Installation and utilization of residential distribution transformers has not changed substantially over a long period of time. Utilities typically size their transformers based on a formula that takes into account broadly what types and how many dwellings will be connected. Most new residential dwellings feature 200 Amp service per household with an anticipated energy demand of under 20,000 kWh per year. Average electrical energy consumption varies from state to state but averages to 11,280 kWh per year. Energy demand is expected to fall into a typical residential load curve that shows increased demand early in the morning, then decreasing during the day and another peak early to late evening. Distribution transformers are sized at the limit of the combined evening peak with the assumption that the transformer has enough thermal mass to absorb short overloads that may occur when concurrent loading situations among multiple dwellings arise. The assumption that concurrent loading is of short duration and the transformer can cool off during the night time has been validated over the years and has become standard practice. This has worked well when dwelling loads follow an averaging scheme and low level of coincidence. With the arrival of electric vehicles (EV's) this assumption has to be reevaluated. The acquisition of an electric vehicle in a household can drive up energy demand by over 4000 kWh per year. Potentially problematic is the increased capacity of battery packs and the resulting proliferation of Level 2 chargers. The additional load of a single Level 2 charger concurring with the combined evening peak load will push even conservatively sized distribution transformers over their nameplate rating for a substantial amount of time. Additionally, unlike common household appliances of similar power requirements such as ovens or water heaters, a Level 2 battery charger will run at peak power consumption for several hours, and the current drawn by the EVs has very high levels of harmonic distortion. The excessive loading and harmonic profile can potentially result in damaging heat build-up resulting in asset degradation. In this thesis I present a device and method that monitors pole mounted distribution transformers for overheating, collect and wirelessly upload data and initiate commands to chargers to change output levels from Level 2 to Level 1 or shut down EV charging altogether until the transformer returns into safe operational range.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (P-HEV). The strategy can effciently be deployed online without the need for complete knowledge of the entire duty cycle in order to optimize fuel consumption. ARBS improves upon the established Preliminary Rule-Based Strategy (PRBS) which has been adopted in commercial vehicles. When compared to PRBS, the aim of ARBS is to maintain the battery State of Charge (SOC) which ensures the availability of the battery over extended distances. The proposed strategy prevents the engine from operating in highly ineffcient regions and reduces the total equivalent fuel consumption of the vehicle. Using an HEV model developed in Simulink, both the proposed ARBS and the established PRBS strategies are compared across eight short duty cycles and one long duty cycle with urban and highway characteristics. Compared to PRBS, the results show that, on average, a 1.19% improvement in the miles per gallon equivalent (MPGe) is obtained with ARBS when the battery initial SOC is 63% for short duty cycles. However, as opposed to PRBS, ARBS has the advantage of not requiring any prior knowledge of the engine effciency maps in order to achieve optimal performance. This characteristics can help in the systematic aftermarket hybridization of heavy duty vehicles.

This thesis provides a comprehensive analysis of electric vehicle incentives and investigates their impact on the uptake of electric vehicles within and beyond the European Union over the period of 2010 to 2019. Depending on the kind of benefit they provide and their timing, the incentives are divided into one-time monetary, recurring monetary, and non-monetary incentives. To properly evaluate the effect of incentives, a fixed effects and difference-in-differences methods are employed, allowing us to control for unobserved factors affecting the electric vehicle market. A fixed effects analysis revealed a significant positive effect of one-time monetary incentives on battery electric vehicle sales, and a significant positive effect of both one-time and recurring monetary incentives on plug-in hybrid electric vehicle sales. Additionally, when considering the effect of individual incentives, the most important ones were found to be rebate and point-of-sale tax incentive. A difference-in-differences analysis confirmed a statistically significant effect of rebate on the sale of battery electric vehicles. JEL Classification C33, H71, L62, L98, O31, Q55 Keywords Electric vehicles, Incentives, Tax incentives, Rebates, Technology adoption Title The Impact of Incentives on Electric Vehicle Sales in the...

京都大学 / 0048 / 新制・課程博士 / 博士(エネルギー科学) / 甲第21385号 / エネ博第373号 / 新制||エネ||73(附属図書館) / 京都大学大学院エネルギー科学研究科エネルギー社会・環境科学専攻 / (主査)教授 手塚 哲央, 教授 下田 宏, 准教授 MCLELLAN,Benjamin / 学位規則第4条第1項該当 / Doctor of Energy Science / Kyoto University / DFAM

