A Data-driven Condition Monitoring method to predict the Remaining Useful Life of SiC Power Modules for Traction Inverters

The electric vehicle of the future requires smarter semiconductor power devices to fulfill higher reliability requirements. Several electro-thermal parameters on the chip level can be used to assess the health condition of power electronics systems and to predict the remaining useful life. This paper analyses promising indicators to monitor the degradation level in the chip solder layer of SiC power switches. Active power cycling tests accelerate the aging of a population of SiC power modules for traction inverters. On-state voltage and junction temperature are monitored until the end of life of the devices. The collected data are input to a predictive regression model to estimate the state of health in the power switches. Moreover, a prognostic concept on the system level is introduced. Measurements at operating temperature during the vehicle idle times serve as input to a product-related predictive model. The processor determines the condition of the SiC power switches to issue a maintenance alert and avoid the possible occurrence of unexpected failures. This work provides investigations in data-driven predictive models for wide-bandgap technologies such as SiC power modules and defines an innovative prognostic method on the edge device.