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 (battery-only) 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 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 to 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 pickup , SAEV fleets are predicted to generate an additional 7.1 to 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

The market potential of a fleet of shared autonomous electric vehicles (SAEVs) is explored by using a multinomial logit mode choice model in an agent-based framework and different fare settings. The mode share of SAEVs in the simulated midsize city (modeled roughly after Austin, Texas) is predicted to lie between 14% and 39% when the SAEVs compete with privately owned, manually driven vehicles and city bus service. The underlying assumptions are that SAEVs are priced between $0.75/mi and $1.00/mi, which delivers significant net revenues to the fleet owner–operator under all modeled scenarios; that they have an 80-mi range and that Level 2 charging infrastructure is available; and that automation costs are up to $25,000 per vehicle. Various dynamic pricing schemes for SAEV fares indicate that specific fleet metrics can be improved with targeted strategies. For example, pricing strategies that attempt to balance available SAEV supply with anticipated trip demand can decrease average wait times by 19% to 23%. However, trade-offs exist within this price setting: fare structures that favor higher revenue-to-cost ratios—by targeting travelers with a high value of travel time (VOTT)—reduce SAEV mode shares, while those that favor larger mode shares—by appealing to a wider VOTT range—produce lower payback.

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