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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).

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.

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).

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.

This work presents a modeling approach for estimation of the equivalent greenhouse gas (GHG) emissions of plugin hybrid electric vehicles (PHEVs) for real driving patterns and charging behaviors. In general, modeling of the equivalent GHG for a trip made by a PHEV not only depends on the trip trace in question, but also on the electric range of the vehicle and energy consumption in previous trips since the last charging event. This can significantly increase the necessary computational burden of estimating the GHG emissions using numerical simulation tools, which are already computationally-expensive. The proposed approach allows a trip numerical simulation starting with a fully charged battery to be re-used for GHG estimation of a trip that starts with any initial state of charge by re-allocating the appropriate amount electric energy to an equivalent gas consumption.