UWSpace

UWSpace is the University of Waterloo’s institutional repository for the free, secure, and long-term home of research produced by faculty, students, and staff.

Depositing Theses/Dissertations or Research to UWSpace

Are you a Graduate Student depositing your thesis to UWSpace? See our Thesis Deposit Help and UWSpace Thesis FAQ pages to learn more.

Are you a Faculty or Staff member depositing research to UWSpace? See our Waterloo Research Deposit Help and Self-Archiving pages to learn more.

Photo by Waterloo staff

Communities in UWSpace

Select a community to browse its collections.

Recent Submissions

  • Thumbnail Image
    Item type: Item ,
    Aluminum-Air Batteries Across Scales: A Multiscale Framework for Electrochemical Characterization, Materials Optimization, and Electric Vehicle Integration
    (University of Waterloo, 2026-06-10) Shabeer, Yasmin
    Aluminum-air (Al-air) batteries have emerged as a promising energy storage technology due to their exceptionally high theoretical energy density, material abundance, and potential for low-cost deployment. However, their practical implementation remains constrained by challenges related to electrochemical performance, parasitic corrosion, electrolyte management, and system-level integration. This thesis presents a comprehensive multiscale investigation of Al-air batteries, integrating techno-economic analysis, experimental characterization, data-driven modeling, and system-level simulation to evaluate their viability as advanced energy storage systems and range extenders in electric vehicles (EVs). The study begins with a techno-economic assessment of metal-air batteries in EV applications. A comparative framework is developed to evaluate the performance of Al-air systems relative to conventional lithium-ion batteries, incorporating key metrics such as gravimetric energy density, vehicle energy consumption, and cost. The analysis shows that Al-air batteries, with practical energy densities of approximately 700-900 Wh kg⁻¹, significantly outperform Li-ion systems (150-250 Wh kg⁻¹), offering strong potential for range extension. Simulated vehicle scenarios indicate that Al-air integration can extend driving range by a factor of 2-5, depending on system configuration and operating conditions. However, these benefits are offset by trade-offs related to system complexity, auxiliary components, and cost, highlighting the need for integrated evaluation frameworks. Experimental investigations are conducted to examine the electrochemical performance of Al-air batteries under varying electrolyte compositions and operating conditions. Using a novel galvanic generator-type Al-air system with a rotating electrode configuration, multiple prototype units provided by AlumaPower were evaluated. The rotating electrode design enhances mass transport, reduces passivation, and promotes uniform anodic dissolution, enabling improved discharge stability compared to conventional static systems. Systematic experiments reveal the alkaline electrolytes, particularly in the range of 6-8 M concentration, provide optimal performance by balancing ionic conductivity and electrochemical kinetics. Peak power densities exceeding 500 mW cm⁻² are achieved under controlled conditions, while discharge tests at moderate current densities (~80-100 mA cm⁻²) exhibit stable voltage profiles in the range of 1.0-1.2 V. The results further demonstrate that increasing electrolyte concentration beyond optimal levels accelerates parasitic corrosion and hydrogen evolution, leading to reduced efficiency and highlighting the importance of electrolyte optimization. To address the critical challenge of aluminum corrosion, a data-driven predictive modeling framework is developed. Artificial neural networks (ANNs) are trained on experimental datasets to model the relationship between electrolyte composition, temperature, and electrochemical variables with corrosion metrics such as corrosion potential (Ecorr) and corrosion current density (Icorr). The ANN models achieve high predictive accuracy, with coefficient of determination (R²) values exceeding 0.99, demonstrating their capability to capture complex nonlinear relationships in electrochemical systems. To further enhance system performance, genetic algorithms (GA) and multi-objective optimization (NSGA-II) are integrated with the ANN framework to identify optimal operating conditions. The optimization results reveal trade-offs between maximizing Ecorr and minimizing Icorr, enabling the identification of optimal electrolyte conditions that balance performance and degradation. This integrated modeling approach represents a significant advancement over conventional empirical methods by enabling predictive and systematic optimization of corrosion behavior. At the system level, the thesis develops a comprehensive modeling framework for integrating Al-air batteries within EV architectures. Using MATLAB and Simulink, a dual-energy storage system is implemented in which Al-air batteries function as range extenders for lithium-ion battery packs. The system incorporates experimentally informed battery models and employs state-of-charge (SOC)-based control strategies to manage power flow between energy sources. Simulations conducted under standard driving cycles, including UDDS, WLTP, and HWFET, demonstrate that Al-air integration can significantly mitigate SOC depletion and extend vehicle range, particularly in reduced-capacity Li-ion configurations (e.g., 50% and 35% baseline energy). The results highlight the importance of control strategy design, power limitations, and system configuration in achieving optimal performance. Collectively, the findings of this thesis establish a comprehensive framework linking electrochemical behavior, corrosion kinetics, and system-level performance of Al-air batteries. The integration of experimental characterization, data-driven modeling, and vehicle-level simulation provides new insights into the practical feasibility of Al-air systems and identifies key design and operational parameters governing their performance. The use of industrially relevant prototype systems further enhances the applicability of the research and bridges the gap between laboratory studies and real-world implementation. This work demonstrates that Al-air batteries, supported by optimized electrolyte conditions, predictive corrosion modeling, and intelligent system integration, represent a viable pathway for next-generation energy storage and EV range extension, while advancing both scientific understanding and practical development.
