Many of today's automotive control system simulation tools are suitable for simulation, but they provide rather limited support for model building and management. Setting up a simulation model requires more than writing down state equations and running them on a computer. The role of a model library is to manage the models of physical components of the system and allow users to share and easily reuse them. In this paper, we describe how modern software techniques can be used to support modeling and design activities; the objective is to provide better system models in less time by assembling these system models in a "plug-and-play" architecture. With the introduction of hybrid electric vehicles, the number of components that can populate a model has increased considerably, and more components translate into more possible drivetrain configurations. To address these needs, we explain how users can simulate a large number of drivetrain configurations. The proposed approach could be used to establish standards within the automotive modeling community.

Plug-in Hybrid Electric Vehicles (PHEVs) offer a great opportunity to significantly reduce petroleum consumption. The potential fuel displacement is influenced by several parameters, including powertrain configuration, component technology, drive cycle, distance… The objective of this paper is to evaluate the impact of component assumptions on fuel efficiency using Monte Carlo analysis. When providing simulation results, researchers agree that a single value cannot be used due to large amount of uncertainties. In previous papers, we have used triangular distribution, but assuming that all inputs were correlated lead to improper results. Monte Carlo allows users to properly evaluate uncertainties while taking dependencies into account. To do so, uncertainties are defined for several inputs, including efficiency, mass and cost. For each assumption, an uncertainty distribution will be defined to evaluate the fuel efficiency and cost of a particular vehicle with a determined probability.

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