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The widespread expansion of Electric Vehicles (EV) throughout the world creates a requirement for charging stations. While Cybersecurity research is rapidly expanding in the field of Electric Vehicle Infrastructure, efforts are impacted by the availability of testing platforms. This paper presents a solution called the “Open Charge Point Protocol (OCPP) Cyber Range.” Its purpose is to conduct Cybersecurity research against vulnerabilities in the OCPP v1.6 protocol. The OCPP Cyber Range can be used to enable current or future research and to train operators and system managers of Electric Charge Vehicle Supply Equipment (EVSE). This paper demonstrates this solution using three attack types, Denial of Service, Machine-in-the-Middle, and Log4shell.

In recent years, electric vehicles (EVs) have continued to grow. According to one forecast, EV share among registered cars will reach about 7% within a decade. The high share of EVs will accelerate the deficit in government revenues from gasoline taxes. Therefore, a vehicle miles traveled (VMT) tax is being discussed as an alternative to the automobile fuel tax. The government is considering the policy shift from a gasoline tax to a VMT tax, that affects almost populations, but few studies have evaluated the new policy while taking into account the growth of EVs. This paper bridges the gap between assessing the VMT tax and considering increasing EV penetration.Chapter 1 discusses the distributional effect of a VMT tax when considering the penetration of electric vehicles. It studies the impact of a VMT tax introduction on vehicle choice and utilization in a two-period model that links two decisions on vehicle choice and subsequent driving. Also, it examines the consumer surplus changes in the short term due to policy shifts from a gasoline tax to a VMT tax according to vehicle types, fuel economy, and household attributes. The results show that the revenue-neutral VMT tax would increase consumer surplus by a modest $2 per vehicle per year. It also suggests that even if the government imposes the same federal VMT tax rate, each state could be a winner or loser depending on the average MPG, miles driven, and VMT elasticity. Ultimately, the results show that shifting policy towards a VMT tax becomes more efficient as the penetration of EVs grows. When the EV share reaches 5%, the incremental EV share generates an additional surplus equal to twice the surplus from adopting the revenue-neutral VMT tax. It can also reduce revenue and expenditure discrepancies without changing the tax rate.Chapter 2 explores a VMT tax to increase government revenues as electric vehicle penetration grows and the long-term impact of that tax on consumer surplus. By taking the estimates from the two-period structural model for vehicle choice and utilization, it conducts a counterfactual analysis to increase government revenue by 30%. Based on the results, the following sensitivity analyses target the cases where government revenue increases from 30% to 100%, and the share of electric vehicles increases from 3% to 10%. As a result, the VMT tax generates a small but positive net consumer surplus compared to the gasoline tax when moderately increasing government revenues. Net consumer surplus from the VMT tax rises incrementally as the growth rate in government tax revenue increases. If the proportion of electric vehicles rises, a relatively more significant additional net surplus will occur. It implies that when raising revenue, the tax rate significantly affects consumer surplus more than the tax type. However, as the proportion of electric vehicles increases, tax type also becomes important in determining a tax rate and changing the consumer surplus.Chapter 3 discusses the second-best optimal taxes by the three tax types considering the expansion of EVs in the US: gasoline tax, VMT tax, and combined tax of gas and VMT taxes. According to the model developed in this paper, the second-best optimal tax rates of flat VMT and flat VMT+gas taxes, converted in cents per gallon, are higher than the second-best optimal gas tax. However, both rates can improve social welfare more than the gas tax. The flat VMT+gas tax generates a significant welfare benefit as much as the VMT tax at the second-best optimal rates. In the early stage of introducing electric vehicles, among the best revenue-neutral taxes to maintain the current government revenue, only the flat VMT+gas tax provides positive welfare benefits. As long as the EV share does not exceed 40%, the flat VMT+gas tax is more favorable than the flat VMT tax. The EV VMT+gas tax is more advantageous regardless of the EV share than the VMT tax types capped with the current government revenue.These three papers contribute to understanding of how the share of electric vehicles affects the surplus from the policy shift from a gasoline tax to a VMT tax. The cost of switching tax policies makes policymakers hesitant to adopt the new tax policy. However, the results of this dissertation provide evidence that adopting the new tax policy can benefit both private and public alike if sustained EV growth is to be expected.

