Traction inverter, as a critical component in electrified transportation, has been the subject of many research studies in terms of topologies, modulation, and control schemes. Recently, some of the well-known electric vehicle manufacturers have utilized higher-voltage batteries to benefit from lower current, higher power density, and faster charging times. With the ongoing trend toward higher voltage DC-link in electric vehicles, some multilevel structures have been investigated as a feasible and efficient option for replacing the two-level inverters. Higher efficiency, higher power density, better waveform quality, and inherent fault-tolerance are the foremost advantages of multilevel inverters which make them an attractive solution for this application. The first contribution of this thesis is to investigate and present a comprehensive review of the multilevel structures in traction applications. Secondly, this thesis proposes an electro-thermal model based on foster equivalent thermal networks for a designed three-level active neutral point clamped (ANPC) inverter, as well as a modified sinusoidal pulse-width modulation (SPWM) -based technique. This electro-thermal model and the modulation technique enable temperature estimation in the inverter and minimization of the hotspot temperature and hence, increase the power density. Based on the experimental results derived from the implemented setup, a 12% increase in power density is achieved with the proposed technique. The other contribution is a reduced-complexity model-predictive controller (MPC) for the three-level ANPC inverter without weighting factors in which the number of calculations has dropped from 27 to 12 in each sampling period. The improvements to the structure and control system of the inverter are supported by theoretical analysis, simulation results, and experimental tests. A three-level inverter is implemented for 800 V, 70 kW operation and tested. 750 V Silicon Carbide (SiC) switches are utilized in the inverter structure. Finally, future trends and suggestions for the following studies are stated in this thesis. / Thesis / Doctor of Philosophy (PhD)

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There has been an increased necessity to search for alternative transportation methods, mainly driven by limited fuel availability and the negative impacts of climate change and exhaust emissions. These factors have lead to increased regulations and a societal shift towards a cleaner and more e cient transportation system. Automotive and technology companies need to be looking for ways to reshape mobility, enhance safety, increase accessibility, and eliminate the ine ciencies of the current transportation system in order to address such a shift. Hybrid vehicles are a popular solution that address many of these goals. In order to fully realize the bene ts of hybrid vehicle technology, the power distribution decision needs to be optimized. In the past, global optimization techniques have been dismissed because they require knowledge of the journey of the vehicle in advance, and are generally computationally extensive. Recent advancements in technologies, like sensors, cameras, lidar, GPS, Internet of Things, and computing processors, have changed the basic assumptions that were made during the vehicle design process. In particular, it is becoming increasingly possible to know future driving conditions. In addition to this, autonomous vehicle technology is addressing many safety and e ciency concerns. This thesis considers and integrates recent technologies when de ning a new approach to hybrid vehicle supervisory controller design and optimization. The dynamic programming algorithm has been systematically applied to an autonomous vehicle with a power-split hybrid electric powertrain. First, a more realistic driving cycle, the Journey Mapping cycle, is introduced to test the performance of the proposed control strategy under more appropriate conditions. Techniques such as vectorization and partitioning are applied to improve the computational e ciency of the dynamic programming algorithm, as it is applied to the hybrid vehicle energy management problem. The dynamic programming control algorithm is benchmarked against rule-based algorithms to substantively measure its bene ts. It is proven that the DP solution improves vehicle performance by at least 9 to 17% when simulated over standard drive cycles. In addition, the dynamic programming solution improves vehicle performance by at least 32 to 39% when simulated over more realistic conditions. The results emphasize the bene ts of optimal hybrid supervisory control and the need to design and test vehicles over realistic driving conditions. Finally, the dynamic programming solution is applied to the process of adaptive control calibration. The particle swarm optimization algorithm is used to calibrate control variables to match an existing controller's operation to the dynamic programming solution. / Thesis / Master of Applied Science (MASc)

no / The research presented in this paper addresses the challenge of developing functional models for complex systems that have multiple modes of operation or use cases. An industrial case study of an electric vehicle is used to illustrate the proposed methodology, which is based on a systematic modelling of functions through nested systems using the system state flow diagram (SSFD) method. The paper discusses the use of SSFD parameter based state definition to identify physical and logical conditions for joining function models, and the use of heuristics to construct complex function models.