The projected and current adoption rates of electric vehicles are increasing. Since electric vehicles require that they be recharged continually over time, the energy needs to support them is immense and growing. Given existing infrastructure is insufficient to supply the projected energy needs, models are necessary to help decision makers plan for how to best expand the power grid to meet this need. A successful power grid expansion is one that enables charging stations to service the electric vehicle community. Thus, plans for power expansion need to be coordinated between the power grid and charging station investors. The infrastructure for the charging stations has to also be resilient and reliable to absorb this increase in load. Charging stations therefore should be included in the plans for post power disruption planning. In this work, two two-stage stochastic programming models are developed that can be used to determine a power grid expansion plan that sup- ports the energy needs, or load, from an uncertain set of electric vehicles geographically dispersed over a region. Another three-stage stochastic programming model is presented, where the decisions are made first to select which charging stations to install and expand uninterruptible power supply units and renewable energy sources. Then, when the disrup- tion occurs in the second-stage, repairs in power system and charging stations take place ahead of the arrival of panicked population to prepare for the expected surge in power de- mand. Finally, as demand is unveiled, managerial and operational decisions at the charging stations are made in the third-stage. To solve the mathematical models, we utilize hybrid approaches which mainly make use of Sample Average Approximation and Progressive Hedging algorithm. To validate the proposed model and gain key insights, we perform computational experiments using realistic data representing the Washington, DC area. Our computational results indicate the robustness of the proposed algorithm while providing a number of managerial insights to the decision makers.

This thesis focuses on developing an intelligent energy management strategy for eco-driving in Connected and Autonomous Hybrid Electric Vehicles (CA-HEV's), which can be implemented in real-time. The strategy is divided into two layers, i.e. the upper level controller and the lower level controller. The upper level controller can be executed on the remote server. It is responsible for extracting the information from the driver about the trip and the vehicle information using the communication capabilities of the CA-HEV. The gathered information is then utilized by dynamic programming (DP), which is implemented in a bi-layer fashion to reduce the computation burden on the server. The outer layer of the DP algorithm and the optimal velocity trajectory and the inner layer optimizes the power distribution in the powertrain to minimize fuel consumption alongside maintaining charge balance conditions. These global optimal results are evaluated for an ideal environment without any traffic information. The lower level controller is responsible for real-time implementation on vehicles in the real world environment and is based on a well-accredited reinforcement learning (RL) strategy, i.e., Q-learning. The RL-based controller optimally distributes the power in a CA-HEV and maintains charge balance conditions. Furthermore, the RL-based controller is also trained on the remote server based on global optimal results obtained from the DP algorithm. The optimal parameter information is then resent to the vehicle's embedded controller for real-time implementation. Simulations are performed for Toyata Prius (2010) on MATLAB and Simulink, and road information is gathered from SUMO. Simulation results provide a comparative study between the global optimal and the RL-based controller. To validate the adaptiveness of the RL-based controller, it is also tested on two approximate real-world drivecycles and its performance is compared against global optimal results evaluated using DP. / Thesis / Master of Applied Science (MASc)

This thesis consists of three main objectives: fundamental and literature review of EV batteries, experimental development, and validation of two liquid cooling battery modules, thermal modeling and comparison of the inter-cell cooling battery module. / The traditional vehicles with internal combustion engine have resulted in severe environmental pollution, which motivates the development of electric vehicles and hybrid electric vehicles. Due to a low energy density and long refueling time of the battery pack, it is still hard for electric vehicles and hybrid electric vehicles to be widely accepted by the consumers. As the batteries with a better ultra-fast charging capability are massively produced, the range anxiety issue is somewhat alleviated. During a charging with large current magnitude, the battery generally has a great amount of heat generation and evident temperature rise. Therefore, a thermal management system is necessary to effectively dissipate the battery loss and minimize the degradation mechanisms caused by extreme temperature. The motivation of this thesis is to study the discipline of the battery thermal management system as an application for electric vehicles. The design methodologies are presented in both experiment test and numerical simulation. For the comparative study between active liquid cooling methods for a lithium-ion battery module using experimental techniques, two battery modules with three Kokam Nickel Manganese Cobalt battery cells connected in parallel are developed. One has liquid coolant flowing along the edge of the model, and another with liquid coolant flowing between the cells. Several characterization tests, including thermal resistance tests, fast charging tests up to 5C, and drive cycle tests are designed and performed on the battery module. The inter-cell cooling module has a lower peak temperature rise and faster thermal response compared to the edge cooling module, i.e., 4.1⁰C peak temperature rise under 5C charging for inter-cell cooling method and 14.2⁰C for edge cooling method. The thermal models built in ANSYS represent the numerical simulation of the inter-cell cooling module as a comparison with the experiment. A cell loss model is developed to calculate the battery heat generation rate under ultra-fast charging tests and a road trip test, which are further adopted as the inputs to the thermal models. The simulation of the 5C ultra-fast charging test gives the peak temperature rise just 0.47⁰C lower than the experimental measurement, it indicates that the FEA thermal models can provide an accurate temperature prediction of the battery module. / Thesis / Master of Applied Science (MASc) / With a demanding market of electric vehicles, battery technologies have grown rapidly in recent years. Among all the battery research topics, the development of ultra-fast charging, that can fully charge the battery pack within 15 minutes, is the most promising direction to address the range anxiety and improve the social acceptance of electric vehicles. Nevertheless, the application of ultra-fast charging has many challenges. In particular, an efficient thermal management system is significant to guarantee the safety and prolong the service life of the battery pack. This thesis contributes to study the fundamentals of the battery field, and design liquid cooling systems to observe the thermal behavior of a battery prototype module under fast charging and general use. FEA thermal modeling of the battery module is developed to provide a guide for further test validation.

