This dissertation presents the systematic analysis and the critical assessment of the AC side soft-switching inverters in electric vehicle (EV) applications. Although numerous soft-switching inverter techniques were claimed to improve the inverter performance, compared with the conventional hard-switching inverter, there is the lack of comprehensive investigations of analyzing and evaluating the performance of soft-switching inverters. Starting with an efficiency comparison of a variety of the soft-switching inverters using analytical calculation, the dissertation first reveals the effects of the auxiliary circuit's operation and control on the loss reduction. Three types of soft-switching inverters realizing the zero-voltage-transition (ZVT) or zero-current-transition (ZCT) operation are identified to achieve high efficiency operation. Then one hard-switching inverter and the chosen soft-switching inverters are designed and implemented with the 55 kW power rating for the small duty EV application. The experimental evaluations on the dynamometer provide the accurate description of the performance of the soft-switching inverters in terms of the loss reductions, the electromagnetic interference (EMI) noise, the total harmonic distortion (THD) and the control complexity. An analysis of the harmonic distortion caused by short pulses is presented and a space vector modulation scheme is proposed to alleviate the effect. To effectively analyze the soft-switching inverters' performance, a simulation based electrical modeling methodology is developed. Not only it extends the EMI noise analysis to the higher frequency region, but also predicts the stress and the switching losses accurately. Three major modeling tasks are accomplished. First, to address the issues of complicated existing scheme, a new parameter extraction scheme is proposed to establish the physics-based IGBT model. Second, the impedance based measurement method is developed to derive the internal parasitic parameters of the half-bridge modules. Third, the finite element analysis software is used to develop the model for the laminated bus bar including the coupling effects of different phases. Experimental results from the single-leg operation and the three-phase inverter operation verify the effectiveness of the presented systematic electrical modeling approach. With the analytical tools verified by the testing results, the performance analysis is further extended to different power ratings and different bus voltage designs. / Ph. D.

Automotive manufacturers are facing increasing pressure from legislative bodies and consumers to reduce fuel consumption and greenhouse gas emissions of vehicles. This has led to many automotive manufacturers starting production of Plug-in Hybrid Electric Vehicles (PHEV's) and Battery Electric Vehicles (BEV's). Another method that helps to reduce the environmental effect of transportation is EcoRouting. The standard Global Positioning System (GPS) navigation offers route alternatives between user specified origin and destination. This technology provides multiple routes to the user and focuses on reducing the travel time to reach to the destination. EcoRouting is the method to determine a route that minimizes vehicle energy consumption, unlike traditional routing methods that minimize travel time. An EcoRouting system has been developed as a part of this thesis that takes in information such as speed limits, the number of stop lights, and the road grade to calculate the energy consumption of a vehicle along a route. A synthesis methodology is introduced that takes into consideration the distance between the origin and destination, the acceleration rate of the vehicle, cruise speed and jerk rate as inputs to simulate driver behavior on a given route. A new approach is presented in this thesis that weighs the energy consumption for different routes and chooses the route with the least energy consumption, subject to a constraint on travel time. A cost function for quantifying the effect of travel time is introduced that assists in choosing the EcoRoute with an acceptable limit on the travel time required to reach the destination. The analysis of the EcoRouting system with minimum number of conditional stops and maximum number of conditional stops is done in this thesis. The effect on energy consumption with the presence and absence of road-grade information along a route is also studied. A sensitivity study is performed to observe the change in energy consumption of the vehicle with a change in acceleration rates and road grade. Three routing scenarios are presented in this thesis to demonstrate the functionality of EcoRouting. The EcoRouting model presented in this thesis is also validated against an external EcoRouting research paper and the energy consumption along three routes is calculated. The EcoRoute solution is found to vary with the information given to the variable acceleration rate model. The synthesis and the results that are obtained show that parameters such as acceleration, deceleration, and road grade affect the overall energy consumption of a vehicle and are helpful in determining the EcoRoute. / Master of Science