This paper examines state and county-exclusive incentives on battery electric vehicle (BEV) registration in the United States. Using two main methods, a differences-in-differences method and a sigmoidal growth rate equation, I examine the impact of non-federal incentives on the total amount of electric vehicles between 2017 and 2022, as well as estimate the years that each state will reach its net-zero goals for carbon emissions in the transportation sector. I hope to provide a deeper understanding of the effectiveness of incentive policy, based on differing levels of incentive policy between regions, in order to best increase electric vehicle adoption in a cost-effective method. In addition, I hope that my estimates of net-zero projections will serve as a beneficial comparison to track states’ respective progress towards sustainable energy in vehicles. These findings can be used to assist policymakers in determining appropriate BEV adoption policies based on regional consumer demographics and needs, as well as visualize a timeline for the next century of rapid electric vehicle growth.

Transportation electrification is clearly the road toward the future. Compared to internal combustion engine, the electrified vehicle has less carbon-dioxide emission, less maintenance costs and less operation costs. It also offers higher efficiency and safety margin. More importantly, it relieves human’s dependence on conventional fossil energy. In the electrification progress, the revolution of electric traction drive systems is one of the most important milestone. The traction system should keep high efficiency to avoid performance reduction. Moreover, the motor drive should be designed within limited space without sacrificing output power rating. Based on the road map from US Drive Electrical and Electronics Technical Team, US Department of Energy, a gap is still there between roadmap target and the state-of-art. To fill the gap, this dissertation performs a systematic research in motor drive system for traction inverters. This paper starts from optimal theoretical design of power converters by using loss model and real-time simulation system. Based on optimal paper design, hardware design is implemented. The component design for converter, such as the laminated busbar, are the focus in this dissertation. The optimized busbar structure can effectively reduce stray inductance in the current-commutation loop, reducing switching overshoots of power modules and increasing semiconductor reliability. The system-level design and trade-off is also analyzed and illustrated by using a 250kW three-level T-type neutral-point clamped converter. The design has reached high efficiency and high-power density. The converter system is also evaluated through comprehensive tests, such as double-pulse tests and continuous tests. The test setup, test condition and test result analysis are discussed in the dissertation. In the end, the dissertation also proposed an improved impedance characterization method for components parasitic inductance measurement in traction drive systems, such as laminated busbar, power module and capacitors. The characterization shares better accuracy and can be customized for device under test with any geometry.

Electric vehicles (EV) are growing in popularity and therefore adoption rate. Best estimates predict a 6.2% EV adoption rate by 2035 in the southeastern United States. With this level of EV adoption, utility planners must begin to consider the impact that EVs will have on the power grid. This paper aims to help predict these EV impacts on the power grid. Specifically, an urban-commercial feeder is analyzed in detail to provide worst-case and most-likely results of varying levels of EV impact. Results show that a 26.2% peak increase is the most likely result for this feeder in 2035. Mitigation techniques are used to lower the impact that EVs will have on the power grid. A number of mitigation techniques are specifically analyzed for this urban-commercial feeder. The cost of each of these mitigation techniques is compared to their effectiveness. The best mitigation strategy is chosen to be a combination of time-of-use and a battery energy storage system because it gives the best results relative to cost and provides emergency capabilities. In this study, system data is extracted from this urban-commercial feeder and combined with other feeder types to provide a utility scale EV impact. The scale-up model provides the most-likely scenario for future EV impacts on the entire utility’s power grid. This system level scale-up data can be used for integrated resource planning purposes.

The main Investor-Owned Utilities in California charge electricity prices that are way above the social marginal cost of consuming electricity. This results in economic inefficiency which previous studies prove to have negative implications for inequality and to slow down the electrification of the transportation sector. This paper seeks to build on the existing knowledge by investigating how the high electricity prices affect low and high income house-holds’ electric vehicle adoption differently in California, and it aims to quantify the extent of such difference. The results show that EV adoption among low income households would be considerably higher under efficient retail pricing, and that low income households are more negatively affected than their high income counterparts. However, the results relative to high income households specifically are inconclusive, and therefore the extent to which the two income groups are affected differently cannot be determined. Further research should tackle this issue by including additional variables such as income by year and EV quality, as well as more granular gasoline price data.

