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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.

A control algorithm is developed for active/semi-active suspensions which can provide more comfort and better handling simultaneously. A weighting parameter is tuned online which is derived from two components - slow and fast adaptation to assign weights to comfort and handling. After establishing through simulations that the proposed adaptive control algorithm can demonstrate a performance better than some controllers in prior-art, it is implemented on an actual vehicle (Cadillac STS) which is equipped with MR dampers and several sensors. The vehicle is tested on smooth and rough roads and over speed bumps.

The analysis of nickel metal hydride (Ni-MH) battery performance is very important for automotive researchers and manufacturers. The performance of a battery can be described as a direct consequence of various chemical and physical phenomena taking place inside the container. In this paper, a physics-based model of a Ni-MH battery will be presented. To analyze its performance, the efficiency of the battery is chosen as the performance measure, which is defined as the ratio of the energy output from the battery and the energy input to the battery while charging. Parametric sensitivity analysis will be used to generate sensitivity information for the state variables of the model. The generated information will be used to showcase how sensitivity information can be used to identify unique model behavior and how it can be used to optimize the capacity of the battery. The results will be validated using a finite difference formulation.

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.

In this paper, a consensus framework for cooperative parameter estimation within the vehicular network is presented. It is assumed that each vehicle is equipped with a dedicated short range communication (DSRC) device and connected to other vehicles. The improvement achieved by the consensus for parameter estimation in presence of sensor’s noise is studied, and the effects of network nodes and edges on the consensus performance is discussed. Finally, the simulation results of the introduced cooperative estimation algorithm for estimation of the unknown parameter of road condition is presented. It is shown that due to the faster dynamic of network communication, single agents’ estimation converges to the least square approximation of the unknown parameter properly.

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).

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.

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.

In this study, a finite element software application (SORPAS®) is used to simulate the effect of pulsing on the expected weld thermal cycle during resistance spot welding (RSW). The predicted local cooling rates are used in combination with experimental observation to study the effect pulsing has on the microstructure and mechanical properties of Zn-coated DP600 AHSS (1.2mm thick) spot welds. Experimental observation of the weld microstructure was obtained by metallographic procedures and mechanical properties were determined by tensile shear testing. Microstructural changes in the weld metal and heat affect zone (HAZ) were characterized with respect to process parameters.

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.

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.

The present work investigates weld failure modes during formability tests of multi-gauge aluminum Tailor Welded Blanks (TWBs). The limiting dome height test is used to evaluate formability of TWBs. Three gauge combinations utilizing aluminum alloy 5754 sheets are considered (2 to 1 mm, 1.6 to 1 mm and 2 to 1.6 mm). Three weld orientations have been considered: transverse, longitudinal and 45°. Interaction of several factors determines the type of failure that occurs in a TWB specimen. These factors are weld orientation, morphology and distribution of weld defects, and the magnitude of constraint imposed by the thicker sheet to the thin sheet. The last factor depends on the difference in thickness of the sheet pair and is usually expressed in terms of gauge ratio. In general TWBs show two different types of fracture: weld failure and failure of the thin aluminum sheet. Only the former will be discussed in this paper.

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.

The automobile industry has been undergoing a transition from fossil fuels to a low emission platform due to stricter environmental policies and energy security considerations. Electric vehicles, powered by lithium-ion batteries, have started to attain a noticeable market share recently due to their stable performance and maturity as a technology. However, electric vehicles continue to suffer from two disadvantages that have limited widespread adoption: charging time and energy density. To mitigate these challenges, vehicle Original Equipment Manufacturers (OEMs) have developed different vehicle architectures to extend the vehicle range. This work seeks to compare various powertrains, including: combined power battery electric vehicles (BEV) (zinc-air and lithium-ion battery), zero emission fuel cell vehicles (FCV)), conventional gasoline powered vehicles (baseline internal combustion vehicle), and ICE engine extended range hybrid electric vehicle.

Lithium-ion batteries, which are nowadays common in laptops, cell phones, toys, and other portable electronic devices, are also viewed as a most promising advanced technology for electric and hybrid electric vehicles (EVs and HEVs), but battery manufacturers and automakers must understand the performance of these batteries when they are scaled up to the large sizes needed for the propulsion of the vehicle. In addition, accurate thermo-physical property input is crucial to thermal modeling. Therefore, a designer must study the thermal characteristics of batteries for improvement in the design of a thermal management system and also for thermal modeling. This work presents a purely experimental thermal characterization in terms of measurement of the temperature gradient and temperature response of a lithium-ion battery utilizing a promising electrode material, LiFePO4, in a prismatic pouch configuration.

In this study, application of differential braking strategy to remove the oscillatory instability or snaking behavior of an articulated steer vehicle is presented. First, a linearized model of the vehicle is described that is used to represent the equations of motion in the state-space form. Then, this model is utilized for designing a sliding mode controller to adjust the differential braking on the rear axle to stabilize the vehicle during the snaking. The performance of the resulting active control system is evaluated in different driving conditions by using the linearized model. Finally, the control system is incorporated into a virtual prototype of the vehicle in ADAMS, and its operation is examined. The results from the linear model analysis and simulations in ADAMS are reasonably consistent.