Comparative Research on SOC Prediction Models Based on Deep Learning
The physical and electrochemical properties of the state of charge(SOC)of lithium-ion batteries are very com-plex,so it is often difficult to directly measure the value of SOC.The machine learning model of big data has become an important technical method to predict SOC.In recent years,with the development of deep learning algorithms of neural networks,SOC estima-tion models based on deep learning have made breakthroughs.This paper summarizes the deep learning method of lithium-ion bat-tery state prediction method,mainly analyzes and compares the classical model methods and characteristics of CNN,GRU,LSTM,CNN-LSTM and CNN-GRU,and compares the effect of each model through the analysis of experimental data.In this paper,differ-ent working conditions under different room temperature environments are selected as test sets in the comparative experiment.Through the error evaluation of prediction results,it is found that convolutional neural network has greatly improved the optimization of prediction results of cyclic neural network,among which CNN-LSTM has a particularly significant effect.
国家科技期刊平台
NSTL
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