The transportation sector is the largest contributor to anthropogenic U.S. greenhouse gas(GHG) emissions. If the U.S. is to address climate change, a key mechanism will be through a transition to a decarbonized transportation sector. Given environmental externalities, it is clear that the private market will produce too little low-carbon transportation infrastructure. Policy-makers are attempting to overcome this scale problem by encouraging the build out of alternative transportation infrastructure. In this dissertation, I examine existing clean infrastructure policies — the spatial efficiency of infrastructure installed to-date, their effect on adoption of the new transportation mode, and their effect on other transportation modes.In the first chapter of this dissertation, I study the location of electric vehicle chargingstations. Absent policy, it is well understood that EV charging network size may be inefficient because of pollution and network externalities. This paper argues that there will also be spatial inefficiencies in the location of electric vehicle fast charging stations. I empirically test for the presence of a spatial inefficiency in the free market by comparing the location decisions of a vertically integrated firm that sells vehicles and provides a charging network to those of a non-vertically integrated charging network. I define a metric, enabled e-miles, that captures whether a charging station mitigates consumers’ range anxiety. I then combine estimates from a spatial demand model and a simulation of charging behavior to compare charging demand and enabled e-miles at charging stations in California. I find strong evidence in favor of a spatial inefficiency in the location decisions of non-vertically integrated charging providers. These findings show that EV policy should not be location-neutral, but should consider spatial inefficiencies as well as network size.In the second chapter of this dissertation, I examine whether the availability of home- andworkplace-charging infrastructure for renters has a detectable effect on EV adoption. Policies supporting charging infrastructure in workplaces and multi-unit dwellings (MUDs) are nascent. I aim to address two questions: (i) are such policies effective in increasing investment in charging stations, and (ii) whether they are effective in promoting EV adoption. I provide the first causal evidence on whether charging infrastructure represents a barrier to adoption for MUD occupants. I find that the subsidies increased the number of charging stations in a census block group (CBG) by .01. Against the sample average of 0.053, this represents a 20% increase. I did not detect any effect of charging stations on EV adoption. In the third chapter of this dissertation, I explore the installation of a bike sharing system in New York City and its interaction with existing vehicle infrastructure. Bike sharing is one form of shared mobility service. These increasingly ubiquitous services provide shared use of a vehicle, bicycle, scooter, or other mode of transportation. They can simultaneously complement and conflict with existing transportation infrastructure. New York City’s Citibike is the largest bike-sharing system in the country and among its goals is the relief of traffic congestion. I estimate the causal effect of Citibike on historical street speeds on Manhattan avenues. I employ Google Maps data to chart the routes between Citibike docks. I match these routes to rider counts and to novel data on traffic speeds at a 10-meter spatial resolution. This allows us to exploit variation in treatment intensity along and across avenues in fine resolution. I control for bike lanes, and other changes in street conditions over time. I find that the Citibike system decreased speeds on avenues in Manhattan. Overall, I estimate that the system decreased speed by 2.3% on average. At the maximum observed system utilization, travel time increased by 9.6%.Together, these chapters employ travel data, spatial variation and tools, causal identification,and a combination of data and economic reasoning to draw policy relevant insights about how to decarbonize the transportation sector. A theme throughout is that the efficient deployment of infrastructure supporting low carbon transportation should take into account the location, scale, and utilization of existing infrastructure, be it the installed base of charging stations, or alternative transportation options.

