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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.

Dynamic modelling of the contact between the tires of automobiles and the road surface is crucial for accurate and effective vehicle dynamic simulation and the development of various driving controllers. Furthermore, an accurate prediction of the rolling resistance is needed for powertrain controllers and controllers designed to reduce fuel consumption and engine emissions. Existing models of tires include physics-based analytical models, finite element based models, black box models, and data driven empirical models. The main issue with these approaches is that none of these models offer the balance between accuracy of simulation and computational cost that is required for the model-based development cycle. To address this issue, we present a volumetric approach to model the forces/moments between the tire and the road for vehicle dynamic simulations.

Accurately predicting the future trajectories of surrounding traffic agents is important for ensuring the safety of autonomous vehicles. To address the scenario of frequent interactions among traffic agents in the highway merging area, this paper proposes a trajectory prediction method based on interactive graph attention mechanism. Our approach integrates an interactive graph model to capture the complex interactions among traffic agents as well as the interactions between these agents and the contextual map of the highway merging area. By leveraging this interactive graph model, we establish an agent-agent interactive graph and an agent-map interactive graph. Moreover, we employ Graph Attention Network (GAT) to extract spatial interactions among trajectories, enhancing our predictions. To capture temporal dependencies within trajectories, we employ a Transformer-based multi-head self-attention mechanism.

The present work is aimed at investigating the tribological factors influencing the LDH test. The material used was AKDQ cold-rolled bare steel, 0.82mm thick. The investigated factors included: test speed (0.833, 4.167, 6.667, and 8.333 mm/s), lubricant viscosity (4.5, 7.0, and 12.5 mm2/s), punch roughness (0.033 and 0.144 μm Ra), and test temperature (25 and 50 °C). Test speed and lubricant viscosity form a variation of the numerator of the Stribeck curve's x-axis (ηV). With ηV increasing from 4 to 120 mm3/s2 friction decreased, resulting in a 0.5 mm higher LDH. Increasing the punch roughness decreased friction producing an increase of 0.25 mm in the LDH. There appears to be an optimum roughness -- at which the roughness features act as lubricant reservoirs but the asperities do not break through the lubricant film -- resulting in minimum friction, therefore, maximum LDH.

Details of the development of metal transfer and friction were studied by drawing cold-rolled bare, galvannealed, electrogalvanized, and hot-dip galvanized strips with a mineral-oil lubricant of 30 cSt viscosity at 40 C, over a total distance of 2500 mm by three methods. An initial high friction peak was associated with metal transfer to the beads and was largest with pure zinc and smallest with Fe-Zn coatings. Insertion of a new strip disturbed the coating and led to the development of secondary peaks. Long-term trends were governed by the stability of the coating. Stearic acid added to mineral oil delayed stabilization of the coating and increased contact area and thus friction with pure zinc surfaces. The usual practice of reporting average friction values can hide valuable information on lubrication mechanisms and metal transfer.

A series plug-in hybrid electric powertrain with all-wheel drive is designed using real-world drive cycles as part of the EcoCAR 2 competition. A stock 2013 Chevrolet Malibu Eco is being re-engineered to reduce fuel consumption and emissions while improving consumer acceptability. Waterloo utilizes a 18.9 kWh A123 energy storage system (ESS), which powers two 105 kW TM4 traction motors. A 2.4 L LE9 General Motors coupled to a 105 kW TM4 motor provides range extending performance. Each step of the design process is discussed, including a novel approach to powertrain selection and controls requirement selection that uses real-world drive cycles. The mechanical integration and unique ESS design is also discussed.

The development and calibration of stress state-dependent failure criteria for advanced high-strength steel (AHSS) and aluminum alloys requires characterization under proportional loading conditions. Traditional tests to construct a forming limit diagram (FLD), such as Marciniak or Nakazima tests, are based upon identifying the onset of strain localization or a tensile instability (neck). However, the onset of localization is strongly dependent on the through-thickness strain gradient that can delay or suppress the formation of a tensile instability so that cracking may occur before localization. As a result, the material fracture limit becomes the effective forming limit in deformation modes with severe through-thickness strain gradients, and this is not considered in the traditional FLD. In this study, a novel bending test apparatus was developed based upon the VDA 238-100 specification to characterize fracture in plane strain bending using digital image correlation (DIC).

