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Hole expansion of a dual phase steel, DP600, was numerically investigated using a damage-based constitutive law to predict failure. The parameters governing void nucleation and coalescence were identified from an extensive review of the x-ray micro-tomography data available in the literature to ensure physically-sound predictions of damage evolution. A recently proposed technique to experimentally quantify work-hardening and damage in the shear-affected zone is incorporated into the damage model to enable fracture predictions of holes with sheared edges. Finite-element simulations of a hole expansion test with a conical punch were performed for both a punched and milled hole edge condition and the predicted hole expansion ratios are in very good agreement with the experiment values reported by several researchers.

Small crack growth from notches under variable amplitude loading requires that crack opening stress be followed on a cycle by cycle basis and taken into account in making fatigue life predictions. The use of constant amplitude fatigue life data that ignores changes in crack opening stress due to high stress overloads in variable amplitude fatigue leads to non-conservative fatigue life predictions. Similarly fatigue life predictions based on small crack growth calculations for cracks growing from flaws in notches are non-conservative when constant amplitude crack growth data are used. These non-conservative predictions have, in both cases, been shown to be due to severe reductions in fatigue crack closure arising from large (overload or underload) cycles in a typical service load history.

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.

This paper presents the numerical validation of the impact response of a hot formed B-pillar component with tailored properties. A laboratory-scale B-pillar tool is considered with integral heating and cooling sections in an effort to locally control the cooling rate of an austenitized blank, thereby producing a part with tailored microstructures to potentially improve the impact response of these components. An instrumented falling-weight drop tower was used to impact the lab-scale B-pillars in a modified 3-point bend configuration to assess the difference between a component in the fully hardened (martensitic) state and a component with a tailored region (consisting of bainite and ferrite). Numerical models were developed using LS-DYNA to simulate the forming and thermal history of the part to estimate the final thickness and strain distributions as well as the predicted microstructures.

The effects of bead surface roughness on friction, die pickup, and sheet surface damage in the drawbead test were investigated. Beads of HRC 58 hardness were prepared from centerless-ground rod by circumferential honing to 0.05 μm roughness, followed by finishing with 100, 400, or 600 grit SiC paper in the axial direction. Paraffinic base oils with viscosities of 4.5, 30, and 285 mm2/s were used neat and in conjunction with stearic acid. The effects of bead roughness depended on the nature of metal transfer, especially its distribution and firmness of attachment. The presence of a boundary additive increased, decreased, or had no effect on friction depending on the particular coating and bead finish.

It is imperative to have accurate tire models when trying to control the trajectory of a vehicle. With the emergence of autonomous vehicles, it is more important than ever before to have models that predict how the vehicle will operate in any situation. Many different types of tire models have been developed and validated, including physics-based models such as brush models, black box models, finite element-based models, and empirical models driven by data such as the Magic Formula model. The latter is widely acknowledged to be one of the most accurate tire models available; however, collecting data for this model is not an easy task. Collecting data is often accomplished through rigorous testing in a dedicated facility. This is a long and expensive procedure which generally destroys many tires before a comprehensive data set is acquired. Using a Vehicle Measurement System (VMS), tires can be modeled through on-road data alone.

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 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).

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.

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.

Ride performance characteristics of a road vehicle involving different suspension system layouts are investigated. The suspension layouts consist of conventional rectangular 4-wheel, novel diamond-shaped 4-wheel, triangular 3-wheel and inverse-triangular 3-wheel. A generalized full-vehicle model integrating different suspension system layouts is formulated. The fundamental suspension properties are compared in terms of bounce-, roll- and pitch-mode. The ride dynamic responses and power consumption characteristics are explored under two measured road roughness excitations and a range of vehicle speeds. The results demonstrate that the novel diamond-shaped suspension system layout could yield significantly enhanced vehicle ride performance in an energy-saving manner.

This paper examines the application of damage models in tube bending and subsequent hydroforming of AlMg3.5Mn aluminum alloy tubes. An in-house Gurson-based damage model, incorporated within LS-DYNA, has been used for the simulations. The applied damage model contains several void nucleation and growth parameters that must be determined for each material. A simpler straight tube hydroforming process was considered first to check the damage parameters and predicted ductility. Then the model was applied to a sequence of bending and hydroforming. The damage history from pre-bending was mapped to the hydroforming stage, to allow prediction of the overall ductility. The applied forming parameters in the simulation were based on data extracted during the experimental tests. Finally, the numerical results were compared to the experimental data.

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.

This research examines the effect of endfeed on the thickness and strains during bending of steel tubes. The tubes were bent using an instrumented rotary draw tube bender and subsequently hydroformed into a diamond-profile outside corner fill die. DQAK tubes with an OD of 76.2 mm and a thickness of 1.55 mm were investigated. Endfeed during bending was found to have a significant effect on the thickness and strains within the tube after bending, and numerical models that were generated showed good agreement with the experimental data. It is shown how slight changes in thickness can cause localized failure during hydroforming, and how excessive die clearances can cause large strains in undesired areas.

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.