<div>The purpose of this dissertation is to study the effectiveness of public policies in generating fuel savings and emissions reductions. I focus on applying various empirical methods to analyze consumer responses to policy changes on both extensive and intensive margins. This dissertation consists of two chapters.</div><div><br></div><div>In the first chapter, I compare the effectiveness of fuel taxes and product taxes on reducing gasoline consumption of new car buyers. I employ a unified data source for vehicle choice and subsequent vehicle use to estimate a random effects logit demand model that explicitly accounts for vehicle use heterogeneity. My demand estimation suggests that new car buyers fully value the fuel-saving benefits from improved vehicle fuel efficiency when they initially purchase their cars. My policy simulations indicate that high-mileage drivers are more responsive to a change in fuel taxes than to a change in product taxes, even as low-mileage drivers are more responsive to product taxes. By capturing such heterogeneous consumer response to policies, I show that a counterfactual increase of the fuel tax is more effective than a revenue-equivalent product tax in reducing the total gasoline consumption of new car buyers. Further, when accounting for its effects on consumer response on both extensive and intensive margins, a change in fuel taxes has a clear advantage over a change in product taxes in reducing the consumption of gasoline even when the magnitude of tax increase is small. More importantly, a model not accounting for vehicle use heterogeneity understates the fuel saving effects of both policies and misleads us about the relative effectiveness when comparing different policies. </div><div><br></div><div>The second chapter explores how changes in the marginal cost of driving affect consumers decisions about passenger vehicle utilization, as measured by average daily miles traveled per vehicle. This intensive margin of consumer response has important implications for the effectiveness of usage-based policies, such as the fuel tax and the mileage tax, that designed to address externalities of driving. I estimate the elasticity of driving with respect to fuel cost per mile using a large panel data that covers 351 towns and cities in Massachusetts over 24 quarters. While most researchers in this literature apply fixed effects estimators to examine the elasticity of driving, I use a factor model econometric setup to account for unobserved common factors and regional heterogeneity. Residual diagnostics confirm that the factor model setup does a better job of removing the cross-section dependence than fixed effects estimators do. Given low consumer responsiveness to changes in the marginal cost of driving engendered by current usage-based policies, rights-based approaches like congestion charges might be better alternatives to influence vehicle utilization and vehicle ownership.</div>

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This research presents a two-part study on a fuel cell electric van (FCEV), focusing on vehicle modelling and developing different control strategies for the modelled vehicle. The modelling phase accounts for the aging effects on the fuel cell (FC) and battery, analyzing FCEV behavior over time. This includes estimating and integrating the degradation impacts on characteristic curves, such as the FC’s polarization and efficiency curves, the battery’s charging and discharging resistance curves, and the open-circuit voltage curve. A simplified fuel cell system (FCS) model is designed to consider power losses in multiple components, including the FC stack, air compressor, and others. The dynamic limits of the FC are also included to yield more realistic results. The model is based on the vehicle Opel Vivaro FC specifications, incorporating parameters like maximum FC power, battery capacity, vehicle weight, and tire dimensions. Subsequently, various control strategies are applied to analyze their effectiveness in FC and battery State-of-Health (SOH) degradation and hydrogen consumption. A rule-based energy management system (EMS) is implemented first, which operates with five different operational modes dependent on the vehicle’s state. This is followed by a look-up table (LUT) based strategy, which uses two two-dimensional tables generated by a Neural Network (NN). The network is trained with discretized optimal / Thesis / Master of Applied Science (MASc)

The design and analysis of an automotive structure is an important stage of the vehicle design process. The structural characteristics have significant impact on the vehicle performance. During the design process it is necessary to have knowledge about the structural characteristics; however in the preliminary design stages detailed information about the structure is not available. During this period of the design process the structure is often simplified to a representative model that can be analyzed and used as the input for the detailed design process. A vehicle model is developed based on the space frame structures where the frame is the load carrying portion of the structure. Preliminary design analysis is conducted using a static load condition applied to the vehicle as pure bending and pure torsion. The deflections of the vehicle based on these loading conditions are determined using the finite element method which has been implemented in developed software. The structural response, measured as the bending and torsion stiffness, is used to evaluate the structural design. An optimization program is implemented to improve the structural design with the goal of reducing weight while increasing stiffness. Following optimization the model is completed by estimating suitable plate thicknesses using a method of substructure analysis. The output of this process will be an optimized structural model with low weight and high stiffness that is ready for detailed design. / UOIT