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Electric vehicles have become a trend as a replacement to gasoline-powered vehicles and will be a sustainable substitution to conventional vehicles. As the number of electric vehicles in cities increases, the charging demand has surged. The optimal location of the charging station plays an important role in the electric vehicle transit system. This chapter discusses the planning of electric vehicle charging infrastructure for urban. The purpose of this work develops an electric vehicle fast-charging facility planning model by considering battery degradation and vehicle heterogeneity in driving range, and considering various influencing factors such as traffic conditions, user charging costs, daily travel, charging behavior, and distribution network constraints. This work identifies optimal fast-charging stations to minimize the total cost of the transit system for deploying fast-charging networks. Besides, this chapter also analyzes some optimization modeling approach for the fast charging location planning, and point out future research directions.
Electric vehicles (EVs) are widely considered a sustainable substitution to conventional vehicles to mitigate fossil fuel dependence and reduce tail-pipe emissions. However, limited ranges, long charging times, and lack of charging infrastructure have hindered EV's market acceptance. This calls for more investments in building charging stations and advancing battery and charging technologies to obviate issues associated with EVs and increase their market share and improve sustainability. This study introduces modeling frameworks to optimize fast-charging infrastructure locations at the network level to address the challenges associated with EVs. Furthermore, it investigates the required charging investments for the current and future EV market shares, technology advancements, and seasonal demand variations. First, this study seeks an optimal configuration for plug-in electric vehicle charging infrastructure that supports their long-distance intercity trips at the network level. A mathematical optimization model is proposed which minimizes the total system cost and considers the range anxiety, multiple refueling, maximum capacity, charging delay, and detour time. This study considers the impacts of charging station locations on the traffic assignment problem with a mixed fleet of electric and conventional vehicles considering a user equilibrium framework. This study fills existing gaps in the literature by capturing realistic patterns of travel demand and considering flow-dependent charging delays at charging stations in intercity networks. Then, the study focuses on Michigan and its future needs to support the intercity trips of EVs across the state in two target years of 2020 and 2030, considering monthly traffic demand and battery performance variations, as well as different battery sizes and charger technologies, the main contributing factors in defining the infrastructure needs of EV users, particularly in states with adverse weather conditions. This study incorporates the developed intercity model to suggest the optimal locations of EV fast chargers to be implemented in Michigan.Next, this study introduces an integrated framework for urban fast-charging infrastructure to address the range anxiety issue in urban networks. Unlike intercity trips that start with fully charged batteries, urban trips might start with any state of charge because of home/work chargers' unavailability, being part of a trip chain, and forgetting to charge overnight. A mesoscopic simulation tool is incorporated to generate trip trajectories, and a state-of-the-art tool is developed to simulate charging behavior based on various trip attributes for these trajectories. The resulting temporal charging demand is the key element in finding the optimum charging infrastructure. The solution quality and significant superiority in the computational efficiency of the decomposition approach are confirmed in comparison with the implicit enumeration approach. Finally, this study generates forecasting models to estimate the number of chargers and charging stations to support the EV charging demand for urban areas. These models provide macro-level estimates of the required infrastructure investment in urban areas, which can be easily implemented by policy-makers and city planners. This study incorporates data obtained from applying a disaggregate optimization-based charger placement model, for multiple case studies to generate the required data to calibrate the macro-level models, in the state of Michigan.
Planning the charging infrastructure for electric vehicles (EVs) is a new challenging task. This book treats all involved aspects: charging technologies and norms, interactions with the electricity system, electrical installation, demand for charging infrastructure, economics of public infrastructure provision, policies in Germany and the EU, external effects, stakeholder cooperation, spatial planning on the regional and street level, operation and maintenance, and long term spatial planning.
