An Approach for Estimating Contributions of Real-World Factors towards Attained Well-to-Wheels Greenhouse Gas Emissions of Plug-in Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) conceptually aim to offer the “best of both worlds” of battery-only electric vehicles (BEVs) in terms of utilizing grid electricity to power an appreciable portion of vehicle miles travelled (VMT), as well as long driving range, fast refueling while maintaining excellent fuel economy comparable to regular (non-plug-in) hybrid electric vehicles (HEVs) when travelling longer distances. However, theoretical estimates of the well-to-wheels (WtW) greenhouse gas (GHG) emissions from PHEVs rely on several idealization assumptions, any/all of which may not necessarily be realized in the real world. With many real-world factors involved, including daily VMT profile, charging behavior, weather conditions and drive aggressiveness, all of which possibly having complex interactions, quantitative analysis of the contribution of each factor towards the real-world/attained WtW GHG can be a daunting task. This research proposes an approach for estimating the contributions of the various real-world factors in a mismatch in WtW GHG, by first considering the fully idealized GHG estimate (corresponding to the theoretical estimate of WtW GHG), and then progressively relaxing the idealization assumptions, one assumption at a time. The successive relaxation of idealization assumptions then allows construction of a series of “GHG mismatch”-indices, with each index corresponding to one category of real-world factors. The proposed approach is demonstrated via real-world data accumulated via one week of data collection from three PHEVs. While width and depth of the data (number of vehicles and duration of monitoring) are not enough to infer or even imply representativeness of PHEVs at large, the approach can be readily scaled to larger datasets.