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Connected and automated vehicle (CAV) technologies promise a substantial decrease in traffic accidents and traffic jams, and bring new opportunities for improving vehicle’s fuel economy. However, testing autonomous vehicles in a real world traffic environment is costly, and covering all corner cases is nearly impossible. Furthermore, it is very challenging to create a controlled real traffic environment that vehicle tests can be conducted repeatedly and compared fairly. With the capability of allowing testing more scenarios than those that would be possible with real world testing, simulations are deemed safer, more efficient, and more cost-effective. In this work, a full-scale simulation platform was developed to simulate the infrastructure, traffic, vehicle, powertrain, and their interactions. It is used as an effective tool to facilitate control algorithm development for improving CAV’s fuel economy in real world driving scenarios.

Disc brakes operate with very close proximity of the brake pads and the brake rotor, with as little as a tenth of a millimeter of movement of the pads required to bring them into full contact with the rotor to generate braking torque. It is usual for a disc brake to operate with some amount of residual drag in the fully released state, signifying constant contact between the pads and the rotor. With this contact, every miniscule movement of the rotor pushes against the brake pads and changes the forces between them. Sustained loads on the brake corner, and maneuvers such as cornering, can both produce rotor movement relative to the caliper, which can push it steadily against one or both of the brake pads. This can greatly increase the residual force in the caliper, and increase drag. This dependence of drag behavior on the movement of the brake rotor creates some vehicle-dependent behavior.

The engine operating point (EOP), which is determined by the engine speed and torque, is an important part of a vehicle's powertrain performance and it impacts FC, available propulsion power, and emissions. Predicting instantaneous EOP in real-time subject to dynamic driver behaviour and environmental conditions is a challenging problem, and in existing literature, engine performance is predicted based on internal powertrain parameters. However, a driver cannot directly influence these internal parameters in real-time and can only accommodate changes in driving behaviour and cabin temperature. It would be beneficial to develop a direct relationship between the vehicle-level parameters that a driver could influence in real-time, and the instantaneous EOP. Such a relationship can be exploited to dynamically optimize engine performance.

In the context of vehicle electrification, improving vehicle aerodynamics is not only critical for efficiency and range, but also for driving experience. In order to balance the necessary trade-offs between drag and downforce without significant impact on the vehicle styling, we see an increasing amount of active aerodynamic solutions on high-end passenger vehicles. Active rear spoilers are one of the most common active aerodynamic features. They deploy at high vehicle speed when additional downforce is required [1, 2]. For a vehicle with an active rear spoiler, the aerodynamic performance is typically predicted through simulations or physical testing at different static spoiler positions. These positions range from fully stowed to fully deployed. However, this approach does not provide any information regarding the transient effects during the deployment of the rear spoiler, which can be critical to understanding key performance aspects of the system.

Edge stretchability reduction induced by mechanical trimming is a critical issue in advanced high strength steel applications. In this study, the tooling effects on the trimmed edge damage were evaluated by the specially designed in-plane hole expansion test with the consideration of three punch geometries (flat, conical, and rooftop), three cutting clearances (6%, 14%, and 20%) and two materials grades (DP980 and DP1180). Two distinct fracture initiation modes were identified with different testing configurations, and the occurrence of each fracture mode depends on the tooling configurations and materials grades. Digital Image Correlations (DIC) measurements indicate the materials are subject to different deformation modes and the various stress conditions, which result in different fracture initiation locations.

Cast austenitic stainless steels, such as 1.4837Nb, are widely used for turbo housing and exhaust manifolds which are subjected to elevated temperatures. Due to assembly constraints, geometry limitation, and particularly high temperatures, thermomechanical fatigue (TMF) issue is commonly seen in the service of those components. Therefore, it is critical to understand the TMF behavior of the cast steels. In the present study, a series of fatigue tests including isothermal low cycle fatigue tests at elevated temperatures up to 1100°C, in-phase and out-of-phase TMF tests in the temperature ranges 100-800°C and 100-1000°C have been conducted. Both creep and oxidation are active in these conditions, and their contributions to the damage of the steel are discussed.

