battery health management, which can greatly reduce the probability of system failure. The remaining life of lithium-ion batteries is also called cycle life, which refers to the number of charge and discharge cycles experienced when the battery degrades from the rated capacity to the point where it cannot maintain the equipment or degrades to less than 70% or 80% of the r...

AI Technical Summary

Problems solved by technology

[0003] At present, in the remaining life prediction of lithium-ion batteries, there are three problems in establishing a high-precision and adaptable battery life prediction method framework: (1) The amount of data is small: the amount of data representing battery life (such as the capacity of lithium-ion batteries) Increases as batteries age, so less early in forecast

(2) Complicated aging modeling: Since the degradation process of lithium-ion batteries is caused by complex electrochemical reactions inside the battery, the lithium-ion battery model usually presents time-varying and nonlinear characteristics, which is difficult to express with an accurate mathematical model. The influence of other factors such as discharge conditions and ambient temperature

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