EcoRouting refers to the determination of a route that minimizes vehicle energy consumption compared to traditional routing methods, which usually attempt to minimize travel time. EcoRoutes typically increase travel time and in some cases this increase is constrained for a viable route. While significant research on EcoRouting exists for conventional vehicles, incorporating the novel aspects of plug-in hybrids opens new areas to be explored. A prototype EcoRouting system has been developed on the MATLAB platform that takes in map information and converts it to a graph of nodes containing route information such as speed and grade. Various routes between the origin and destination of the vehicle are selected and the total energy consumption and travel time for each route are estimated using a vehicle model. The route with the minimum energy consumption will be selected as the EcoRoute unless there is a significant difference between the minimum time route and the EcoRoute. In this case, selecting a sub-optimal route as the EcoRoute will increase the probability that the driver uses a lower fuel consumption route. EcoRouting has the potential to increase the fuel efficiency for powertrains designed mainly for performance, and we examine the sensitivity of the increased efficiency to various vehicle and terrain features. The reduction in energy consumption can be achieved independent of powertrain modifications and can be scaled using publicly available parameters. / Master of Science

Due to the rapid growth of urban areas, sustainable urbanization is an inevitable task for city planners to address major challenges in resource management across different sectors. Sustainable approaches of energy production, distribution, and consumption must take the place of traditional methods to reduce the negative impacts of urbanization such as global warming and fast consumption of fossil fuels. In order to enable the transition of cities to sustainable ones, we need to have a precise understanding of the city dynamics. The prevalence of big data has highlighted the importance of data-driven analysis on different parts of the city including human movement, physical infrastructure, and economic activities. Sustainable urban mobility (SUM) is the problem domain that addresses the sustainability issues in urban areas with respect to city dynamics and people movements in the city. Hence, to realize an integrated solution for SUM, we need to study the problems that lie at the intersection of energy systems and mobility. For instance, electric vehicle invention is a promising shift toward smart cities, however, the impact of high adoption of electric vehicles on different units such as electricity grid should be precisely addressed. In this dissertation, we use data analytics methods in order to tackle major issues in SUM. We focus on mobility and energy issues of SUM by characterizing transportation networks and energy networks. Data-driven methods are proposed to characterize the energy systems as well as the city dynamics. Moreover, we propose anomaly detection algorithms for control and management purposes in smart grids and in cities. In terms of applications, we specifically investigate the use of electrical vehicles for personal use and also for public transportation (i.e. electric taxis). We provide a data-driven framework to propose optimal locations for charging and storage installation for electric vehicles. Furthermore, adoption of electric taxi fleet in dense urban areas is investigated using multiple data sources. / Ph. D.

EPA testing methods for calculation of fuel economy label ratings, which were revised beginning in 2008, use equations that weight the contributions of fuel consumption results from multiple dynamometer tests to synthesize city and highway estimates that reflect average U.S. driving patterns. The equations incorporate effects with varying weightings into the final fuel consumption, which are explained in this thesis paper, including illustrations from testing. Some of the test results used in the computation come from individual phases within the certification driving cycles. This methodology causes additional complexities for hybrid electric vehicles, because although they are required to have charge-balanced batteries over the course of a full drive cycle, they may have net charge or discharge within the individual phases. The fundamentals of studying battery charge-balance are discussed in this paper, followed by a detailed investigation of the implications of per-phase charge correction that was undertaken through testing of a 2010 Toyota Prius at Argonne National Laboratory's vehicle dynamometer test facility. Using the charge-correction curves obtained through testing shows that phase fuel economy can be significantly skewed by natural charge imbalance, although the end effect on the fuel economy label is not as large. Finally, the characteristics of the current 5-cycle fuel economy testing method are compared to previous methods through a vehicle simulation study which shows that the magnitude of impact from mass and aerodynamic parameters vary between labeling methods and vehicle types. / Master of Science

This thesis develops and implements a testbed for the evaluation of inverter fed motor drives used in electric vehicles. The testbed consists of a computer-controlled dynamometer connected to power analysis and data collection tools. The programming and operation and of the testbed is covered. Then it is used to evaluate three pairs of identical rating inverters. The goal is to analyze the effect of topology and software improvements on motor drive efficiency. The first test analyzes the effect of a soft-switching circuit on inverter and motor efficiency. The second test analyzes the difference between space vector modulation (SVM) and current-band hysteresis. The final test evaluates the effect of both soft-switching and SVM on drive performance. The tests begin with a steady state analysis of efficiency over a wide range of torque and speed. Then drive cycles tests are used to simulate both city and highway driving. Together, these dynamic and steady state test results provide a realistic assessment of electric vehicle drive performance. / Master of Science

