From Cornfields to Canyons: Route-Specific, Physics-Based, Predictive Reliability Modeling of DC–DC Power Converters

The reliability of power electronic converters remains a key bottleneck in electric vehicle (EV) adoption, directly affecting drivetrain longevity and total cost of ownership. Conventional lifetime models evaluate components independently under static thermal conditions, overlooking the coupled and time-varying stresses that dominate real-world operation. In this work, a physics-based degradation framework is developed and experimentally calibrated to capture three layers of a realistic application: (i) coupled component aging induced by self-heating (SH) under shared electrical stress, (ii) thermal cross-talk (TCT) between neighboring components, and (iii) system lifetimes determined by the temperature profiles of EV trips across the U.S. Incorporating SH and TCT results in a 92% reduction in the predicted active lifetime of a DC-DC converter compared to fixed thermal stress model. Integration of the trip profile further extends model applicability, enabling climate-specific lifetime predictions across different U.S. regions. The proposed framework provides a predictive, stress and temperature-aware reliability tool for robust EV power converter design