A New Co-Simulation Approach for Tolerance Analysis on Vehicle Propulsion Subsystem
ISSN: 0148-7191, e-ISSN: 2688-3627
Published September 09, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
An increasing demand for reducing cost and time effort of the design process via improved CAE (Computer-Aided 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 electro-mechanical part variation and software calibration settings.
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CitationMancuso, C., Cavaiuolo, D., Corbo, G., Papadimitriou, I. et al., "A New Co-Simulation Approach for Tolerance Analysis on Vehicle Propulsion Subsystem," SAE Technical Paper 2019-24-0079, 2019, https://doi.org/10.4271/2019-24-0079.
Data Sets - Support Documents
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