X-in-the-Loop Validation of Deep Learning-Based Virtual Sensing for Lifetime Estimation of Automotive Power Electronics Converters

Health degradation issues in automotive power electronics converter (PEC) systems are present due to repetitive thermomechanical stress endured while the vehicle is in real-field operation. This stress results from heat generation, a byproduct of semiconductor operation within PECs, leading to degradation in semiconductor operating life. The best practice in academia and industry is to rely on detailed physics-of-failure (PoF)-based models for lifetime estimation. However, the PoF-based model of PECs requires substantial computation time and robust devices to estimate lifetime accurately. According to the literature surveys, the computation time of the PoF-based models could be reduced further by using a low-fidelity and/or reduced-order model (ROM) that may result in unacceptable accuracy. To fulfill this research gap, this article proposes a real-time executable, deep learning (DL)-based virtual sensing method that enables vehicle manufacturers to estimate the lifetime of the PECs onboard. This computationally efficient virtual sensing method has been integrated into an onboard vehicle validator edge (VVE). At the same time, multiple DL configurations are being explored, and optimization is performed on compositions, hyperparameters, training, and testing datasets to obtain the best DL model. Finally, to demonstrate the feasibility and accuracy of the proposed method before its implementation within the complex VVE, an X-in-the-loop (XiL) test is performed with vehicle frontloading.