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.