<p dir="ltr">Peer-to-peer (P2P) platforms have experienced remarkable growth, driven by advancements in internet technology and mobile applications. This rapid expansion has reshaped markets and introduced complex dynamics that warrant deeper exploration. This dissertation focuses on three critical dimensions of this field: the impact of platform introduction, platform regulation, and environmentally sustainable platform operations.</p><p dir="ltr">First, we study how the emergence of ride-hailing platforms has impacted the automotive industry by influencing both the sales and rental markets. Dealers and rental agencies, which once operated in separate markets, have become indirect competitors because car owners in the sales market offer rides to consumers in both the sales and rental markets through the platform. Therefore, to fully understand the platform’s impact, it is essential to consider these markets simultaneously. To this end, we develop a comprehensive model incorporating the manufacturer, dealer, and rental agency to analyze how a platform’s presence influences firm decisions and total car ownership. We show that the dealer increases its orders for products with high marginal costs due to the value enhancement effect, wherein car ownership becomes more valuable with the presence of a platform. Importantly, we find that neglecting the rental market - as most of the existing literature does - underestimates this effect. While the value enhancement effect does not extend to the rental market, a platform's presence may motivate the rental agency to increase its orders for products with low marginal costs and new-car valuation. However, the increase in rental cars is generally relatively modest compared to the decrease in personally owned cars, resulting in an overall increase in total ownership only for products with sufficiently high marginal costs and rental-car valuation. Moreover, we show that failing to consider both markets and their interactions may lead to inaccurately assessing the total change in ownership compared to the platform's absence. Finally, we discuss the implications of car owners' partial or heterogeneous participation rate in the platform and demonstrate that our results generally hold.</p><p dir="ltr">Second, we focus on the 90-day cap regulation in San Francisco and Berkeley to investigate the effectiveness of this supply restriction in improving the affordability of housing in the city. We specifically investigate 1) whether the regulation accurately targets landlords in the sharing market and increases the supply in the local long-term rental market and 2) whether the regulation achieves its goal of making housing more affordable for the targeted lower-income population in the city. We exploit a detailed dataset on Airbnb and Zillow in this empirical analysis. Using standard DID regression analysis, the paper finds that the regulation significantly decreased the listing number by about 29.6% and increased the overall average daily rate of short-term rentals by about 14.6% on the platform while decreasing the average price of long-term rentals by about 4.1% in the local residential market, in the year following the enforcement of the regulation. Meanwhile, we find that the benefit of the regulation effectively targeted affordable homes in the long-term rental market but did not affect the high-end and single-family markets significantly. In particular, using quantile DID methods, we show that the regulation only reduces the average rental price (of all types of homes) in only about 30% of the lower end of the local long-term rental market. The regulation also made a heterogeneous impact on different types of listings on the platform, making hosted listings increase their supply and benefit from the spillover effects, especially since it works efficiently to figure out landlords and sharers for multi-home host listings. </p><p dir="ltr">Third, we examine a ride-hailing platform's optimal subsidy design to increase electric vehicle (EV) adoption among drivers, which has been a key operational goal for P2P platforms as they increasingly prioritize sustainability. To this end, we model the choices made by drivers when selecting between gasoline gasoline vehicles (GVs) and EVs, considering the heterogeneity of drivers in their time costs. We examine how market segments are shaped by differences in the marginal costs of usage and prices between the two types of vehicles. These analyses reveal the distinct trade-offs faced by drivers with high supply compared to those with low supply. Motivated by practice, we consider three types of subsidies a platform may adopt to achieve full adoption of EVs, i.e., set-up bonuses, earning boosts, and charging discounts. We find that the earning boost subsidy consistently drives greater EV usage than the other subsidy types. However, we also find that a profit-driven platform is more likely to favor earning boosts when its per-unit profit is relatively high, even if this may not align with the most environmentally beneficial outcomes. This highlights the need for careful consideration of the platform’s subsidy design, as profit-maximizing strategies might conflict with environmental objectives. </p>

