Deep belief network and relevance vector machine fusion-based lithium battery residual life prediction method

Abstract

The invention relates to a lithium ion battery cycle life prediction technology, in particular, a deep belief network and relevance vector machine fusion-based lithium battery residual life prediction method. An existing lithium battery residual life prediction method relies on accurate physical models or complex signal processing technologies, as a result, the existing lithium battery residual life prediction method needs heavy investment, or the existing method is based on a shallow structures, as a result, the performance of fault prediction will be limited, and the existing method is vulnerable to curse of dimensionality, while with the method of the invention adopted, the problems of the existing method can be solved. A charging and discharging period-based lithium battery capacity degradation data set is obtained; the data are pre-processed; the fusion models of a DBN (deep belief network) and an RVM (relevance vector machine) are built; a DBN model and a RVM model are trained; and the trained fusion models of the DBN and the RVM is adopted to predict the residual life of a lithium battery. The method of the invention is suitable for predicting the residual life of the lithium battery.

Description

technical field [0001] The invention relates to a lithium ion battery cycle life prediction technology. Background technique [0002] Due to its safe and reliable performance, lithium batteries have become the focus of research in the fields of energy, automotive engineering, and aerospace engineering. Lithium-ion batteries are widely used in aviation, aerospace, satellite, military, electric vehicles and other application systems. However, the performance of lithium batteries will gradually decline with the use of cyclic charge and discharge, and the battery capacity will gradually decay until it cannot be fully charged effectively and is scrapped. Therefore, the cycle life of lithium batteries is limited. Accurately predict the remaining time of lithium batteries Cycle life is key to the safe and reliable operation of these systems. For simplicity of description, the remaining cycle service life is simplified as remaining useful life (RUL) below. Therefore, it is very i...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the existing methods for predicting the remaining life of lithium batteries rely on accurate physical models or complex signal processing techniques, requiring expensive investment, or the existing methods are based on shallow structures, which will limit the performance of fault prediction and easily The problem of suffering from the curse of dimensionality, thus providing a lithium battery remaining life prediction method based on the fusion of deep belief network and correlation vector machine

Images