A Practical Switch Condition Monitoring Solution for SiC Traction Inverters

As automotive manufacturers move toward silicon carbide (SiC) MOSFET-based traction inverters, practical online switch condition monitoring solutions are crucial to address potential reliability concerns. In this article, an end-to-end practical online condition monitoring (OCM) solution is proposed. An online sensing circuit is proposed, which enables online ON-state resistance (
$R_{\text {ds-}\mathrm{\scriptscriptstyle ON}}$
) 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 (ETS) technique is also proposed. The obtained periodic, high-resolution
$R_{\text {ds-}\mathrm{\scriptscriptstyle ON}}$
data are filtered by a Kalman filter stage. With the proposed measurement solution,
$R_{\text {ds-}\mathrm{\scriptscriptstyle ON}}$
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
$R_{\text {ds-}\mathrm{\scriptscriptstyle ON}}$
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.