Method for predicting remaining service life of lithium battery based on wavelet denoising and relevant vector machine

Abstract

The invention provides a method for predicting the remaining service life of a lithium battery based on wavelet denoising and a relevant vector machine and relates to a method for estimating the health condition of the lithium battery and predicting the remaining service life of the lithium battery. The method comprises the following steps of measuring health condition data of the lithium battery along with charging and discharging periods, carrying out secondary wavelet denoising on measured lithium battery capacity data; calculating a capacity threshold that lithium battery loses effect; carrying out optimization selection on a width factor of a relevant vector machine algorithm through a differential evolutionary algorithm based on a lithium battery capacity data sequence and a charging and discharging period data sequence; predicting the remaining service life of the lithium battery through the relevant vector machine algorithm optimized by the differential evolutionary algorithm. The method for predicting the remaining service life of the lithium battery based on wavelet denoising and the relevant vector machine is easy to operate and effective, and the remaining service life of the lithium battery can be accurately predicted.

Description

technical field [0001] The present invention relates to a method for predicting the remaining life of a lithium battery based on wavelet noise reduction and correlation vector machine. Specifically, it involves extracting original data through wavelet secondary noise reduction, and establishing a prediction model based on the data using a correlation vector machine algorithm for the lithium battery. A method for predicting remaining life. Background technique [0002] Lithium batteries are widely used in portable electronic devices, electric vehicles, and military electronic systems. The failure of lithium batteries will cause performance degradation, functional failure, slow response, and other electronic failures. Therefore, it is very necessary to predict the remaining life of lithium batteries. [0003] The health status of a lithium battery is generally characterized by its battery capacity, and the capacity data is obtained through measurement during continuous charge...

AI Technical Summary

Problems solved by technology

The width factor of the kernel function in the correlation vector machine algorithm has a great impact on the prediction accuracy. In the past, empirical methods were used to obtain it, and the prediction accuracy of the remaining life of lithium batteries is low.

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