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A Study of Greenhouse Gas Emissions Reduction Opportunity in Light-Duty Vehicles by Analyzing Real Driving Patterns
Technical Paper
2017-01-1162
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
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). The trip traces were analyzed using a public-domain vehicle fuel economy simulator (FASTSim), for several models of light duty conventional and electric drive vehicles. Fuel economy estimates are then used to divide the population of vehicles (and thus households) into several groups. On a well-to-wheels modeling basis, analysis shows that replacing a conventional vehicle with a EDV in one vehicle group (accounting for ~14% of total miles driven) can save between 1.3 to 2.25 times as much GHG emissions compared to making the same vehicle change in another group (accounting for another ~14% of total miles driven). This paper highlights, but does not address, the open question of how to modify current policies to optimally incentivize the adoption of electric drive vehicles for maximum benefit:
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Laberteaux, K. and Hamza, K., "A Study of Greenhouse Gas Emissions Reduction Opportunity in Light-Duty Vehicles by Analyzing Real Driving Patterns," SAE Technical Paper 2017-01-1162, 2017, https://doi.org/10.4271/2017-01-1162.Data Sets - Support Documents
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