Unexpected field issues may soon be significantly reduced as TRINA scientists demonstrate ability to predict remaining useful life of electronics for electrified vehicles.
TRINA scientists, in collaboration with researchers from the University of Connecticut (UCONN), have developed machine learning based prognostic algorithms to detect anomalies in electrified vehicle inverters and then predict the remaining useful life (RUL) of the power electronics. This technology may reduce vehicle down time by allowing drivers to schedule maintenance and order parts in advance of issues. This work, titled “A Distance-Based Health Indicator for RUL Prediction of Power Electronics,” was presented at the 2023 IEEE Transportation Electrification Conference & Expo (iTEC) in Detroit, Michigan, where it recently received a best paper award.
Link: 2023 IEEE Transportation Electrification Conference & Expo
*The 2023 award winners will be posted soon.