Private versus Shared, Automated Electric Vehicles for US Personal Mobility: Energy Use, Greenhouse Gas Emissions, Grid Integration, and Cost Impacts

被引:19
|
作者
Sheppard, Colin J. R. [1 ,2 ]
Jenn, Alan T. [1 ,3 ]
Greenblatt, Jeffery B. [1 ,4 ]
Bauer, Gordon S. [5 ]
Gerke, Brian F. [1 ]
机构
[1] Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA
[2] Marain Inc, Indianapolis, IN 46202 USA
[3] Univ Calif Davis, Davis, CA 95616 USA
[4] Emerging Futures Inc, Portland, OR 97201 USA
[5] Int Council Clean Transportat, Washington, DC USA
基金
美国能源部;
关键词
Modeled output of private EV charging from Eric Wood; Matteo Muratori (National Renewable Energy Laboratory). Private EV sampling tool by Jerome Carman; Peter Alstone (Humboldt State University). StreetLight Data provided GPS input data for the RISE model. This article and the work described were sponsored by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) under the Vehicle Technologies Analysis Program. The following DOE Office of Energy Efficiency and Renewable Energy (EERE) managers played important roles in establishing the project concept; advancing implementation; and providing ongoing guidance: Rachael Nealer; Jake Ward; Katherine McMahon; Kelly Fleming; and Heather Croteau. The authors also acknowledge Tom Wenzel of Lawrence Berkeley National Laboratory for critical feedback on this manuscript. This work was funded by the U.S. Department of Energy Vehicle Technologies Office under Lawrence Berkeley National Laboratory Agreement No. 32048. Lawrence Berkeley National Laboratory is supported by the Office of Science of the United States Department of Energy and operated under Contract Grant No. DE-AC02-05CH11231;
D O I
10.1021/acs.est.0c06655
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Transportation is the fastest-growing source of greenhouse gas (GHG) emissions and energy consumption globally. While the convergence of shared mobility, vehicle automation, and electrification has the potential to drastically reduce transportation impacts, it requires careful integration with rapidly evolving electricity systems. Here, we examine these interactions using a U.S.-wide simulation framework encompassing private electric vehicles (EVs), shared automated EVs (SAEVs), charging infrastructure, controlled EV charging, and a grid economic dispatch model to simulate personal mobility exclusively using EVs. We find that private EVs with uncontrolled charging would reduce GHG emissions by 46% compared to gasoline vehicles. Private EVs with fleetwide controlled charging would achieve a 49% reduction in emissions from baseline and reduce peak charging demand by 53% from the uncontrolled scenario. We also find that an SAEV fleet 9% the size of today's active vehicle fleet can satisfy trip demand with only 2.6 million chargers (0.2 per EV). Such an SAEV fleet would achieve a 70% reduction in GHG emissions at 41% of the lifecycle cost as a private EV fleet with controlled charging. The emissions and cost advantage of SAEVs is primarily due to reduced vehicle manufacturing compared with private EVs.
引用
收藏
页码:3229 / 3239
页数:11
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