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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.

This paper reviews the relationship between taper wheel bearing damage and vehicle noise and vibration for a body-on-frame pickup truck and a body-on-frame SUV. In addition to understanding how the different levels of bearing damage relate to vehicle noise, it also discusses the level of noise versus the damaged bearing’s position in the vehicle. For this study, the wheel bearing supplier provided front and rear bearings with various amounts of Brinell damage to the bearing raceways. The different bearings were evaluated subjectively for noise in the vehicle. After vehicle testing, the bearing raceway Brinell depths were measured to correlate the level of bearing damage to vehicle noise. The study shows the relationship between bearing Brinell dent depth and vehicle noise for body-on-frame light trucks and SUVs. The noise was most apparent in vehicles between 45 and 60 mph. For bearings with moderate levels of damage, steering inputs were required to hear noise.

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.

This paper summarizes the validation of prototype vehicle-to-infrastructure (V2I) safety applications based on Dedicated Short Range Communications (DSRC) in the United States under a cooperative agreement between the Crash Avoidance Metrics Partners LLC (CAMP) and the Federal Highway Administration (FHWA). After consideration of a number of V2I safety applications, Red Light Violation Warning (RLVW), Curve Speed Warning (CSW) and Reduced Speed Zone Warning with Lane Closure Warning (RSZW/LC) were developed, validated and demonstrated using seven different vehicles (six passenger vehicles and one Class 8 truck) leveraging DSRC-based messages from a Road Side Unit (RSU). The developed V2I safety applications were validated for more than 20 distinct scenarios and over 100 test runs using both light- and heavy-duty vehicles over a period of seven months. Subsequently, additional on-road testing of CSW on public roads and RSZW/LC in live work zones were conducted in Southeast Michigan.

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.

The present production General Motors 2-Mode Hybrid system for full-size SUVs and pickup trucks integrates truck utility functions with a full hybrid system. The 2-mode hybrid system incorporates two electro-mechanical power-split operating modes with four fixed-gear ratios. The combination provides fuel savings from electric assist, regenerative braking and low-speed electric vehicle operation. The combination of two power-split modes reduces the amount of mechanical power that is converted to electric power for continuously variable transmission operation, meeting the utility required for SUVs and trucks. This paper describes how fuel economy functionality was blended with full-size truck utility functions. Truck functions described include: Manual Range Select, Cruise Control, 4WD-Low and continuous high load operation.

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.

Hybrid powertrain vehicles inherently create discontinuous sounds during operation. The discontinuous noise created from the electrical motors during transition states are undesirable since they can create tones that do not correlate with the dynamics of the vehicle. The audible level of these motor whines and discontinuous tones can be reduced via common noise abatement techniques or reducing the amount of regeneration braking. One electronic solution which does not affect mass or fuel economy is Masking Sound Enhancement (MSE). MSE is an algorithm that uses the infotainment system to mask the naturally occurring discontinuous hybrid drive unit and driveline tones. MSE enables a variety of benefits, such as more aggressive regenerative braking strategies which yield higher levels of fuel economy and results in a more pleasing interior vehicle powertrain sound. This paper will discuss the techniques and signals used to implement MSE in a hybrid powertrain equipped vehicle.

Torque ripple of electric motors is a unique feature in Battery Electric Vehicles (BEV) affecting vehicle performance. It is one of the disturbances from electric motors resulting in unpleasant vehicle fore-aft vibrations at specific vehicle speeds. In this study, the torque ripple modeling and simulation procedure has been developed. Critical modeling contents in a full vehicle ADAMS model and a brief overview of the propulsion control are described. Analytical data sets for torque fluctuations (torque ripple) from a couple of different sources are incorporated in the model. The CAE simulation procedure was applied to simulate vehicle performances of a General Motors Battery Electric Vehicle in an early vehicle design phase. Torque ripple phenomena are simulated both in an open-loop and closed-loop propulsion control environment to see how much vehicle fore-aft vibration suppression is achieved by the motor control methods.

Electric motor whine is a major NVH source for electric vehicles. Traditional mitigation methods focus on e-motor hardware optimization, which requires long development cycles and may not be easily modified when the hardware is built. This paper presents a control- and software-based strategy to reduce the most dominant motor order of an IPM motor for General Motors’ Ultium electric propulsion system, using the patented active Torque Ripple Cancellation (TRC) technology with harmonic current injection. TRC improves motor NVH directly at the source level by targeting the torque ripple excitations, which are caused by the electromagnetic harmonic forces due to current ripples. Such field forces are actively compensated by superposition of a phase-shifted force of the same spatial order by using of appropriate current.

Electric motor whine is one of the main noise sources of hybrid and electric vehicles. Motor air gap eccentricity due to propulsion system deflection, part tolerances and manufacturing variation is typically ignored in motor NVH design and analysis. Such eccentricity can be a dominant noise source by amplifying critical motor whine orders up to 10 dB, leading to poor NVH robustness. However, this problem cannot be explained by conventional method based on symmetric 2D approach. New 3D electromagnetic (EM) and NVH analyses are developed and validated to accurately predict air gap induced motor noise to enhance NVH robustness: First, a true 3D full 360-degree electric motor model is developed to model asymmetric air gap distribution along motor stack length. Predicted 3D EM forces are mapped to mechanical finite-element mesh over the cylindrical stator surface.

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.

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.

This paper examines the effect of rear effective backlight angle on vehicle contamination using contamination simulation results of a commercial vehicle. Highly-resolved time accurate computational fluid dynamics simulations were performed using a commercial Lattice-Boltzmann solver, to compare the rear end contamination with five different rear effective backlight angles. Additional aerodynamics simulations presented good correlation with published experimental data. The contamination results were compared with the aerodynamics simulation results in order to find trends between the two simulation types for different effective backlight angles.

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.

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.

This paper presents the work of the SAE Brake NVH Standards Committee in developing a draft Low-Frequency Brake Noise Test Procedure. The goal of the procedure is to be able to accurately measure noise issues in the frequency range below 900 Hz using a conventional shaft brake noise dynamometer. The tests conducted while evaluating alternative test protocols will be discussed and examined in detail. The unique issues encountered in developing a suitable test procedure for low-frequency noise will be discussed, and the results of tests using both shaft brake dynamometers and chassis dynamometers will be described. The current draft procedure incorporating the knowledge gained from this development effort will be described in detail and conclusions as to its applicability will also be presented

Energy management in electric vehicles plays a significant role in both reducing energy consumption and limiting the rate of battery capacity degradation. It is especially important for systems with multiple energy storage units where optimally arbitrating power demand among the energy storage units is challenging. While many optimal control methods exist for designing a good energy management system, in this work a Reinforcement-Learning (RL) methodology is explored to design an energy management system for an electric vehicle with a Hybrid Energy Storage System (HESS) that included a battery and a supercapacitor. The energy management system is designed to optimally divide the traction power request among a battery and a super-capacitor in real-time; while trying to minimize the overall energy consumption and battery degradation.

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.

More than four decades ago, the concept of zero defects was coined by Phillip Crosby. It was only a vision at the time, but the introduction of Artificial Intelligence (AI) in manufacturing has since enabled it to become attainable. Since most mature manufacturing organizations have merged traditional quality philosophies and techniques, their processes generate only a few Defects Per Million of Opportunities (DPMO). Detecting these rare quality events is one of the modern intellectual challenges posed to this industry. Process Monitoring for Quality (PMQ) is an AI and big data-driven quality philosophy aimed at defect detection and empirical knowledge discovery. Detection is formulated as a binary classification problem, where the right Machine Learning (ML), optimization, and statistics techniques are applied to develop an effective predictive system.