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Software tools for simulations of vehicle fuel economy/energy efficiency play an important role strategic decision-making in advanced powertrains. In general, there is a trade-off between the level of detail in a numerical model of a vehicle (higher detail provides better simulation accuracy), and the computational time resources to run the model. However, even with detailed models of a vehicle, there remains some uncertainty about how the vehicle performs in the real-world. Calibration of simulation models versus real-world data is a challenging task due to variations in vehicle usage by different owners. This work utilizes datasets of real-world driving in vehicles that have been equipped with OBD/GPS loggers. The loggers record at fairly high frequency the vehicle speed, road slope, cabin heating/air-conditioning loads, as well as energy/fuel consumption.

Software simulation tools for vehicle fuel economy/energy efficiency can play an important role in strategic decisions about advanced powertrains. One such tool that has been developed by the National Renewable Energy Laboratory (NREL) is known as FASTSim. The philosophy of FASTSim aims to strike a difficult balance between simplifying the task of creating/editing vehicle models, fast computation time and high-fidelity simulation results. In the “baseline” version of FASTSim, which is open-source and freely available in Python or Excel, the instantaneous efficiency of an engine, motor or fuel cell is estimated via reference curves as function of power demand. The reference efficiency curve for each powertrain subsystem (e.g. for a spark-ignition engine) in baseline FASTSim has the same profile irrespective of what vehicle is being modelled, which is a compromise in accuracy in favor of ease of modeling.

Sales of plug-in vehicles (PEVs), which include battery-only electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), have steadily grown in the past years amidst various incentive programs. While incentives are important, or perhaps essential during early phases of market introduction, they may not be feasibly sustainable when/if PEV sales ramp up to mass-market levels. As such, much interest and speculation exist on how soon could PEVs become cost-competitive without incentives. Research in this paper adopts a bottom-up approach for estimating sale price and total cost of ownership (TCO) for new vehicles. A critical review is also conducted for various publicly available sources of information pertaining to PEVs and conventional internal combustion-engine (CICE) vehicles. Due to the existence of some uncertainty about current costs, let alone future costs, a sensitivity analysis is conducted.

Plug-in hybrid electric vehicles (PHEVs) combine some of the attractive traits of both fully electric vehicles (EVs) and non-plug-in hybrid vehicles (HVs). EV traits shared by PHEVs include the capability to charge the battery via electricity from the grid while the vehicle is parked and the ability to drive an appreciable distance without having to turn the engine on, in what is known as charge depletion mode. HV traits shared by PHEVs include the ability to use the engine to maintain the state of charge (SOC) of the batteries within certain limits, in what is known as charge sustaining mode. Charge sustaining mode allows a PHEV to drive long distances without requiring stops for electrical charging (unlike EVs) but comes at the trade-off that fuel needs to be used.