  • Thumbnail Image
    Item type: Item ,
    Autonomous Driving System Rule Learning Using Expert-Defined Causality
    (University of Waterloo, 2026-06-10) Bouchard, Frédéric
    An increasing number of road users are travelling freely in urban environments. Each of them has their own motion preferences but is expected to comply with the traffic laws. To cope with the motion discrepancies, autonomous vehicles require highly sophisticated reactive decision-making that can adapt their motion given the surrounding environment and the applicable traffic laws. Such decision-makers must be trustworthy, since each mistake can lead to a fatality, and performant, since they must estimate, at a high frequency, which behaviour to implement. This thesis describes how such functionality can be achieved with a practical rule engine learned from expert driving decisions and a precise notion of requirements. We first demonstrate the feasibility of planning the motion of an autonomous vehicle by implementing a prototype that, given a curated training suite of driving examples, can create and maintain a two-layer rule-based theory. Assuming perfect perception, we then design a method that learns the rules based on a precise notion of requirements. An expert anticipates that the decision-maker can enter a state for which a requirement is unmet and therefore specifies with a set of template rules the cause of each anticipated violation. For each template rule, its antecedent entails a notion of causality, and its consequent specifies the behaviour to implement. The set of template rules are used as a labelling function. Namely, each time the decision-maker fails to satisfy a requirement, an associated template rule is used to address the misbehaviour. The rules of the rule-based theory are based on templates. The antecedent of such rules are automatically learned and may have been significantly altered to include new relevant constraints that are expected to cope better with the requirements. Finally, considering that autonomous vehicles rely on sensor capabilities, we thereafter extend our method to compete in the Carla Leaderboard operational design domain. Using the same computer vision as the best performer for which there is code available, we demonstrate that our system can learn a policy that is explainable while performing better than our competitor on the set of provided requirements. This thesis has been divided into three phases, each of which strongly correlates with a paper submitted to a conference or journal for publication. In the first phase, we assess the feasibility of the proposed rule-based architecture by implementing/deploying a rule engine prototype in a level-3 autonomous vehicle driving for 110 kilometres of field tests in an urban environment of the city of Waterloo. Namely, the prototype has an algorithm to create and iteratively refine a rule-based behaviour planner, using a two-layer rule-based theory. The first layer determines a set of feasible parametrized behaviours, given the perceived state of the environment. From these, a resolution function chooses the most conservative high-level maneuver. The second layer then reconciles the parameters into a single behaviour. Based on the set of traffic rules described in a driver handbook, an expert produces a set of training examples expressing the relevant change of behaviours. The algorithm presented in that paper performs hierarchical rule-based machine learning. In the second phase, we formalize the construction of the training suite of driving examples that inevitably comes with the iterative development of the autonomous driving technology infrastructure of software and hardware. Namely, we explore how to extract knowledge from counterexamples encountered while driving in a city generated by the CARLA simulator. For that, we convert the requirements of the CARLA Autonomous Driving Leaderboard into a specification that is used to learn a rule-based policy. We assess the generalization of the learned rule-based policy by evaluating its performance on an unseen city generated by the same simulator. We then compare our performance with InterFuser, a state-of-the-art competing approach, and demonstrate that our method outperforms their method. In the third phase, we use the computer vision and tracker of InterFuser and create our own path generator inspired by the route planner of TransFuser to demonstrate that our method can cope with sensor noise while achieving state-of-the-art performance. In this phase, we use the six official towns that form the CARLA Autonomous Driving Leaderboard as the training towns and attempt to generalize to two unseen towns. Although our initial goal was to become an entry on the CARLA Autonomous Driving Leaderboard, the evaluation infrastructure has become unavailable. Therefore, to be convincing that our approach achieves state-of-the-art performance, we create our own challenge by randomly generating novel routes both on the six official towns and two additional unseen towns that have been released by the same officials. Although we demonstrate that our method outperforms a state-of-the-art end-to-end approach, we list in Limitations a number of issues that have not yet been addressed and constitute limitations to the results presented in this thesis. Thereafter, we speculate on how our method can be extended to mitigate some of these limitations.
