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. / M.S. / There is an enhanced rate of global warming due to emissions and increased usage of fossil fuels in the transportation sector. As a feasible solution, electrification of transportation has become a necessary step towards an environment-friendly future. The escalation in plug-in electric vehicles (PEVs) has increased the impact on loading and voltage fluctuations in the distribution grid due to uncoordinated charging. This puts on extra stress on the grid system and compromises the system performance. As a measure to control the vehicle charging in a residential setup, a real-time optimal charging scheduling algorithm is developed which is implemented at the neighborhood level. To increase the charging performance with the limited available resources, an aggregator is introduced. The charging profit is maximized as the PEV charging problem is solved optimally by the aggregator. This facilitates the reduction in night-time grid congestion and maximization of number of PEVs getting charged with limited dependency on communication to avoid long delays in charging control. The proposed technique guarantees the complete charging of the selected PEVs in the stipulated time while considering the power grid operational constraints. It also reduces the impact of peak load demand by flattening the base load demand curve. To demonstrate the efficiency of the proposed mixed integer linear programming optimization algorithm, numerical tests for an IEEE 13 node feeder are performed. The results are discussed to give an outlook on the balance between system and user requirements by meeting the demand of the PEV users.

The transport sector, particularly electric vehicles (EVs), plays a pivotal role in mitigating climate change. However, the use of lithium-ion (li-ion) batteries in EVs raises sustainability concerns due to their limited lifespan, high costs, potential raw material shortages, and the environmental impact of raw material mining. These concerns necessitate the incorporation of sustainable development strategies into EV innovation plans. With a booming EV market, both luxury and non-luxury brands are being urged to adopt circular economy strategies, such as battery reuse and recycling, to enhance the brand-customer relationship. While the automotive industry has begun implementing these strategies, there is limited research on how they affect customer acceptance and the impact on luxury versus non-luxury brands. This thesis examines the existence of differences and influence of these circular economy strategies on the EV industry, with a particular focus on li-ion batteries, and analyzes their impact on luxury and non-luxury automotive EV brands.

There is a growing interest in electric transportation, and the number of electric vehicles (EVs) is increasing. The resulting increase in EV charging power demand has an adverse impact on the existing power grids, especially the distribution transformers. The repeated and continued overloading caused by EV charging can significantly reduce their operational life. This dissertation aims to comprehensively study the adverse impacts of EV charging on distribution transformers and provide robust and practical solutions to mitigate it. A typical North American secondary distribution system with different EV penetration levels and four realistic residential EV charging scenarios are used for the analyses. The IEEE Standard C57.91-2011 is used to quantify transformer life under different scenarios and to validate the efficacy of the proposed overloading mitigation strategies. It is observed that EV charging can have a significant impact on the life of distribution transformers. To mitigate the impact of EV charging on the distribution transformer, first, a practical solution based on reactive power compensation is proposed. The method is based on reducing the over- all transformer losses by providing a component of the residential reactive power demand through non-unity power factor operation of the EV charger. A centralized recursive control structure is proposed to compute and communicate the required reactive power values to the individual EVs. It is shown that the proposed technique increases the distribution transformer’s life by an average of nearly 47% in all four scenarios considered. Moreover, the proposed controller’s structure makes it effective even on low-bandwidth, high-latency communication networks. To verify this, the proposed controller’s stability under communication delays and its robustness against potential communication failures is also validated. This research also studies potential concerns about the charger’s reliability by non-unity power factor operation. Accordingly, an alternative overloading mitigation strategy is also proposed based on fixed charging current magnitude. This second method is shown to be more effective in reducing transformer overloading at the cost of a marginal decrease in the charging rate. Lastly, a high-level overview of the existing vehicle-to-grid communication standards is presented to provide a better context for practical implementation and identify potential challenges.

