High-Performance Computing-Based Fast Virtual Prototyping of Power Electronics Converters for Ground Vehicle Powertrain Systems

EasyChair Preprint 10950

Abstract

Power electronics converters, as an enabler for future power and energy system, are heavily used in military ground vehicle powertrain systems to provide reliable and efficient power conversion. Virtual prototyping of power electronics converters using computer-aided modeling and simulation to create and test power electronics circuits and systems before they are physically constructed can help engineers to optimize designs, reduce development time, and lower costs. This paper demonstrates the effectiveness of developing a High-Performance Computing (HPC)-based power electronics modeling and simulation approach to speed up the entirety of simulation time to support high-speed power electronics-enabled power architecture for demanding ground vehicle powertrain applications. First, a multiple power electronics building block (PEBBs)-based model for the virtual prototyping of power electronics converters is developed in MATLAB. This is then followed by a parallel implementation of the same model in Julia utilizing its highperformance compiled programming language’s fast computing capability and a variety of supported parallel computing programming interfaces. Later, more PEBBs are added to the multi-PEBB model to test the scalability of the parallel simulation. It is observed that the Message Passing Interface-based parallel multi-PEBBs simulation in Julia is both fast and scalable without a noticeable increase in execution times when more PEBBs are added to the system.

Keyphrases: Faster-Than-Real-Time Simulation, HPC, Power Electronics Simulation

@booklet{EasyChair:10950,
  author    = {Sushma Amara and Yi Li and Cayden Wagner and Shuangshuang Jin and Zheyu Zhang and Christopher Edrington},
  title     = {High-Performance Computing-Based Fast Virtual Prototyping of Power Electronics Converters for Ground Vehicle Powertrain Systems},
  howpublished = {EasyChair Preprint 10950},
  year      = {EasyChair, 2023}}