This study addresses the problem of locating fast charging stations for electric vehicles in the early stages of infrastructure implementation. The primary objectives are to contextualise the problem and to subsequently develop the existing methodology found in the literature. In particular, this study aims to design an optimal location model for the infrastructure of fast charging stations for electric vehicles, using the city of Barcelona as a case study. The most notable contribution is the implementation of a multi-objective model, applying the weighted sums methodology. Using this approach, multiple solutions can be obtained and used to observe the trade-offs between conflicting objectives, providing valuable information to decision makers. In addition, an alternative outlook is taken with regards to electric vehicle user behaviour; the concept of minimising deviations is introduced into the optimisation problem with an aim to better reflect driver behaviour, and account for all users in the model. The model is first formulated mathematically and then implemented using the GAMS.. The objective of the work is to apply modeling and optimization techniques to design a method for locating public EV charging facilities. This method must take into account the legal framework of the EV charging facilities as well as the EV users characteristics and necessities.
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.
Fast-Charging Infrastructure for Electric and Hybrid Electric Vehicles Comprehensive resource describing fast-charging infrastructure in electric vehicles, including various subsystems involved in the power system architecture needed for fast-charging Fast-Charging Infrastructure for Electric and Hybrid Electric Vehicles presents various aspects of fast-charging infrastructure, including the location of fast-charging stations, revenue models and tariff structures, power electronic converters, power quality problems such as harmonics & supraharmonics, energy storage systems, and wireless-charging, electrical distribution infrastructures and planning. This book serves as a guide to learn recent advanced technologies with examples and case studies. It also considers problems that arise, and the mitigation methods involved, in fast-charging stations in global aspects and provides tools for analysis. Sample topics covered in Fast-Charging Infrastructure for Electric and Hybrid Electric Vehicles include: Selection of fast-charging stations, advanced power electronic converter topologies for EV fast-charging, wireless charging for plug-in HEV/EVs, and batteries for fast-charging infrastructure Standards for fast-charging infrastructure and power quality issues (analysis of harmonic injection and system resonance conditions due to large-scale penetration of EVs and supraharmonic injection) For professionals in electric vehicle technology, along with graduate and senior undergraduates, professors, and researchers in related fields, Fast-Charging Infrastructure for Electric and Hybrid Electric Vehicles is a useful, comprehensive, and accessible guide to gain an overview of the current state of the art.
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 (EV) market growth is critical to achieving sustainable development goals, governing aspirations to achieve full-scale electrification targets across the globe. Increasing EV sales have shifted the focus of researchers from EV adoption to new operational challenges such as the optimal deployment of charging stations and grid load management, which in turn also affects EV adoption. These challenges require an accurate characterization of EV user charging behavior, especially with evolving battery technology and driving ranges. This study critically reviews approaches and data sources used to elicit EV charging behavior and patterns from a demand-side perspective and investigates how supply-side studies on charging infrastructure deployment and management incorporate charging behavior. We observe a noticeable disconnect between both strands of the literature, as supply-side studies still rely on simplistic assumptions about charging behavior and focus on a handful of aspects in isolation. More specifically, several studies either consider personal EVs or ride-hailing services with only public fast-charging infrastructure while ignoring available home/work charging infrastructure. We recommend shifting from this silo approach to a system-level dynamic planning framework where future charging demand is forecasted by combining charging behavior models with the models to forecast travel demand and EV adoption, followed by an integration of demand information into the supply-side optimization. The framework can thus capture complex supply-demand interactions and inform the charging infrastructure planning policies, laying out a roadmap for emerging and mature EV markets.
Modern power systems increasingly rely on distributed energy resources (DERs), which requires new planning practices. This paper proposes a strategy to solve the facility allocation problem for fast charging stations (FCSs) of electric vehicles (EVs). The mixed-integer nonlinear programming (MINLP) model minimizes investment and total operation costs, considering the building of FCSs with photovoltaic (PV) systems over carports and battery energy storage systems (BESSs) as planning alternatives. The aspects of the set covering problem are adapted to evaluate candidates to accommodate FCSs. A preprocessing strategy is proposed to define an EV fleet and the required energy demand of each FCS. A multiobjective approach is used to obtain a compromise optimal solution for the MINLP model. The combined strategies lead to a reduced computational burden, allowing full-scale studies of electromobility infrastructure planning. Results from studies use a real-world Brazilian case to certify the benefits of the proposed strategy in optimizing the FCS allocation problem. The optimized operation demonstrates the reduction in the operation cost considering the renewable alternatives.