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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

Due to the diversity of Heavy Duty Vehicles (HDV), the European CO2 and fuel consumption monitoring methodology for HDVs will be based on a combination of component testing and vehicle simulation. In this context, one of the key input parameters that need to be accurately defined for achieving a representative and accurate fuel consumption simulation is the vehicle's aerodynamic drag. A highly repeatable, accurate and sensitive measurement methodology was needed, in order to capture small differences in the aerodynamic characteristics of different vehicle bodies. A measurement methodology is proposed which is based on constant speed measurements on a test track, the use of torque measurement systems and wind speed measurement. In order to support the development and evaluation of the proposed approach, a series of experiments were conducted on 2 different trucks, a Daimler 40 ton truck with a semi-trailer and a DAF 18 ton rigid truck.

The reliability of electronic components is of increasing importance for further progress towards automated driving. Thermal aging processes such as electromigration is one factor that can negatively affect the reliability of electronics. The resulting failures depend on the thermal load of the components within the vehicle lifetime - called temperature collective - which is described by the temperature frequency distribution of the components. At present, endurance testing data are used to examine the temperature collective for electronic components in the late development stage. The use of numerical simulation tools within Vehicle Thermal Management (VTM) enables lifetime thermal prediction in the early development stage, but also represents challenges for the current VTM processes [1, 2]. Due to the changing focus from the underhood to numerous electronic compartments in vehicles, the number of simulation models has steadily increased.

The object of the validation study presented in this paper is a generic vehicle, the so-called SAE body, developed by a consortium of German car manufacturers (Audi, Daimler, Porsche, Volkswagen). Many experiments have been performed by the abovementioned consortium on this object in the past to investigate its behavior when exposed to fluid flow. Some of these experiments were used to validate the simulation results discussed in the present paper. It is demonstrated that the simulation of the exterior flow is able to represent the transient hydrodynamic structures and at the same time both the generation of the acoustic sources and the propagation of the acoustic waves. Performing wave number filtering allows to identify the acoustic phenomena and separate them from the hydrodynamic effects. In a next step, the noise transferred to the interior of the cabin through the glass panel was calculated, using a Statistical Energy Analysis approach.

The human-robot interaction (HRI) is involved in a lift assistant system of manufacturing assembly line. The admittance model is applied to control the end effector motion by sensing intention from force of applied by a human operator. The variable admittance including virtual damping and virtual mass can improve the performance of the systems. But the tuning process of variable admittance is un-convenient and challenging part during the real test for designers, while the offline simulation is lack of learning process and interaction with human operator. In this paper, the Iterative learning algorithm is proposed to emulate the human learning process and facilitate the variable admittance control design. The relationship between manipulate force and object moving speed is demonstrated from simulation data. The effectiveness of the approach is verified by comparing the simulation results between two admittance control strategies.

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 study evaluated the interactions between sheet steel, lubricant and measurement system under typical sheet forming conditions using a fixed draw bead simulator (DBS). Deep drawing quality mild steel substrates with bare (CR), electrogalvanized (EG) and hot dip galvanized (HDG) coatings were tested using a fixed DBS. Various lubricant conditions were targeted to evaluate the coefficient of friction (COF) of the substrate and lubricant combinations, with only rust preventative mill oil (dry-0 g/m2 and 1 g/m2), only forming pre-lube (dry-0 g/m2, 1 g/m2, and >6 g/m2), and a combination of two, where mixed lubrication cases, with incremental amounts of a pre-lube applied (0.5, 1.0, 1.5 and 2.0 g/m2) over an existing base of 1 g/m2 mill oil, were analyzed. The results showed some similarities as well as distinctive differences in the friction behavior between the bare material and the coatings.

The piston temperature has to be carefully controlled to achieve effective and efficient thermal management in the internal combustion engines. One of the common methods to cool piston is by injecting oil from the crankcase underside to the piston under-crown area. In the present study, a novel 3-D multiphase thermal-fluid coupled model was developed using the commercial CFD software SimericsMP+ to study the piston cooling using the oil jet. In this model, an algorithm was proposed to couple the fluid and solid computation domain to account for the different timescale of heat transfer in the fluid and solid due to the high thermal inertia of the solid piston. The heat transfer coefficient (HTC) and reference temperature were mapped to the piston top surface and the liner temperature distribution was also used as the boundary condition. The temperature-dependent material properties, piston motion, and thermal contact resistance between the ring and piston were also accounted for.

Fuel cells convert a fuel together with oxygen in a highly efficient electrochemical reaction to electricity and water. Automotive fuel cell systems mainly use compressed onboard stored hydrogen as fuel. Oxygen from ambient air is fed to the cathode of the fuel cell stack by an air supply subsystem. For its current and next generation air supply subsystem NuCellSys has employed screw type compressor technology, which in the automotive area initially was developed for supercharged internal combustion (IC) engines. As NVH expectations to fuel cell vehicles differ very much from IC-engine driven vehicles, specific efforts have to be taken to address the intense noise and vibration profile of the screw compressor. This paper describes different counter measures which have been implemented into the NuCellSys next generation air supply subsystem.

