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Although electric vehicles (EVs) are theoretically capable of emissions-free driving, their market penetration is still pending, which is reflected in their low sales numbers. This is mainly due to three major barriers to the widespread adoption of these vehicles, with one of them being their limited average driving distance. Although the limited range of these cars would theoretically be sufficient to match the usage patterns of most drivers, they are generally unwilling to accept it. In this regard, users often report serious concerns about not reaching their planned destinations due to battery depletion, which is commonly referred to as range anxiety. Within this cumulative dissertation, four research questions were derived, aiming to investigate measures that mitigate range anxiety and thus positively affect the attitude toward using EVs. To answer these research questions, six studies were conducted. The insights gained from analyzing the data provide researchers with an in-depth knowledge for investigating the influence of information systems on range anxiety. In addition, practitioners find decision support for addressing the phenomenon of range anxiety in implementing and designing information systems.
The book's website (with databases and other support materials) can be accessed here. Praise for the Second Edition: The second edition introduces an especially broad set of statistical methods ... As a lecturer in both transportation and marketing research, I find this book an excellent textbook for advanced undergraduate, Master’s and Ph.D. students, covering topics from simple descriptive statistics to complex Bayesian models. ... It is one of the few books that cover an extensive set of statistical methods needed for data analysis in transportation. The book offers a wealth of examples from the transportation field. —The American Statistician Statistical and Econometric Methods for Transportation Data Analysis, Third Edition offers an expansion over the first and second editions in response to the recent methodological advancements in the fields of econometrics and statistics and to provide an increasing range of examples and corresponding data sets. It describes and illustrates some of the statistical and econometric tools commonly used in transportation data analysis. It provides a wide breadth of examples and case studies, covering applications in various aspects of transportation planning, engineering, safety, and economics. Ample analytical rigor is provided in each chapter so that fundamental concepts and principles are clear and numerous references are provided for those seeking additional technical details and applications. New to the Third Edition Updated references and improved examples throughout. New sections on random parameters linear regression and ordered probability models including the hierarchical ordered probit model. A new section on random parameters models with heterogeneity in the means and variances of parameter estimates. Multiple new sections on correlated random parameters and correlated grouped random parameters in probit, logit and hazard-based models. A new section discussing the practical aspects of random parameters model estimation. A new chapter on Latent Class Models. A new chapter on Bivariate and Multivariate Dependent Variable Models. Statistical and Econometric Methods for Transportation Data Analysis, Third Edition can serve as a textbook for advanced undergraduate, Masters, and Ph.D. students in transportation-related disciplines including engineering, economics, urban and regional planning, and sociology. The book also serves as a technical reference for researchers and practitioners wishing to examine and understand a broad range of statistical and econometric tools required to study transportation problems.
For a century, almost all light-duty vehicles (LDVs) have been powered by internal combustion engines operating on petroleum fuels. Energy security concerns about petroleum imports and the effect of greenhouse gas (GHG) emissions on global climate are driving interest in alternatives. Transitions to Alternative Vehicles and Fuels assesses the potential for reducing petroleum consumption and GHG emissions by 80 percent across the U.S. LDV fleet by 2050, relative to 2005. This report examines the current capability and estimated future performance and costs for each vehicle type and non-petroleum-based fuel technology as options that could significantly contribute to these goals. By analyzing scenarios that combine various fuel and vehicle pathways, the report also identifies barriers to implementation of these technologies and suggests policies to achieve the desired reductions. Several scenarios are promising, but strong, and effective policies such as research and development, subsidies, energy taxes, or regulations will be necessary to overcome barriers, such as cost and consumer choice.
The electric vehicle offers many promises--increasing U.S. energy security by reducing petroleum dependence, contributing to climate-change initiatives by decreasing greenhouse gas (GHG) emissions, stimulating long-term economic growth through the development of new technologies and industries, and improving public health by improving local air quality. There are, however, substantial technical, social, and economic barriers to widespread adoption of electric vehicles, including vehicle cost, small driving range, long charging times, and the need for a charging infrastructure. In addition, people are unfamiliar with electric vehicles, are uncertain about their costs and benefits, and have diverse needs that current electric vehicles might not meet. Although a person might derive some personal benefits from ownership, the costs of achieving the social benefits, such as reduced GHG emissions, are borne largely by the people who purchase the vehicles. Given the recognized barriers to electric-vehicle adoption, Congress asked the Department of Energy (DOE) to commission a study by the National Academies to address market barriers that are slowing the purchase of electric vehicles and hindering the deployment of supporting infrastructure. As a result of the request, the National Research Council (NRC)--a part of the National Academies--appointed the Committee on Overcoming Barriers to Electric-Vehicle Deployment. This committee documented their findings in two reports--a short interim report focused on near-term options, and a final comprehensive report. Overcoming Barriers to Electric-Vehicle Deployment fulfills the request for the short interim report that addresses specifically the following issues: infrastructure needs for electric vehicles, barriers to deploying the infrastructure, and possible roles of the federal government in overcoming the barriers. This report also includes an initial discussion of the pros and cons of the possible roles. This interim report does not address the committee's full statement of task and does not offer any recommendations because the committee is still in its early stages of data-gathering. The committee will continue to gather and review information and conduct analyses through late spring 2014 and will issue its final report in late summer 2014. Overcoming Barriers to Electric-Vehicle Deployment focuses on the light-duty vehicle sector in the United States and restricts its discussion of electric vehicles to plug-in electric vehicles (PEVs), which include battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). The common feature of these vehicles is that their batteries are charged by being plugged into the electric grid. BEVs differ from PHEVs because they operate solely on electricity stored in a battery (that is, there is no other power source); PHEVs have internal combustion engines that can supplement the electric power train. Although this report considers PEVs generally, the committee recognizes that there are fundamental differences between PHEVs and BEVs.
