首页 | 本学科首页   官方微博 | 高级检索  
     


Shared autonomous electric vehicle (SAEV) operations across the Austin,Texas network with charging infrastructure decisions
Affiliation:1. Department of Engineering and Technology, Texas A&M University - Commerce, 2200 Campbell St, Commerce, TX 75429-3011, USA;2. Department of Systems and Industrial Engineering, University of Arizona, 1127 E. James E. Rogers Way, Room 111, Tucson, AZ 85721-0020, USA;3. Metropia, Inc., 3701 Executive Center Dr. STE 209, Austin, TX 78750, USA;4. Department of Civil Engineering and Engineering Mechanics, The University of Arizona, 1209 E. Second St., Room 206A, Tucson, AZ, USA;5. AAA Foundation for Traffic Safety, 601 14th Street, NW, Suite 201, Washington, DC 2005-2000, USA
Abstract:Shared autonomous vehicles, or SAVs, have attracted significant public and private interest because of their 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 6-county region. The simulation works in sync with the agent-based simulator MATSim, with SAEV modeling as a new mode. Charging stations are placed, as needed, to serve all trips requested (under 75 km or 47 miles in length) over 30 days of initial model runs. Simulation of distinctive fleet sizes requiring different charge times and exhibiting different ranges, suggests that the number of station locations depends almost wholly on vehicle range. Reducing charge times does lower fleet response times (to trip requests), but increasing fleet size improves response times the most. Increasing range above 175 km (109 miles) does not appear to improve response times for this region and trips originating in the urban core are served the quickest. Unoccupied travel accounted for 19.6% of SAEV mileage on average, with driving to charging stations accounting for 31.5% of this empty-vehicle mileage. This study found that there appears to be a limit on how much response time can be improved through decreasing charge times or increasing vehicle range.
Keywords:Charging station placement  Electric vehicle charging  Shared autonomous vehicles  Taxi fleet simulations
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号