Method for predicting remaining service life of lithium battery based on FPCA (functional principal component analysis) and Bayesian updating

Abstract

The invention discloses a method for predicting remaining service life of a lithium battery based on FPCA (functional principal component analysis) and Bayesian updating. The method comprises the following steps of utilizing FPCA to build a lithium battery non-parameter degrading model, predicting the remaining service life of the lithium battery based on the degrading model, and analyzing the influence of different amount of modeling data on the predicting accuracy. The method has the advantages that an empirical Bayesian method is provided for updating the lithium battery degrading model in real time; the lithium battery degrading model is corrected in real time by the Bayesian method, so as to obtain the more accurate lithium battery degrading model; the distributing of the remaining service life of the lithium battery is calculated by a parameter bootstrap method, and the confidence interval is calculated.

Description

technical field [0001] The invention relates to the technical field of lithium battery remaining life prediction, in particular to a lithium battery remaining life prediction method based on functional principal component analysis and Bayesian update. Background technique [0002] With the development of lithium-ion batteries, research on life prediction of lithium-ion batteries began in the 1980s. Effective battery life prediction can not only predict potential risks, thus provide effective guidance for battery use, but also reduce related losses caused by battery failure. [0003] There are currently many methods for battery life prediction, most of which are model-based and data-driven prediction methods. Existing model-based lifetime prediction methods can be classified into electrochemical model-based methods, equivalent circuit-based methods, performance-based methods, and analytical model-based methods. The electrochemical model is based on porous electrode theory a...

AI Technical Summary

Problems solved by technology

However, most current data-driven lithium battery life prediction methods require feature extraction from battery data, and inappropriate feature selection may significantly affect the accuracy of battery life prediction

At the same time, most methods can only give a point estimate of battery life, but cannot give its confidence interval and the remaining life distribution when the battery capacity reaches the failure threshold.

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