The rising use of fossil fuels and the resulting rise in environmental harm have fueled the advancement of automobiles that are fuel-efficient. A severe existential challenge facing the planet earth has given rise to hybrid electric vehicles (HEVs), which have developed from their incipient stage and are shown promise as a solution. Additionally, when needed to produce peaking power, batteries' efficiency is reduced. Instead, supercapacitors have smaller energy storage capacity but can withstand peaking power. Designing a clever method to manage the energy balance between a supercapacitor and a battery is the main goal of this research. Different topologies are used to study the battery-supercapacitor energy storage system in great detail. Nitrogen oxides (NOx), carbon monoxide (CO), hydrocarbons (HC), and other harmful gases are less released when a battery-supercapacitor energy storage system is integrated. Additionally, it can lower the load on the battery, extending its life and improving its performance in HEVs.

Indiana University-Purdue University Indianapolis (IUPUI) / Plug-in Hybrid Electric Vehicles (PHEV) are new generation Hybrid Electric Vehicles (HEV) with larger battery capacity compared to Hybrid Electric Vehicles. They can store electrical energy from a domestic power supply and can drive the vehicle alone in Electric Vehicle (EV) mode. According to the U.S. Department of Transportation 80 % of the American driving public on average drives under 50 miles per day. A PHEV vehicle that can drive up to 50 miles by making maximum use of cheaper electrical energy from a domestic supply can significantly reduce the conventional fuel consumption. This may also help in improving the environment as PHEVs emit less harmful gases. However, the Energy Management System (EMS) of PHEVs would have to be very different from existing EMSs of HEVs. In this thesis, three different Energy Management Systems have been designed specifically for PHEVs using simulated study. For most of the EMS development mathematical vehicle models for powersplit drivetrain configuration are built and later on the results are tested on advanced vehicle modeling tools like ADVISOR or PSAT. The main objective of the study is to design EMSs to reduce fuel consumption by the vehicle. These EMSs are compared with existing EMSs which show overall improvement. x In this thesis the final EMS is designed in three intermediate steps. First, a simple rule based EMS was designed to improve the fuel economy for parametric study. Second, an optimized EMS was designed with the main objective to improve fuel economy of the vehicle. Here Particle Swarm Optimization (PSO) technique is used to obtain the optimum parameter values. This EMS has provided optimum parameters which result in optimum blended mode operation of the vehicle. Finally, to obtain optimum charge depletion and charge sustaining mode operation of the vehicle an advanced PSO EMS is designed which provides optimal results for the vehicle to operate in charge depletion and charge sustaining modes. Furthermore, to implement the developed advanced PSO EMS in real-time a possible real time implementation technique is designed using neural networks. This neural network implementation provides sub-optimal results as compared to advanced PSO EMS results but it can be implemented in real time in a vehicle. These EMSs can be used to obtain optimal results for the vehicle driving conditions such that fuel economy is improved. Moreover, the optimal designed EMS can also be implemented in real-time using the neural network procedure described.

Indiana University-Purdue University Indianapolis (IUPUI) / Today transportation is one of the rapidly evolving technologies in the world. With the stringent mandatory emission regulations and high fuel prices, researchers and manufacturers are ever increasingly pushed to the frontiers of research in pursuit of alternative propulsion systems. Electrically propelled vehicles are one of the most promising solutions among all the other alternatives, as far as; reliability, availability, feasibility and safety issues are concerned. However, the shortcomings of a fully electric vehicle in fulfilling all performance requirements make the electrification of the conventional engine powered vehicles in the form of a plug-in hybrid electric vehicle (PHEV) the most feasible propulsion systems. The optimal combination of the properly sized components such as internal combustion engine, electric motor, energy storage unit are crucial for the vehicle to meet the performance requirements, improve fuel efficiency, reduce emissions, and cost effectiveness. In this thesis an application of Particle Swarm Optimization (PSO) approach to optimally size the vehicle powertrain components (e.g. engine power, electric motor power, and battery energy capacity) while meeting all the critical performance requirements, such as acceleration, grade and maximum speed is studied. Compared to conventional optimization methods, PSO handles the nonlinear constrained optimization problems more efficiently and precisely. The PHEV powertrain configuration with the determined sizes of the components has been used in a new vehicle model in PSAT (Powertrain System Analysis Toolkit) platform. The simulation results show that with the optimized component sizes of the PHEV vehicle (via PSO), the performance and the fuel efficiency of the vehicle are significantly improved. The optimal solution of the component sizes found in this research increased the performance and the fuel efficiency of the vehicle. Furthermore, after reaching the desired values of the component sizes that meet all the performance requirements, the overall emission of hazardous pollutants from the PHEV powertrain is included in the optimization problem in order to obtain updated PHEV component sizes that would also meet additional design specifications and requirements.