T18.2 - Dynamic Environment-Aware Lifetime Prediction of SiC MOSFET Modules Through LSTM

Wednesday, March 19, 2025

1:50 PM – 2:10 PM ET

Location: Level Three, A302

Technical Lecture Presenter(s)

Md Zakir Hasan (he/him/his)

Mississippi State University

Presentation Paper

Co-Author(s)

Seungdeog Choi

Mississippi State University
PS

Prashant Singh

University of Tennessee - Knoxville
CL

Chun-Hung Liu

Mississippi state university
YA

Youssef Aider

University of Tennessee - Knoxville

The state-of-the-art determines the remaining useful lifetime (RUL) through a steady state, fixed power cycling tests (PCT) without considering the impact of dynamically changing environmental conditions. It has resulted in considerable RUL prediction errors in the real world. However, the dynamic changing conditions (e.g., large temperature swings) may affect the degradation evolution of SiC MOSFET, which could eventually result in RUL changes. Thus, it must be integrated to make accurate predictions. To precisely understand the RUL variation complexity, the junction temperature (Tj) has been measured with a Negative Thermal Coefficient (NTC) thermistor and its profile has been modeled through the thermal model RC foster network using Extended Kalman Filter (EKF). Then, the on-state resistance (Rds,on) variations and Degradation Acceleration Factor (DAF) under the dynamic environment conditions are integrated into a lifetime prediction model to accurately predict the RUL through the Long Short-Term Memory (LSTM) machine learning algorithm.