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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.

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.

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.

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.

Given the current proliferation of active safety features on new vehicles, especially for Advanced Driver Assistance Systems (ADAS) and Highly Automated Driving (HAD) technologies, it is evident that there is a need for testing methods beyond a vehicle level physical test. This paper will discuss the current state of the art in the industry for simulation-based verification and validation (V&V) testing methods. These will include, but are not limited to, "Hardware-in-the-Loop (HIL)", “Software-in-the-Loop (SIL)”, “Model-in-the-Loop (MIL)”, “Driver-in-the-Loop (DIL)”, and any other suitable combinations of the aforementioned (XIL). Aspects of the test processes and needed components for simulation will be addressed, detailing the scope of work needed for various types of testing. The paper will provide an overview of standardized test aspects, active safety software validation methods, recommended practices and standards.

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.

In traditional automotive electronic design, software update has been a component oriented, manual process rather than a systematic designed in capability suitable for automation. In recent days as software content in vehicles grow, the need to update software in vehicles more frequently is becoming a necessity. Moreover, additional attributes for software updates, for example timely delivery of security related update for vehicles, desire to add features using software update, control cost of software updates, etc., requires a system engineered design rather than a component oriented approach. As the automobile domain utilizes various means of mobility (Combustion Engine, Hybrid, Battery, etc.) and various functional domains (Infotainment, Safety, Mobility, Telematics, ADAS (Advance Driving Assist service), Autonomous, etc.), to control the overall cost of future software update for such a diverse environment, it is beneficial to introduce automation in the software update process.

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.

DTS (Double Transition Shift) transmission can achieve multiple-speed automatic transmission by using minimum number of planetary gear sets and clutches. Historically DTS transmission has been overlooked because of the complexity to perform high quality shifts. In this paper, a vehicle model with a traditional AT (Automatic Transmission) is studied first using Matlab/SimDriveline; the simulation results and the vehicle road test data show good agreement. Then a DTS transmission is established based on the same vehicle model. An open-loop control module is developed. All possible parameters which can influence the shift performance are studied.

Scuffing is a common source of failure for many mechanical components in automobiles. 8625 alloy steel is commonly used in camshafts, gears, piston pins, shafts, and splines. The purpose of the research is to study the scuffing resistance of non-treated, carburized, nitrocarburized, and carbonitrided 8625 alloy steels. The scuffing resistance of the 8625 alloy steels was determined through pin-on-disk tests. The hardness and microstructure of the disks were analyzed using electron microscopy to determine wear mechanisms for each surface treated steel. The wear mechanisms were then related to the scuff resistance of the various materials.

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.

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.

Thermal cabin comfort is the largest consumer of battery energy second only to propulsion in Battery Electric Vehicles (BEV’s). Accurate prediction of thermal comfort in the vehicle cabin with fast turnaround times will allow engineers to study the impact of various thermal comfort technologies and develop energy efficient Heating, Ventilation and Air Conditioning (HVAC) systems. In this study a novel data-driven model based on physics-guided Sparse Identification of Nonlinear Dynamics (SINDy) method was developed to predict Equivalent Homogeneous Temperature (EHT), Mean Radiant Temperature (MRT) and cabin air temperature under transient conditions and drive cycles. EHT is a recognized measure of the total heat loss from the human body that can be used to characterize highly non-uniform thermal environments such as a vehicle cabin. The SINDy model was trained on drive cycle data from Climatic Wind Tunnel (CWT) for a representative Battery Electric Vehicle.

Accelerating product development cycles and incentives to reduce costs in product development are strong motivators to move to virtual development and validation processes. Challenges to moving to a virtual paradigm include a wealth of historical data and context for hardware tests, uncertainty over dependencies, and a lack of a clear path of transition to virtual methods. In this paper we will discuss approaches to understanding the value created by hardware tests and aligning that value to virtual processes. We will also discuss the need for a virtual context to be added to SAE J1739 [1] (DFMEA detection criteria), and how to create paths to maximize the value of virtual assessments. Finally, we will also discuss the cultural and organizational changes required to support.

With the emphasis of electrification in automotive industry, tremendous efforts are made to develop electric motors with high efficiency and power density, and reduce noise, vibration and harshness (NVH). A multiphysics simulation workflow is used to predict the eccentricity-induced noise for GM’s Bolt EV motor. Both static and dynamic eccentricities are investigated along with axial tilt. Analysis results show that these eccentricities play a critical role in the NVH behavior of the motor assembly. Transient electromagnetic (EM) analysis is performed first by extruding 2D stator and rotor sections to form 3D EM models. Sector model is duplicated to form full 360-degree model. Stator is split into three rotated sections to characterize stator skew, and the skew between two sections of rotor and magnets are also modelled. Sinusoidal current is applied and lumped-sum forces on each stator tooth are computed.

Vehicle electrification is driven globally due to the increased concerns on carbon emissions. But the challenges in customer acceptance remains esp. in relation to vehicle costs. Virtual simulations can help in cutting down product development cost and enable faster launch of new vehicles. An early stage system model based design iterations can help in cutting down the product development costs and building more robust products. In the current paper, we develop and analyze a battery pack system model for early phase design. We extend a previously developed system model to include critical physics like sub-component level multiphysics for electrical joint integrity. Also, we demonstrate an integration of 3D FEM & system model for improving the accuracy of joint temperature predictions during charging and/or discharging. A typical High Voltage (HV) battery system comprises of battery modules (Li-ion cells, cooling channels, structural frames, interconnect boards) and HV bus bars.

Torque ripple from electric motor can excite a system resonance perceived as vibration at the vehicle seat track. The CAE simulation procedure was applied to analyze the seat track acceleration excited by electric motor torque ripple. In this study, the transfer function between the electric motor torque and vehicle level seat track acceleration was developed, and it incorporates the control capability and vehicle sensitivity subfunctions. The motor level torque ripple requirement was developed, which can support motor design in early vehicle development stage based on vehicle level criteria. The analysis results obtained for motor level torque ripple requirement shows good agreement with the experimental validation using vehicle test data. The variation study on control capability and vehicle sensitivity was investigated, and the results can help to identify the solution to improve vehicle torque ripple response.

Styrene-Butadiene Rubber (SBR), a copolymer of butadiene and styrene, is widely used in the automotive industry due to its high durability and resistance to abrasion, oils and oxidation. Some of the common applications include tires, vibration isolators, and gaskets, among others. This paper characterizes the dynamic behavior of SBR and discusses the suitability of a visco-elastic model of elastomers, known as the Kelvin model, from a mathematical and physical point of view. An optimization algorithm is used to estimate the parameters of the Kelvin model. The resulting model was shown to produce reasonable approximations of measured dynamic stiffness. The model was also used to calculate the self heating of the elastomer due to energy dissipation by the viscous damping components in the model. Developing such a predictive capability is essential in understanding the dynamic behavior of elastomers considering that their dynamic stiffness can in general depend on temperature.