Method and device for estimating residual service life of battery

Abstract

The invention belongs to the technical field of lithium batteries, and provides a method and a device for estimating the residual service life of a battery in order to solve the problem that the prediction precision of the residual service life of the lithium battery is influenced because an extreme learning machine model is influenced by a random initialization weight. The method for estimating the residual service life of the battery comprises the following steps: acquiring historical charge-discharge cycle data of the lithium battery, and extracting a discharge time difference from a first voltage to a second voltage and the battery capacity of a forward cycle from the historical charge-discharge cycle data so as to form an initial feature vector; and based on the initial feature vector and a pre-trained service life prediction model, performing iterative prediction until the predicted capacity is lower than a preset proportion of the rated capacity, and finally obtaining a residual service life prediction value of the lithium battery. The service life prediction model is an extreme learning machine model of which initialization parameters are optimized through a beetle antennae search algorithm. The prediction method improves the precision and stability of the extreme learning machine, and finally improves the accuracy of the prediction value of the remaining service life of the lithium battery.

Description

technical field [0001] The invention belongs to the technical field of lithium batteries, in particular to a method and device for estimating the remaining battery life. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] As the charge-discharge cycle of a lithium-ion battery increases, its capacity decreases and its safety deteriorates. In order to ensure the safety of lithium batteries and balance the efficiency of resource use, it is necessary to predict the capacity of lithium-ion batteries in use so that they can be replaced in advance. The artificial neural network has achieved good results in various fields of data analysis. It continuously adjusts the connection weights between the neural network layers through error back propagation (BP), and then gives the optimal model parameters. Completion of calculations. [0004] Facing the l...

AI Technical Summary

Problems solved by technology

However, the extreme learning machine model is affected by random initialization weights, which ultimately affects the accuracy of lithium battery remaining service life prediction.

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