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A multi vector design tool to accurately predict instrument panel obscuration was developed to insure that critical legal displays in vehicles are not obscured. The concept provides for a computer generated light source shaped to replicate the human eyes. The light source is then projected onto a 3D math based arrangement and the resultant shadows are visible on the instrument panel surface and its displays. Design studios require criteria for the placement of the instrument cluster gages and displays, various controls, switches, and steering column stalks before an interior theme can be completed. Therefore, instrument panel obscuration and visibility must be determined early in the design process. The obscured areas are a function of the instrument panel surface, steering wheel rim, hub, spokes, and the location of the driver's eyes. This light source method allows engineers and designers the ability to quickly determine obscured areas.

e-Thermal is a vehicle level thermal analysis tool developed by General Motors to simulate the transient performance of the entire vehicle HVAC and Powertrain cooling system. It is currently in widespread (global) use across GM. This paper discusses the details of the air-conditioning module of e-Thermal. Most of the literature available on transient modeling of the air conditioning systems is based on finite difference approach that require large simulation times. This has been overcome by appropriately modeling the components using Sinda/Fluint. The basic components of automotive air conditioning system, evaporator, condenser, compressor and expansion valve, are parametrically modeled in Sinda/Fluint. For each component, physical characteristics and performance data is collected in form of component data standards. This performance data is used to curve fit parameters that then reproduce the component performance.

This paper describes a vehicle-level simulation model for climate control and powertrain cooling developed and currently utilized at GM. The tool was developed in response to GM's need to speed vehicle development for HVAC and powertrain cooling to meet world-class program execution timing (18 to 24 month vehicle development cycles). At the same time the simulation tool had to complement GM's strategy to move additional engineering responsibility to its HVAC suppliers. This simulation tool called “e-Thermal” was quickly developed and currently is in widespread (global) use across GM. This paper describes GM's objectives and requirements for developing e-Thermal. The structure of the tool and the capabilities of the simulation tool modules (refrigeration, front end airflow, passenger compartment, engine, transmission, Interior air handling …) is introduced. Model data requirements and GM's strategy for acquiring component data are also described.

The wear test introduced in this paper can be used to determine and rank PV (pressure time velocity) capability of plastic materials for applications where a plastic part is rotating or reciprocating against a metal surface. It provides an accelerated test method to evaluate the wear performance of plastic materials. A single test can provide tribological information at multiple PV conditions. The tribological information obtained from this method includes coefficient of friction, PV (pressure times velocity) limits, and interface temperature profile. This test is currently used by General Motors Corporation to develop plastic materials for transmission thrust washer and dynamic seal applications. The test is running in two sequences (A & B), capable of a PV range from 50,000 psi-ft/min 500,000 psi-ft/min, under dry conditions. The PV steps in sequence A are combinations of high pressure and low velocity - for applications where high loads are expected, such as thrust washers.

A common occurrence in computer aided design is the need to make changes to an existing CAD model to compensate for shape changes which occur during a manufacturing process. For instance, finite element analysis of die forming or die tryout results may indicate that a stamped panel springs back after the press line operation so that the final shape is different from nominal shape. Springback may be corrected by redesigning the die face so that the stamped panel springs back to the nominal shape. When done manually, this redesign process is often time consuming and expensive. This article presents a computer program, FESHAPE, that reshapes the CAD or finite element mesh models automatically. The method is based on the technique of volume morphing pioneered by Sederberg and Parry [Sederberg 1986] and refined in [Sarraga 2004]. Volume morphing reshapes regions of surfaces or meshes by reshaping volumes containing those regions.

Aerodynamic performance testing of heavy duty fans involve complicated test setups with specialized equipment and measurement systems as summarized in ANSI 210-07 standard. This paper describes virtual testing and simulation methods to obtain the fan aerodynamic performance data using commercial Computational Fluid Dynamics (CFD) software. Two different virtual test environments were used during the analysis. The first one is a virtual test chamber which is constructed based on the actual fan system installation. The second one is a virtual flow tube which approximates a fan flow test set-up as outlined in ANSI 210-07. The virtual fan is created from (i) the laser scan of the actual fan and (ii) the design specifications of the fan. The virtual test conditions simulate the actual test arrangement by imposing free boundary at flow inlet/outlet and proper fan rotation. The aerodynamic flow rate is controlled by a variable orifice located at the virtual test chamber outlet.

