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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.

We present a parametric analysis of electric vehicle (EV) adoption rates and the corresponding contribution to greenhouse gas (GHG) reduction in the US light-duty vehicle (LDV) fleet through 2050. The analysis is performed with a system dynamics based model of the supply-demand interactions among the fleet, its fuels, and the corresponding primary energy sources. The differentiating feature of the model is the ability to conduct global sensitivity and parametric trade-space analyses. We find that many factors impact the adoption rates of EVs. These include, in particular, policy initiatives that encourage consumers to consider lifetime ownership costs, the price of oil, battery performance, as well as the pace of technological development for all powertrains (conventional internal combustion engines included). Widespread EV adoption can have noticeable impact on petroleum consumption and GHG emissions by the LDV fleet.