The automotive industry is continuously striving to reduce vehicle mass by reducing the mass of components including wheel bearings. A typical wheel bearing assembly is mostly steel, including both the wheel and knuckle mounting flanges. Mass optimization of the wheel hub has traditionally been accomplished by reducing the cross-sectional thickness of these components. Recently bearing suppliers have also investigated the use of alternative materials. While bearing component performance is verified through analysis and testing by the supplier, additional effects from system integration and performance over time also need to be comprehended. In a recent new vehicle architecture, the wheel bearing hub flange was reduced to optimize it for low mass. In addition, holes were added for further mass reduction. The design met all the supplier and OEM component level specifications.

The importance of achieving good (low) assembled lateral runout of the brake disc is well recognized in the industry - it is a critical feature for avoiding issues such as wear-induced disc thickness variation and vibration/shudder during braking. Significant efforts and expense has been invested by the industry into reducing disc brake lateral runout. However, wheel assemblies also have some inherent runout, which in turn cause cyclical forces to act on the brake corner during vehicle movement. Despite the stiffness of the wheel bearing (which aligns the brake disc with the caliper and knuckle), these “tire non-uniformity” forces can be sufficient to promote deflection of the assembly that is appreciable compared to typical disc lateral runout tolerances. This paper covers measurements of this phenomenon on three different vehicles (compact, mid-size, and large cars), under a variety of operating conditions such as speed, wheel assembly runout, and wheel assembly balance.

This study evaluates utilizing an accelerated test method that correlates customer interaction with a vehicle seat where bagginess and wrinkling is produced. The evaluation includes correlation from warranty returns as well as test vehicle results for test verification. Consumer metrics will be discussed within this paper with respect to potential application of this test method, including but not limited to JD Power ratings. The intent of the test method is to aid in establishing appropriate design parameters of the seat trim covers and to incorporate appropriate design measures such as tie downs and lamination. This test procedure was utilized in a Design for Six Sigma (DFSS) project as an aid in optimizing seat parameters influencing trim cover performance using a Design of Experiment approach. Presenter Lisa Fallon, General Motors LLC

Fabric materials have diverse applications in the automotive industry which include upholstery, carpeting, safety devices, and interior trim components. The textile industry has invested substantial effort toward development of standard testing techniques for characterizing mechanical properties of different fabric types (e.g. woven and knitted). However, there are presently no standards for determination of Young's modulus, Poisson's ratio and tensile stress-strain properties required for the detailed modeling of fabric materials in vehicle structural simulations. This paper presents results from uniaxial tensile tests of different automotive seat cover fabric materials. Digital image correlation, a full field optical method for measuring surface deformation, was used to determine tensile properties in both the warp/wale and the weft/course directions. The fabrics were tested with and without the foam backing.

Although advanced driver assistance systems (ADAS) have been widely introduced in automotive industry to enhance driving safety and comfort, and to reduce drivers’ driving burden, they do not in general reflect different drivers’ driving styles or customized with individual personalities. This can be important to comfort and enjoyable driving experience, and to improved market acceptance. However, it is challenging to understand and further identify drivers’ driving styles due to large number and great variations of driving population. Previous research has mainly adopted physical approaches in modeling drivers’ driving behavior, which however are often very much limited, if not impossible, in capturing human drivers’ driving characteristics. This paper proposes a reinforcement learning based approach, in which the driving styles are formulated through drivers’ learning processes from interaction with surrounding environment.

Commercially available Generation 3 (GEN3) advanced high strength steels (AHSS) have inherent capability of replacing press hardened steels (PHS) using cold stamping processes. 980 GEN3 AHSS is a cold stampable steel with 980 MPa minimum tensile strength that exhibits an excellent combination of formability and strength. Hot forming of PHS requires elevated temperatures (> 800°C) to enable complex deep sections. 980 GEN3 AHSS presents similar formability as 590 DP material, allowing engineers to design complex geometries similar to PHS material; however, its cold formability provides implied potential process cost savings in automotive applications. The increase in post-forming yield strength of GEN3 AHSS due to work and bake hardening contributes strongly toward crash performance in energy absorption and intrusion resistance.

