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The increased use of zinc coatings on steels has led to a decrease in their weldability. Weld current and time need to be increased in order to achieve sound welds on these materials compared to uncoated steels, and electrode tip life suffers greatly due to rapid alloying and degradation. In this work, typical uncoated Class II electrodes were tested along with a TiC metal matrix composite (MMC) coated electrode. Tests were conducted to study the weldability and process of nugget formation for both electrodes on HDG (hot dipped galvanized) HSLA (high strength low alloys) steels. Current and time ranges were constructed for both types of electrodes by varying either the weld current or weld time while holding all other parameters constant. Analysis of weld microstructures was conducted on cross-sectioned welds using SEM (scanning electron microscopy). Using the coated electrodes reduced weld current and times needed to form MWS (minimum weld size) on the coated steels.

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.

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.

Lithium-ion batteries (LIBs) are one of the best candidates as energy storage systems for automobile applications due to their high power and energy densities. However, durability in comparison to other battery chemistries continues to be a key factor in prevention of wide scale adoption by the automotive industry. In order to design more-durable, longer-life, batteries, reliable and predictive battery models are required. In this paper, an effective model for simulating full-size LIBs is employed that can predict the operating voltage of the cell and the distribution of variables such as electrochemical current generation and battery state of charge (SOC). This predictive ability is used to examine the effect of parameters such as current collector thickness and tab location for the purpose of reducing non-uniform voltage and current distribution in the cell. It is identified that reducing the non-uniformities can reduce the ageing effects and increase the battery durability.

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 electrified vehicle applications, the heat generated of lithium-ion (Li-ion) cells may significantly affect the vehicle range and state of health (SOH) of the pack. Therefore, a major design task is creation of a battery thermal management system with suitable control and cooling strategies. To this end, the thermal behavior of Li-ion cells at various temperatures and operating conditions should be quantified. In this paper, two different commercial pouch cells for plug-in hybrid electric vehicles (PHEVs) are studied through comprehensive thermal performance tests. This study employs a fractional factorial design of experiments to reduce the number of tests required to characterize the behavior of fresh cells while minimizing the effects of ageing. At each test point, the effects of ambient temperature and charge/discharge rate on several types of cell efficiencies and surface heat generation are evaluated.

New advanced Pd only, Pd:Rh and Pt:Pd:Rh catalysts are compared with a current platinum rhodium catalyst after poisoning and thermal ageing. The results indicate that at equivalent precious metal cost (at 1994 prices) the advanced palladium based catalysts achieve significantly improved performance compared with current Pt, Rh and Pd technology. The new Pd:Rh formulation is recommended for close coupled locations and the Pt:Pd:Rh formulation recommended for underfloor locations where residual fuel lead may be present. The formation of H2S is shown to be low with the palladium based catalysts. Finally, it is shown that the new catalysts with balanced oxidation and reduction capability perform better in multi-brick systems than addition of a highly loaded palladium only front brick.

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.

Safety assurance is a central concern for the development and societal acceptance of automated driving (AD) systems. Perception is a key aspect of AD that relies heavily on Machine Learning (ML). Despite the known challenges with the safety assurance of ML-based components, proposals have recently emerged for unit-level safety cases addressing these components. Unfortunately, AD safety cases express safety requirements at the system level and these efforts are missing the critical linking argument needed to integrate safety requirements at the system level with component performance requirements at the unit level. In this paper, we propose the Integration Safety Case for Perception (ISCaP), a generic template for such a linking safety argument specifically tailored for perception components. The template takes a deductive and formal approach to define strong traceability between levels.

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 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.

Progress made in reducing engine-out particulate emissions has prompted a revival in the design of flow-through oxidation catalysts for diesel engine applications. Effort in this area has focused primarily in the area of SOF control for the further reduction of particulate emissions. The work reported here covers some of the catalyst design parameters important for SOF and gas phase pollutant control. This is illustrated with both laboratory reactor and engine evaluation data for several formulary and operating parameters. Platinum-based catalysts are shown to be generally the most active, but they require treatments or additives to reduce the inherently high activity of platinum for the oxidation of SO2 present in the exhaust. The effect of additives and their loading on the oxidation activity of Pt/alumina for HC, CO, SOF and SO2 oxidation is discussed in detail and additives are identified which reduce SO2 oxidation with minimal effect on HC, CO or SOF oxidation activity.

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.

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.

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.