Lithium battery residual life prediction method based on MFF multi-core GPR algorithm

Abstract

The invention provides a lithium battery residual life prediction method based on an MFF multi-core GPR algorithm, and the method comprises the following steps: obtaining a battery data set, taking the battery capacity as a lithium battery life prediction index, carrying out the extraction of a plurality of features of a charging and discharging curve of the battery data set, and forming a plurality of single feature sets; performing weighted linear combination on the extracted features, and calculating the combined features; preliminarily selecting a plurality of single kernel functions according to trend characteristics of the feature indexes, and performing weighted linear combination on the kernel functions to obtain a combined kernel function; dividing the data set into training data and test data, taking the combined features obtained in the step 2 as input features of the GPR algorithm, substituting the combined kernel function obtained in the step 3 into the GPR algorithm, training a GPR model on the training data set, then performing prediction, and stopping prediction until the predicted battery capacity reaches a failure threshold value, and calculating the residual life value RUL of the lithium battery according to the time when the failure threshold value is reached.

Description

technical field [0001] The invention belongs to the technical field of batteries, and relates to a method for predicting the remaining life of a lithium battery based on an adaptive MFF (multi-feature fusion) multi-core GPR (Gaussian process regression) algorithm. Background technique [0002] Lithium-ion batteries have been widely used in airplanes, electric vehicles and portable electronic devices due to their light weight and environmental protection. As the lithium-ion battery undergoes complex physical and chemical reactions during the continuous charge and discharge process, the performance of the battery will degrade until it fails, which will affect the safe operation of the entire system. Therefore, estimating the state of health (SOH) and predicting the remaining useful life (RUL) of Li-ion batteries are important issues in battery management systems. Accurately predicting the remaining life of lithium-ion batteries can effectively predict the future health of lit...

AI Technical Summary

Problems solved by technology

At present, those skilled in the art can predict the RUL of the battery by extracting four features from the charging voltage curve, however, the limitation of this method is that they cannot ensure that the extracted features and labels are highly correlated

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