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.

This edited book comprises papers about the impacts, benefits and challenges of connected and automated cars. It is the third volume of the LNMOB series dealing with Road Vehicle Automation. The book comprises contributions from researchers, industry practitioners and policy makers, covering perspectives from the U.S., Europe and Japan. It is based on the Automated Vehicles Symposium 2015 which was jointly organized by the Association of Unmanned Vehicle Systems International (AUVSI) and the Transportation Research Board (TRB) in Ann Arbor, Michigan, in July 2015. The topical spectrum includes, but is not limited to, public sector activities, human factors, ethical and business aspects, energy and technological perspectives, vehicle systems and transportation infrastructure. This book is an indispensable source of information for academic researchers, industrial engineers and policy makers interested in the topic of road vehicle automation.

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 (100-mile by 100-mile) discrete-time, agent-based model. The dissertation 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 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. 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 households, and thus be competitive with current manually-driven carsharing services and significantly less expensive than on-demand driver-operated transportation services. The mode share of SAEVs in the simulated mid-sized city is predicted to be between 14 and 39%, when competing against privately-owned, manually-driven vehicles and city bus service. This assumes SAEVs are priced between $0.75 and $1.00 per mile, which delivers significant net revenues to the fleet owner-operator, under all modeled scenarios, assuming 80-mile-range EVs and remote/cordless Level II charging infrastructure and $10,000-per-vehicle automation costs.

The climate crisis requires substantial shifts in the transportation and energy sectors. Greater use of intermittent renewable energy sources requires demand- and supply-side flexibility in electricity markets. Deployment of on-demand, shared, fully automated, and electric vehicle (SAEV) fleets offers natural synergies in meeting such challenges. Smart charging (and discharging) of electric vehicles (EVs) can shift loads away from peak demand to reduce, or at least delay, expensive infrastructure upgrades, while fleet managers lower emissions and power costs in real time. This dissertation explores (1) optimization-based idle-vehicle dispatch strategies to improve SAEV fleet operations in the Austin metro, (2) integration of power and transportation system (EV-use) modeling across the Chicago metro area, and (3) a case study of demand response participation and charging station siting in a region with multiple energy suppliers. Optimizing SAEV repositioning and charging dispatch strategies jointly lowered rider wait times by 39%, on average, and increased daily trips served per SAEV by 28% (up to 6.4 additional riders), compared to separate range-agnostic repositioning and heuristic charging strategies. Joint strategies may also decrease the SAEV fleet’s empty travel by 5.7 to 12.8 percentage points (depending on geofencing and charging station density). If fleets pay dynamic electricity prices and wish to internalize their upstream charging emissions damages, a new multi-stage charging problem is required. A day-ahead energy transaction problem provides targets for a within-day idle-vehicle dispatch strategy that balances charging, discharging, repositioning, and maintenance decisions. This strategy allowed the Austin SAEV fleet to lower daily power costs (by 15.5% or $0.79/day/SAEV, on average) while reducing health damages from generation-related pollution (2.8% or $0.43/day/SAEV, on average). Fleet managers obtained higher profits ($8 per SAEV per day) by serving more passengers per day than with simpler (price-agnostic) dispatch strategies. This dissertation also coupled an agent-based travel demand simulator (POLARIS) with an electricity grid model (A-LEAF) to evaluate charging impacts on the power grid across seasons, household-EV adoption levels, SAEV mode shares, and dynamic ride-sharing assumptions in 2035 for the Chicago, Illinois metro. At relatively low EV penetration levels (8% to 17%), an increase in electricity demand will require at most 1 GW of additional generation capacity. Illinois’ transition to intermittent variable renewable energy (VRE) and phase-out of coal-fired power plants will likely not noticeably increase wholesale power prices, even with unmanaged personal EV charging at peak hours. However, wholesale power prices will increase during peak winter hours (by +$100/MWh, or $0.10/kWh) and peak summer hours (+$300/MWh) due to higher energy fees and steep congestion fees on Illinois’ 2015-era transmission system. Although a smart-charging SAEV fleet uses wholesale prices to reduce electricity demand during peak hours, spreading charging demand in hours before and after the baseline peak creates new "ridges" in energy demand, which raise prices for all. These simulation results underscore the importance of investing in transmission system expansion and reducing barriers to upgrading or building new transmission infrastructure. If vehicles and chargers support bidirectional charging, SAEVs can improve grid reliability and resilience at critical times through demand response (DR) programs that allow load curtailment and vehicle-to-grid (V2G) power. Scenario testing of DR requests in Austin ranging from 1 MW to 12 MW between 4 and 5 PM reveals break-even compensation costs (to SAEV owners) that range from $86/kW to $4,160/kW (if the city imposes unoccupied travel fees), depending on vehicle locations and battery levels at the time of the DR request. Smaller requests can be met without V2G by reducing charging speeds, usually from 120 kW speed to Level 2 charging. Finally, an incremental charging station heuristic was designed to capture differences in land costs and electricity rate structures from different energy suppliers in the same region. The daily amortized costs over 10 years of hardware, installation, and land costs were estimated to be nearly $0.30/SAEV/day, compared to $0.38/SAEV/day with a baseline heuristic strategy ignoring land costs and marginal costs of expanding existing sites. SAEV charging costs showed no substantial difference between heuristic strategies, although combined daily energy fees were more expensive at $0.43/SAEV/day. Including land costs in charging station investment heuristics is necessary, and modelers should include spatially varying energy prices since the average daily per-vehicle energy costs are higher than the physical station costs. Taken together, this dissertation’s contributions offer hope for a decarbonizing world that provides affordable, clean, and convenient on-demand mobility

The various sharing initiatives seen in the Nordic countries over the last years within transportation, housing/accommodation, sharing/renting of smaller capital goods and personal services could yield considerable benefits for consumers due to better quality and/or lower prices of the services. They also have a potential for emissions reductions of CO2 and local pollutants. However, savings from lower prices could lead to increased emissions from increased demand of the services (particularly transport) and increased spending on other goods and services. Depending on how consumers spend their savings, these changes could partly, wholly or more than offset the initial emission reductions. The impacts on overall CO2 emissions depend on whether the emissions are taxed, part of the emissions trading system EU ETS or not regulated at all.

Researchers developed two scenarios to envision the future of mobility in China in 2030. Economic growth, the presence of constraints on vehicle ownership and driving, and environmental conditions differentiate the scenarios. By making potential long-term mobility futures more vivid, the team sought to help decisionmakers at different levels of government and in the private sector better anticipate and prepare for change.