Current lithium-ion battery (LiB) recycling infrastructure is limited for strategic metals such as lithium and cobalt, despite projections that millions of electric vehicles (EVs) will hit the road in the next decade. Governments have labeled lithium and cobalt as “strategic” due to their importance in emerging green technologies as well as “critical” due to the increased risk of supply disruption resulting from geographic supply concentration, low substitutability, and low end-of-life recycling rates (EOLRR’s). This paper aims to assist stakeholders conceptualize EV battery packs as a source of strategic metals and to help improve EOL-RR’s for lithium and cobalt. Specifically, this paper demonstrates the value of EV battery packs as a source of metals compared to natural resources and outlines advantages and disadvantages of thermal and mechanical recycling processes in terms of energy consumption, variable costs and maximum recoverable metal value. Findings suggest that EV battery packs contain favorable concentrations – often 1 magnitude higher – of lithium, cobalt, nickel and copper compared to respective economic ores. Disassembling the EV battery pack to the cell level increases cobalt, lithium and nickel concentrations since they are part of the cathode active material. Between thermal and mechanical recycling processes, the key tradeoff is between process time and the ability to recover lithium. Thermal recycling requires less processing time than mechanical recycling, resulting in lower variable costs per battery pack, but mechanical recycling yields a higher maximum recoverable metal value since lithium can be recovered along with cobalt, copper and nickel.

In the United States, more than a quarter of greenhouse gas (GHGs) emissions (27%) are attributed to the transportation sector which comprises mainly of vehicles powered by internal combustion engines (ICE). To reduce the dependence on fossil fuels and the resulting GHG emissions associated with conventional ICE vehicles, plug-in hybrid vehicles are being promoted as a viable near-term vehicle technology. This paper is a comparative experimental study of two types of hybrid systems: parallel (also known as plug-in hybrid) and series (also known as extended-range electric) hybrid systems. The two hybrid systems are modelled on an electric bicycle platform and field tested to analyze their performance. The fuel economy was measured and compared in L/100km and the electric powertrain efficiency of the system was measured and compared in watt-hours per kilometer (Wh/km). A sensitivity analysis is carried out in terms of different transmission gear ratios and the variable setpoints in the hybrid control logic to access the impact these factors have on the performance of the hybrid system. This paper focuses only on the technological aspect of the hybrid system and any social and policy aspects associated are not considered. The constructive modeling of the hybrid system, the limitations faced during the process and the results of the field tests are presented.

Exploring the impact of charging station location on electric vehicle (EV) demand is crucial for understanding electric vehicle demand and developing additional policies. This paper examines how the number of charging stations near the town/city center, the distance between charging stations and the nearest highway, and the number of stations with facilities (car dealers, parking lots, office buildings, and hotels) affect electric vehicle demand. Based on quarterly data of EV sales and charging station locations in all counties of New York State from 2014 to 2021, this study concludes that more stations installed near the town/city center and highways, as well as more stations equipped with facilities like office buildings, could boost EV demand. These findings imply that future subsidies for charging infrastructure may be location-based.

