A Practical Switch Condition Monitoring Solution for SiC Traction Inverters

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Abstract
As automotive manufacturers move towards Silicon Carbide (SiC) MOSFET based traction inverters, practical online switch condition monitoring solutions are crucial to address potential reliability concerns. In this paper, an end-to-end practical online condition monitoring solution is proposed. An online sensing circuit is proposed which enables online on-state resistance ( Rds-on ) measurement for all six switches of the inverter. To address the challenge of periodic data acquisition alongside higher priority motor control tasks, a fast, code-efficient out-of-order equivalent time sampling technique is also proposed. The obtained periodic, high resolution Rds-on data is filtered by a Kalman filter stage. With the proposed measurement solution, Rds-on obtained at the motor current peak has an error of < 1.5%. Furthermore, the symmetrical nature of the inverter’s operation is exploited to propose a Bayesian inference solution for independent online state-of-health (SoH) estimation for all six switches. This technique isolates aging related Rds-on change from operating conditions related changes. In particular, by automatically accounting for device and system level variations in the model, the proposed Bayesian SoH estimation solution eliminates the need for extensive system/device specific calibration. The efficacy and robustness of the proposed solution are tested by inducing bond-wire failure in several decapsulated discrete SiC MOSFETs.
Funding Information
  • Texas Instruments Inc., Sugarland, TX, USA

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