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As material cleanliness and bearing lubrication have improved, wheel bearings are experiencing less raceway spalling failures from rotating fatigue. Warranty part reviews have shown that two of the larger failure modes for wheel bearings are contaminant ingress and Brinell damage from curb and pothole impacts. Warranty has also shown that larger wheels have higher rates of Brinell warranty. This paper discusses the Brinell failure mode for bearings. It reviews a vehicle test used to evaluate Brinell performance for wheel bearings. The paper also discusses a design of experiments to study the effects of factors such as wheel size, vehicle loading and vehicle position versus the bearing load from a vehicle side impact to the wheel. As the trend in vehicle styling is moving to larger wheels and low profile tires, understanding the impact load can help properly size wheel bearings.

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 presents recent advances in automotive microprocessor, operating system, and supporting software technology that supports regulatory and/or functional safety graphics within vehicle cockpit displays. These graphics include “virtual switches” that replace physical switches in the vehicle, as well as “virtual indicators” that replace physical indicator lights. We discuss the functional safety design process and impacts to software and hardware architecture as well as the software design methods to implement End-To-End [E2E] network protection between different ECUs and software processes. We also describe hardware monitoring requirements within the display panel, backlighting, and touch screen and examine an example system design to illustrate the concepts.

GM's R oad-to- L ab-to- M ath (RLM) initiative is a fundamental engineering strategy leading to higher quality design, reduced structural cost, and improved product development time. GM started the RLM initiative several years ago and the RLM initiative has already provided successful results. The purpose of this paper is to detail the specific RLM efforts at GM related to powertrain controls development and calibration. This paper will focus on the current state of the art but will also examine the history and the future of these related activities. This paper will present a controls development environment and methodology for providing powertrain controls developers with virtual (in the absence of ECU and vehicle hardware) calibration capabilities within their current desktop controls development environment.

Heating devices are effective technologies to strengthen emission robustness of AfterTreatment Systems (ATS) and to guarantee emission compliance in the new boundaries given by upcoming legislations. Moreover, they allow to manage the ATS warm-up independently from engine operating conditions, thereby reducing the need for specific combustion strategies. Within heating devices, an attractive solution to provide the required thermal power without mandating a 48V platform is the fuel burner. In this work, a model-based control coordinator to manage the interaction between engine, ATS and fuel burner device has been developed, virtually validated, and optimized. The control function features a burner model and a control logic to deliver the needed amount of thermal energy, while ensuring ATS hardware protection.

Complex chassis systems operate in various environments such as low-mu surfaces and highly dynamic maneuvers. The existing metrics for lateral motion hazard by Neukum [13] and Amberkar [17] have been developed and correlated to driver behavior against disturbances on straight line driving on a dry surface, but do not cover low-mu surfaces and dynamic driving scenarios which include both linear and nonlinear region of vehicle operation. As a result, an improved methodology for evaluating vehicle yaw dynamics is needed for safety analysis. Vehicle yaw dynamics safety analysis is a methodical evaluation of the overall vehicle controllability with respect to its yaw motion and change of handling characteristic.

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.

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.

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.

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.

Wheel bearing friction torque (“drag”) directly contributes to vehicle fuel economy and CO2 emissions. At the same time, one of the most important factors for long-term durability of wheel bearings is effective seal performance. Since these two factors are often in conflict, it is important to balance the desire for low friction with the need for optimal sealing. One factor that affects wheel bearing sealing performance is the distortion of the outer ring that occurs when the bearing is mounted to the steering knuckle with fasteners. Minimizing this distortion is not just important for sealing, however. This paper explores the relationship between the outer ring distortion and the resulting friction torque. A design of experiments (DOE) approach was used in order to study the effects of the fastening bolt torque, constant velocity joint (CVJ) fastening torque, and outer ring distortion on component-level drag.

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.

Transmission Control Module (TCM) bracket is mounted on the vehicle chassis and is subjected to the random load excitation due to the uneven surface of the road. Assembly of the TCM bracket on the vehicle chassis induces some constant stress on it due to bolt preload, which acts as a mean stress along with the varying random loads. It is important for a design engineer and CAE analyst to understand the effect of all sources of loads on vehicle mount brackets while designing them. The objective of this study is to consider the effect of mean stress in the random vibration fatigue assessment of TCM bracket. The random vibration fatigue analyses are performed for all the three directions without and with consideration of mean loads and results are compared to show the significance of mean stresses in random vibration fatigue life.

The purge pump is used to pull evaporative gases from canister and send to engine for combustion in Turbocharged engines. The purge pump with impeller at one end and electric motor at the other end is supported by the ball bearing assembly. A bearing kinematic model to predict forcing function due to defect in ball bearing arrangement, coupled with bearing dynamic model of rotor because of rotating component, is proposed in this paper to get accumulated effect on transmitted force to the purge pump housing. Rotor dynamic of purge pump rotor components only produces certain order forcing responses which can be simulated into the multibody software environment, knowing the ball bearing geometry parameters hence providing stiffness parameter for rotor system.

Gasoline Particulate Filters (GPF) are widely employed in exhaust aftertreatment systems of gasoline engines to meet the stringent particulate emissions requirements of Euro6 and China6 standard. While providing an effective filtration of particles, the GPF increases the engine backpressure as a penalty due to accumulation of soot. To clean the accumulated soot, periodical burning of soot is achieved by the onboard control models and lot of effort is spent on calibrating the same. In order to understand pressure drop behavior across GPF, detailed pressure drop measurements were conducted at clean, soot and ash loaded conditions at engine dynamometer and at vehicle conditions. Effect of degreening of GPF was studied to take into account any change in pressure drop characteristics of onboard control models during GPF aging in the vehicle.

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.

The power loss of bearings is a significant factor in the overall efficiency in a drive unit system. Such bearings are subject to combined radial and axial loading needed to support the gear mesh forces. An experimental methodology has been developed to perform sets of power loss measurements on TRB, 4PCBB and DGBB. These measurements were performed under a variety of speed, load, temperature, and lubrication conditions. The loss behaviors of these types of the bearings are discussed, along with the tradeoff of different bearing arrangements for the fuel economy cycles. Several power loss models are employed to assess the accuracy of the estimations as compared to the experimental measurements. At low speed some models showed good correlations for TRB and DGBB, while at higher speed, they start deviating from the testing results. A higher fidelity model for estimating the losses at high speed, especially speed around 20krpm and beyond, needs to be developed.