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The performance, life cycle cost, and safety of electric and hybrid electric vehicles (EVs and HEVs) depend strongly on their energy storage system. Advanced batteries such as lithium-ion (Li-ion) polymer batteries are quite viable options for storing energy in EVs and HEVs. In addition, thermal management is essential for achieving the desired performance and life cycle from a particular battery. Therefore, to design a thermal management system, a designer must study the thermal characteristics of batteries. The thermal characteristics that are needed include the surface temperature distribution, heat flux, and the heat generation from batteries under various charge/discharge profiles. Therefore, in the first part of the research, surface temperature distribution from a lithium-ion pouch cell (20Ah capacity) is studied under different discharge rates of 1C, 2C, 3C, and 4C.

System identification is an important aspect in model-based control design which is proven to be a cost-effective and time saving approach to improve the performance of hybrid electric vehicles (HEVs). This study focuses on modeling and parameter estimation of the longitudinal vehicle dynamics for Toyota Prius Plug-in Hybrid (PHEV) with power-split architecture. This model is needed to develop and evaluate various controllers, such as energy management system, adaptive cruise control, traction and driveline oscillation control. Particular emphasis is given to the driveline oscillations caused due to low damping present in PHEVs by incorporating flexibility in the half shaft and time lag in the tire model.

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

The vision for the future automotive chassis is to interconnect the lateral, longitudinal, and vertical dynamics by separately controlling driving, braking, steering, and damping of each individual wheel. A major advantage of all wheel drive electric vehicles with four in-wheel motors is the possibility to control the torque and speed at each wheel independently. This paper proposes a traction controller for such a vehicle. It estimates the road's adhesion potential at each wheel and adjusts each motor voltage, such that the longitudinal slip is kept in an optimal range. For development and validation, a full vehicle model is designed in ADAMS/View software, in co-simulation with motor and control elements, modeled in MATLAB/Simulink.

The performance of a commercial Cu-zeolite SCR catalyst after differing degrees of hydrothermal aging (aged for 72 hours at 500, 700 and 800°C with 10% moisture balanced with air) was studied by spatially resolving different key reactions using gas-phase FTIR measurements. Gases were sampled along a channel at different positions and analyzed using FTIR, which overcomes the interference of water and nitrogen on ammonia concentration detection encountered in standard mass spectrometer-based spatial resolution measurements. The NO:NO₂ concentration ratio was changed so that the standard (NO:NO₂ = 1:0), fast (NO:NO₂ = 1:1) and NO₂ (NO:NO₂ = 0:1) SCR reactions could be investigated as a function of the catalyst's hydrothermal aging extent. In addition, the effects of hydrothermal aging on the activity of NH₃ and NO oxidation were also investigated. Hydrothermal aging had little effect on NO oxidation activity.

The conversion design strategy, and emissions and performance results for a dedicated propane, vapour injected, 1995 Dodge Dakota truck are reported. Data is obtained from the University of Waterloo entry in the 1997 Propane Vehicle Challenge. A key feature of the design strategy is its focus on testing and emissions while preserving low engine speed power for drivability. Major changes to the Dakota truck included the following: installation of a custom shaped fuel tank, inclusion of a fuel temperature control module, addition of a vaporizer and a fuel delivery metering unit, installation of a custom vapour distribution manifold, addition of an equivalence ratio electronic controller, inclusion of a wide range oxygen sensor, addition of an exhaust gas recirculation cooler and installation of thermal insulation on the exhaust system. A competition provided natural gas catalyst was used.

A serpentine flow channel is one of the most common and practical channel layouts for a PEM fuel cell since it ensures the removal of water produced in a cell. While the reactant flows along the flow channel, it can also leak or cross to neighboring channels via the porous gas diffusion layer due to a high pressure gradient. Such a cross flow leads to effective water removal in a gas diffusion layer thus enlarging the active area for reaction although this cross flow has largely been ignored in previous studies. In this study, neutron radiography is applied to investigate the liquid water accumulation and its effect on the performance of a PEM fuel cell. Liquid water tends to accumulate in the gas diffusion layer adjacent to the flow channel area while the liquid water formed in the gas diffusion layer next to the channel land area seems to be effectively removed by the cross leakage flow between the adjacent flow channels.

