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This work presents a simulation-based modeling of the equivalent greenhouse gas (GHG) of plugin hybrid electric vehicles (PHEVs) for real driving patterns obtained from monitoring of real vehicles in public survey data sets such as the California Household Travel Survey (CHTS). Aim of the work is to highlight differences in attainable GHG reduction by adopting a PHEV instead of a conventional vehicle (CV) for different driving patterns obtained from real-world sub-populations of vehicles. Modeling of the equivalent GHG for a trip made by a PHEV can be challenging since it not only depends on the vehicle design and driving pattern of the trip in question, but also on: i) all electric range (AER) of the PHEV, ii) “well to tank” (W2T) equivalent GHG of the electricity used to charge the battery, as well as, iii) battery depletion in previous trips since the last charging event.

Plugin hybrid electric vehicles (PHEVs) have several attractive features in terms of reduction of greenhouse gas (GHG) emissions. Compared to conventional vehicles (CVs) that only have an internal combustion engine (ICE), PHEVs have better energy efficiency like regular hybrids (HEVs), allow for electrifying an appreciable portion of traveled miles, and have no range anxiety issues like battery-only electric vehicles (BEVs). However, in terms of criteria emissions (e.g., NOx, NMOG, HC), it is unclear if PHEVs are any better than HEVs or CVs. Unlike GHG emissions, criteria emissions are not continuously emitted in proportional quantities to fossil fuel consumption. Rather, the amount and type of criteria emissions is a rather complex function of many factors, including type of fuel, ICE temperature, speed and torque, catalyst temperature, as well as the ICE controls (e.g., fuel-to-air ratio, valve and ignition timing).

Electric drive vehicles (EDV) have the potential to greatly reduce greenhouse gas (GHG) emissions and thus, there are many policies in place to encourage the purchase and use of gasoline-hybrid, battery, plug-in hybrid, and fuel cell electric vehicles. But not all vehicles are the same, and households use vehicles in very different ways. What if policies took these differences into consideration with the goal of further reducing GHG emissions? This paper attempts to answer two questions: i) are there certain households that, by switching from a conventional vehicle to an EDV, would result in a comparatively large GHG reduction (as compared to other households making that switch), and, if so, ii) how large is the difference in GHG reductions? The paper considers over 65,000 actual GPS trip traces (generated by one-second interval recording of the speed of approximately 2,900 vehicles) collected by the 2013 California Household Travel Survey (CHTS).

Future Automotive Systems Technology Simulator (FASTSim) is a free and open-source tool developed by National Renewable Energy Lab (NREL). Among the attractive capabilities of the FASTSim is that it can perform computationally efficient fuel economy simulations of automotive vehicles with reasonable accuracy for standard or arbitrary drive cycles. The modeling capability includes vehicles with various types of powertrains such as: conventional vehicles (CVs), hybrid-electric vehicles (HEVs), plugin hybrid electric vehicles (PHEVs) and battery-only electric vehicles (BEVs). The public version of FASTSim available from NREL is implemented in Excel, which achieves the goal of good accessibility to a broad audience, but has some limitations, including: i) bottleneck in computations when importing arbitrary drive cycles, ii) slower computations in general than other scripting or programming languages, and iii) less portable to integration with other applications and/or other platforms.