Integrated Network Based Models For Evaluating And Optimizing The Impact Of Electric Vehicles On The Transportation System

The adoption of plug-in electric vehicles (PEV) requires research for models and algorithms tracing the vehicle assignment incorporating PEVs in the transportation network so that the traffic pattern can be more precisely and accurately predicted. To attain this goal, this dissertation is concerned with developing new formulations for modeling travelling behavior of electric vehicle drivers in a mixed flow traffic network environment. Much of the work in this dissertation is motivated by the special features of PEVs (such as range limitation, requirement of long electricity-recharging time, etc.), and the lack of tools of understanding PEV drivers traveling behavior and learning the impacts of charging infrastructure supply and policy on the network traffic pattern. The essential issues addressed in this dissertation are: (1) modeling the spatial choice behavior of electric vehicle drivers and analyzing the impacts from electricity-charging speed and price; (2) modeling the temporal and spatial choices behavior of electric vehicle drivers and analyzing the impacts of electric vehicle range and penetration rate; (3) and designing the optimal charging infrastructure investments and policy in the perspective of revenue management. Stochastic traffic assignment that can take into account for charging cost and charging time is first examined. Further, a quasi-dynamic stochastic user equilibrium model for combined choices of departure time, duration of stay and route, which integrates the nested-Logit discrete choice model, is formulated as a variational inequality problem. An extension from this equilibrium model is the network design model to determine an optimal charging infrastructure capacity and pricing. The objective is to maximize revenue subject to equilibrium constraints that explicitly consider the electric vehicle drivers' combined choices behavior. The proposed models and algorithms are tested on small to middle size transportation networks. Extensive numerical experiments are conducted to assess the performance of the models. The research results contain the author's initiative insights of network equilibrium models accounting for PEVs impacted by different scenarios of charging infrastructure supply, electric vehicles characteristics and penetration rates. The analytical tools developed in this dissertation, and the resulting insights obtained should offer an important first step to areas of travel demand modeling and policy making incorporating PEVs.

This book discusses the technical, economic, and environmental aspects of electric vehicles and their impact on electrical grids and energy systems. The book is divided into three parts that include load modeling, integration and optimization, and environmental evaluation. Theoretical background and practical examples accompany each section and the authors include helpful tips and hints in the load modeling and optimization sections. This book is intended to be a useful tool for undergraduate and graduate students, researchers and engineers who are trying to solve power and engineering problems related electric vehicles. Provides optimization techniques and their applications for energy systems; Discusses the economic and environmental perspectives of electric vehicles; Contains the most comprehensive information about electric vehicles in a single source.

Rising concerns related to the effects of traffic congestion have led to the search for alternative transportation solutions. Advances in battery technology have resulted in an increase of electric vehicles (EVs), which serve to reduce the impact of many of the negative consequences of congestion, including pollution and the cost of wasted fuel. Furthermore, the energy-efficiency and quiet operation of electric motors have made feasible concepts such as Urban Air Mobility (UAM), in which electric aircraft transport passengers in dense urban areas prone to severe traffic slowdowns. Electrified transportation may be the solution needed to combat urban gridlock, but many logistical questions related to the design and operation of the resultant transportation networks remain to be answered.

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.

Abstract: The U.S. federal government is seeking useful applications of Vehicle-Infrastructure Integration (VII) to encourage a greener and more efficient transportation system; Plug-in Hybrid Electric Vehicles (PHEVs) are considered as one of the most promising automotive technologies for such an application. In this study, the author demonstrates a strategy to improve PHEV energy efficiency via the use of VII. This dissertation, which is composed of three published peer-reviewed journal articles, demonstrates the efficacies of the PHEV-VII system as regards to both the energy use and environmental impact under different scenarios. The first article demonstrates the capabilities of and benefits achievable for a power-split drivetrain PHEV with a VII-based energy optimization strategy. With the consideration of several real-time implementation issues, the results show improvements in fuel consumption with the PHEV-VII system under various driving cycles. In the second article, a forward PHEV model with an energy management system and a cycle optimization algorithm is evaluated for energy efficiency. Prediction cycles are optimized using a cycle optimization strategy, which resulted in 56-86% fuel efficiency improvements for conventional vehicles. When combined with the PHEV power management system, about 115% energy efficiency improvements were achieved. The third article focuses on energy and emission impacts of the PHEV-VII system. At a network level, a benefit-cost analysis is conducted, which indicated that the benefits outweighed costs for PHEV and Hybrid Electric Vehicle (HEV) integrated with a VII system at the fleet penetration rate of 20% and 30%, respectively.