Technology exists to fulfill functions in society, and technological innovations are continuously proposed to fulfill a particular function more effectively than an incumbent technology. These innovations are disseminated through society in a process called technology diffusion, and may ultimately replace an incumbent system in what is known as a technology transition. Due to the complex and uncertain underlying processes of technology adoption and diffusion, technical systems are resistant to transition to possibly superior alternatives. To address market, systemic, and structural failures preventing a desired technology transition, a policymaker, or other motivated agent, may strategically intervene to stimulate or accelerate the diffusion process. The success or failure of such policy intervention carries crucial implications for climate change mitigation, healthcare advances, and any other aspect of society that technology touches. However, existing models of optimal technology policy design omit or otherwise offer crude representations of these underlying processes and are largely case-specific at the expense of gleaning generalizable insights. The goal of this dissertation is to advance the operations research modeling of technology transitions and the role of policy support. Through a variety of powerful operations research methodologies and relevant case studies, the individual projects in this dissertation offer novel models of technology transitions and insights into real-world technology policy, especially in the energy and climate domain. The three core chapters of this dissertation begin with the development of an applied energy system optimization model to assess a real-world climate policy, then move on to present two novel theoretical models that yield more general, analytical insights into technology policy decision making. Chapter 2 addresses the growing importance of cities in climate change mitigation with the development of an energy system optimization model for urban-scale decarbonization. Our optimization model determines the least-cost power and transportation technology pathways to achieve a policy goal of net-zero greenhouse gas emissions and is used to analyze the Community Climate Plan adopted by Austin, Texas. We find that the policy objective can be achieved at a modest 2.7% increase in net present power and transportation costs relative to business-as-usual. The optimal decarbonization pathway proceeds through two distinct stages, first reducing power sector emissions, then electrifying transportation. Solar PV expands in the long run with or without the climate plan based on favorable cost projections, but the policy causes wind to replace natural gas as a complement to solar PV. Our findings also highlight the substantial value of intelligently scheduled battery storage operations and electric vehicle charging. While the energy system optimization model of Chapter 2 captures numerous decisions for a complex urban energy system, it carries limiting assumptions about how technology diffusion occurs and the role of a policymaker in supporting a technology transition. Addressing these larger questions motivates the project in Chapter 3, which describes the development of two stylized models of technology policy decision making under uncertainty. The first model is a Markov reward process (MRP) that represents policy interventions with one-time, upfront costs, while the second is a Markov decision process (MDP) that represents interventions with recurring costs. For each model, we derive analytical expressions for the policymaker's willingness to pay (WTP) to raise the probabilities of advancing a technology development or diffusion process at various stages and compare and contrast the behaviors of the MRP and MDP models. Most notably, our analytical findings elucidate how the different cost-accounting schemes and the possibility of regressing from a more advanced development or diffusion stage back to an earlier one affect the WTP. Then, we conduct numerical sensitivity analysis to explore how the optimal technology policy portfolio varies with certain parameters, and present a case study on lithium-ion batteries for electric vehicles to demonstrate the practical application of our model to technology policy decision making. In Chapter 4, we narrow our focus on technology transitions to infrastructure-dependent technologies common in energy, transportation, and telecommunications systems. Policymakers seeking to promote the diffusion of infrastructure-dependent technologies are often confronted with the chicken-and-egg problem: consumers are reluctant to adopt the technology without adequate infrastructure available, and firms are reluctant to invest in infrastructure without a sufficient number of adopters. This chicken-and-egg problem can hinder the diffusion of new technologies and prolong the timeframe over which existing technological systems remain locked-in. In this paper, we formulate a stylized model of technology policy decision making from the perspective of a policymaker who seeks to stimulate the market penetration of an infrastructure-dependent technology. Our model is a bilevel optimization problem in which a policymaker (leader) maximizes net social benefits by setting the levels of two incentives: a subsidy for a profit-maximizing firm (follower) to invest in infrastructure that raises the benefit of adoption to consumers, and a direct subsidy for consumers to adopt the technology. We analytically derive the firm's optimal infrastructure investment response to the upper-level policy decisions, and show that the bilevel model is equivalent to a quadratic program. To bypass non-convexity, we develop a custom solution strategy based on decomposition, and find that it performs better than directly applying an off-the-shelf solver to the potentially non-convex problem. Finally, we present a case study on the diffusion of battery electric vehicles and obtain insights into how a policymaker should allocate resources to charging infrastructure and vehicle incentives. The three projects of this dissertation employ operations research methods to model technology transitions and the role of policy support. While each captures a variety of phenomena affecting technology transitions and optimal technology policy decision making, there remain thought-provoking questions that future research can address. We conclude this dissertation with proposed research directions and contemplate the high-level, real-world implications of this work.

The rapid escalation in plug-in electric vehicles (PEVs) and their uncoordinated charging patterns pose several challenges in distribution system operation. Some of the undesirable effects include overloading of transformers, rapid voltage fluctuations, and over/under voltages. While this compromises the consumer power quality, it also puts on extra stress on the local voltage control devices. These challenges demand a well-coordinated and power network-aware charging approach for PEVs in a community. This paper formulates a realtime electric vehicle charging scheduling problem as a mixed-integer linear program (MILP). The problem is to be solved by an aggregator that provides charging services in a residential community. The proposed formulation maximizes the profit of the aggregator, enhancing the utilization of available infrastructure. With prior knowledge of load demand and hourly electricity prices, the algorithm uses a moving time horizon optimization approach, allowing an unknown number of arriving vehicles. In this realistic setting, the proposed framework ensures that power system constraints are satisfied and guarantees the desired PEV charging level within the stipulated time. Numerical tests on an IEEE 13-node feeder system demonstrate the computational and performance superiority of the proposed MILP technique.

