Environmental and Policy Implications of Vehicle Automation and Electrification

• Main Author: Mersky, Avi Chaim
• Format: Others
• Published: Research Showcase @ CMU 2017
• Online Access: http://repository.cmu.edu/dissertations/1068; http://repository.cmu.edu/cgi/viewcontent.cgi?article=2107&context=dissertations

Mobility and transport underpin a society’s economic and physical life. Travel, however, has significant external costs, not solely borne by those performing or requesting the service. In addition to the direct cost of building and maintaining the necessary infrastructure, an individual’s decision to travel or transport goods affects the time others must travel, via congestion; injuries and fatalities; environmental health; and national energy security. When fueled by oil, for example, these costs add up to approximately $4.00 a gallon, depending on the specific vehicle. Two sets of technologies have the potential to drastically reduce the externalities associated with passenger travel: vehicle electrification and automation. Ensuring a socially optimal outcome from changes in vehicle technology requires four components. The first is determining whether adopting a set of new technologies would provide a net social benefit. The second is knowing how to effectively encourage adoption of a technology that has been determined to provide a net social benefit. The third is knowing how to optimally construct necessary infrastructure for the technology, while considering how future changes in the technology or other technologies may affect this process. The fourth component is being able to effectively regulate a technology. This dissertation addresses each of these issues by focusing on specific novel applications and case studies. It then discusses the joint implications and questions raised by these chapters. Chapter I introduces the environmental and safety externalities associated with passenger vehicle mobility. Chapter II focuses on the issue of determining the social value of implementing a new technology. A municipality evaluating a potential transition to an electrified vehicle fleet has its own set of decision criteria, which may be different than other actors. Of the passenger vehicle models that the City of Pittsburgh is considering, battery electric vehicles (BEVs), but not plug-in hybrid electric vehicles, were found to have lower life-cycle GHG emissions than conventional vehicles in Pittsburgh. However, vehicle electrification was found likely to have higher total social emissions costs than conventional options. Chapter III focuses on technology adoption by investigating the statistical significance of demographics and incentives on electric vehicle sales in Norway. Chapter III shows that access to BEV charging infrastructure, being adjacent to major cities, and regional incomes have the greatest predictive power for the growth of BEV sales. While Chapter III does not test for causation, vehicle chargers are necessary for BEV adoption and the results show that charging infrastructure is significantly correlated with BEV adoption in Norway. This suggests the need to plan for charging infrastructure concurrently with BEV adoption. Chapter IV focuses on how to optimally construct necessary infrastructure for electric vehicles when accounting for vehicle automation. For our simulation of about 2,000 trips in the greater Seattle, Washington area, moving from levels 0-3 to level 4 reduced peak electric load by about one-third and level 5 automation about two-thirds. Moving from no automation to level 4 automation nearly halved operator costs, while not having any significant effect on commuter costs. Moving to level 5 automation decreased operator costs by about 75% due to reduced number of charging stations, but shifted a portion of this reduction onto commuters. Chapter V focuses on how to effectively regulate technologies so that their future development increases social value, focusing on the specific problem of measuring the fuel economy of autonomous vehicles. The results showed that autonomous vehicles following algorithms designed without considering efficiency could degrade fuel economy by up to 3%, while efficiency-focused control strategies may equal or slightly exceed the existing EPA fuel economy test results by up to 5%, when compared to the base EPA cycles that they were simulated as following.