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Plug-in hybrid electric vehicles (PHEVs) have the capability to drive an appreciable fraction of their miles travelled on electric power from the grid, similar to battery-only electric vehicles (BEVs). However, unlike BEVs which cannot drive unless charged, PHEVs can automatically switch to gasoline power and operate similar to a regular (non-plug-in) hybrid electric vehicle (HEV). Though operating similar to HEV is already beneficial in terms of fuel economy, greenhouse gas (GHG) emissions and criteria pollutants compared to conventional internal combustion engine (ICE) vehicles, much of the attractiveness and allure of PHEVs comes from their capability to drive “almost like a BEV”, but without range anxiety about running out of battery charge.

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.

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.

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.

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.

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).

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.

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).

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.