A Link Between the Lab and the Real World - A Setup for Accelerated Aging of Power Electronics Using Mission Profiles from the Field
By Mattias P. Eng, Madhav Mishra, Wilhelm Söderkvist Vermelinm, Dag Andersson, Klas Brinkfeldt
Published in EuroSimE
IEEE
2024
Abstract
To generate data used for developing schemes and models for CM, PHM, and for estimating RUL of power electronic devices, accelerated aging experiments in the form of power cycling are often performed. In these experiments, a set current is passed through the power devices and is turned on and off in regular cycles. Due to the mismatch in CTEs of the materials in the devices, the on/off cycles will generate thermally induced stress in the various material interfaces, which is the main cause of failures. Most of the power cycling setups that are currently used can only manage a single set on-state current level and fixed on/off times (which is also the common standard for lifetime testing); a condition that is very far from most real applications. The experimental setup described here is based on a Gamry Reference 3000AEpotentiostat/galvanostat/ZRA working with a Gamry 30k Booster, which can be programmed to generate a variable load current profile and will thus enable the application of more realistic conditions for accelerated aging of power electronic devices in the lab. This will improve prognostics model development and provide excellent use cases for evaluating the capabilities of the prognostics algorithms for generalization to field conditions. The application of variable load profiles from the field, instead of the regular on/off cycles traditionally used, is not compatible with the commonly used method of using the chip itself as a temperature sensor. Instead, we here present a novel method of estimating the junction temperature using a device specific derivation of thermal parameters from the measured cooling block temperature, case temperature, and dissipated power in conjunction with simulations using the PySpice simulation package implemented in Python. The setup coupled with the new junction temperature estimation is an important step in enabling predictive maintenance of power devices that is currently missing from the power electronics community.