Method for predicting residual life of lithium ion battery based on WDE optimization LSTM network

Abstract

The invention provides a method for predicting the residual life of a lithium ion battery based on a WDE optimization LSTM network, and relates to the technical field of lithium ion batteries. The method comprises the steps of firstly, constructing two groups of lithium ion battery monitoring indexes; acquiring lithium ion battery monitoring data, and extracting lithium ion battery monitoring index data and lithium ion battery capacity data from the lithium ion battery monitoring data; then determining an LSTM network structure, and building a model for indirectly predicting the residual lifeof the lithium ion battery based on LSTM; utilizing a weighted differential evolution algorithm to optimize key parameters in the model for indirectly predicting the residual life of the lithium ion battery; utilizing optimization data to determine an optimal model for indirectly predicting the residual life of the lithium ion battery; and finally, predicting later lithium ion battery capacity data by utilizing the optimal model for indirectly predicting the residual life of the lithium ion battery. According to the method for predicting the residual life of the lithium ion battery based on the WDE optimization LSTM network, the change rule of the lithium ion battery capacity data can be accurately predicted, and the residual life of the lithium ion battery can be effectively evaluated.

Description

technical field [0001] The invention relates to the technical field of lithium-ion batteries, in particular to a method for predicting the remaining life of a lithium-ion battery based on a WDE optimized LSTM network. Background technique [0002] Lithium-ion batteries have the advantages of no memory effect, low self-discharge rate, high working voltage, high energy density and long cycle life, and have been rapidly and widely used in various fields, such as: new energy vehicles, aircraft and aviation detectors, Industrial production and uninterruptible power supply systems, etc. Lithium-ion battery remaining life prediction and health status monitoring play a vital role in the development of new energy technologies. During the use of lithium-ion batteries, with the increase of charge and discharge times, the performance degradation of lithium-ion batteries is inevitable. By effectively predicting the capacity of lithium-ion batteries, the continuous and stable developmen...

AI Technical Summary

Problems solved by technology

Although the fusion model can improve the accuracy of the prediction results to a certain extent, this type of method also has some shortcomings, such as: the fusion model has a large uncertainty and the calculation complexity is too high, so in actual use, it is still There are certain limitations, and the feasibility of this type of method still needs to be improved

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