A fuel cell hybrid powertrain design is implemented and optimized by the University of Waterloo Alternative Fuels Team for the ChallengeX competition. A comprehensive set of bench-top and in-vehicle validation results are used to generate accurate fuel cell vehicle models for SIL/HIL control strategy testing and tuning. The vehicle is brought to a “99% buy-off” level of production readiness, and a detailed crashworthiness analysis is performed. The vehicle performance is compared to Vehicle Technical Specifications (VTS).

A serpentine flow channel can be considered as neighboring channels connected in series, and is one of the most common and practical channel layouts for PEM fuel cells since it ensures the removal of liquid water produced in a cell with excellent performance and acceptable parasitic load. During the reactant flows along the flow channel, it can also leak or cross directly to the neighboring channel via the porous gas diffusion layer due to the high pressure gradient caused by the short distance. Such a cross flow leads to a larger effective flow area resulting in a substantially lower amount of pressure drop in an actual PEM fuel cell compared to the case without cross flow. In this work, an analytical solution is obtained for the cross flow in a PEM fuel cell with a serpentine flow channel based on the assumption that the velocity of cross flow is linearly distributed in the gas diffusion layer between two successive U-turns.

Porous silicon as gas/chemical sensing material has been widely investigated in recent years. In this paper, the humidity sensing property of n-type porous silicon with ordered structure is studied for the first time. The ordered porous silicon used in this experiment has uniform pore size, pore shape and distribution. Both the membrane and closed bottom samples were studied. The resistance change of the porous silicon was measured. A 22-28% decrease of resistance was observed when relative humidity was changed from 1% to 100%. Both the response time and the recovery time were within 10 minutes, and 90% of the response can be reached in 6 minutes for the PS membrane sample. The possible sensing mechanism and future work are also discussed in this paper.

Traditional electrode materials for lithium-ion storage cells are typically crystalline layered structures such as metal oxides, and graphitic carbons. These materials power billions of portable electronic devices in today's society. However, large-scale, high-capacity storage devices capable of powering hybrid electric vehicles (HEV″s) or their plug-in versions (PHEV's) have much more demanding requirements with respect to safety, cost, and the power they must deliver. Recently, nanostructured solid state materials, which are comprised of two more compositional or structural phases, have been found to show exciting possibilities to meet these criteria.

The response to increasingly stringent light duty diesel emission regulation is a nearly unanimous increase in heavy Exhaust Gas Recirculation (EGR) application to reduce feedgas NOx emissions. Little attention has been paid to the fact that heavy EGR usage is likely to push the engine operating conditions towards less efficient or even unstable regions of conventional centrifugal compressor operating maps. Moreover, the low oxygen content at part load operation also poses transient response challenges. Therefore, improving turbocharger efficiency at part load and extending the stable operating range is becoming critical for viable future low emission diesel engines. In this study of a turbocharger compression system, encompassing the airflow geometry from compressor impeller inlet to volute exit, a dual volute compressor concept was introduced, and Computational Fluid Dynamics (CFD) was used to investigate its effects on the overall expected performance level and range.

In this work, a new air hybrid engine is introduced in which two throttles are used to manage the engine load in three modes of operation i.e. braking, air motor, and conventional mode. The concept includes an air tank to store pressurized air during braking and rather than a fully variable valve timing (VVT) system, two throttles are utilized. Use of throttles can significantly reduce the complexity of air hybrid engines. The valves need three fixed timing schedules for the three modes of operation. To study this concept, for each mode, the results of engine simulations using GT-Power software are used to generate the operating maps. These maps show the maximum braking torque as well as maximum air motor torque in terms of air tank pressure and engine speed. Moreover, the resulting maps indicate the operating conditions under which each mode is more effective. Based on these maps, a power management strategy is developed to achieve improved fuel economy.