An increase emphasis has been placed on the automotive industry to develop advanced technology vehicles which meet increasing strict government regulations and standards for emissions and fuel economy while maintaining the safety, performance, and consumer appeal of the vehicle. In response to these requirements, hybrid and electric vehicle technologies have become more complex as the necessity for vehicles with an overall better environmental impact. Modern engineers must understand the current methods used to analyze and evaluate risk with the new hybrid technologies to ensure the continued customer satisfaction and safety while meeting new government and agency standards. The primary goal of this work is to maintain consistent definitions, standards, and protocols for risk analysis using design failure modes and effects analysis. Throughout the entire automotive sector there exist standards for risk analysis and methods for analysis, however these models can be difficult to relate to the atmosphere under which educational competitions occur. The motor system case study within this work aims to allow the process for DFMEA to be simple and easily implemented and understood when it is appropriate to start. After defining the model, an electric motor system for hybrid vehicle is analyzed for mechanical and inverter system risks. The end result being a 32% reduction in motor system risk due to recommended actions for mitigating top motor systems risks for future motor system design and implementation, all to meet customer requirements. This work aims to provide an additional tool that when implemented will accelerate the next generation of automotive engineers. / Master of Science

In a world with a growing population there is a trend toward higher and higher energy usage. Because of the cost involved in producing extra energy, there is a need for more efficient usage of the energy that is already available. The issue of efficiency rings home especially clear with electric motors. Although induction motors are used in many different applications, the motors used in electric vehicles must be able to generate a large starting torque as well as operate over a wide speed range. This work analyzes the restrictions placed on the motor and inverter drive system. It also looks at the best method for controlling the drive in order to achieve the highest efficiency out of the drive. While other works have shown methods of achieve high efficiency out of the motor, it is the assertion of this work that the efficiency of the total drive is more important. It is to that end that this work analyzes the performance of an induction motor under low torque and speed where a traction drive utilizes the most energy. / Master of Science

With the introduction of electronic control units to automotive vehicles, system complexity has increased. With this change in complexity, new standards have been created to ensure safety at the system level for these vehicles. Furthermore, vehicles have become increasingly complex with the push for electrification of automotive vehicles, which has resulted in the creation of hybrid electric and battery electric vehicles. The goal of this thesis is to provide an example of a hazard and operability analysis as well as a hazard and risk analysis for a hybrid electric vehicle. Additionally, the safety standards developed do not align well with educational prototype vehicles because the standards are designed for corporations. The hybrid vehicle supervisory controller example within this thesis demonstrates how to define a system and then perform system-level analytical techniques to identify potential failures and associated requirements. Ultimately, through this analysis suggestions are made on how best to reduce system complexity and improve system safety of a student built prototype vehicle. / Master of Science / With the introduction of electronic control units to automotive vehicles, system complexity has increased. With this change in complexity, new standards have been created to ensure safety at the system level for these vehicles. Furthermore, vehicles have become increasingly complex with the push for electrification of automotive vehicles, which has resulted in the creation of hybrid electric and battery electric vehicles. There are different ways for corporations to demonstrate adherence to these standards, however it is more difficult for student design projects to follow the same standards. Through the application of hazard and operability analysis and hazard and risk analysis on the hybrid vehicle supervisory controller, an example is provided for future students to follow the guidelines established by the safety standards. The end result is to develop system requirements to improve the safety of the prototype vehicle with the added benefit of making design changes to reduce the complexity of the student project.

