Shared Autonomous Electric Vehicle (SAEV) operations across the Austin, Texas region, with a focus on charging infrastructure provision and cost calculations
Loeb, Benjamin Jesse
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Shared autonomous vehicles, or SAVs, have attracted significant public and private interest because of the opportunity to simplify vehicle access, avoid parking costs, reduce fleet size, and, ultimately, save many travelers time and money. One way to extend these benefits is through an electric vehicle (EV) fleet. EVs are especially suited for this heavy usage due to their lower energy costs and reduced maintenance needs. As the price of EV batteries continues to fall, charging facilities become more convenient, and renewable energy sources grow in market share, EVs will become more economically and environmentally competitive with conventionally-fueled vehicles. EVs are limited by their distance range and charge times, so these are important factors when considering operations of a large, electric SAV (SAEV) fleet. This study simulated performance characteristics of SAEV fleets serving travelers across the Austin, Texas 5,301 square-mile, 6-county region. The simulation works in synch with the agent-based, open-source, simulator MATSim, with SAEVs as a new mode. Charging stations are placed, as needed, to serve all trips requested over 30 days of initial model runs. This model uses a mixed fleet where one third of the vehicles in use are gasoline hybrid-electric vehicles which serve all trips in excess of 35 miles, to prevent these low-range EVs from being burdened by long trips. Travelers may sometimes share rides, when practical, up to four travelers per vehicle. Hundreds of simulations of distinctive fleet sizes with different ranges and various charge times suggest that the number and location of stations depend almost wholly on vehicle range. Reducing charge times, as well as independently increasing vehicle range, does lower fleet response times (to trip requests). Increasing fleet size improves response times the most. The effects of dynamic ridesharing and the number of charging stations available are also studied here. The station generation algorithm produced 170 charging stations for a fleet of SAEVs with 60-mile range. A 200-mile range fleet resulted in just 19 stations. When testing a fleet of 200-mile range and 30-minute charge times with the set of 170 charging stations, average response times were low at 6.8 minutes per request. Empty vehicle miles traveled (empty VMT) accounted for 15% of total travel over the course of the simulation day and just 3.7% of this empty VMT was driving to charging stations (or 0.6% of total VMT). It is estimated that this fleet will cost $0.60 to $1.09 per passenger-mile assuming a 10 year return on investment for capital costs (e.g. land acquisition and charging facilities). This is compared to a base case of a fully gasoline-powered fleet which can achieve average response times of 6.4 minutes per trip and 9.73% empty VMT for the same sized fleet. A lower-performance fleet, with 60-mile ranges and 240-minute charge times, meets requests with an average response time of 33.1 minutes creating 25.7% empty VMT. 19% of this empty VMT (4.82% of total VMT) is to access charging stations. Cost calculations estimate this fleet would cost between $0.59 and $0.97 per passenger-mile to operate. A gasoline fleet is estimated to operate at just $0.30 to $0.62 per passenger mile. These savings are thanks to the presence of existing fueling stations that do not need to be maintained by the fleet manager. For all but very large fleet sizes, DRS showed substantial changes to response times. With a fleet size of 5 travelers per SAEV, response times fell by 32 minutes on average with an average imposed delay of 11 minutes per traveler. DRS also halved empty VMT for a fleet size of 5 travelers per vehicle. Increasing the number of charging stations from 19 to 170 improved response times and empty VMT but for most fleet sizes these improvements were not substantial.