​This book defines and charts the barriers and future of vehicle-to-grid technology: a technology that could dramatically reduce emissions, create revenue, and accelerate the adoption of battery electric cars. This technology connects the electric power grid and the transportation system in ways that will enable electric vehicles to store renewable energy and offer valuable services to the electricity grid and its markets. To understand the complex features of this emergent technology, the authors explore the current status and prospect of vehicle-to-grid, and detail the sociotechnical barriers that may impede its fruitful deployment. The book concludes with a policy roadmap to advise decision-makers on how to optimally implement vehicle-to-grid and capture its benefits to society while attempting to avoid the impediments discussed earlier in the book.
As more and more communities around the world are turning to electric vehicles (EVs) to help the environment and save energy, we face a big challenge. The systems that deliver power to our homes and businesses are having a tough time keeping up, especially with the increasing use of EVs. This challenge is a major issue for the experts in the energy field who are working hard to figure out how to make sure our power systems stay reliable. The main goal for these experts right now is to create a strong, flexible system that can smoothly handle the integration of EVs, making sure the power flows well, the grid stays stable, and the systems remain eco-friendly. E-Mobility in Electrical Energy Systems for Sustainability is a comprehensive guide to navigating the complexities of e-mobility integration. Delving into crucial aspects such as architectural reconfiguration, restoration strategies, power quality control, and regulatory frameworks, the book provides solutions on how to address the challenges posed by the integration of EVs into distribution systems. Its examination of advanced technologies, including communication-enabled EV charging systems, battery management systems, and power grid cybersecurity measures, equips readers with the knowledge needed to start the transformative journey towards sustainable electric transportation. This book is a great resource for those seeking to understand, engage with, and contribute to the landscape of e-mobility integration.
This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models. Following a gentle introduction to latent variable modeling, the authors clearly explain and contrast a wi
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.
Fully updated throughout, Electric Vehicle Technology, Second Edition, is a complete guide to the principles, design and applications of electric vehicle technology. Including all the latest advances, it presents clear and comprehensive coverage of the major aspects of electric vehicle development and offers an engineering-based evaluation of electric motor scooters, cars, buses and trains. This new edition includes: important new chapters on types of electric vehicles, including pickup and linear motors, overall efficiencies and energy consumption, and power generation, particularly for zero carbon emissions expanded chapters updating the latest types of EV, types of batteries, battery technology and other rechargeable devices, fuel cells, hydrogen supply, controllers, EV modeling, ancillary system design, and EV and the environment brand new practical examples and case studies illustrating how electric vehicles can be used to substantially reduce carbon emissions and cut down reliance on fossil fuels futuristic concept models, electric and high-speed trains and developments in magnetic levitation and linear motors an examination of EV efficiencies, energy consumption and sustainable power generation. MATLAB® examples can be found on the companion website www.wiley.com/go/electricvehicle2e Explaining the underpinning science and technology, this book is essential for practicing electrical, automotive, power, control and instrumentation engineers working in EV research and development. It is also a valuable reference for academics and students in automotive, mechanical, power and electrical engineering.
Im Zeitalter der Digitalisierung müssen Unternehmen ihre Prozesse, Produkte und Geschäftsmodelle anpassen, um ihre Innovations- sowie Wettbewerbsfähigkeit aufrecht zu erhalten. Aufgrund dessen müssen Unternehmen ihre bestehenden Kompetenzen erneuern, indem sie neues Wissen aus der digitalen Sphäre wertstiftend integrieren. Dies stellt jedoch eine erhebliche Herausforderung dar, insbesondere für etablierte Unternehmen, da die konvergente und generative Natur der digitalen Innovation nicht nur den Prozess der Wissensintegration dynamischer macht, sondern auch die Vielfalt und Menge des Wissens erhöht, das über die Unternehmensgrenzen hinweg integriert werden muss. Um diese Herausforderung näher zu beleuchten und Lösungsansätze zu evaluieren, wurden vier Studien durchgeführt und in dieser kumulativen Dissertation zusammengestellt. Diese Studien geben Einblicke in die Besonderheiten der Wissensintegration im Kontext digitaler Innovationen und fördern das Verständnis dafür, wie etablierte Unternehmen die damit verbundenen Herausforderungen bewältigen können. Dabei wird insbesondere der Einsatz von Grenzressourcen, wie bspw. Programmierschnittstellen (APIs), untersucht, da sie einen wesentlichen Mechanismus zur Skalierung der Wissensintegration in digital(isierenden) Geschäftsökosystemen darstellen. In the age of digitalization, companies need to adapt their processes, products, and business models to maintain their capability to innovate and thus to ensure their competitiveness. As a result, companies need to renew their existing competencies by integrating new and valuable knowledge from the digital sphere. However, this poses a significant managerial challenge, especially for established companies, as the convergent and generative nature of digital innovation not only makes the process of knowledge integration more dynamic, but also increases the diversity and amount of knowledge that needs to be integrated across organizational boundaries. To shed more light on this challenge and evaluate possible solutions, four studies were conducted and compiled in this cumulative dissertation. These studies provide insights into the unique nature of knowledge integration in the context of digital innovation and advance the understanding of how incumbent firms can address the associated challenges. In particular, the use of boundary resources such as application programming interfaces (APIs) is examined, as they represent a key mechanism for scaling knowledge integration in digital(izing) business ecosystems.