Near Automatic Translation of Autonomie-Based Power Train Architectures for Multi-Physics Simulations Using High Performance Computing 2017-01-0267
The Powertrain Analysis and Computational Environment (PACE) is a powertrain simulation tool that provides an advanced behavioral modeling capability for the powertrain subsystems of conventional or hybrid-electric vehicles. Due to its origins in Argonne National Lab’s Autonomie, PACE benefits from the reputation of Autonomie as a validated modeling tool capable of simulating the advanced hardware and control features of modern vehicle powertrains. However, unlike Autonomie that is developed and executed in Mathwork’s MATLAB/Simulink environment, PACE is developed in C++ and is targeted for High-Performance Computing (HPC) platforms. Indeed, PACE is used as one of several actors within a comprehensive ground vehicle co-simulation system (CRES-GV MERCURY): during a single MERCURY run, thousands of concurrent PACE instances interact with other high-performance, distributed MERCURY components. A proof-of-concept implementation of PACE, as applied to a conventional powertrain architecture, was presented at the SAE2016 conference. Since then, a C++ library of components implementing the functionality of the corresponding Simulink subsystems has been developed, followed by streamlining the process of the generation of the C++ code for a particular powertrain; the native Simulink XML representation of the architecture (components and their connectivity) is used for an automatic generation of the simulation workflow and thus effectively reproducing the functionality of the Autonomie models while making it compliant to MERCURY requirements. The resulting PACE models are rigorously verified and validated against results generated by Simulink runs. Furthermore, the whole process is largely automated, thus providing time savings when implementing new vehicle architectures.
Citation: Haupt, T., Henley, G., Card, A., Mazzola, M. et al., "Near Automatic Translation of Autonomie-Based Power Train Architectures for Multi-Physics Simulations Using High Performance Computing," SAE Int. J. Commer. Veh. 10(2):483-488, 2017, https://doi.org/10.4271/2017-01-0267. Download Citation
Tomasz Haupt, Gregory Henley, Angela Card, Michael S. Mazzola, Matthew Doude, Scott Shurin, Christopher Goodin
Mississippi State Univ, US Army TARDEC, US Army ERDC
WCX™ 17: SAE World Congress Experience
SAE International Journal of Commercial Vehicles-V126-2, SAE International Journal of Commercial Vehicles-V126-2EJ
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