Remaining useful life prediction of IGBT in electric vehicles
As one of the core components of electric vehicles,IGBTs'health monitoring and remaining life prediction play a vital role in proactive maintenance.The Bi-LSTM model based on Bayesian optimiza-tion and attention mechanism is proposed to predict the remaining useful life of IGBT in this paper.The proposed method can effectively improve the accuracy of IGBT remaining service life prediction.VCE-on through IGBT accelerated aging test is collected in this study,verifying its feasibility as a failure character-istic parameter.This data is used as an experimental data set to validate the proposed method through sim-ulation.The experimental analysis results show that the proposed hybrid prediction model has lower deg-radation prediction error than the classical LSTM and other prediction models,demonstrating significant theoretical and practical value.
electric vehicles of IGBTremaining life predictionBayesian optimization algorithmatten-tion mechanismbidirectional long short-term memory
万方数据
维普