As a virtual manufacturing press line, line die forming simulation provides a full range math-based engineering tool for stamping die developments of automotive structure and closure panels. Much beyond draw-die-only formability analysis that has been widely used in stamping simulation community during the last decade, the line die formability analysis allows incorporating more manufacturing requirements and resolving more potential failures before die construction and press tryout. Representing the most difficult level in formability analysis, conducting line die formability analysis of automotive body side outers exemplifies the greatest technological challenge to stamping CAE community. This paper discusses some critical issues in line die analysis of the body side outers, describes technical challenges in applications, and finally demonstrates the impact of line die forming simulation on the die development.

Reducing the high cost of hardware testing with analytical methods has been highly accelerated in the automotive industry. This paper discusses an analytical model to simulate the driveline boom test for the transverse engine with all wheel drive configuration on a front-wheel drive base (TFWD/AWD). Driveline boom caused by engine firing frequency that excites the bending mode of the propeller shaft becomes a noise and vibration issue for the design of TFWD/AWD driveline. The major source of vibrations and noise under the investigation in this paper is the dominant 3rd order engine torque pulse disturbance that excites the bending of the propeller shaft, the bending of the powertrain and possible the bending of the rear halfshaft. All other excitation sources in this powertrain for a 60° V6 engine with a pushrod type valvetrain are assessed and NVH issues are also considered in this transient dynamic model.

An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction.

Prediction of vehicle velocity is important since it can realize improvements in the fuel economy/energy efficiency, drivability, and safety. Velocity prediction has been addressed in many publications. Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets. Using these inclusive datasets of sensors in deep neural networks, high accuracy velocity predictions can be achieved. This research builds upon previous findings that Long Short-Term Memory (LSTM) deep neural networks provide low error velocity prediction. We developed an LSTM deep neural network that uses different groups of datasets collected in Fort Collins, Colorado.

Damping treatments are widely used in passenger vehicles, but the knowledge of damping treatments is often fragmentary in the industry. In this study, vibro-acoustics behavior of a set of vehicle floor and dash panels with various types of damping treatments was investigated. Sound transmission loss, sound radiation efficiency as well as damping loss factor were measured. The damping treatments ranged from laminated steel construction (thin viscoelastic layer) and doubler plate construction (thick viscoelastic layer) to less structural “bake-on” damping and self-adhesive aluminum foil-backed damping treatments. In addition, the bare vehicle panels were tested as a baseline and the fully carpeted floor panel was tested as a reference. The test data were then examined together with analytical modeling of some of the test configurations. As expected, the study found that damping treatments add more than damping. They also add mass and change body panel stiffness.

The strong focus on reducing brake drag, driven by a historic ramp-up in global fuel economy and carbon emissions standards, has led to renewed research on brake caliper drag behaviors and how to measure them. However, with the increased knowledge of the range of drag behaviors that a caliper can exhibit comes a particularly vexing problem - how should this complex range of behaviors be represented in the overall road load of the vehicle? What conditions are encountered during coastdown and fuel economy testing, and how should brake drag be measured and represented in these conditions? With the Environmental Protection Agency (amongst other regulating agencies around the world) conducting audit testing, and the requirement that published road load values be repeatable within a specified range during these audits, the importance of answering these questions accurately is elevated. This paper studies these questions, and even offers methodology for addressing them.

Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor.

The dash mat is one of the most important acoustic components in the vehicle for both powertrain noise and road noise attenuation. To optimize acoustic performance and mass requirements in the advanced development stage, analytical modeling is essential. The development of a detailed Statistical Energy Analysis (SEA) model of a dash mat is discussed in this paper. Modeling techniques and correlation with test are presented for two different production dash mat designs, a barrier-decoupler conventional system and a dual layer dissipative system without a mass barrier. The material properties and thickness distribution are used in the SEA model together with the geometry information of the dash panel. With the SEA model suitably correlated, trade-off studies are conducted to investigate the relationship between mass reduction of the barrier and change in decoupler thickness. The effects of air gaps are also considered in both modeling and testing.

