Junction Temperature Prediction of IGBT Power Module Based on BP Neural Network

  • Wu, Junke (State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University) ;
  • Zhou, Luowei (State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University) ;
  • Du, Xiong (State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University) ;
  • Sun, Pengju (State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University)
  • Received : 2013.08.14
  • Accepted : 2013.12.13
  • Published : 2014.05.01


In this paper, the artificial neural network is used to predict the junction temperature of the IGBT power module, by measuring the temperature sensitive electrical parameters (TSEP) of the module. An experiment circuit is built to measure saturation voltage drop and collector current under different temperature. In order to solve the nonlinear problem of TSEP approach as a junction temperature evaluation method, a Back Propagation (BP) neural network prediction model is established by using the Matlab. With the advantages of non-contact, high sensitivity, and without package open, the proposed method is also potentially promising for on-line junction temperature measurement. The Matlab simulation results show that BP neural network gives a more accuracy results, compared with the method of polynomial fitting.


IGBT;Junction temperature prediction;BP neural network;Saturation voltage drop;Collector current


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