The rise in overall powertrain complexity and the stringent performance requirements of a hybrid electric vehicle (HEV) have elevated the role of its powertrain control strategy to considerable importance. Iterative modeling and simulation form an integral part of the control strategy design process and industry engineers rely on proprietary ?legacy? models to rapidly develop and implement control strategies. However, others must initiate new algorithms and models in order to develop production-capable control systems. This thesis demonstrates the development and validation of a charge-sustaining control algorithm for a through-the-road (TTR) parallel hybrid (diesel-electric) powertrain. Some unique approaches used in powertrain-level control of other commercial and prototype vehicles have been adopted to incrementally develop this control strategy. The real-time performance of the control strategy has been analyzed through on-road and chassis dynamometer tests over several standard drive cycles. Substantial quantitative improvements in the overall HEV performance over the stock configuration, including better acceleration and fuel-economy have been achieved.

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The main objective in optimizing train control is to eliminate the waste associated with classical design where train separation is determined through the use of "worst case" assumptions to calculate Safe Braking Distances that are invariant to the system. In fact, the worst case approach has been in place since the beginning of train control systems. Worst case takes the most conservative approach to the determination of train stopping distance, which is the basis for design and capacity of all train control systems. This leads to stopping distances that could be far more than actually required under the circumstances at the time the train is attempting to brake. A new train control system is proposed that utilizes information about the train and the conditions ahead to optimize and minimize the Safe Braking Distance. Two methods are proposed to reduce safe braking distance while maintaining an appropriate level of safety for the system. The first introduces a statistical method that quantifies a braking distance with various hazards levels and picks a level that meets the safety criteria of the system. The second method uses train mounted sensors to determine the adhesion level of the wheel and rail to determine the appropriate braking rate for the train under known circumstances. Combining these methods provides significant decreases in Safe Braking Distances for trains. A new train control system is utilized to take advantage of these features to increase overall system capacity. / Electrical and Computer Engineering

no / Regenerative braking would extend the working range of an EV or HV provided that any extra energy consumption from increased vehicle mass and system losses did not outweigh the saving from energy recuperation, also reduce duty levels on the brakes themselves, giving advantages including extended brake rotor and friction material life, but more importantly reduced brake mass, minimise brake pad wear. The objective of this research is to define thermal performance on lightweight disc brake models. Thermal performance was a key factor which was studied using the 3D model in FEA simulations. Ultimately a design method for lightweight brakes suitable for use on any car-sized hybrid vehicle was used from previous analysis. The design requirement, including reducing the thickness, would affect the temperature distribution and increase stress at the critical area. Based on the relationship obtained between rotor weight, thickness, undercut effect and offset between hat and friction ring, criteria have been established for designing lightweight brake discs in a vehicle with regenerative braking.

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)

Tires are the most influential component of the vehicle as they constitute the only contact between the vehicle and the road and have to generate and transmit forces necessary for the driver to control the vehicle. The demand for the tire models are increasing due to the need to study the variations of force generation mechanisms due to various variables such as load, pressure, speed, and road surface irregularities. Another need from the vehicle manufactures is the study of potential incompatibilities associated with safety systems such as Anti-lock Braking System (ABS) and Electronic Stability Control (ESC) and tires. For vehicle dynamic simulations pertaining to the design of safety systems such as ABS, ESC and ride controllers, an accurate and computationally efficient tire model is required. As these control algorithms become more advanced, they require accurate and extended validity in the range of frequencies required to cover dynamic response due to short wavelength road disturbances, braking and steering torque variations. Major thrust has been provided by the tire industry to develop simulation models that accurately predict the dynamic response of tires without the use of computationally intensive tools such as FEA. The objectives of this research are • To develop, implement and validate a rigid ring tire model and a simulation tool to assist both tire designers and the automotive industry in analyzing the effects of tire belt vibrations, road disturbances, and high frequency brake and steering torque variations on the handling, braking, and ride performances of the vehicle. • To further enhance the tire model by considering dynamic stiffness changes and temperature dependent friction properties. • To develop, and implement novel control algorithms for braking, stability, and ride performance improvements of the vehicle / Ph. D.