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 thesis is divided into two parts. The first evaluates the role of the built environment in life-cycle energy consumption, by comparing different neighborhood and city styles. Through a holistic modeling and accounting framework, this work identifies the largest energy-consuming sectors, among residential and commercial buildings, personal vehicles and transit trips, and supporting infrastructure (roads, sidewalks, parking lots, water pipes, street lighting). Life-cycle energy calculations include operational energy use (e.g., gasoline for vehicles, electricity and natural gas for buildings) and embodied energy used to produce materials and construct buildings and infrastructure. Case study neighborhoods in Austin, Texas, and larger-scale regional models suggest that building energy demands comprise around 50% of life-cycle energy demands, while transportation demands (from driving and infrastructure alike) contribute around 40%, across all cases. However, results also suggest that population density and average residential unit size play a major role in defining per-capita energy consumption. Operational demands made up about 90% of life-cycle energy demands, suggesting that v most urban energy savings can be obtained from reduced personal vehicle trips and more efficient vehicles and buildings. Case study comparisons suggest that neighborhoods and regions with greater density and higher share of multi-family housing units tend to reduce operational (and thus life-cycle) energy demands with less travel demand and decreased home and work energy use, per capita. The second part of this modeled plug-in electric vehicle (PEV) emissions impacts in Texas, by considering four possible vehicle adoption scenarios (where PEVs make up 1, 5, 10, and 25% of total passenger vehicles). The analysis anticipates PEV electricity demand and emissions rates, based on current Texas power grid data. Results indicate that PEV emissions depend significantly on which specific power plants are used to power the vehicles, but that PEVs' average per-mile emissions rates for NO[subscript x], PM, and CO₂ are all likely to be lower than today's average passenger car, when today's average mix is used. Power produced from 100% coal plants could produce 14 times as much NO[subscript x], 3,200 times as much SO₂, nearly 10 times as much CO₂ and CO₂eq, 2.5 times as much PM₁₀, and VOCs, and nearly 80 times the NO₂ compared to a grid with 100% natural gas plants. / text