Research on prediction of remaining useful life of lithium-ion batteries based on convolutional attention mechanism
The accurate estimation and prediction of the remaining useful life of lithium-ion battery is of great significance for lithium-ion battery power management system.On the one hand,it can improve the reliability of the actual circuit system,and prolong the useful life of the battery.The degradation process of lithium-ion batteries shows complex and nonlinear characteris-tics and is accompanied by capacity regeneration,which leads to the low accuracy of traditional prediction models for Remaining Useful Life(RUL)prediction of lithium-ion batteries.In order to further improve the prediction accuracy of lithium-ion battery RUL,this paper firstly analyzed the historical battery degradation data and constructed a new deep learning framework based on convolutional attention mechanism.Through the research on the aging cycle process of lithium-ion battery,the capacity data were selected as the Health Indicator(HI),and the convolution and pooling operation of Convolutional Neural Network(CNN)was used to mine the internal information of the data.The data complexity was reduced and the timing characteristics of battery data were extracted.The feature data were output to the constructed Attention Mechanism(AM)deep network to capture the location information of the global time series data and analyze the relationship between the internal information in the time series data,so as to obtain accurate RUL prediction.Finally,the proposed model was verified on the public NASA and CALCE datasets,and compared with several other prediction models.The results showed that the proposed model had higher prediction accuracy and generalization adaptability.
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