A timely introduction to the revolutionary technologies reshaping the global energy market The search for more efficient and sustainable ways to meet society’s energy requirements has driven recent technological innovation on an unprecedented scale. The energy needs of a growing population coupled with concerns about climate change have posed unique challenges that necessitate novel energy technologies . The transition of modern energy grids towards multi-energy networks, or MENs, promises to be a fundamental transformation in the way we energize our world. Interconnected Modern Multi-Energy Networks and Intelligent Transportation Systems presents an overview of the foundational methodologies and technologies underlying MENs and the groundbreaking vehicle systems that bring them together. With the inclusion of transformative technologies from radically different sectors, the content covered in this book will be of high value for researchers interested in future energy systems. Readers will also find: In-depth examination of the process of switching from conventional transportation systems to modern intelligent transportation ones Detailed discussions of topics including self-driving vehicles, hybrid energy technologies, grid-edge, and more The introduction of a holistic, reconfigurable system adaptable to vastly different conditions and forms of network interaction Interconnected Modern Multi-Energy Networks and Intelligent Transportation Systems is useful for researchers in electrical, mechanical, civil, architectural, or environmental engineering, as well as for telecommunications researchers and for any industry professionals with an interest in energy transportation.

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

Electric Vehicle Integration in a Smart Microgrid Environment The growing demand for energy in today’s world, especially in the Middle East and Southeast Asia, has been met with massive exploitation of fossil fuels, resulting in an increase in environmental pollutants. In order to mitigate the issues arising from conventional internal combustion engine-powered vehicles, there has been a considerable acceleration in the adoption of electric vehicles (EVs). Research has shown that the impact of fossil fuel use in transportation and surging demand in power owing to the growing EV charging infrastructure can potentially be minimalized by smart microgrids. As EVs find wider acceptance with major advancements in high efficiency drivetrain and vehicle design, it has become clear that there is a need for a system-level understanding of energy storage and management in a microgrid environment. Practical issues, such as fleet management, coordinated operation, repurposing of batteries, and environmental impact of recycling and disposal, need to be carefully studied in the context of an ageing grid infrastructure. This book explores such a perspective with contributions from leading experts on planning, analysis, optimization, and management of electrified transportation and the transportation infrastructure. The primary purpose of this book is to capture state-of-the-art development in smart microgrid management with EV integration and their applications. It also aims to identify potential research directions and technologies that will facilitate insight generation in various domains, from smart homes to smart cities, and within industry, business, and consumer applications. We expect the book to serve as a reference for a larger audience, including power system architects, practitioners, developers, new researchers, and graduate-level students, especially for emerging clean energy and transportation electrification sectors in the Middle East and Southeast Asia.

The objectives of this research were to investigate the impact of electric vehicle battery charging on grid demand at a national level and on the steady state parameters of distribution networks. An agent-based control system that coordinates the battery charging of electric vehicles according to electric vehicle owner preferences, distribution network technical limits and electricity prices was designed and developed and its operation was tested experimentally. The impact on grid demand peak increases at the national systems of Great Britain and Spain was evaluated using low and high electric vehicle uptake levels of 7% and 48.5% of the car fleet for the year 2030 with a deterministic method. It was found that a low uptake will not raise significantly the grid demand peaks in both countries under investigation. However, a high uptake will raise significantly the grid demand peaks. The impact from residential electric vehicle battery charging on steady state voltages, power line losses, transformers' and cables' loadings of distribution networks was evaluated using a deterministic and a probabilistic method. It was found that low and medium uptake levels of electric vehicles equivalent to 12.5% and 33% per residential area of 384 customers in 2030, can be safely accommodated by reinforcing the distribution network. A combination of reinforcements, installation of microgenerators and control of electric vehicle battery charging will be required to accommodate safely a high uptake of 71% with regards to the constraints studied. An agent-based control system that coordinates the battery charging of electric vehicles was designed and developed. Search techniques and neural networks were used for the decision making processes. The ability of the agent-based control system to operate successfully in both normal and abnormal conditions for the electrical network was proved with experimental validation in the laboratory of Tecnalia research institute in Spain.

Computational intelligence is a well-established paradigm, where new theories with a sound biological understanding have been evolving. The current experimental systems have many of the characteristics of biological computers (brains in other words) and are beginning to be built to perform a variety of tasks that are difficult or impossible to do with conventional computers. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. This book comprising of 17 chapters offers a step-by-step introduction (in a chronological order) to the various modern computational intelligence tools used in practical problem solving. Staring with different search techniques including informed and uninformed search, heuristic search, minmax, alpha-beta pruning methods, evolutionary algorithms and swarm intelligent techniques; the authors illustrate the design of knowledge-based systems and advanced expert systems, which incorporate uncertainty and fuzziness. Machine learning algorithms including decision trees and artificial neural networks are presented and finally the fundamentals of hybrid intelligent systems are also depicted. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques, machine learning and data mining would find the comprehensive coverage of this book invaluable.