Exploring the integration of multi-speed gearboxes in electric vehicle (EV) drivetrains, this research presents a comprehensive analysis through detailed gearbox modelling, empirical traction machine testing, and analytical drive unit loss evaluations. The study utilizes two distinct automotive-grade electric machines – an axial-flux permanent magnet synchronous machine and an interior permanent magnet machine, the latter coupled with a single-speed gearbox – to demonstrate how multi-speed gearboxes can enhance drivetrain efficiency and performance for a subcompact EV. Extensive dynamometer testing, incorporating a variety of electrical and thermal conditions, characterizes both traction machines. Findings reveal that despite the incremental churning losses from additional gear pairs, two-speed gearboxes facilitate a more efficient operation of the electric machine, inverter, and gearbox, particularly when optimized through strategic gear ratio selection. Dynamometer testing under no-load conditions and at different temperatures underscores the impact of gearbox churning and bearing drag losses and the potential for their reduction. Detailed examinations of load-dependent and independent losses within the drive unit elucidate the interactions among drivetrain components across various gear ratios. Optimized two-speed gearboxes are shown to reduce vehicle energy consumption by up to 9% and increase driving range compared to conventional single-speed configurations, supported by strategic gear ratio selections and optimizations aimed at achieving vehicle performance targets, such as acceleration, gradeability, and top speed. This research contributes to advancing the field of electric vehicle technology by illustrating the complex trade-offs and potential enhancements achievable with multi-speed drivetrains, setting a precedent for future studies to refine gearbox performance and explore novel technologies to optimize powertrain performance across diverse operational landscapes. / Thesis / Doctor of Philosophy (PhD)

During the past two decades desperate need for energy-efficient vehicles which has less emission have led to a great attention to and development of electrified vehicles like pure electric, Hybrid Electric Vehicle (HEV) and Plug-in Hybrid Electric Vehicles (PHEVs). Resultantly, a great amount of research efforts have been dedicated to development of control strategies for this type of vehicles including PHEV which is the case study in this thesis. This thesis presents a real-time control scheme to improve the fuel economy of plug-in hybrid electric vehicles (PHEVs) by accounting for the instantaneous states of the system as well as the future trip information. To design the mentioned parametric real-time power management policies, we use dynamic programming (DP). First, a representative power-split PHEV powertrain model is introduced, followed by a DP formulation for obtaining the optimal powertrain trajectories from the energy cost point of view for a given drive cycle. The state and decision variables in the DP algorithm are selected in a way that provides the best tradeoff between the computational time and accuracy which is the first contribution of this research effort. These trajectories are then used to train a set of linear maps for the powertrain control variables such as the engine and electric motor/generator torque inputs, through a least-squares optimization process. The DP results indicate that the trip length (distance from the start of the trip to the next charging station) is a key factor in determining the optimal control decisions. To account for this factor, an additional input variable pertaining to the remaining length of the trip is considered during the training of the real-time control policies. The proposed controller receives the demanded propulsion force and the powertrain variables as inputs, and generates the torque commands for the engine and the electric drivetrain system. Numerical simulations indicate that the proposed control policy is able to approximate the optimal trajectories with a good accuracy using the real-time information for the same drive cycles as trained and drive cycle out of training set. To maintain the battery state-of-charge (SOC) above a certain lower bound, two logics have been introduced a switching logic is implemented to transition to a conservative control policy when the battery SOC drops below a certain threshold. Simulation results indicate the effectiveness of the proposed approach in achieving near-optimal performance while maintaining the SOC within the desired range. / MS / During the past two decades desperate need for energy-efficient vehicles which has less emission have led to a great attention to and development of electrified vehicles like pure electric, Hybrid Electric Vehicle (HEV) and Plug-in Hybrid Electric Vehicles (PHEVs). Resultantly, a great amount of research efforts have been dedicated to development of control strategies for this type of vehicles including PHEV which is the case study in this thesis. This thesis presents a real-time control scheme to improve the fuel economy of plug-in hybrid electric vehicles (PHEVs) by accounting for the instantaneous states of the system as well as the future trip information. To design the mentioned parametric real-time power management policies, we use dynamic programming (DP). First, a representative power-split PHEV powertrain model is introduced, followed by a DP formulation for obtaining the optimal powertrain trajectories from the energy cost point of view for a given drive cycle. The state and decision variables in the DP algorithm are selected in a way that provides the best tradeoff between the computational time and accuracy which is the first contribution of this research effort. These trajectories are then used to train a set of linear maps for the powertrain control variables such as the engine and electric motor/generator torque inputs, through a least-squares optimization process. The DP results indicate that the trip length (distance from the start of the trip to the next charging station) is a key factor in determining the optimal control decisions. To account for this iv factor, an additional input variable pertaining to the remaining length of the trip is considered during the training of the real-time control policies. The proposed controller receives the demanded propulsion force and the powertrain variables as inputs, and generates the torque commands for the engine and the electric drivetrain system. Numerical simulations indicate that the proposed control policy is able to approximate the optimal trajectories with a good accuracy using the real-time information for the same drive cycles as trained and drive cycle out of training set. To maintain the battery state-of-charge (SOC) above a certain lower bound, two logics have been introduced a switching logic is implemented to transition to a conservative control policy when the battery SOC drops below a certain threshold. Simulation results indicate the effectiveness of the proposed approach in achieving near-optimal performance while maintaining the SOC within the desired range.

