Rapid estimation of electric vehicle acceptance using a general description of driving patterns. (February 2015)

• Record Type: Journal Article
• Authors: Tamor, Michael A.; Moraal, Paul E.; Reprogle, Briana; Milačić, Miloš
• Is Part Of: Transportation research. Volume 51(2015)
• Page Start: 136
• Page End: 148
• Publication Date: 2015-02
• Subjects: Electric vehicle -- Hybrid electric vehicles -- Plug-in hybrid -- Electric range -- Acceptance; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2014.10.010
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Usage of personal vehicles in three US cities and Germany is analyzed. Potential for acceptance of EVs in those regions is estimated. Usage patterns and acceptance are found to differ only by a simple distance scale factor. A very simple description of the general usage pattern yields essentially identical results. This statistical representation can also be inverted to generate synthetic vehicle populations. Abstract: A reliable estimate of the potential for electrification of personal automobiles in a given region is dependent on detailed understanding of vehicle usage in that region. While broad measures of driving behavior, such as annual miles traveled or the ensemble distribution of daily travel distances are widely available, they cannot be predictors of the range needs or fuel-saving potential that influence an individual purchase decision. Studies that record details of individual vehicle usage over a sufficient time period are available for only a few regions in the US. In this paper we compare statistical characterization of four such studies (three in the US, one in Germany) and find remarkable similarities between them, and that they can be described quite accurately by properly chosen set of distributions. This commonality gives high confidence that ensemble data can be used to predict the spectrum of usage and acceptance of alternative vehicles in general. This generalized representation of vehicle usage may also be a powerful tool in estimating real-world fuel consumption and emissions.