Transient Thermal Modeling of Power Semiconductors for Long-Term Load Profiles

This paper presents an improved thermal analysis method focusing on the long-term profiles of power modules. A shift-invariant dictionary method is developed to extract the critical atoms to sparsely represent time series signals. Then the temperature response of the power module can be approximated by a sparse linear combination of basis functions. The performance of the proposed method is compared with the convolution method, the result indicates that the transient junction temperature of the power module can be predicted with less computational effort. The simulation and experimental results also prove that the proposed method can effectively reconstruct the thermal behavior, exhibits good predictive capability and is suitable for temperature prediction under long time scales.