An Online Junction Temperature Estimating Method for SiC MOSFETs Based on Steady-State Features and GPR
计算机科学
人工智能
情报检索
作者
Qinghao Zhang,W. Li,Pinjia Zhang
出处
期刊:IEEE Transactions on Industrial Electronics [Institute of Electrical and Electronics Engineers] 日期:2024-01-19卷期号:71 (10): 13299-13309
标识
DOI:10.1109/tie.2024.3349585
摘要
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.