Aerodynamic design and thermal management are some of the most important tasks when developing new concepts for the flow around tractor-trailers. Today, both experimental and numerical studies are an integral part of the aerodynamic and thermal design processes. A variety of studies have been conducted how the aerodynamic design reduces the drag coefficient for fuel efficiency as well as for the construction of radiators to provide cooling on tractor-trailers. However, only a few studies cover the combined effect of the aerodynamic and thermal design on the air temperature of the under hood flow [8, 13, 16, 17, 20]. The objective of this study is to analyze the heat transfer through forced convection for a scaled Cab-over-Engine (CoE) tractor-trailer model with under hood flow. Different design concepts are compared to provide low under hood air temperature and efficient cooling of the sub components.

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 General Motors Aero Lab’s Full-Scale Wind Tunnel Facility, which came into operation in August of 1980[1], has undergone the significant upgrade of installing a state-of-the-art moving ground plane system. After almost four decades of continuous use the full-scale wind tunnel also received some significant maintenance to other areas, including a new heat exchanger, main fan overhaul, and replacement of the test section acoustic treatment. A 5-belt system was installed along with an integrated vehicle lift system. The center belt measures 8.5m long and can accommodate two belt widths of 1100mm and 950mm. Flow quality and other wind tunnel performance parameters were maintained to prior specifications which are on par with the latest industry standards [2]. The new 5-belt rolling road system maintains GM’s industry leading vehicle aerodynamic development and the improved acoustic panels ensure GM continues to develop vehicles with leading class acoustics.

Over the past several years a task group within the SAE Automotive Corrosion and Protection (ACAP) Committee has conducted extensive on-vehicle field testing and numerous accelerated lab tests with the goal of establishing a standard accelerated test method for cosmetic corrosion evaluations of finished aluminum auto body panels. This project has been a cooperative effort with OEM, supplier, and consultant participation and was also supported in part by DOE through USAMP (AMD 309). The focus of this project has been the identification of a standardized accelerated cosmetic corrosion test that exhibits the same appearance, severity, and type of corrosion products that are exhibited on identical painted aluminum panels exposed to service relevant environments. Multi-year service relevant exposures were conducted by mounting panels on-vehicles in multiple locations in the US and Canada.

This paper summarizes a common project of Mercedes-Benz and FKFS (Research Institute of Automotive Engineering) to apply numerical methods to thermal soak issues in a very early stage of the development phase of a new car. “Thermal soak” results from driving the vehicle at high load followed by shutting off the engine and a cool down phase. After stopping, the underhood flow is only driven by natural convection. The thermal soak behaviour is discussed in principal and the numerical challenges are summarized. Four different issues are identified: the need for a transient computation including transient thermal load pattern, a method to compute natural convection in the underhood after the shutdown of the engine, the complex geometry and the lack of a single computational program to consider all three modes of heat transfer, which results in a coupled numerical approach.

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.

This report describes a new driving simulator capability at General Motors (GM) Research and Development's (R&D) Vehicle Development Research (VDR) Laboratory and its application in an iterative HMI development process. The paper also provides an overview of three recent simulator usability tests supporting HMI development.

Brake system development and testing is spread over vehicle manufacturers, system and component suppliers. Test equipment from different sources, even resulting from different technology generations, different data analysis and report tools - comprising different and sometimes undocumented algorithms - lead to a difficult exchange and analysis of test results and, at the same time, contributes to unwanted test variability. Other studies regarding the test variability brought up that only a unified and unambiguous data format will allow a meaningful and comparative evaluation of these data and only standardization will reveal the actual reasons of test variability. The text at hand illustrates that a substantial part of test variability is caused by a misinterpretation of data and/or by the application of different algorithms.

As the automotive industry quickly moves towards hybridized and electrified vehicles, the optimal integration of power electronics in these vehicles will have a significant impact not only on the cost, performance, reliability, and durability; but ultimately on customer acceptance and market success of these technologies. If properly executed with the right cost, performance, reliability and durability, then both the industry and the consumer will benefit. It is because of these interdependencies that the pace and scale of success, will hinge on effective collaboration. This collaboration will be built around the convergence of automotive and industrial technology. Where real time embedded controls mixes with high power and voltage levels. The industry has already seen several successful collaborations adapting power electronics to the automotive space in target vehicles.

Model-based control system design improves quality, shortens development time, lowers engineering cost, and reduces rework. Evaluating a control system's performance, functionality, and robustness in a simulation environment avoids the time and expense of developing hardware and software for each design iteration. Simulating the performance of a design can be straightforward (though sometimes tedious, depending on the complexity of the system being developed) with mathematical models for the hardware components of the system (plant models) and control algorithms for embedded controllers. This paper describes a software tool and a methodology that not only allows a complete system simulation to be performed early in the product design cycle, but also greatly facilitates the construction of the model by automatically connecting the components and subsystems that comprise it.

There is a growing need for life-cycle data – so-called collectives – when developing components like elastomer engine mounts. Current standardized extreme load cases are not sufficient for establishing such collectives. Supplementing the use of endurance testing data, a prediction methodology for component temperature collectives utilizing existing 3D CFD simulation models is presented. The method uses support points to approximate the full collective. Each support point is defined by a component temperature and a position on the time axis of the collective. Since it is the only currently available source for component temperature data, endurance testing data is used to develop the new method. The component temperature range in this data set is divided in temperature bands. Groups of driving states are determined which are each representative of an individual band. Each of the resulting four driving state spaces is condensed into a substitute load case.