Automotive vehicle manufacturers have been facing increased pressures from legislative bodies and consumers to reduce the fuel consumption and harmful emissions of their newly produced vehicles as a result of new research showing the detrimental effects these emissions have on the environment. These pressures are encouraging manufactures and researchers to invest billions of dollars into the development of new advanced vehicle technologies. Some of these investments have resulted in substantial progress in powertrain technologies that have led to the preliminary adoption of electrified powertrain vehicles. Other areas of research are actively working to reduce the energy consumption of a vehicle, regardless of its powertrain, by influencing driver behavior and by optimizing the way a vehicle travels between an origin and destination. This intelligent vehicle routing is done by analyzing a range of possible routes and selecting the route that consumes the least amount of fuel. An accurate method for predetermining vehicle energy expenditure along a given route before it is driven is needed to effectively implement intelligent vehicle routing systems. One common method is the generation of a road network-wide database with energy use figures for each section of road. This method requires expensive experimentation trials or network simulation software. Individual-level vehicle predictive energy estimation eliminates the need for costly fuel use generation by utilizing vehicle velocity generation techniques and vehicle powertrain models. Estimation of individual vehicle energy consumption along a route is done by identifying an origin-destination pair, detecting required full-stops along the path, and synthesizing multiple stop-to-stop velocity modes between each set of stops. The resulting velocity profile is paired with a specific vehicle powertrain model to determine fuel consumption. A drawback of this route generation technique is that the vehicle path is assumed to be one-dimensional and lacks inclusion of road curves and their associated velocity changes to maintain passenger comfort. This thesis evaluates the merit of discounting road curves in predictive vehicle energy consumption analyses and presents a technique for modeling common road corners that require velocity changes to limit passenger discomfort. The resulting corner synthesis method is combined with a validated vehicle powertrain model to complete full route consumption modeling. Two routes, an urban and highway, are modeled and driven to evaluate the accuracy of the full simulation model when compared with on-road data. The results show that corners can largely be ignored during energy consumption analysis for highways. The cornering effects on a vehicle during urban driving, however, should be included in urban route analyses with multiple road curves. Inclusion of the cornering effects during an example urban route analysis decreased the error between the on-road consumption data and the simulation results. / Master of Science / Automotive vehicle manufacturers have been facing increased pressures from legislative bodies and consumers to reduce the fuel consumption and harmful emissions of their newly produced vehicles as a result of new research showing the detrimental effects these emissions have on the environment. These pressures are encouraging manufactures and researchers to invest billions of dollars into the development of new advanced vehicle technologies. Some of these investments have resulted in substantial progress in powertrain technologies that have led to the preliminary adoption of electrified powertrain vehicles. Other areas of research are actively working to reduce the energy consumption of a vehicle, regardless of its powertrain, by influencing driver behavior and by optimizing the way a vehicle travels between an origin and destination. This intelligent vehicle routing is done by analyzing a range of possible routes and selecting the route that consumes the least amount of fuel. An accurate method for predetermining vehicle energy expenditure along a given route before it is driven is needed to effectively implement intelligent vehicle routing systems. One common method is the generation of a road network-wide database with energy use figures for each section of road. This method requires expensive experimentation trials or network simulation software. Individual-level vehicle predictive energy estimation eliminates the need for costly fuel use generation by utilizing vehicle velocity generation techniques and vehicle powertrain models. Estimation of individual vehicle energy consumption along a route is done by identifying an origin-destination pair, detecting required full-stops along the path, and synthesizing multiple stop-to-stop velocity modes between each set of stops. The resulting velocity profile is paired with a specific vehicle powertrain model to determine fuel consumption. A drawback of this route generation technique is that the vehicle path is assumed to be one-dimensional and lacks inclusion of road curves and their associated velocity changes to maintain passenger comfort. This thesis evaluates the merit of discounting road curves in predictive vehicle energy consumption analyses and presents a technique for modeling common road corners that require velocity changes to limit passenger discomfort. The resulting corner synthesis method is combined with a validated vehicle powertrain model to complete full route consumption modeling. Two routes, an urban and highway, are modeled and driven to evaluate the accuracy of the full simulation model when compared with on-road data. The results show that corners can largely be ignored during energy consumption analysis for highways. The cornering effects on a vehicle during urban driving, however, should be included in urban route analyses with multiple road curves. Inclusion of the cornering effects during an example urban route analysis decreased the error between the on-road consumption data and the simulation results.

