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Zero Emission Shared-Use Autonomous Vehicles: A Deployment Construct and Associated Energy Grid and Environmental Impacts

  • Author(s): Wifvat, Van Thomas
  • Advisor(s): Samuelsen, Scott
  • et al.
Abstract

For decades, the leading cause of death for American youth has been the car accident, and the largest source of domestic Greenhouse Gas (GHG) and many Criteria Air Pollutants (CAPs) has been the transportation sector. The advent of the autonomous vehicle in combination with Battery-Electric Vehicles (BEVs) and Fuel-Cell Electric Vehicles (FCEVs) presents an opportunity to transcend both pernicious challenges. In particular, the evolution of safer and more efficient autonomous (i.e., robotic) driving behavior via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, increased use of electric vehicles, and greater access to affordable and convenient shared (i.e., pooled) rides portend societal benefits including a significant reduction in energy demand and associated pollution. This dissertation evaluates the impact of Shared Autonomous Electric Vehicles (SAEVs, “Saves”) on the California energy grid, GHG emissions, and CAPs.

Vehicle-centric impacts (i.e., efficiency changes due to vehicle design and driving behavior) are measured using a vehicle design tool together with a microscopic traffic simulation model to (1) design prototype SAEVs, and (2) measure their energy efficiency for standard and eco-driving scenarios and an array of performance characteristics (e.g., different electric drivetrains, various communication protocols, etc.). Fleet-centric impacts (i.e., changes to vehicle allocation and usage) are measured using ArcGIS with a Caltrans travel demand model dataset to allocate and size SAEV stations, where SAEVs recharge/refuel and are sent to serve nearby trips in a hypothetical SAEV-deployment construct. The Holistic Energy Grid modelling tool (HiGRID) is used to measure SAEV impacts on the California electric grid and grid GHG and CAPs. The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model (GREET) is used to measure corresponding transportation sector GHG and CAP impacts.

Vehicle-centric energy impacts from SAEV-enabled eco-driving and platooning averaged net efficiency improvements of approximately 6-18%. Fleet-centric impacts include VMT changes from -11% to +36%, largely depending on ridesharing. Depending on SAEV design and operation, over 375,000 metric tons of annual CO2-equivalent GHG emissions could be reduced by adopting the proposed SAEV-deployment construct in lieu of the projected conventionally-driven vehicle fleet. Corresponding CAP impacts include a net reduction of over 250 metric tons of annual NOx emissions.

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