Lithium ion battery remaining life prediction method based on wolf pack optimization LSTM network

Abstract

The invention provides a lithium ion battery remaining life prediction method based on a wolf pack optimization LSTM network, and relates to the technical field of lithium ion batteries. The method comprises the following steps: firstly, acquiring monitoring data of a lithium ion battery, and extracting lithium ion battery capacity data from the monitoring data; determining a long short-term memory network structure, and constructing an LSTM-based lithium ion battery remaining life prediction model; secondly, optimizing key parameters in the lithium ion battery remaining life direct predictionmodel by utilizing a wolf pack algorithm to obtain a direct prediction model based on a wolf pack optimization LSTM network; determining an optimal lithium ion battery remaining life direct prediction model by using the optimization data; and finally predicting later-stage lithium ion battery capacity data by using the optimal lithium ion battery residual life direct prediction model. According to the lithium ion battery remaining life prediction method based on the wolf pack optimization LSTM network provided by the invention, the remaining life of the lithium ion battery can be accurately predicted.

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 lithium-ion batteries based on gray wolf group optimization LSTM network. Background technique [0002] The remaining life of a lithium-ion battery is used to describe the number of charge-discharge cycles when the capacity of a recycled lithium-ion battery reaches a certain threshold and cannot continue to work. At present, the prediction methods of lithium-ion battery life can be roughly divided into two categories: prediction methods based on experience and prediction methods based on performance. The experience-based method mainly uses the historical data of the battery to estimate its life, which can also be called the basic statistical law method, mainly including the cycle number method, the ampere-hour method and weighted ampere-hour method, and the event-oriented aging accumulation method. method. These three ...

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Problems solved by technology

These three methods can only give a rough estimate of the remaining life of lithium-ion batteries. They are based on the statistics of lithium-ion battery monitoring data and can only be applied to special conditions. Although they have a faster calculation speed, However, it is impossible to give an accurate description of the physical and chemical change process inside the battery, and it has poor adaptability and cannot adapt to the prediction problem under complex conditions.

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