In this dissertation, I present a body of work that advances our understanding of the technical and economic potential for vehicle grid integration based on a variety of methodological approaches that quantify the opportunity at multiple scales, across multiple geographies, and that cover scenarios with both personally owned plug-in electric vehicles (PEVs) and shared autonomous electric vehicles (SAEVs). The key research questions addressed in this dissertation include:* How can charging infrastructure be cost-effectively deployed to maximize utilization and value to PEV drivers?* How much flexibility exists in the charging demand from PEVs? * What is the economic opportunity to manage the charging of PEVs to occur at lower cost time periods?* How will fleets of electrified autonomous vehicles serving mobility on-demand differ in how they are managed to minimize the cost of charging or to serve as a source of electricity for buildings?These questions are motivated by the fact that transportation electrification and emerging forms of mobility are dramatically changing how the transportation system is planned, operated, and analyzed. PEVs present new challenges and constraints around the siting and operation of refueling infrastructure. Electric load from PEVs can exacerbate grid congestion at either transmission or distribution scales if left unmanaged. Sharing and autonomy are changing mobility which will have unique implications for the grid integration of PEVs. Meanwhile, there are strong social and environmental forces compelling planners, regulators, and private industry to electrify transportation as soon as possible. The transportation sector is the largest emitter of greenhouse gases in the United States. With the exception of the great recession, emissions in the transportation sector have been growing for the last three decades, in contrast to the electric power and industrial sectors which have been on a downward trend in emissions. Transportation, therefore, represents one of the primary challenges to achieving deep decarbonization of the U.S. economy.In the electric power sector, policy and economic forces are upending incumbent generation technologies (coal and natural gas) in favor of lower cost and lower carbon alternatives, particularly wind and solar power. As these intermittent renewable resources increase in capacity, the incidence of renewable energy (RE) curtailment increases due to time periods when supply is greater than demand and generators are turned down or shut off from the level that they would otherwise be producing. Curtailment raises the overall system cost of supplying electricity. In addition, some utilities must meet an energy production standard to satisfy state mandates for renewable production. Renewable curtailment forces utilities to either acquire more RE or introduce sources of grid flexibility to relieve the curtailment. One low cost strategy to mitigate these challenges is to manage the temporal profile of electricity demand to make use of the renewable resources when they are available.PEVs are generally analyzed through modeling using one of two approaches, statistical modeling and activity-based modeling. Statistical models typically summarize or infer travel patterns from travel survey data and use them to characterize the need for PEV charging and the temporal opportunities to charge. The key disadvantage of such approaches is that they cannot account for the individual mobility constraints of travelers and they typically require an assumption that charging infrastructure is unlimited. Another common approach is to develop Markov Chain models of mobility and PEV charging. In these models, transitions between states are treated as random events. Because they lack a representation of the causal mechanism for these transitions, these models are difficult to generalize and their utility is degraded if applied in prospective contexts assuming a transportation system with dramatically different characteristics than present. Activity-based models make use of travel diaries from surveys or GPS data logging which are then provided as input to mobility and charging simulations. Agent-based models are a subset of activity-based models, in so far as they treat travelers individually and require a representation of each individual's activity schedule in order to model the travel necessary to engage in those activities. What distinguishes agent-based models are two key features: 1) wrapping the individuals in a virtual environment (e.g. the transportation system) with detailed representation of transportation supply and 2) dynamically simulating the agents' interactions with the virtual environment and with each other. These interactions open the opportunity to model the choices of the agents based on empirical studies of human behavior as well as to make agent behavior contingent on the time-evolving state of their environment and other agents.In the electric power and grid modeling domain, load from PEVs are typically represented as static or derived from very rudimentary estimation techniques. Studies either ignore flexibility entirely or they make simplistic assumptions