The objective of this paper is to help students optimize project component selection or design by detailing, through two specific examples, the University of Waterloo's Alternative Fuels Team's (UWAFT's) successful design process. UWAFT successfully designed a fuel cell powered vehicle for the ChallengeX student engineering competition. The use of a formal, structured design process enabled this team to achieve great confidence in both the feasibility of their design and their ability to manifest the design. This design process is model-based whereby a parameterized software model is created. This paper hopefully assists students to overcome a common reluctance to implementing a model-based design process. After a component is constructed and tested, students can update their software model, which can help them assess the strength of their design.

A method to improve the computational efficiency of analyzing wheeled vehicle systems on three-dimensional (3-D) roads has been developed. This was accomplished by creating a technique to incorporate the tire on a 3-D road in a multibody dynamics model of the vehicle with an approach that formulates the governing equations using symbolic formulation. For general handling analysis performed on the vehicle, the tire forces and moments are determined using a tire model that represents the tire as a set of mathematical expressions. Since these expressions need numerical values to determine the forces and moments, a symbolic solution does not exist. Therefore, the evaluation of the tire forces and moments needs to be done during simulation. However, symbolic operations can be used when the governing equations are formulated to develop an efficient method to evaluate these forces.

This work presents comparisons of finite element model predictions of static and dynamic denting with experimental results. Panels were stamped from 0.81, 0.93 and 1.00mm AA6111-T4 and then paint-baked to produce representative automotive outer body panels. Each type of panel was statically and dynamically dented at three locations using a 25.4mm steel ball. Static denting was accomplished with incremental loading of 22.24N loads up to a maximum of 244.48N. Dynamic denting was accomplished by dropping the steel ball from heights ranging from 200mm to 1200mm. Multi-stage finite element analysis was performed using LS-DYNA1 and ABAQUS2 to predict the entire process of forming, spring-back, denting and final spring-back of the dented panels. The predicted results show good correlation with the experiments, but also highlight the sensitivity of the predictions to formulation of the finite element problem.

Electromagnetic forming of aluminum alloys provides improved forming limits, minimal springback and rapid implementation. The ability to predict the minimum energy required in electromagnetic forming is essential in developing an efficient process. Understanding the development of the strain distribution over time in the blank is also highly desired. A numerical model is needed that offers insight into these areas and the electromagnetic forming process in general that cannot easily be extracted from experiments. To address these concerns, ANSYS/EMAG is used to model the time varying currents that are discharged through the coil in order to obtain the transient magnetic forces acting on the blank. The body forces caused by electromagnetic induction are then used as the boundary condition to model the high velocity deformation of the blank with LS-DYNA, an explicit dynamic finite element code.

Driving Automation Systems (DAS) are subject to complex road environments and vehicle behaviors and increasingly rely on sophisticated sensors and Artificial Intelligence (AI). These properties give rise to unique safety faults stemming from specification insufficiencies and technological performance limitations, where sensors and AI introduce errors that vary in magnitude and temporal patterns, posing potential safety risks. The Safety of the Intended Functionality (SOTIF) standard emerges as a promising framework for addressing these concerns, focusing on scenario-based analysis to identify hazardous behaviors and their causes. Although the current standard provides a basic cause-and-effect model and high-level process guidance, it lacks concepts required to identify and evaluate hazardous errors, especially within the context of AI. This paper introduces two key contributions to bridge this gap.

The present paper offers a status report on round-robin tests conducted with the participation of ten laboratories, with drawbead simulation (DBS) as the test method. The results showed that, in most laboratories, the coefficient of friction (COF) derived from the test is repeatable within an acceptable range of ±0.01. Repeatability between laboratories was less satisfactory. Five laboratories reported results within the desirable band, while some laboratories found consistently higher values. In one instance this could be traced to incomplete transfer of clamp forces to the load cell, in other instances inaccurate test geometry is suspected. Therefore, numerical values of COF from different laboratories are not necessarily comparable. Irrespective of these inter-laboratory variations, the relative ranking of lubricants was not affected, and data generated within one laboratory can be used for relative evaluations and for a resolution of production problems.