The performance characteristics of a commercial lean-NOX trap catalyst were evaluated between 200 and 500°C, using H2, CO, and a mixture of both H2 and CO as reductants before and after different high-temperature aging steps, from 600 to 750°C. Tests included NOX reduction efficiency during cycling, NOX storage capacity (NSC), oxygen storage capacity (OSC), and water-gas-shift (WGS) and NO oxidation reaction extents. The WGS reaction extent at 200 and 300°C was negatively affected by thermal degradation, but at 400 and 500°C no significant change was observed. Changes in the extent of NO oxidation did not show a consistent trend as a function of thermal degradation. The total NSC was tested at 200, 350 and 500°C. Little change was observed at 500°C with thermal degradation but a steady decrease was observed at 350°C as the thermal degradation temperature was increased.

A so-called damage percolation model is coupled with Gurson-based finite element (FE) approach in order to accommodate the high strain gradients and localized ductile damage. In doing so, void coalescence and final failure are suppressed in Gurson-based FE modeling while a measured second phase particle field is mapped onto the most damaged mesh area so that percolation modeling can be performed to capture ductile fracture in real sheet forming operations. It is revealed that void nucleation within particle clusters dominates ductile fracture in aluminum alloy sheet forming. Coalescence among several particle clusters triggered final failure of materials. A stretch flange forming is simulated with the coupled modeling.

Stretch flange features are commonly found in the corner regions of commercial parts, such as window cutouts, where large strains can induce localization and necking. In this study, laboratory-scale stretch flange forming experiments on AA5182 and AA5754 were conducted to address the formability of these aluminum alloys under undergoing this specific deformation process. Two distinct cracking modes were found in the stretch flange samples. One is radial cracking at the inner edge of flange (cutout edge) while the other is circumferential cracking away from the inner edge at the punch profile radius. Numerical simulation of the stretch flange forming operations was conducted with an explicit finite element code-LS-DYNA. A coalescence-suppressed Gurson-based material model is used in the finite element model. Void coalescence and final failure in stretch flange is simulated through measured second-phase particle fields with a so-called damage percolation model.

The benefits of deliberately modulating air-to-fuel ratio over a three-way catalyst are disputed. In this work, engine test cell experiments were carried out to assess the performance of a warmed-up Pd/Rh three-way catalyst. The objectives were threefold: first, to determine the best mode of operation; second, to determine if air-to-fuel ratio modulation enhances robustness to transient air-to-fuel ratio disturbances; third, to determine if the conversion efficiency can be manipulated by controlling the shape of the air-to-fuel ratio oscillation. It was observed that the highest conversion efficiency is obtained using a steady air-to-fuel ratio just rich of stoichiometric; however, this mode of operation lacks robustness with respect to transient disturbances and UEGO sensor errors. Robustness can be improved using an oscillating air-to-fuel ratio, but with a sacrifice in peak conversion efficiency.

Robust lane marking detection remains a challenge, particularly in temperate climates where markings degrade rapidly due to winter conditions and snow removal efforts. In previous work, dynamic Bayesian networks with heuristic features were used with the feature distributions trained using semi-supervised expectation maximization, which greatly reduced sensitivity to initialization. This work has been extended in three important respects. First, the tracking formulation used in previous work has been corrected to prevent false positives in situations where only poor RANSAC hypotheses were generated. Second, the null hypothesis is reformulated to guarantee that detected hypotheses satisfy a minimum likelihood. Third, the computational requirements have been greatly reduced by computing an upper bound on the marginal likelihood of all part hypotheses upon generation and rejecting parts with an upper bound less likely than the null hypothesis.

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. In this paper, two types unsupervised clustering methods are used to build a driver braking intention predictor. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. The proposed unsupervised learning algorithms can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different algorithms which are K-means and Gaussian mixture model (GMM) will be compared.

The drive to improve and optimize hybrid vehicle performance is increasing with the growth of the market. With this market growth, the automotive industry has recognized a need to train and educate the next generation of engineers in hybrid vehicle design. The University of Waterloo Alternative Fuels Team (UWAFT), as part of the EcoCAR 3 competition, has developed a control strategy for a novel parallel-split hybrid architecture. This architecture features an engine, transmission and two electric motors; one pre-transmission motor and one post-transmission motor. The control strategy operates these powertrain components in a series, parallel, and all electric power flow, switching between these strategies to optimize the energy efficiency of the vehicle. Control strategies for these three power flows are compared through optimization of efficiencies within the powertrain.