Adiabatic heating during plastic straining can slow the diffusionless shear transformation of austenite to martensite in steels that exhibit transformation induced plasticity (TRIP). However, the extent to which the transformation is affected over a strain rate range of relevance to automotive stamping and vehicle impact events is unclear for most third-generation advanced high strength TRIP steels. In this study, an 1180MPa minimum tensile strength TRIP steel with carbide-free bainite is evaluated by measuring the variation of retained austenite volume fraction (RAVF) in fractured tensile specimens with position and strain. This requires a combination of servo-hydraulic load frame instrumented with high speed stereo digital image correlation for measurement of strains and ex-situ synchrotron x-ray diffraction for determination of RAVF in fractured tensile specimens.

Third generation advanced high strength steels (AHSS) that rely on the transformation of austenite to martensite have gained growing interest for implementation into vehicle architectures. Previous studies have identified a dependency of the rate of austenite decomposition on the amount of strain and the associated strain path imposed on the sheet. The rate and amount of austenite transformation can impact the work hardening behavior and tensile properties. However, a deeper understanding of the impact on toughness, and thus crash performance, is not fully developed. In this study, the strain path and strain amounts were systematically controlled to understand the associated correlation to impact toughness in the end application condition (strained and baked). Impact toughness was evaluated using an instrumented Charpy machine with a single sheet v-notch sample configuration.

Third generation advanced high strength steels (3GAHSS) are increasingly used in automotive for light weighting and safety body structure components. However, high material strength usually introduces higher springback that affects the dimensional accuracy. The ability to accurately predict springback in simulations is very important to reduce time and cost in stamping tool and process design. In this work, tension and compression tests were performed and the results were implemented to generate Isotropic/Kinematic hardening (I/KH) material models on a 3GAHSS steel with 980 MPa minimum tensile strength. Systematic material model parametric studies and evaluations have been conducted. Case studies from full-scale industrial parts are provided and the predicted springback results are compared to the measured springback data. Key variables affecting the springback prediction accuracy are identified.

The high performance brake systems of today are usually in a delicate balance - walking the fine line between being overpowered by some of the most potent powertrains, some of the grippiest tires, and some of the most demanding race tracks that the automotive world has ever seen - and saddling the vehicle with excess kilograms of unsprung mass with oversized brakes, forcing significant compromises in drivability with oversized tires and wheels. Brake system design for high performance vehicles has often relied on a very deep understanding of friction material performance (friction, wear, and compressibility) in race track conditions, with sufficient knowledge to enable this razor’s edge design.

Robust engineering is an integral part of the quality initiative, Design For Six Sigma (DFSS), in most companies to enable good designs and products for reliability and durability. Taguchi’s signal-to-noise ratio has been considered as a good performance index for robustness for many years. An alternate approach that is direct and simple for measuring robustness is proposed. In this approach, robustness is measured in terms of an augmented output response and it is a composite index of variation and efficiency of a system. This formulation represents an engineering design intent of a product in a statistical sense, so engineers can understand, communicate, and resonate at ease. Robust formulations are illustrated and discussed with case studies for smaller-the-better, nominal-the-best, and dynamic responses. Confirmation runs of optimization show good agreement of the augmented response with the additive predictive models.

Retained austenite stability to both mechanically induced transformation and athermal transformation is of great importance to the fabrication and in-vehicle performance of automotive advanced high strength steels. Selected cold-rolled advanced high strength steels containing retained austenite with minimum tensile strengths of 980 MPa and 1180 MPa were pre-strained to pre-determined levels under uniaxial tension in the rolling direction and subsequently cooled to temperatures as low as 77 K. Room temperature uniaxial tensile results of pre-strained and cooled steels indicate that retained austenite is stable to athermal transformation to martensite at all tested temperatures and pre-strain levels. To evaluate the combined effects of temperature and pre-strain on impact behavior, stacked Charpy impact testing was conducted on the same 980 MPa minimum tensile strength steel following similar pre-straining in uniaxial tension.

The validation of brake discs has remained, to this day, heavily reliant on “Thermal Abuse” or “Thermal Cracking” type testing, with many procedures so dated that most engineers active in the industry today cannot even recall the origin of the test. These procedures - of which there are many variants - all share the trait of greatly accelerating durability testing by performing repeated high power (high speed and high deceleration) brake applies to drive huge temperature gradients and internal stress, and often allowing the disc to get very hot, to where the strength of the material from which the disc is constructed is significantly degraded. There is little debate about whether these procedures work; by and large disc durability issues in the field are extremely rare.

A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture.