In this master thesis the dynamics of a modern main battle tank's turret traverse and gun elevation have been modelled. The models of dynamic motion have been coupled to two different types of electric motors, namely a direct-current motor and an induction motor. These have been modelled in MATLAB and SIMULINK together with the mechanical systems in the turret traverse and gun elevation. The goal of this project was to develop non-ideal models of the combined mechanical and electrical systems, but the main focus has been the dynamics of the electric motors. / I denna examensavhandling har modeller av elektriska motorer som driver tornet samt elevation av eldröret på en stridsvagn tagits fram. De två motorer som undersökts är en likströmsmotor och en induktionsmotor. Dessa har kopplats till mekaniska system som representerar rotation av stridsvagnens torn och elevation av eldröret. Modelleringen har gjorts i MATLAB och SIMULINK. Målet med denna studie var att ta fram icke-ideala modeller av både de elektriska motorerna och de mekaniska systemen för torn- och eldrörsdrift.

This thesis outlines the design and implementation process of an electrified powertrain for use in an American muscle car. Designed as McMaster University's entrant to the EcoCAR 3 Advanced Vehicle Technology Competition (AVTC), an electrified powertrain was developed to provide a Chevrolet Camaro with the performance expected by the American muscle car market while maintaining ever increasing fuel economy regulations. A background of current trends in vehicle electrification, including the prominent market segments experiencing these trends, will be explored along with the history of the classic and modern American muscle car's technical specifications. Following an investigation into existing vehicle electrification trends, the selected hybrid architecture will be discussed. The process of converting a conventional combustion powertrain into a series-parallel hybrid electric powertrain will be explored from the component-level through to full system design. Following a review of the design process for the powertrain, a high-level testing plan will be proposed using a number of test cells available within the facility. This plan will begin at the component-level exploring specific areas of potential complication and move up to complete system-level testing of powertrain functionality. / Thesis / Master of Applied Science (MASc) / Until recently, hybrid electric vehicles have tended to be available in a fairly limited market segment with few offerings for performance-oriented vehicle customers. The introduction of high performance hybrid vehicles suggests that this trend is likely to change. Increasingly more stringent fuel economy and emissions standards means that performance vehicle segments such as American muscle cars must adopt new technologies to retain their performance characteristics. Hybrid powertrains are one solution to providing and improving on the iconic performance of American muscle while meeting future regulatory changes. The addition of a number of electrified components to a gasoline powertrain can assist in achieving desired performance while reducing fuel economy. This thesis investigates the detailed design process adopted to make these modifications while maintaining the functionality expected by muscle car owners. After the design and assembly of the hybrid muscle car powertrain, a specific testing plan was laid out to ensure that the system is capable of sustaining the expected power output. This design and testing process can help introduce new hybrid vehicles to the market which are capable of meeting both the upcoming fuel economy regulations as well as the ongoing performance expectations of the muscle car market.