Electric vehicles are increasingly a part of human everyday life. Accordingly, a key issue is how these vehicles should sound to make the traffic environment safer and more sustainable. The aim of this thesis is to map, understand and discuss the communicative properties of AVAS sound in an electric vehicle and to examine these sounds as a semiotic resource for meaning making. The research questions are: (i) What characterizes the AVAS sounds that are represented in traffic today; (ii) in what way can AVAS technology be developed to contribute to a safer and thus more sustainable traffic environment, and (iii) in what ways can a sound be designed to contribute to a safer and more secure traffic environment? Two studies were carried out: Study A conducted an analysis of already existing AVAS sounds found in traffic today and in Study B an AVAS prototype was created, built and installed. On the basis of the results, chord as a semiotic resource, the absence of low frequencies, pulsating effect and the vehicle as a semiotic resource are discussed. In conclusion, the issue of the vehicle as a participant in the public space is discussed.

With electric vehicles becoming increasingly prevalent in the automotive market consumers are becoming more conscientious of total driving range. In light of this trend, reliable and accurate modeling methods are necessary to aid the development of more energy efficient vehicles with greater drivable range. Many methods exist for evaluating energy consumption of current and future vehicle designs over the US certification drive cycles. This work focuses on utilizing the well-established Willans line approximation and proposes a simplified modeling method to determine electric vehicle energy consumption and powertrain efficiency. First, a backwards physics-based model is applied to determine tractive effort at the wheel to meet US certification drive cycle demand. Second, the Willans line approximation then augments the tractive effort model and parameterizes the vehicle powertrain to establish a bi-directional power flow method. This bi-directional approach separates propel and brake phases of the vehicle over the certification City and Highway drive cycles to successfully isolate the vehicle powertrain from non-intrinsic losses, such as parasitic accessory loads. The proposed method of bi-directional modeling and parameter tuning provides significant insight to the efficiency, losses, and energy consumption of a modeled electric vehicle strictly using publicly available test data. Results are presented for eight electric vehicles with production years varying from 2016 to 2021. These electric vehicles are chosen to encapsulate the electric vehicle market as performance electric vehicles to smaller commuter electric vehicles are selected. All vehicles are modeled with an accessory load constrained between 300 and 850 W and a regenerative braking ("regen") low-speed cutoff of 5 mph with six of the eight vehicles modeled with a regenerative braking fraction of 94%. The bi-directional Willans line is then tuned to reach agreement with the net EPA energy consumption test data for each vehicle with the results presented as representative of the chosen vehicle. Lastly, a transfer function relating major model inputs to the output is derived and lends considerable insight for the sensitivity of the modeling method. Sensitivity of the proposed modeling method is conducted for a 2017 BMW i3 with the model deemed reasonably resilient to changes in input parameters. The model is most sensitive to changes in powertrain marginal efficiency with a 6% decrease of marginal efficiency leading to a 0.404 kW and 0.793 kW cycle average net battery power increase for the City and Highway drive cycles respectively. Additionally, the model is also sensitive to changes in vehicle accessory load with a direct relationship between increases of vehicle accessory load to increases of cycle average net battery power for the City and Highway cycles. The sensitivity results justify the use of the proposed model as a method for evaluating vehicle energy consumption and powertrain efficiency solely using publicly available test data. / Master of Science / Developing robust and accurate methods for analyzing electric vehicle energy consumption and powertrain efficiency is of great interest. For the purposes of this paper, powertrain refers to a motor / inverter pair which is coupled to a simple gear reduction for torque multiplication. Many vehicles are designed with motors of varying power and torque capabilities which can present challenges when attempting to effectively compare electric vehicles from different manufacturers. The proposed modeling method presented in this work utilizes public test data to derive detailed vehicle and powertrain information. Vehicle energy consumption is also modeled and compared to net EPA test data. Eight electric vehicles are modeled with each vehicle representing a specific segment of the current electric vehicle market. A bi-directional Willans line is applied to model the propel and brake phases of each electric vehicle over the US certification drive cycles. The bi-directional approach effectively isolates the vehicle powertrain from non-intrinsic losses. From the derived powertrain parameters and modeled energy consumption, the proposed method is deemed accurate and highly useful for translating public test data to detailed vehicle information. Lastly, a sensitivity analysis is presented with the proposed method deemed reasonably resilient to changes in input parameters. The modeling method is most sensitive to changes of powertrain marginal efficiency and vehicle accessory load but constraining these inputs to reasonable ranges for electric vehicles proves sufficient.

