Real-Time Nonlinear Behavioral Electrothermal Device-Level Emulation of IGBT on Heterogeneous Adaptive Compute Acceleration Platform

Abstract

Power converter design evaluation by means of real-time simulation techniques is prevalent, although it is mostly restricted to simple power semiconductor switch models that exclude device-level physical details. In this work, the nonlinear high-order electro-thermal model of the Insulated-gate bipolar transistor (IGBT) is developed and then deployed onto the heterogeneous digital hardware for real-time implementation. As the complexity of the nonlinear behavioral model (NBM) of the IGBT poses a significant computational burden on real-time hardware emulation, machine learning (ML) methodology is utilized so that the trained model can reproduce the characteristics of its original counterpart as much as possible and then it is implemented on the Adaptive Compute Acceleration Platform (ACAP), which composes of the processing system (PS), programmable logic (PL), and Artificial Intelligent Engine (AIE). The vector multiplication feature of the AIE caters to mathematical operations of the ML-based model particularly well and consequently enables it to be executed in real-time with remarkable speedup over the original model with which matrix inversion is otherwise mandatory. Finally, the validation for real-time device-level results and system-level results of a multi-converter system is provided by SaberRD® and MATLAB/Simulink®.