The deployment of Automated Vehicles (AV) is starting to become widespread throughout transportation, resulting in the recognition and awareness by legislative leaders of the potential impact on transportation operations. To assist transportation operators in making the needed preparations for these vehicles, an in-depth study regarding the impact of AV and Connected Automated Vehicles (CAV) is needed. In this research, the impact of AV and CAV on the highway setting is studied. This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, such as the Intelligent Driver Model (IDM), the IDM with traffic adaptive driving Strategy (SIDM), the Improved IDM (IIDM), the IIDM with Constant-Acceleration Heuristic (CAH), and the MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) were examined with the state-of-the-art vehicle trajectory data. The Highway Drone dataset (HighD) were analyzed through the implementation of genetic algorithm to gain more insight about the trajectories of these vehicles. In 2020, there is no commercially available gully automated vehicle available to the public, although many companies are conducting in field testing. This research generated AV trajectories based on the actual vehicle trajectories from the High-D dataset and adjusts those trajectories to account for ideal AV operations. The analysis from the fitted trajectory data shows that the calibrated IIDM with CAH provides a best fit on AV behavior. Next, the AV and CAV were modeled in microscopic perspective to show the impact of these vehicles on a corridor. The traffic simulation software, VISSIM, modified by implementing an external driver model to govern the interactions between Legacy Vehicles (LV), AV, and CAV on a basic and merging highway segment as well as a model of the Interstate 95 corridor south of Richmond, Virginia. From the analysis, this research revealed that the AV and CAV could increase highway capacity significantly. Even with a small portion of AV or CAV, the roadway capacity increased. On I-95, CAV performed better than AV because of Cooperative Adaptive Cruise Control (CACC) and platooning due to CAV's ability to coordinate movement through communication; however, in weaving segments, CAV underperformed AV. This result indicates that the CAV algorithms would need to be flexible in order to maintain flow in areas with weaving sections. Lastly, diverse operational conditions, such as different heavy vehicle market penetration and different aggressiveness were examined to support traffic operators transition to the introduction of AV and CAV. Based on the analysis, the study concludes that the different aggressiveness could mitigate congestion in all cases if the proper aggressiveness level is selected considering the current traffic condition. Overall, the dissertation provides guidance to researchers, traffic operators, and lawmakers to model, simulate, and evaluate AV and CAV on highways. / Doctor of Philosophy / The deployment of Automated Vehicles (AV) is starting to become widespread throughout transportation, resulting in the recognition and awareness by legislative leaders of the potential impact on transportation operations. To assist transportation operators in making the needed preparations for these vehicles, an in-depth study regarding the impact of AV and Connected Automated Vehicles (CAV) is needed. In this research, the impact of AV and CAV on the highway setting is studied. This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, such as the Intelligent Driver Model (IDM), the IDM with traffic adaptive driving Strategy (SIDM), the Improved IDM (IIDM), the IIDM with Constant-Acceleration Heuristic (CAH), and the MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) were examined with the state-of-the-art vehicle trajectory data. The Highway Drone dataset (HighD) were analyzed through the implementation of genetic algorithm to gain more insight about the trajectories of these vehicles. In 2020, there is no commercially available gully automated vehicle available to the public, although many companies are conducting in field testing. This research generated AV trajectories based on the actual vehicle trajectories from the High-D dataset and adjusts those trajectories to account for ideal AV operations. The analysis from the fitted trajectory data shows that the calibrated IIDM with CAH provides a best fit on AV behavior. Next, the AV and CAV were modeled in microscopic perspective to show the impact of these vehicles on a corridor. The traffic simulation software, VISSIM, modified by implementing an external driver model to govern the interactions between Legacy Vehicles (LV), AV, and CAV on a basic and merging highway segment as well as a model of the Interstate 95 corridor south of Richmond, Virginia. From the analysis, this research revealed that the AV and CAV could increase highway capacity significantly. Even with a small portion of AV or CAV, the roadway capacity increased. On I-95, CAV performed better than AV because of Cooperative Adaptive Cruise Control (CACC) and platooning due to CAV's ability to coordinate movement through communication; however, in weaving segments, CAV underperformed AV. This result indicates that the CAV algorithms would need to be flexible in order to maintain flow in areas with weaving sections. Lastly, diverse operational conditions, such as different heavy vehicle market penetration and different aggressiveness were examined to support traffic operators transition to the introduction of AV and CAV. Based on the analysis, the study concludes that the different aggressiveness could mitigate congestion in all cases if the proper aggressiveness level is selected considering the current traffic condition. Overall, the dissertation provides guidance to researchers, traffic operators, and lawmakers to model, simulate, and evaluate AV and CAV on highways.

An energy management strategy for a prototype P4 parallel hybrid Chevrolet Blazer is developed for the EcoCAR Mobility Challenge. The objective of the energy management strategy is to reduce energy consumption while maintaining the drive quality targets of a conventional vehicle. A comprehensive model of the hybrid powertrain and vehicle physics is constructed to aid in the development of the control strategy. To improve fuel efficiency, a Willans line model is developed for the conventional powertrain and used to develop a rule-based torque split strategy. The strategy maximizes high efficiency engine operation while reducing round trip losses. Calibratable parameters for the torque split operating regions allow for battery state of charge management. Torque request and filtering algorithms are also developed to ensure the hybrid powertrain can smoothly and reliably meet driver demand. Vehicle testing validates that the hybrid powertrain meets acceleration response targets while delivering an enjoyable driving experience. Simulation testing shows that the energy management strategy improved fuel economy in most drive cycles with improvements of 8.8% for US06, 9.8% for HWFET, and 0.1% for the EcoCAR Mobility Challenge Cycle. Battery state of charge management behavior is robust across a variety of drive cycles using inputs from both simulated and test drivers. The resulting energy management strategy delivers an efficient, responsive, and reliable hybrid electric vehicle. / Master of Science / A control strategy for a hybrid vehicle is developed to improve fuel efficiency without sacrificing vehicle responsiveness. Efficiency improvements are achieved by the strategy intelligently selecting to use the engine, motor, or a combination of the two to minimize fuel consumption. The strategy also handles the important tasks of maintaining the battery pack charge and smoothly transitioning between the engine and motor power. All together, this results in a hybrid vehicle with both improved fuel economy and an enjoyable driving experience.