The increasing penetration of electric vehicles (EVs) and other distributed energy resources (DERs) offers enhanced flexibility and resilience. During extreme conditions, grid-connected EVs and DERs can provide electricity service and restore critical loads when the utility system is unavailable. On the other hand, during normal operation, these proactive devices can provide ancillary services to alleviate voltage fluctuations and support frequency regulation. In comparison with other DERs, EVs are more flexible in providing ancillary services due to their mobile nature. However, the proliferation of EVs and DERs also introduces operational challenges to the distribution grid. For instance, EVs primarily fulfill their transportation needs. Uncoordinated charging of a large number of EVs can increase the burden on the distribution system. Due to the limited charging rate and battery size, it is generally impractical for a single EV to directly participate in the ancillary service market. A conventional distribution system is designed for unidirectional flow of electric energy. With the growing installation of DERs on the distribution system, the flow of electric energy is bi-directional and, therefore, there is a higher risk of protection miscoordination due to the fault currents resulting from DERs. With limited communication capability, these undetected protective device (PD) actuations can cause uncertainties and delay the service restoration process. This dissertation makes contributions to the coordination of EVs and DERs. It introduces four innovative models for EV coordination: 1) A transactive energy (TE) trading mechanism is proposed to coordinate EVs and aggregators. 2) Optimal tools are provided to assist EVs and aggregators in optimal decision making while participating in TE. 3) A charging station model is developed to allow EVs to provide ancillary service aligned with their mobile nature. 4) A utility function model is presented to capture the EV owners' behaviors for providing ancillary services and charging vehicles. Charging stations can estimate the electric energy demand and optimize ancillary service provision to meet their goals. Simulation cases validated that the proposed optimization tools can align EV owners' preferences in providing ancillary service to enhance distribution system operation flexibility. To enhance the resilience of distribution systems, two novel optimization strategies are presented: 1) An advanced outage management (AOM) is proposed to utilize smart meters and fault indicators (FIs) to identify the most credible outage scenario and fault locations. 2) An advanced feeder restoration (AFR) is developed to provide an optimal restoration strategy to enhance system resilience. The proposed optimization models have been validated with realistic simulation cases. / Doctor of Philosophy / As Electric Vehicles (EVs) and other Distributed Energy Resources (DERs) become more common, they are changing how our distribution systems work. For example, during power outages, grid-connected DERs and EVs can be deployed to sustain essential electricity services such as hospitals and communications. On the other hand, during a normal operating condition, they can help maintain the stability of our electricity systems. It is a technical challenge to integrate these new EV and DER devices into the existing power grid. For example, EVs are mainly designed for transportation. Their clustered charging patterns can significantly increase the electrical demand if they are not managed properly. Also, the limited battery capacity and charging speed make it difficult for a single vehicle to provide meaningful support to the grid operation. For the EV management side, this research is concerned with how to better integrate EVs and similar technologies into the power grid. Four key contributions of this dissertation are: 1) Developing a trading mechanism for EVs and aggregators of EVs to exchange energy and ancillary services efficiently; 2) Creating computational technologies to help these entities optimize their decisions while meeting their requirements; 3) Structuring charging station operations that cater to the preferences of EV owners while supporting grid operation; and 4) Modeling EV owners' decision-making to set optimal pricing and service strategies at charging stations. These mechanisms and strategies will allow EV owners to support the power grid while meeting their transportation needs. Moreover, the study addresses the issue of enhancement of the distribution system's capability to restore services under extreme conditions. It provides an advanced outage management method that utilizes remote monitoring and control technologies, including smart meters and fault indicators, to identify the location of electrical faults and reduce the outage areas. The advanced feeder restoration method determines an optimal strategy to restore the electricity service efficiently while keeping the distribution grid stable.

