An Online Junction Temperature Estimating Method for SiC MOSFETs Based on Steady-State Features and GPR

The thermal sensitivity electrical parameter (TSEP) method has gained popularity for online junction temperature (
T
_j
) estimation to enhance the operational reliability of SiC
mosfet
s. However, the performance of existing TSEP methods is affected by varying operating conditions. Achieving a balance between
T
_j
estimation accuracy and cost remains a challenge. To address these issues, this article proposes an online
T
_j
estimation method.
First
, the
on
-state voltage and
on
-state body diode voltage drop are combined as features for
T
_j
estimation. The selection of these two features with different sensitivities under various load current cases improves the accuracy of
T
_j
estimation and enhances the overall sensitivity.
Second
, Gaussian Process Regression is employed to eliminate the effect of load current from the
T
_j
estimation model, ensuring robustness to operating conditions.
Finally
, an online
T
_j
estimation strategy based on these innovations is proposed and its effectiveness is validated through multiple experiments in a dc–dc converter under various operating conditions. Compared to conventional methods, the proposed approach demonstrates higher accuracy and stronger robustness against operating conditions.