Electric Vehicles (EVs) are active loads as they increase the demand for electricity and introduce several challenges to electrical distribution feeders during charging. Demand Response (DR) or performing load control in commercial buildings along with the deployment of solar photovoltaic (PV) and ice storage systems at the building level can improve the efficiency of electricity grids and mitigate expensive peak demand/energy charges for buildings. This research aims to provide such a solution to make EV penetration transparent to the grid. Firstly, this research contributes to the development of an integrated control of major loads, i.e., Heating Ventilation and Air Conditioning (HVAC), lighting and plug loads while maintaining occupant environmental preferences in small- and medium-sized commercial buildings which are an untapped DR resource. Secondly, this research contributes to improvement in functionalities of EnergyPlus by incorporating a 1-minute resolution data set at the individual plug load level. The research evaluates total building power consumption performance taking into account interactions among lighting, plug load, HVAC and control systems in a realistic manner. Third, this research presents a model to study integrated control of PV and ice storage on improving building operation in demand responsive buildings. The research presents the impact of deploying various combinations of PV and ice storage to generate additional benefits, including clean energy generation from PV and valley filling from ice storage, in commercial buildings. Fourth, this research presents a coordinated load control strategy, among participating commercial buildings in a distribution feeder to optimally control buildings' major loads without sacrificing occupant comfort and ice storage discharge, along with strategically deployed PV to absorb EV penetration. Demand responsive commercial building load profiles and field recorded EV charging profiles have been added to a real world distribution circuit to analyze the effects of EV penetration, together with real-world PV output profiles. Instead of focusing on individual building's economic benefits, the developed approach considers both technical and economic benefits of the whole distribution feeder, including maintaining distribution-level load factor within acceptable ranges and reducing feeder losses. / Ph. D. / Utilities generally meet peak demand through expensive peaking units which are operated only for short periods of time. At the same time the growing demand for Electric Vehicles (EVs) in the U.S. impacts the already burdened distribution feeder during peak hours. EVs are active loads as they increase the distribution feeder’s demand when charging. EV charging may bring about several challenges to the distribution feeder, including reduced load factors, potential transformer overloads, feeder congestion and violation of statutory voltage limits. On the other hand, building owners want to make buildings demand responsive so that they can participate in a demand response program offered by a regional electric grid operator to earn additional revenues. Allowing buildings to be demand-responsive by controlling buildings’ major loads, including HVAC (Heating, Ventilation and Air Conditioning), lighting and plug loads based on demand reduction signals from the grid has proven to provide tremendous savings. Additionally, optimized peak demand reductions at the building level by means of coordinated control of building loads, solar photovoltaic (PV) and ice storage systems can play a major role in flattening the building load shape, thereby decreasing its peak electricity consumption and at the same time mitigating grid stress conditions when needed. This study discusses the impacts of EV charging on a distribution feeder serving demand responsive commercial customers and develops a mitigation strategy to make EV penetration transparent to the grid. The mitigation strategy relies on coordinated control of major loads in demand responsive commercial buildings, ice storage discharge, along with strategically deployed PV. The analysis presented in this study shows that the developed approach can help mitigate EV penetration challenges by reducing the peak distribution system load, reducing feeder losses and improving distribution system load factor.

<p dir="ltr">This motorsport thesis explores the complete electrification of an IndyCar by simulations. Initial research was conducted on stock IndyCar specifications, and concurrently, a sequential approach was developed for MATLAB-based simulations to generate comprehensive results. The study aims to integrate extensive insights gained from courses such as Vehicle Dynamics, Aerodynamics, Data Acquisition, and Electric Powertrains, alongside practical experience from racing internships. The goal is to comprehend the impact of this conversion on engineering parameters. The analysis specifically emphasizes the engineering aspects, with a particular focus on the longitudinal dynamics of the vehicle through quarter-mile simulations.</p>