Изучение особенностей развития рынков электромобильного транспорта России и Казахстане остается актуальным вопросом по нескольким причинам. Во-первых, переход на электромобили может значительно снизить выбросы вредных веществ и уменьшить зависимость от нефтепродуктов. Во-вторых, развитие электромобильной технологии продолжает продвигаться вперед, включая улучшения в батарейных технологиях, зарядных устройствах и автономных системах. В-третьих, переход на электромобильный транспорт также может иметь экономические выгоды, включая сокращение затрат на топливо, повышение энергоэффективности и создание новых рынков для производителей электромобилей и связанных с ними услуг. В-четвертых, многие страны в мире вводят программы по стимулированию электромобильного транспорта через налоговые льготы, субсидии на покупку электромобилей и развитие инфраструктуры зарядных станций. Цель работы - разработка направлений совместного развития национальных рынков электромобильного транспорта России и Казахстана. Объект: национальные рынки электромобильного транспорта России и Казахстана. Практическая значимость заключается в возможности применения достигнутых результатов при принятии отдельных управленческих решений, а также формирования стратегических планов развития площадок для производства и экспорта электромобильного транспорта и сопровождающий рынок инфраструктуры. / The study of the peculiarities of the electric vehicle markets in Russia and Kazakhstan remains an urgent issue for several reasons. First, switching to electric vehicles can significantly reduce emissions of harmful substances and reduce dependence on petroleum products. Secondly, the development of electric vehicle technology continues to move forward, including improvements in battery technology, chargers and autonomous systems. Third, switching to electric vehicles can also have economic benefits, including reducing fuel costs, improving energy efficiency, and creating new markets for manufacturers of electric vehicles and related services. Fourthly, many countries in the world are introducing programs to stimulate electric vehicle transport through tax incentives, subsidies for the purchase of electric vehicles and the development of charging station infrastructure. The purpose of the work is to develop directions for the joint development of the national electric vehicle transport markets of Russia and Kazakhstan. Object: national electric vehicle transport markets of Russia and Kazakhstan. The practical significance lies in the possibility of applying the results achieved in making individual management decisions, as well as forming strategic plans for the development of sites for the production and export of electric vehicles and the accompanying infrastructure market.

The popularity of the internal combustion engine (ICE) vehicles has contributed to global warming problem and degradation of air quality around the world. Furthermore, the vehicles??? massive demand on gas has played a role in the depletion of fossil fuel reserves and the considerable rise in the gas price over the past twenty years. Those existing challenges force the auto-industry to move towards the technology development of vehicle electrification. An electrified vehicle is driven by one or more electric motors. And the electricity comes from the onboard energy storage system (ESS). Currently, no single type of green energy source could meet all the requirements to drive a vehicle. A hybrid energy storage system (HESS), as a combination of battery and ultra-capacitor units, is expected to improve the overall performance of vehicles??? ESS. This thesis focuses on the design of HESS and the development of a HESS prototype for electric vehicles (EVs) and hybrid electric vehicles (HEVs). Battery unit (BU), ultra-capacitor unit (UC) and a DC/DC converter interfacing BU and UC are the three main components of HESS. The research work first reviews literatures regarding characteristics of BU, UC and power electronic converters. HESS design is then conducted based on the considerations of power capability, energy efficiency, size and cost optimization. Besides theoretical analysis, a HESS prototype is developed to prove the principles of operation as well. The results from experiment are compared with those from simulation.