  • Thumbnail Image
    Item type: Item ,
    Exploring Structure, Agency and Equity in Cross-Sector Partnerships for Advancing Sustainability and Climate Goals
    (University of Waterloo, 2026-06-10) Samuel, Naima
    The Sustainable Development Goals (SDGs) position partnerships as a central mechanism for advancing sustainability objectives by enabling coordinated efforts across multiple actors and sectors, particularly in addressing complex challenges like climate change. Despite the prominence of partnerships, there remains limited understanding of how cross-sector partnerships function in practice, particularly in local contexts where implementation occurs across municipal, community, and private-sector actors. Existing research has often emphasized formal structures, early phases of collaboration, or normative commitments, providing relatively limited insight into how partnerships are enacted during implementation, how they deliver outcomes, and how equity is embedded and sustained over time. Organized as a three-paper thesis, this dissertation combines a systematic review of sustainability partnerships situated within the SDGs with a comparative qualitative analysis of twelve Canadian local climate action partnerships to examine how cross-sector partnerships are structured, enacted, and adapted, and how effectiveness and equity emerge through the interaction of partnership structures and partner agency. Drawing on document analysis and semi-structured interviews, the analysis is informed by structuration theory, which provides a lens for examining how structures enable and constrain action and how partners reproduce or adapt these arrangements through practice. The findings show that the effectiveness of cross-sector partnerships in local climate action depends on more than formal design alone. Outcomes are shaped through the interaction of partnership structures and partner agency, as structural arrangements influence coordination, participation, and resource allocation while partners interpret, enact, and adapt these arrangements over time. Equity is similarly shaped through these dynamics, not through representational diversity or stated commitments alone, but through deliberate adjustments to decision-making, engagement, and resourcing structures. Co-design emerges as a central practice through which partners collectively reshape partnership arrangements and sustain equity over time. The dissertation contributes an integrated understanding of how cross-sector partnerships support effective and equitable action toward sustainability goals, using local climate mitigation as a site of implementation within the broader sustainability agenda. It extends structuration theory by showing how structure–agency dynamics are enacted through collective practice in multi-actor implementation contexts, highlighting the role of co-design, the influence of partnership arrangements and lifecycle dynamics, and the importance of aligning structures and agency to support both effectiveness and equity. It also offers practical insights for understanding, designing, and adapting partnerships to better support coordinated, inclusive, and effective local climate action.
  • Thumbnail Image
    Item type: Item ,
    Distance functions and optimal taxation
    (University of Waterloo, 2026-01-08) Burbidge, John
    Governments use taxes to pay for some of their expenditures. Setting aside the benefits of the expenditures, the taxes economic agents have to pay reduce their well being. One objective of the optimal taxation literature is to find tax systems that minimize the loss of well being, given the government's revenue requirement. Thus one way to frame the optimization problem is to have the government choose tax rates (or prices) to maximize individual utility given the revenue requirement. Given the prevalence of price-times-quantity expressions in budget constraints, taking derivatives with respect to tax rates or prices yields rules expressed in terms of quantities. One of Terence Gorman's many insights was that if the objective is to find rules about prices, reframe the problem so that the government chooses quantities; derivatives of p times q with respect to q yield rules about p or tax rates. Below I show that some optimal tax problems are simplifi ed by assuming the government chooses quantities to maximize revenue subject to a fixed level of individual utility. The distance function, which is de nied as the number by which one must scale the arguments of the utility function to yield a particular level of utility, plays a central role.
  • Thumbnail Image
    Item type: Item ,
    Taxation for redistribution: Optimal tax structures along the utility possibility frontier
    (University of Waterloo, 2026-01-08) Burbidge, John
    Mirrlees (1971) examined taxation for redistribution with imperfect information - the government can observe earnings but not wage rates or hours worked. Mirrlees assumed a one-good model with a continuum of types. Stiglitz (1982) worked out the two-type example of the Mirrlees model. This paper extends the distance-function approach to the Ramsey (1927) problem in Burbidge (2025a) to study optimal tax structures along the utility possibility frontier, upf, with two types and two goods.