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 contributed volume collects insights from industry professionals, policy makers and researchers on new and profitable business models in the field of electric vehicles (EV) for the mass market. This book includes approaches that address the optimization of total cost of ownership. Moreover, it presents alternative models of ownership, financing and leasing. The editors present state-of-the-art insights from international experts, including real-world case studies. The volume has been edited in the framework of the International Energy Agency’s Implementing Agreement for Cooperation on Hybrid and Electric Vehicles (IA-HEV). The target audience primarily comprises practitioners and decision makers but the book may also be beneficial for research experts and graduate students.

Smart sustainable mobility ecosystems promise to address society’s expectation of environmentally friendly on-demand mobility. While the technology stack to build such ecosystems is just around the corner in the form of connected, automated, and electric vehicles, strategies to deploy and operate such fleets in a coordinated manner must still be advanced. Most of such optimization challenges highly depend on the nature of customer demand, vehicle supply, and environmental influences. Hence, this dissertation investigates how available data streams from mobility ecosystems can be leveraged in Information Systems to solve related decision problems. The overarching goal of this work is to generate design knowledge to improve vehicle availability, provider profitability, and environmental sustainability for such ecosystems. Applying quantitative methods to real-world data from shared vehicle systems generates insights into the nature of demand and supply. Combining it with an analysis of empirical research on vehicle relocation algorithms builds the foundation for two artifact designs. The first artifact enables the development and simulation-based evaluation of operation modes for vehicle fleets. The second artifact enables artificial intelligence-based decision support for the vehicle rebalancing problem. The insights are finally incorporated and generalized to a nascent design theory on data-enabled operational decision-making in the context of smart sustainable mobility environments. The findings have multifaceted implications for researchers concerned with data-enabled value creation in Green IS, shared economy and smart mobility, and business analytics and data science. Furthermore, guidance for fleet providers to improve system attractiveness and for society to experience the potential amount of vehicle access without personal ownership is provided.

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.

This book examines electric car sharing in cities from a variety of perspectives, from service design to simulation, from mathematical modeling to technology deployment, and from energy use improvement to the integration of different kinds of vehicle. The contents reflect the outcomes of the Green Move project, undertaken by Politecnico di Milano with the aim of fostering an innovative and easily accessible electric vehicle sharing system. The first section of the book illustrates the car sharing service, covering service design, the configuration of the vehicle sharing model and the Milan mobility pattern, analysis of local demand and supply, testing of the condominium-based car sharing model, and communication design for social engagement. The second section then explains the technological choices, from the architecture of the system and dynamic applications to information management, the smartphone-based energy-oriented driving assistance system, automatic fleet balancing systems, and real-time monitoring of vehicle positions. In the final section, readers will find descriptions of the simulation model, a model to estimate potential users of the service, and a model for a full-scale electric car sharing service in Milan.

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

Master's Thesis from the year 2018 in the subject Engineering - Automotive Engineering, Technical University of Munich, language: English, abstract: This thesis is an initial approach to analyze the design and implementation of an e-vehicle sharing system in the P3 Group oÿce in Paris. An overview of the electric vehicle charging infrastructure, along with the relevant aspects of charging modes is provided. A showcase of the analysis of di ̇erent car-sharing models within Europe is given, after which a specific case study is analyzed in greater detail. The parameters and features for the system were derived from a competitive benchmark of the car-sharing models on the market today. The objective was to assist the company in planning and managing a corporate e-vehicle sharing system in a profitable way while o ̇ering the employees good quality service. Therefore, the cost of designing and installing the P3 EV charging station was evaluated. On this matter, empirical data was gathered from P3 employees to better understand their daily commute, their needs and their expectations of the system. An optimization model for distances, cost and charging patterns was discussed and formalized as an integer linear program in MATLAB. Given the complexity inherent to this optimization model, stochastic distribution was employed to minimize the cost for the company, taking into consideration the trips paid and the costs involved–namely, the personal wage of an employee. A focus on the optimal design of an e-vehicle sharing system was necessary, while considering the problem’s dimensionality (number of vehicles, parking places, battery capacities, etc.) and employee relocation time. This study determines if the system provides higher net benefits to the company than available transportation alternatives. As a result of this pricing comparison, a significant reduction in total cost could be achieved for the company. The data set conclusively supports the implementation of the e-vehicle sharing system, which provides a decreased cost versus the use of public transportation. A possible avenue of future research is to extend the functionality of the developed model by adding a responsive user demand and possibly, maximizing the car-sharing ridership between employees.

This edited volume presents new insights and challenges in the field of electric mobility in relation to new mobility and infrastructure concepts as well as to renewable energies. The book covers the socio-economic view on the topic as well as technical aspects and thus offers valuable knowledge for future business models. It primarily addresses practitioners and researchers in the field but may also be of use to graduate students.

This edited monograph collects theoretical, empirical and political contributions from different fields, focusing on the commercial launch of electric mobility, and intending to shed more light on the complexity of supply and demand. It is an ongoing discussion, both in the public as well as in academia, whether or not electric mobility is capable of gaining a considerable market share in the near future. The target audience primarily comprises researchers and practitioners in the field, but the book may also be beneficial for graduate students.

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