While driving behavior is generally governed by the nature and the driving objectives of the driver, there are many situations (typically in crowded traffic conditions) where tacit communication between vehicle drivers and pedestrians govern driving behavior, significantly influencing transportation safety. The study aimed to formalize the tacit communication between vehicle drivers and pedestrians, in order to inform an investigation on effective communication mechanisms between autonomous vehicle and humans. Current autonomous vehicles engage in decision making primarily controlled by on-board or external sensory information, and do not explicitly consider communication with pedestrians. The study was a within subject 2x2x2 factorial experimental design. The three independent variables were driving context (normal driving vs. autonomous vehicle placard), driving route (1 vs. 2), and narration (yes vs. no). The primary outcome variable was driver-yield behavior. Each of the ten drivers completed the factorial design, requiring eight total drives. Data were collected using a data acquisition system (DAS) designed and installed on the experimental vehicle by the Virginia Tech Transportation Institute. The DAS collected video, audio, and kinematic data. Videos were coded using a proprietary software program, Hawkeye, based on an a priori data directory. Recommendations for future autonomous vehicle research and programming are provided. / Ph. D. / To improve traffic safety and efficiency, the current study examined factors of pedestrian-vehicle interactions. Driving is a dangerous endeavor for all parties, however, pedestrians are an especially vulnerable group. Many different solutions have been suggested including; education and training of road users, high visibility law enforcement, infrastructure changes, and vehicle solutions. Of all proposed, the vehicle solution, autonomous vehicles, shows great promise in improving traffic safety. Autonomous vehicles provide an opportunity for a high degree of safety, yet, inefficiencies exist. For instance, a vehicle might stop at all crosswalks regardless of pedestrian proximity. To this end, the current study was a scientific exploration of the factors relating to pedestrian-vehicle interactions. The exploratory nature of this work provided an opportunity to provide recommendations for programming of autonomous vehicles to balance safety and efficiency.

<p dir="ltr">E-commerce, fueled by the digital revolution, has become a cornerstone of modern retail, driving demand for efficient last-mile logistics services. As online sales soar past $4 trillion, the need for streamlined, cost-effective delivery solutions is urgent, particularly in markets like China, where complex traffic conditions and high customer expectations complicate last-mile delivery. Autonomous driving technology offers a promising approach to meeting these challenges, enabling lower costs and improved delivery efficiency. However, cities such as Suzhou present unique obstacles for autonomous delivery vehicles (ADVs), with unpredictable traffic and diverse obstacles like pedestrians and bicycles. To tackle these issues, this research developed a high-capacity simulation platform capable of executing 300,000 scenarios weekly. It incorporates advanced routing algorithms, such as the Shortest Path Faster Algorithm (SPFA), and high-definition mapping (HDMap) for precise localization, supporting rigorous testing across varied urban logistics scenarios. The platform’s modular microservices architecture ensures scalability, enabling thorough validation of both software and hardware components in unmanned logistics vehicles. Findings demonstrate that the platform’s architecture, particularly its modular microservices and Protocol Buffers for data handling, optimizes the reliability and safety of autonomous systems in dense urban environments. Realistic scenario generation through SPFA routing and HDMap integration provides a robust environment for decision-making tests, contributing to enhanced operational stability and efficiency.</p><p dir="ltr">Practical Implications extend beyond autonomous driving, suggesting relevance to intelligent transportation systems, delivery drones, and smart cities. The platform’s high-throughput capacity underscores the importance of large-scale testing, enabling rapid development cycles with minimal dependence on real-world testing. This research provides a foundation for future improvements in simulation efficiency, scenario diversity, and applications across various sectors, paving the way for further advancements in autonomous technology.</p>

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