A New Co-Simulation Approach for Tolerance Analysis on Vehicle Propulsion Subsystem

An increasing demand for reducing cost and time effort of the design process via improved CAE (ComputerAided Engineer) tools and methods has characterized the automotive industry over the past two decades. One of the main challenges involves the effective simulation of a vehicle’s propulsion system dealing with different physical domains: several examples have been proposed in the literature mainly based on co-simulation approach which involves a specific tool for each propulsion system part modeling. Nevertheless, these solutions are not fully suitable and effective to perform statistical analysis including all physical parameters. In this respect, this paper presents the definition and implementation of a new simulation methodology applied to a propulsion subsystem. The reported approach is based on the usage of Synopsis SABER as dominant tool for co-simulation: models of electronic circuitry, electro-mechanical components and control algorithm are implemented in SABER to perform tolerance analysis; in addition, a dynamic link with engine plant model developed in GT-SUITE environment has been established via a dedicated procedure. Moreover, a HPC Grid (High Performance Computing Grid) is used with the aim to execute simulations of long engine maneuvers as well as to parallelize jobs while applying Monte-Carlo methods. The overall approach is tested on the active thermal management subsystem of a General Motors internal combustion engine in order to evaluate the robustness of control algorithm against electromechanical part variation and software calibration settings. Introduction Nowadays, the development cycle of each software/ hardware part and component of a vehicle propulsion system is inevitably assisted by a simulation phase which implies the usage of specific CAE tools and analysis: these allow engineers to verify system performance according to design requirements and predict any potential issue on the field with the advance to reduce hardware costs and compress development timing. Moreover, the continuous enhancements of simulator features and computational resources is opening to new breakthroughs and scenarios for the simulation of whole vehicle system and environment. Being one of the greatest automotive company in the world, General Motors has been put large effort, since many years, to create internal processes and dedicated infrastructures capable to fully sustain the virtualization of vehicle manufacturing and testing. For instance, Bogden et al. [5] introduced the SDSS (Signal-Delivery Sub-System) process which is intended to perform robust analysis on an engine control subsystem including electronics, algorithm and engine parts modeling. In [4] Goodwin et al. applied the process and method to study the impact of electronics and electro/mechanical parts variation on the operation of variable valve timing (VVT) subsystem, modelled in Synopsis SABER environment. In particular, they showed how to accomplish simulation of huge number of statistical samples via MonteCarlo approach using distributed computing. As further improvement to the SDSS methodology, in [3] authors describe a procedure, referred as CAL-SIL, to include also the control algorithm and calibrations within same simulation environment. All described applications dealt with the simulation of engine subsystems which didn’t require the usage of an engine plant model since the interaction of sensors/actuators with thermal, hydraulic and mechanical portion of the engine was obtained using fixed test conditions or experimental profiles (i.e. pressure, flow-rate, rpm, etc..), as reported in figure 1. Recently, the demand to extend all features developed for SDSS methodology on more complex vehicle and engine subsystems resulted in the need to include also modeling of engine plant within the simulation environment. Usually, models of internal combustion engines are developed in specific CAE simulators, such as GT-SUITE from Gamma Technologies, which allows to properly model, with high level of accuracy, the © 2019 Synopsys, Inc.; General Motors LLC. Published by SAE International. This Open Access article is published under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits distribution, and reproduction in any medium, provided that the original author(s) and the source are credited. Downloaded from SAE International by SAE International Sales Team Use Internal Use ONLY, Wednesday, April 01, 2020