Simulations of the US light duty vehicle stock help policy makers, investors, and auto manufacturers make informed decisions to influence the future of the stock and its associated green house gas emissions. Such simulations require an underlying framework that captures the key elements of consumer purchasing decisions, which can be uncertain. This uncertainty in a simulation’s logic is usually convolved with uncertainty in the underlying assumptions about the futures of energy prices and technology innovation and availability. By comparing simulated alternative energy vehicle (AEV) sales to historical sales data, one can assess the simulation’s ability to capture the dynamics of consumer choice, independent of many of those underlying uncertainties, thereby determining the factors that most strongly impact sales. The market for diesel vehicles, hybrid electric vehicles, and to a lesser extent plug-in hybrid electric vehicles and all-electric vehicles, has now matured sufficiently to make such a study possible. In this work, we measure the results of the Sandia ParaChoice model under a variety of input assumptions against historical sales data. We observe that (1) the underlying simulation logic is sound, capturing key drivers of consumer choice, (2) AEV model availability has a significant impact on sales, and (3) AEV consumers are very likely aware of purchasing incentives and factoring those incentives into their purchasing decisions.