Life cycle greenhouse gas emissions for last-mile parcel delivery by automated vehicles and robots
dc.contributor.author | Li, Luyao | |
dc.contributor.advisor | Keoleian, Greg | |
dc.date.accessioned | 2021-05-01T20:23:25Z | |
dc.date.issued | 2021-04 | |
dc.date.submitted | 2021-04 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/167295 | |
dc.description.abstract | Increased E-commerce and demand for contactless delivery during the COVID-19 pandemic have fueled interest in robotic package delivery. We evaluate life cycle greenhouse gas (GHG) emissions for automated ground delivery systems consisting of a vehicle (last-mile) and a robot (final-50-feet) in a suburban setting. Small and large cargo vans (125 and 350 cubic feet; V125 and V350) with internal combustion engine (ICEV) and battery electric (BEV) powertrains were assessed for three delivery scenarios: (i) conventional, human-driven vehicle with human delivery; (ii) partially automated, human-driven vehicle with robot delivery; and (iii) fully automated: connected automated vehicle (CAV) with robot delivery. The robot’s contribution to life cycle GHG emissions is small (2-6%). CAV auxiliary loads offset operational benefits from automated driving. Compared to the conventional scenario, full automation results in 7% lower GHG emissions for the V350-ICEV but 5% higher for the V125-BEV. Conventional delivery with a V125-BEV provides the lowest GHG emissions, 160 g CO2e/package, while partially automated delivery with a V350-ICEV generates the most at 450 g CO2e/package. Sensitivity analysis shows delivery density and fuel economy are key parameters determining GHG emissions for all scenarios, while CAV power requirements and efficiency benefits have a smaller impact on automated scenario emissions. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | automated vehicle | en_US |
dc.subject | life cycle assessment | en_US |
dc.subject | greenhouse gas emissions | en_US |
dc.title | Life cycle greenhouse gas emissions for last-mile parcel delivery by automated vehicles and robots | en_US |
dc.type | Thesis | en_US |
dc.description.thesisdegreename | Master of Science (MS) | en_US |
dc.description.thesisdegreediscipline | School for Environment and Sustainability | en_US |
dc.description.thesisdegreegrantor | University of Michigan | en_US |
dc.contributor.committeemember | Kim, Hyung Chul | |
dc.identifier.uniqname | lluyao | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/167295/1/Li_Luyao_Thesis.pdf | |
dc.identifier.doi | https://dx.doi.org/10.7302/970 | |
dc.working.doi | 10.7302/970 | en_US |
dc.owningcollname | Dissertations and Theses (Ph.D. and Master's) |
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