The electrification of shared fleets offers numerous benefits, including the reduction of local emissions of pollutants, which leads to ecological improvements such as the improvement of air quality. Electric Vehicles in Shared Fleets considers a holistic concept for a socio-technical system with a focus on three core areas: integrated mobility solutions, business models for economic viability, and information systems that support decision-making for the successful implementation and operation of electric vehicles in shared fleets.In this book, we examine different aspects within these areas including multimodal mobility, grid integration of electric vehicles, shared autonomous electric vehicle services, relocation strategies in shared fleets, and the challenge of battery life of electric vehicles. Insights into the future of transport are provided, which is predicted to be shared, autonomous, and electric. This will require the expansion of the charging infrastructure to provide adequate premises for the electrification of transportation and to create market demand.

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.

Front Cover -- About Island Press -- Subscribe -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- 1. Will the Transportation Revolutions Improve Our Lives-- or Make Them Worse? -- 2. Electric Vehicles: Approaching the Tipping Point -- 3. Shared Mobility: The Potential of Ridehailing and Pooling -- 4. Vehicle Automation: Our Best Shot at a Transportation Do-Over? -- 5. Upgrading Transit for the Twenty-First Century -- 6. Bridging the Gap between Mobility Haves and Have-Nots -- 7. Remaking the Auto Industry -- 8. The Dark Horse: Will China Win the Electric, Automated, Shared Mobility Race? -- Epilogue -- Notes -- About the Contributors -- Index -- IP Board of Directors

This edited open access book gives a comprehensive overview of small and lightweight electric three- and four-wheel vehicles with an international scope. The present status of small electric vehicle (SEV) technologies, the market situation and main hindering factors for market success as well as options to attain a higher market share including new mobility concepts are highlighted. An increased usage of SEVs can have different impacts which are highlighted in the book in regard to sustainable transport, congestion, electric grid and transport-related potentials. To underline the effects these vehicles can have in urban areas or rural areas, several case studies are presented covering outcomes of pilot projects and studies in Europe. A study of the operation and usage in the Global South extends the scope to a global scale. Furthermore, several concept studies and vehicle concepts on the market give a more detailed overview and show the deployment in different applications.

Electric vehicle (EV) sharing services have received growing attention from investors and city dwellers in the decade. However, due to high operating costs and the increasing competition, profitability has become the bottleneck for many EV sharing service providers to succeed in the long run. My dissertation research focuses on developing mathematical models to design finer operational strategies for large-scale EV sharing fleets, especially in the stochastic and unbalanced transportation background. The basic blueprint is to upgrade EV fleet management from myopic strategies to location-based, energy-based, and environment-responsive policies. Specifically, we develop models to incorporate dynamic origin-destination pricing, congestion-responsive deployment, and battery health management into centralized EV sharing systems. First, we consider the dynamic pricing and dispatching of EVs given stochastic, time-varying, and heterogeneous customer demand. The EV operator monitors the fleet distribution and the demand signals to make real-time decisions. We adopt approximate dynamic programming (ADP) methods to solve the system. In particular, we develop neural network value function approximation (VFA) techniques that improve the policy performance. Our case study suggests that, with the demand-responsive pricing instrument, the EV fleet can effectively increase its expected profit, reduce the need for manual rebalancing, and smoothen the electricity usage across time. Next, we further investigate the interaction between the EV fleet and the congested transportation network. We extend the preliminary work to build a spatiotemporal network where the fleet operation and traffic states are captured by an approximated fluid model. The ADP algorithm maintains its effectiveness. We further design VFA methods to meet the learning need in the augmented state space. Numerical results demonstrate the benefit of dispatching vehicles using congestion-aware strategies. Finally, we consider the battery health management problem in an EV sharing fleet. We propose a continuous model to address the joint vehicle charging and moving problems for a large-scale EV sharing system. Under reasonable assumptions, the formulation is reduced to the continuous Kantorovich-Rubinstein transshipment and a battery-related optimization. On this basis, we obtain a near-optimal battery charging/replacing policy. Our model supports a shared EV fleet's decisions on charging device installation, vehicle relocation, and battery charging/replacing.

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.