Research And Implementation Of Lithium Battery Life Prediction Based On BiGRU Network

Li-ion battery life prediction is an important part of battery health management.It is an important topic to accurately and quickly predict the remaining life of lithium batteries,and it also has very important practical significance.At present,the remaining service life of lithium batteries has been studied for many years,and many methods have been found accordingly.Each method has different focuses and different prediction capabilities.This article summarizes the many methods of previous researchers and compares their advantages and disadvantages.At the same time,for the physical and chemical process of lithium batteries,a simple,accurate and rapid method for predicting the remaining service life of lithium-ion batteries is proposed.The main contents are as follows:First,it describes the background and practical significance of the subject of the remaining service life prediction of lithium batteries,summarizes current domestic and foreign research,analyzes various current prediction methods,and further observes various physical and chemical parameters of lithium batteries and other characteristics.The degradation mechanism of lithium battery performance.As there are many factors that affect the life of lithium-ion batteries,and there are inherent correlations among various factors,the difficulty of analyzing the complexity of the entire system has suddenly increased.In order to simplify the model,choose among various characteristic parameters to better characterize lithium.The battery capacity of the ion battery life is used as a health factor to analyze the correlation between the battery capacity and the battery life.In view of the difficulties in predicting the service life of lithium-ion batteries and the one-way LSTM neural network cannot make full use of data information and other problems.This paper proposes a neural network model based on Bi GRU,selecting battery capacity data as the key factor,and at the same time,in order to segment the data,a sliding window function is used to segment the original capacity data,and the Bi GRU neural network is applied to lithium-ion battery cycle life prediction problem.Experiments show that the Bi GRU-based neural network has higher prediction accuracy and fewer training parameters,which verifies the effectiveness of the Bi GRU model.Finally,design a battery remaining service life prediction system,input the original battery capacity data,set the preprocessing method,window function size,select the kind of neural network,the number of neurons,activation function and other parameters to start training and It is predicted that the effect of predicting the remaining service life of the battery can be achieved in the end.