<p dir="ltr">Car design is a complex task because of how highly integrated system of systems it is. Fine?designed car models take years of design and optimization and are usually done by specialty teams who are dedicated to each sub-system. This thesis delves into designing a simplified electric race car from scratch with focus on the performance envelope of it. First, a 3D CAD model was done using SolidWorks. That section deals with spatial engineering and strategic placement of major car components for best performance. Having most of the parts in place gives a rough estimate of CoG (Center of Gravity) location, which is needed for vehicle dynamics analysis, which are discussed later in the report. The target for this project car is to have innovative aerodynamics features which might not have been used before because of bulky internal combustion engines restricting available space. One of them is an airfoil-like fascia which makes the center part of the car act as a one big wing. That is believed to give a significant reduction in drag loads on the car. The approach for aerodynamics design and analysis started with a model representing the car’s OML (Outer Mold line) which was simulated separately using Siemens StarCCM+. After understanding the car’s body aero behavior, a rear wing was added to provide extra rear downforce for better handling and stability. The rear wing design was explained in detail. Unfortunately, due to time restrictions as well as software access issues, the aerodynamic analysis of the full car with rear wing is left for future work. After having an estimate about aero loads acting on the car, vehicle dynamics analysis could start. The first subject studied in vehicle dynamics was front-view suspension geometry analysis. Taking the available packaging and geometry into consideration, a 2D model was done in SolidWorks to optimize camber gain. This analysis gave the motion ratio of the front and rear pushrod suspension system which was needed to analyze the performance of the one-eighth car model, ½ car pitch model, and ½ car roll model. These models gave insights into the decision-making process for spring and damping rates to reach a good balance between performance and comfort. This project acts as a hub for further development and studies related to car design.</p>

This thesis explores the intersection of spatial accessibility, social equity, and neighbourhood demographics within the context of emerging urban challenges related to plug-in electric vehicle (PEV) infrastructure. Incorporating sociological theories such as spatial accessibility and spatial segregation, combined with statistical methods including network routing analyses and multilevel modelling, it examines how neighbourhood demographics shape patterns of spatial accessibility to public charging infrastructure across the city of Gothenburg. In considering neighbourhood demographics in urban planning processes, municipalities can develop more equitable urban development strategies that facilitate EV adoption outside of prevalent demographics. The findings highlight current disparities in access to public charging opportunities, particularly in segregated neighbourhoods, and stress the need for targeted planning strategies to promote social inclusion in this area. This can contribute to local PEV charging network expansion by providing key insights and points for further research and provides a foundation for discussions on related areas, including the provision of emerging transport initiatives such as public car-sharing points.

Background: The adoption of electric vehicles (EVs) has been largely discussed and investigated by the industry and researchers as a means to support the transition from fossil-fueled-based vehicles to more environmentally sustainable solutions, as the EVs are. However, adoption is not ramping-up as the industry expected even though the technology proved to be ready for the market. The reason for it could vary, nevertheless common factors have been identified. Purpose: This work aims to understand which factors appear as a concern regarding willingness to purchase EVs in Sweden and what strategies original equipment manufacturers (OEMs) should adopt to enhance the widespread EV adoption. Methodology: A questionnaire based on 5 level Likert scale was distributed to the Swedish population to investigate factors that affect EV adoption such as the high cost of the EVs, lack of charging station infrastructure, EVs’ driving experience, EVs’ driving range, removal of governmental subsidies for EV, availability of different EV models, battery life and EVs safety aspects. Responses were also gathered on the perceived effectiveness of OEMs adoption strategies. A multiple linear regression was used to test 8 hypotheses on willingness to purchase EVs. Results and Analysis: Of the 8 factors analyzed, only two, namely driving experience and range were significant to the model generated on willingness to purchase EV, in which 66% agreed or strongly agreed that they are willing to purchase EVs. On the OEM strategies, the study observed that OEMs should invest in strategies to allow the public to experience driving an EV, as well as educate the population about the technology, its benefits, and the total cost of ownership (TCO). Conclusion: The results indicate that in Sweden factors such as driving experience and range are still relevant to influence the willingness to acquire an EV, on the other hand, the cost of EVs, removal of government subsidies, and the other factors previously mentioned do not impact the respondent’s willingness. To boost adoption, OEMs should focus their strategies on refreshed market segmentation, customer education, test drive experience, and marketing. Recommendation for future research: It is recommended that future research should have a higher sample size that is distributed based on Swedish population density to enhance the statistical significance of the analysis. Additionally, more factors affecting EV adoption could be investigated in the future, such as the sustainability of battery material sourcing.