This paper seeks to identify the refrigeration load of a hybrid electric truck in order to find the demand power required by the energy management system. To meet this objective, in addition to the power consumption of the refrigerator, the vehicle mass needs to be estimated. The Recursive Least Squares (RLS) method with forgetting factors is applied for this estimation. As an example of the application of this parameter identification, the estimated parameters are fed to the energy control strategy of a parallel hybrid truck. The control system calculates the demand power at each instant based on estimated parameters. Then, it decides how much power should be provided by available energy sources to minimize the total energy consumption. The simulation results show that the parameter identification can estimate the vehicle mass and refrigeration load very well which is led to have fairly accurate power demand prediction.

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.

This paper discusses modeling of a power-split hybrid electric vehicle and the design of a longitudinal dynamics controller for the University of Waterloo’s self-driving vehicle project. The powertrain of Waterloo’s vehicle platform, a Lincoln MKZ Hybrid, is controlled only by accelerator pedal actuation. The vehicle’s power management strategy cannot be altered, so a novel approach to grey-box modeling of the OEM powertrain control architecture and dynamics was developed. The model uses a system of multiple neural networks to mimic the response of the vehicle’s torque control module and estimate the distribution of torque between the powertrain’s internal combustion engine and electric motors. The vehicle’s power-split drivetrain and longitudinal dynamics were modeled in MapleSim, a modeling and simulation software, using a physics-based analytical approach.

Torque converters are used as coupling devices in automobile powertrains involving automatic transmissions. Efficient modeling of torque converters capturing various modes of operation is important for powertrain design and simulation, (Hroval and Tobler 1, Ishihara and Emori 2) optimization and control applications. Models of torque converters are available in various commercial simulation packages, Hadi et. al. 3. The information about the effect of model parameters on torque converter performance is valuable for any design operation. In this paper, a symbolic sensitivity analysis of a torque converter model will be presented. Direct differentiation (Serban and Freeman 4) is used to generate the sensitivity equations which results in equations in symbolic form. By solving the sensitivity equations, the effect of a perturbation of the model parameters on the behavior of the system is determined.

Battery packs of electric vehicles are typically composed of lithium-ion batteries with aluminum and copper acting as cell terminals. These terminals are joined together in series by means of connector tabs to produce sufficient power and energy output. Such critical electrical and structural cell terminal connections involve several challenges when joining thin, highly reflective and dissimilar materials with widely differing thermo-mechanical properties. This may involve potential deformation during the joining process and the formation of brittle intermetallic compounds that reduce conductivity and deteriorate mechanical properties. Among various joining techniques, laser welding has demonstrated significant advantages, including the capability to produce joints with low electrical contact resistance and high mechanical strength, along with high precision required for delicate materials like aluminum and copper.

Parameter identification of a math-based spark-ignition engine model is studied in this paper. Differential-algebraic equations governing the dynamic behavior of the engine combustion model are derived using a quasi-dimensional modelling scheme. The model is developed based on the two-zone combustion theory with turbulent flame propagation through the combustion chamber [1]. The system of equations includes physics-based equations combined with the semi-empirical Wiebe function. The GT-Power engine simulator software [2], a powerful tool for design and development of engines, is used to extract the reference data for the engine parameter identification. The models is GT-Power are calibrated and validated with experimental results; thus, acquired data from the software can be a reliable reference for engine validation purposes.

This paper presents an optimal cooperative path planning method considering driver’s driving intention for shared control to address target path conflicts during the driver-automation interaction by using the convex optimization technique based on the natural cubic spline. The optimal path criteria (e.g. the optimal curvature, the optimal heading angle) are formulated as quadratic forms using the natural cubic spline, and the initial cooperative path profiles of the cooperative path in the Frenet-based coordinate system are induced by considering the driver’s lane-changing intention recognized by the Support Vector Machine (SVM) method. Then, the optimal cooperative path could be obtained by the convex optimization techniques. The noncooperative game theory is adopted to model the driver-automation interaction in this shared control framework, where the Nash equilibrium solution is derived by the model predictive control (MPC) approach.