Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions
There are natural synergies between shared autonomous vehicle (AV) fleets and electric vehicle (EV) technology, since fleets of AVs resolve the practical limitations of today’s non-autonomous EVs, including traveler range anxiety, access to charging infrastructure, and charging time management. Fleet-managed AVs relieve such concerns, managing range and charging activities based on real-time trip demand and established charging-station locations, as demonstrated in this paper. This work explores the management of a fleet of shared autonomous electric vehicles (SAEVs) in a regional, discrete-time, agent-based model. The simulation examines the operation of SAEVs under various vehicle range and charging infrastructure scenarios in a gridded city modeled roughly after the densities of Austin, Texas. Results based on 2009 national household travel survey (NHTS) trip distance and time-of-day distributions indicate that fleet size is sensitive to battery recharge time and vehicle range, with each 80-mile range SAEV replacing 3.7 privately owned vehicles and each 200-mile range SAEV replacing 5.5 privately owned vehicles, under Level II (240-volt AC) charging. With Level III 480-volt DC fast-charging infrastructure in place, these ratios rise to 5.4 vehicles for the 80-mile range SAEV and 6.8 vehicles for the 200-mile range SAEV. SAEVs can serve 96–98% of trip requests with average wait times between 7 and 10 minutes per trip. However, due to the need to travel while “empty” for charging and passenger pick-up, SAEV fleets are predicted to generate an additional 7.1–14.0% of travel miles. Financial analysis suggests that the combined cost of charging infrastructure, vehicle capital and maintenance, electricity, insurance, and registration for a fleet of SAEVs ranges from $0.42 to $0.49 per occupied mile traveled, which implies SAEV service can be offered at the equivalent per-mile cost of private vehicle ownership for low-mileage households, and thus be competitive with current manually-driven carsharing services and significantly cheaper than on-demand driver-operated transportation services. When Austin-specific trip patterns (with more concentrated trip origins and destinations) are introduced in a final case study, the simulation predicts a decrease in fleet “empty” vehicle-miles (down to 3–4% of all SAEV travel) and average wait times (ranging from 2 to 4 minutes per trip), with each SAEV replacing 5–9 privately owned vehicles.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/09658564
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Supplemental Notes:
- Abstract reprinted with permission of Elsevier.
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Authors:
- Chen, T Donna
- Kockelman, Kara
- Hanna, Josiah P
- Publication Date: 2016-12
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: pp 243-254
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Serial:
- Transportation Research Part A: Policy and Practice
- Volume: 94
- Publisher: Elsevier
- ISSN: 0965-8564
- Serial URL: http://www.sciencedirect.com/science/journal/09658564
Subject/Index Terms
- TRT Terms: Electric vehicle charging; Electric vehicles; Fleet management; Infrastructure; Intelligent agents; Intelligent vehicles; Vehicle fleets; Vehicle operations; Vehicle sharing
- Uncontrolled Terms: Agent based models
- Geographic Terms: Austin (Texas)
- Subject Areas: Energy; Highways; Operations and Traffic Management; Vehicles and Equipment;
Filing Info
- Accession Number: 01619131
- Record Type: Publication
- Files: TRIS
- Created Date: Dec 21 2016 11:29AM