This dissertation presents two essays that explore the antecedents of consumer decision-making when choices or behaviors have significant environmental consequences. The first essay involves theoretical development and experimental testing of a conceptual model describing the process by which a car-buyer evaluates the choice between an electric vehicle (EV) and a gasoline vehicle, while the second essay consists of empirical analysis of a large panel dataset of household-level 15-minute interval electricity consumption data to identify the drivers of different behavioral response patterns to electric utility requests for energy conservation on hot summer afternoons. The first essay is motivated by the observation that increased consumer adoption of battery-powered EVs is important for commercial and environmental reasons, but EV adoption is currently inhibited by both an up-front price disadvantage and the inconveniences associated with battery recharging. The research presented in the first essay leverages the Theory of Reasoned Action as well as the literature on identity signaling to develop a model on how consumers with interests in the environmental and/or technological implications of EV ownership evaluate the potential purchase of an EV versus a conventional automobile. The model generates ten pairs of hypotheses that are tested via estimation of a structural equation model using data from three online experiments. Bayesian pooling of the three sets of estimated path analysis coefficients finds considerable support for the conceptual model. These pooled results show that EV ownership signals the owner's concern about both environmental protection and technology advancement, but the effect of the environmental signal on EV purchase likelihood is positive whereas the effect of the technology signal on EV purchase likelihood is negative. Moreover, in addition to lowering EV purchase likelihood via a direct effect, the perceived inconveniences associated with EV ownership (e.g., needs for battery charging) offset the negative effect of technology signaling on EV purchase likelihood, while the corresponding interaction of inconvenience with environmental signaling value was found to be not significant. Meanwhile, a larger EV price premium had a direct negative effect on EV purchase likelihood but did not moderate the effects of either technology signaling value or environmental signaling value on EV purchase likelihood. Among other findings, specific knowledge about how EVs affect technological advancement has a direct positive influence on EV purchase likelihood. However, all downstream effects of specific knowledge about EVs effects on environmental protection are mediated by perceptions of EV effectiveness in benefitting the environment. Meanwhile, the second essay investigates consumer behavior concerning household electricity consumption. Utilities use demand response (DR) programs to induce customers to reduce electricity consumption during selected hot summer afternoons when power generation supplies may be challenged to satisfy regional demand levels. The research presented in the second essay leverages panel data on electricity consumption from households in a community where an experimental pro-social DR program was conducted to explore drivers of household responses to utility requests to voluntarily reduce electricity consumption. Analysis of the panel data shows that, on average, households with solar rooftops respond differently to utility DR notifications than non-solar households: solar households reduce electricity consumption as requested by the utility, whereas non-solar households receiving the same request actually increase electricity consumption. However, although solar households respond favorably to DR notification, they also consume significantly more electricity than non-solar households during most hours. These empirical results – greater responsiveness to DR notifications, but otherwise higher levels of electricity consumption – beg reconciliation and explanation. An experimental research study is proposed for a future examination of alternative psychological explanations for the observed differences in behavioral responses between solar and non-solar households. / Doctor of Philosophy / This dissertation presents two essays that explore how and why individuals make decisions with environmental consequences. The first essay investigates how and why individuals choose to purchase a higher-cost big-ticket durable good (i.e., an electric vehicle) that results in substantially lower air emissions over the lifetime of the product, while the second essay investigates how and why individuals make environmentally-friendly behavioral decisions (i.e., conserving electricity on a hot summer afternoon) when the stakes are modest and transitory. The first essay discusses the findings from three experiments in which on-line survey respondents were asked to imagine being in the market to buy a new car and then indicate how likely they would buy an electric vehicle (EV) rather than an otherwise identical gasoline automobile. Before indicating EV purchase likelihood, participants were informed to assume different levels of price premium and inconvenience (e.g., associated with battery recharging) resulting from EV ownership. Participants were also asked a series of questions to measure their attitudes about environmental protection and technology advancement, as well as the ability of EVs to help both of those dimensions of social progress. Of particular interest, participants were asked how much driving an EV sends a public signal of the owner's commitment to environmental and technology improvement. Among other findings, statistical analysis of the data collected from these experiments indicates that EV ownership sends a strong signal of the owner's commitment to both environmental protection and technology advancement. However, the environmental signal of EV ownership positively influences EV purchase likelihood, whereas the technology signal of EV ownership negatively influences EV purchase likelihood. Of further interest, this negative relationship between technology signaling value and EV purchase likelihood is offset by the perceived inconveniences associated with EV ownership (i.e., battery charging), such that the negative effect of technology signaling on EV purchase likelihood can be overcome if the prospective EV buyer also believes EV ownership is highly burdensome. The second essay presents the findings from analysis of 15-minute interval electricity consumption data during the summer of 2021 from 307 households in a master-planned community that was the site of an experimental utility demand response (DR) program. In this community, at 2 pm on seven particularly hot weekday afternoons that summer, the local electric utility issued a DR text message to a randomly-selected subset of households, asking them to conserve energy between 4 and 8 pm in order to help alleviate tight supplies of power generation. Any difference in average electricity consumption patterns between households that were asked to reduce electricity consumption (i.e., "treatment" households) and those that were not asked (i.e., "control" households) can be considered a "DR effect": a change in behavior induced by the utility's request to reduce electricity consumption. While initial analysis of the electricity consumption data revealed no DR effects, subsequent identification and segmentation of solar households (I.e., households with rooftop solar electricity production systems) from non-solar households enabled discovery of statistically-significant DR effects for both solar and non-solar households. Of particular interest, while solar households responded to the utility's DR text message in the intended manner by reducing electricity consumption, non-solar households responded by increasing electricity consumption instead. Experimental research is planned to investigate why solar households and non-solar households respond so differently to the same message from the utility.