In the automotive industry, vehicle durability analysis is based on test schedule encompassing multiple road surfaces (events) including rough roads, potholes, etc. Traditionally, in the Computer Aided Engineering (CAE) world, road load data for various road surfaces are measured/predicted and fatigue life is predicted for each individual road surface. Fatigue life for the complete test schedule is then calculated with Miner’s rule by summing fatigue damage for each road surface with an appropriate number of repetitions. A major pitfall of this approach is that it does not consider the effect of the largest rainflow range across the entire test schedule. The method described in this paper was developed to perform fatigue analysis of structures subjected to diverse road surfaces and also consider the case in which the maximum overall peak and minimum overall valley do not occur over the same road surface.

This paper discusses a study conducted by GM to better understand the factors that influence injury potential in vehicle-to-vehicle side impacts. A number of other studies have been done which focus primarily on frontal vehicle-to-vehicle compatibility. GM focused on side impact compatibility in this study due to the risk of harm generally associated with this type of crash. Real world field performance was studied through an extensive six-state field analysis of recent model year (‘94+) vehicles. Of particular interest in this study was an efficacy analysis of the MVSS 214 dynamic side impact standard, which was phased-in starting with some 1994 model year passenger cars. Physical side impact crash testing of a 1997 passenger car was used to investigate the relationship of impacting mass, speed, geometric profile and stiffness on side impact intrusion and occupant injury.

In recent years, the automotive industry has seen a rapid decrease in product development cycle time and a simultaneous increase in the variety of vehicles offered in the marketplace. These trends require a rigorous yet efficient systems engineering approach to the development of automotive braking systems. This paper provides an overview of an objective process for developing and predicting vehicle-level brake performance through an approach using both laboratory subsystem testing and math modeling.

Consumer expectations for automated vehicle operations such as automatic locking, remote ignition control, navigation, and entertainment are primary drivers for the increasing complexity of embedded automotive electronics modules. The prevalent practice for procuring these modules is to develop a written behavioral specification that is then used by an outside supplier to build and test the module. Validation test plans are written separately based on an understanding of the requirements. The challenges posed by the current practice include the inability to completely specify the expected behavior in a timely manner, the need to balance the design between low cost and new features demanded by the customer, and ensuring that the product exactly implements the specified behavior. Moreover, vehicle manufacturers desire the ability to explore sensitivity of specifications by identifying constraints on the system and assessing the product for ease of implementation.

The Advanced Engineering (AE) group within General Motors Powertrain (GMPT) develops next generation engines and transmissions for automotive and marine products. As a research organization, AE needs to prototype design ideas quickly and inexpensively. To this end, AE has embraced model-based development techniques and is currently investigating the benefits of software in-the-loop (SIL) testing. The underlying obstacle faced in developing a practical SIL system lays in the ability to integrate a plant model with sufficient fidelity together with target application software. ChiasTek worked with AE utilizing their CosiMate tool chain to eliminate these barriers and delivered a flexible SIL system simulation solution.

While machine learning in autonomous vehicles development has increased significantly in the past few years, the use of reinforcement learning (RL) methods has only recently been applied. Convolutional Neural Networks (CNNs) became common for their powerful object detection and identification and even provided end-to-end control of an autonomous vehicle. However, one of the requirements of a CNN is a large amount of labeled data to inform and train the neural network. While data is becoming more accessible, these networks are still sensitive to the format and collection environment which makes the use of others’ data more difficult. In contrast, RL develops solutions in a simulation environment through trial and error without labeled data. Our research expands upon previous research in RL and Proximal Policy Optimization (PPO) and the application of these algorithms to 1/18th scale cars by expanding the application of this control strategy to a full-sized passenger vehicle.