about the timing and degree to which PEV load can be shaped. The inaccuracy in these modeling choices have had a relatively low impact in the recent past due to the still relatively low penetration of PEVs in the national vehicle fleet. But within a decade it will no longer suffice to ignore or simplify PEV load, which could eventually make up more than 20% of U.S. electricity demand.This dissertation addresses these gaps by coupling models of electric mobility and the grid at multiple scales. Each paper presented in this dissertation was produced in collaboration with co-authors across multiple projects and contexts. I employ reduced-form models in the context of optimization to solve the charger scheduling and vehicle mobility problems, as well as detailed agent-based models that simulate context-specific traveler behaviors and the dynamics of resource-constrained charging infrastructure.To address the infrastructure siting problem, I develop a spatially explicit agent-based simulation model that represents charging infrastructure, charging behavior, competition for scarce chargers, and driver adaptation. A differential evolution and a heuristic optimization scheme are employed to find a cost-effective distribution of charging infrastructure. I then address the question of flexibility in two ways. First I develop a scheme for optimizing the charging profiles of individual PEV drivers based an objective that simultaneously considers the costs of charging and the benefits associated with providing ancillary services to the grid. Then I employ a much higher fidelity approach to simulate both the electrified mobility system as well as the power sector. I develop the BEAM modeling framework (Behavior, Energy, Mobility, and Autonomy), which is an agent-based model of PEV mobility and charging behavior designed as an extension to MATSim (the Multi-Agent Transportation Simulation model). I apply BEAM to the San Francisco Bay Area and conduct a preliminary calibration and validation of its prediction of charging load based on observed charging infrastructure utilization for the region in 2016. I link the BEAM model with PLEXOS, an industry standard production cost model that accurately characterizes grid dispatch constraints.Finally, I consider the impact of grid-integrated fleets of SAEVs providing mobility on-demand. In two separate studies I develop models to consider how such fleets could be used to serve building energy demand during power outages as well as a more general analysis of the battery and charging infrastructure requirements to serve nationwide mobility.The key findings across all of this work are the following:* In today's energy markets, PEV flexibility can reach values of $155/year/vehicle for NYISO and $98/year/vehicle for CAISO. The annual cost savings due to optimizing dispatch come more from savings on the price of energy (74% in CAISO and 61% in NYISO) but providing ancillary services (in the form of regulation) also contributes value to the solution (26% in CAISO and 39% in NYISO).* When we project the energy market of California to a future year when renewables make up 50% of the annual energy produced, PEV flexibility is even more beneficial to the power sector, primarily in lowering grid operating cost and the amount of RE that must be curtailed to avoid over-generation when supply and demand are mismatched. For example, if treated as flexible loads, 2.5 million smart charging PEVs avoid 50\% of incremental system operating costs annually and reduce renewable energy curtailment by 27% annually relative to when the same number of unmanaged charging PEVs are added to the grid. * When SAEVs serve power to buildings during an extreme outage, the fleet can generate 32%-40% more revenue than is earned serving mobility alone. While the overall value of providing on-demand power depends on the frequency and severity of power outages, the results show that serving power demand during large-scale outages can provide a substantial value stream, comparable to what can be earned providing grid services. * All mobility in the United States currently served by 276 million personally owned vehicles could be served by 12.5 million SAEVs at a cost of $0.27/vehicle-mile or $0.18/passenger-mile. The energy requirements for this fleet would be 1142 GWh/day (8.5% of 2017 U.S. electricity demand) and the peak charging load 76.7 GW (11% of U.S. power peak). In total, this body of work contributes new insights into the opportunity for electric mobility to reduce the cost of operating the electric grid, enabling deeper and faster adoption of renewable power in the electric sector, and providing reliable mobility to travelers in the transportation sector. The domain of vehicle grid integration is still relatively new, there are many areas of research that require additional attention, such as increased research on traveler preferences around PEV charging, the intersection between electric mobility and the distribution grid, electrification of medium and heavy duty vehicles, as well as properly incentivizing electric vehicles to ride hail drivers in the gig economy.

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.