Guo, Sunyu, Yang, Yuan, Ma, Haohao, and Huang, Tao (2026) Junction Temperature Estimation Method for Power Modules Based on Gate Voltage Undershoot Amplitude-Phase 2-D Features WithLoad Current Independence. IEEE Transactions on Industrial Electronics, 73 (5). pp. 7937-7948.

PDF (Published Version) - Published Version Restricted to Repository staff only

 

1

Abstract

Junction temperature (T<inf>j</inf>) monitoring plays a crucial role in predicting the lifetime and assessing the reliability of power semiconductor modules. However, existing T<inf>j</inf> estimation methods face three major challenges: insufficient measurement accuracy, strong load current dependence, and vulnerability to bond wire degradation. To address these issues, this article proposes a novel dynamic junction temperature monitoring method based on 2-D amplitude-phase feature fusion derived from gate voltage undershoot signals. The core innovations are twofold: 1) fusion of time-domain amplitude and phase characteristics constructs a 2-D temperature-sensitive feature, leveraging multidimensional complementarity to improve T<inf>j</inf> estimating accuracy and enhance anti-interference capabilities; and 2) integration of Gramian angular field (GAF) encoding with convolutional neural networks (CNNs) enables load-current-independent junction temperature estimation. Moreover, the proposed method demonstrates inherent immunity to bond wire degradation. Its effectiveness is validated through experimental double-pulse tests, which show that the maximum T<inf>j</inf> estimation error remains within ±2 ^◦C, even under varying load conditions.

Actions (Repository Staff Only)

Item Control Page