Manufacturers of passenger vehicles are experiencing increased pressure from consumers and legislators due to the impact of transportation on the environment. Automotive manufacturers are responding by designing more sustainable forms of transportation through a variety of efforts, including increased vehicle efficiency and the electrification of vehicle powertrains (plug in hybrid electric vehicles (PHEV) and battery electric vehicles (BEV)). An additional method for reducing the environmental impact of personal transport is eco-routing, a methodology which selects routes on the basis of energy consumption. Standard navigation systems offer route alternatives between a user clarified origin and destination when there are multiple paths available. These alternatives are commonly weighted on the basis of minimizing either total travel time (TTT) or trip distance. Eco-routing offers an alternative criterion – minimizing route energy consumption. Calculation of the energy consumption of a route necessitates the creation of a velocity profile which models how the route will be driven and a powertrain model which relates energy consumption to the constructed velocity profile. Existing research efforts related to both of these aspects typically require complex analysis and proprietary vehicle properties. A new approach to weighting the energy consumption of different routes is presented within this paper. The process of synthesizing velocity profiles is an improvement upon simpler models while requiring fewer variables as compared to more complex models. A single input, the maximum acceleration, is required to tune driver aggressiveness throughout an entire route. Additionally, powertrain results are simplified through the application of a new parameter, predictive terminal energy. The parameter uses only glider properties as inputs, as compared to dedicated powertrain models which use proprietary vehicle information as inputs which are not readily available from manufacturers. Application of this research reduces computation time and increases the number of vehicles for which this analysis can be applied. An example routing scenario is presented, demonstrating the capability of the velocity synthesis and predictive terminal energy methodologies. / Master of Science

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.

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

With the advent of smart sensor devices and Internet of Things (IoT) in the rapid urbanizing cities, data is being generated, collected and analyzed to solve urban problems in the areas of transportation, epidemiology, emergency management, economics, and sustainability etc. The work in this area basically involves analyzing one or more types of data to identify and characterize their impact on other urban phenomena like traffic speed and ride-sharing, spread of diseases, emergency evacuation, share market and electricity demand etc. In this work, we perform spatio-temporal analysis of various urban datasets collected from different urban application areas. We start with presenting a framework for predicting traffic demand around a location of interest and explain how it can be used to analyze other urban activities. We use a similar method to characterize and analyze spatio-temporal criminal activity in an urban city. At the end, we analyze the impact of nearby traffic volume on the electric vehicle charging demand at a charging station. / 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. / Master of Science

The shift towards electric transportation on a global scale is being primarily driven by regulatory requirements and market demand. The impact of the COVID-19 pandemic on air pollution, energy demand, and CO2 emissions has further accelerated this transition. This transformation necessitates the development of efficient electric propulsion systems, particularly for commercial vehicles. These systems not only have a positive environmental impact but also offer significant financial advantages to fleet owners due to lower overall costs. One of the major challenges in this transition is the design and calibration of regenerative braking strategies, especially for commercial vehicles that exhibit significant variations in weight. This weight difference between curb and gross vehicle weight is a common scenario in the commercial vehicle sector. This thesis introduces the Adaptive One-Pedal Drive (A-OPD) strategy, which is specifically tailored for electric commercial vehicles with varying weight profiles and lacking advanced drive-by-wire braking systems. The thesis focuses on the development and accurate assessment of a model-centric approach for electrified propulsion systems. This approach establishes a strong correlation between the model and physical data, demonstrating its reliability in estimating critical variables such as battery state-of-charge, battery terminal voltage, system high-voltage DC, and wheel torque, even under diverse driving conditions. This model-centric approach serves as a valuable tool for optimizing design and conducting tradeoff analyses, enabling efficient evaluation of energy efficiency and drivability. Selecting the most suitable electrified propulsion system architecture is a crucial decision. The thesis categorizes electrified propulsion system architectures based on their impact on vehicle performance, energy consumption, and total cost of ownership. This selection process involves a multidisciplinary approach that takes into account both technical and business requirements. The central research focus of this thesis centers on regenerative braking systems. It compares series and parallel configurations, traditional one-pedal-drive (OPD), and introduces an innovative Adaptive One-Pedal Drive (A-OPD). The A-OPD relies on vehicle running mass identification using the Recursive Least Square Filter (RLS) and weight classification. This A-OPD strategy significantly enhances energy efficiency in urban traffic scenarios, even when vehicles are partially loaded. It outperforms parallel regenerative braking systems by up to 50% while maintaining performance levels similar to the series regenerative braking strategy. This innovation represents a significant leap in energy efficiency for electric commercial vehicles without the need for complex electronic braking systems. In summary, this thesis advances our understanding of optimizing the performance of electric commercial vehicles. The A-OPD strategy proves to be a practical and valuable tool for enhancing energy efficiency, particularly in dense urban traffic, and it outperforms parallel regenerative braking systems. Utilizing model-in-the-loop and driver-in-the-loop simulations, this thesis offers a comprehensive framework for designing efficient electrified propulsion system architectures. / Thesis / Doctor of Philosophy (PhD)