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

Format:

Book

Conference/Event

Author/Creator:

Hamza, Karim, author.

Contributor:

Chu, Kang-Ching

Laberteaux, Kenneth

Conference Name:

WCX SAE World Congress Experience (2025-04-08 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

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 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 Well-to-wheels (WtW) GHG could become a daunting task. This research proposes an approach for estimating the contributions of the various real-world factors towards mismatch in GHG, by first considering the fully idealized GHG estimate (corresponding to the theoretical estimate of GHG), and then progressively relaxing the idealization assumptions, one assumption at a time. The successive relaxation of idealization assumptions allows construction of a series of "GHG mismatch" metrics corresponding to the contributions of individual 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 imply representativeness of PHEVs at large, the approach can be readily scaled to larger datasets

Notes:

Vendor supplied data

Publisher Number:

2025-01-8543

Access Restriction:

Restricted for use by site license