Modeling personal vehicle energy consumption to assess the potential for electrification and decarbonization

Thesis: Ph. D. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018Cataloged from PDF version of thesis.Includes bibliographical references (pages 145-158).This thesis develops a new model of the energy requirements of personal vehicle travel and uses it to evaluate tools to decarbonize the transport sector. Energy use and carbon emissions from transportation are spread across millions of miles of roadways and hundreds of millions of travelers. This diversity of travel patterns makes it challenging to catalogue and predict those quantities and difficult to characterize the mechanisms that drive them. However, a better understanding of transport energy use patterns is needed to find options for reducing personal vehicle energy requirements and greenhouse gas emissions. Existing research on transportation and climate policy often represents energy use in a fundamentally simplified manner. Some research does not account for the effect of usage patterns on technology performance, missing variation in technology impacts across context of use.Other research informs technology modeling with a simplified picture of travel patterns, missing contexts in which technologies will be used. The research in this thesis adds new insight by assessing technology performance based on a comprehensive picture of travel patterns. This better captures both how travel patterns determine technology performance and how technology performance constrains achievable transformations to the transport sector. It combines high-resolution driving data with comprehensive travel patterns from household travel surveys or a transport network simulation, integrating data at multiple scales to avoid simplifications that mask relationships between technology use, technology performance, and systemwide carbon intensity. The central finding of this thesis is that retaining heterogeneity in travel behavior and technology performance allows us to better understand barriers to and strategies for transport decarbonization that will be missed with simpler methods.Specifically, this thesis addresses electric vehicle range limitations, finding that they provide a constraint on transport electrification that is relatively limited and consistent across locations. This research also reveals interactions between electric vehicle charging and the electricity grid and uncovers how to better align electricity demand and supply under high solar photovoltaics penetration. This understanding will help inform targeted technological development and policies as well as help identify risks and unintended consequences in a transition to a low-carbon transportation system.by Zachary A. Needell.Ph. D. in TransportationPh.D.inTransportation Massachusetts Institute of Technology, Department of Civil and Environmental Engineerin

Micro Transit Simulation of On-Demand Shuttles Based on Transit Data for First- and Last-Mile Connection

We simulate the introduction of shared, automated, and electric vehicles (SAEVs) providing on-demand shuttles service in a large-scale transport digital twin of the San Francisco Bay Area region (California, USA) based on transit supply and demand data, and using the mesoscopic agent-based Behavior, Energy, Autonomy, and Mobility beta software (BEAM) developed at the Lawrence Berkeley National Laboratory (LBNL). The main goal of this study is to test the operations of this novel mobility service integrated with existing fixed-route public transportation service in a mesoscopic simulation of a real case scenario, while testing the BEAM beta software capabilities. In particular, we test the introduction of fleets of on-demand vehicles bound to operate within circular catchment areas centered on high-frequency transit stops, with the purpose of extending the reach of fixed-route transit by providing an alternative first- and last-mile connection at high-frequency public transport stations. Results show that on-demand automated shuttles represent the best solution for some users, increasing the overall transit ridership by 3%, and replacing mostly ride-hail trips, especially those connecting to transit stops, but also some walking trips. This type of service has the potential to reduce overall vehicle miles traveled (VMT), increase transit accessibility, and save energy, but future research is needed to optimize this type of service and make it more attractive to travelers

Photonic Crystal Waveguides for >90% Light Trapping Efficiency in Luminescent Solar Concentrators

Luminescent solar concentrators are currently limited in their potential concentration factor and solar conversion efficiency by the inherent escape cone losses present in conventional planar dielectric waveguides. We demonstrate that photonic crystal slab waveguides tailored for luminescent solar concentrator applications can exhibit >90% light trapping efficiency. This is achieved by use of quantum dot luminophores embedded within the waveguide that absorb light at photon energies corresponding to photonic crystal leaky modes that couple to incoming sunlight. The luminophores then emit at lower photon energies into photonic crystal bound modes that enable highly efficient light trapping in slab waveguides of wavelength-scale thickness. Photonic crystal waveguides thus nearly eliminate escape cone losses, and overcome the performance limitations of previously proposed wavelength-selective dielectric multilayer filters. We describe designs for hole-array and rod-array photonic crystals comprised of hydrogenated amorphous silicon carbide using CdSe/CdS quantum dots. Our analysis suggests that photonic crystal waveguide luminescent solar concentrators using these materials these can achieve light trapping efficiency above 92% and a concentration factor as high as 100