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Since transient vehicle HVAC computational fluids (CFD) simulations take too long to solve in a production environment, the goal of this project is to automatically create a lumped-parameter flow network from a steady-state CFD that solves nearly instantaneously. The data mining algorithm k-means is implemented to automatically discover flow features and form the network (a reduced order model). The lumped-parameter network is implemented in the commercial thermal solver MuSES to then run as a fully transient simulation. Using this network a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track localized temperatures near specific objects (such as equipment in vehicles).

Health Ready Components are essential to unlocking the potential of Integrated Vehicle Health Management (IVHM) as it relates to real-time diagnosis and prognosis in order to achieve lower maintenance costs, greater asset availability, reliability and safety. IVHM results in reduced maintenance costs by providing more accurate fault isolation and repair guidance. IVHM results in greater asset availability, reliability and safety by recommending preventative maintenance and by identifying anomalous behavior indicative of degraded functionality prior to detection of the fault by other detection mechanisms. The cost, complexity and effectiveness of the IVHM system design, deployment and support depend, to a great extent, on the degree to which components and subsystems provide the run-time data needed by IVHM and the design time semantic data to allow IVHM to interpret those messages.

Presented in the paper is a comprehensive analysis for floating piston pin. It is more challenging because it is a special type of journal bearing where the rotation of the journal is coupled with the friction between the journal and the bearing. In this analysis, the multi-degree freedom mass-conserving mixed-EHD equations are solved to determine the coupled pin rotation and friction. Other bearing characteristics, such as minimum film thickness, pin secondary motions in both connecting-rod small-end bearing and piston pin-boss bearing, power loss etc are also determined. The mechanism for floating pin to have better scuffing resistance is discovered. The theoretical and numerical model is implemented in the GM internal software FLARE (Friction and Lubrication Analysis for Reciprocating Engines).

As the new features for driver assistance and active safety systems are growing rapidly in vehicles, the simulation within a virtual environment has become a necessity. The current active safety system consists of Electronic Control Units (ECUs) which are coupled to camera and radar sensors. Two methods of implementation exists, integrated sensors with control modules or separation of sensors form control modules. The subsystem integration testing poses new challenges for virtual environment for simulation of active safety features. The comprehensive simulation environment for integration testing consists of chassis controls, powertrain, driver assistance, body and displays controllers. Additional complexity in the system is the serial communication strategy. Multiple communication protocols such as GMLAN, LIN, standard CAN, and Flexray could be present within the same vehicle topology.

The United States Department of Transportation (USDOT) and the Crash Avoidance Metrics Partnership-Vehicle Safety Communications 2 (CAMP-VSC2) Consortium (Ford, General Motors, Honda, Mercedes-Benz, and Toyota) initiated, in December 2006, a three-year collaborative effort in the area of wireless-based safety applications under the Vehicle Safety Communications-Applications (VSC-A) Project. The VSC-A Project developed and tested Vehicle-to-Vehicle (V2V) communications-based safety systems to determine if Dedicated Short Range Communications (DSRC) at 5.9 GHz, in combination with vehicle positioning, would improve upon autonomous vehicle-based safety systems and/or enable new communications-based safety applications.

Model-Based Development processes in the automotive industry typically use high-level modeling languages to build the reference models of embedded controllers. One can use formal verification tools to exhaustively verify these design models against their requirements, ensuring high quality models and a reduction in the cost and effort of functional testing. However, there is a gap, in terms of processes and tools, between the informal requirements and the formal specifications required by the verification tools. In this paper, we propose an approach that tries to bridge this gap by (i) identifying the verifiable requirements through a categorization process, (ii) providing a set of templates to easily express the verifiable requirements, and (iii) generating monitors that can be used as specifications in design verification tools. We demonstrate our approach using the Simulink Design Verifier tool for design verification of Simulink/Stateflow models.

To cope with the increasing number of advanced features (e.g., smart-phone integration and side-blind zone alert.) being deployed in vehicles, automotive manufacturers are designing flexible hardware architectures which can accommodate increasing feature content with as fewer as possible hardware changes so as to keep future costs down. In this paper, we propose a formal and quantitative definition of flexibility, a related methodology and a tool flow aimed at maximizing the flexibility of an automotive hardware architecture with respect to the features that are of greater importance to the designer. We define flexibility as the ability of an architecture to accommodate future changes in features with no changes in hardware (no addition/replacement of processors, buses, or memories). We utilize an optimization framework based on mixed integer linear programming (MILP) which computes the flexibility of the architecture while guaranteeing performance and safety requirements.

The Kalman and Vold-Kalman order tracking filters have been implemented in commercial software since the early 90's. There are several mathematical formulations of filters that have been implemented by different software vendors. However, there have not been any papers that have been published which sufficiently explain the math behind these filters and discuss the actual implementations of the filters in software. In addition, upon generating the equations represented by these filters, solving the equations for datasets in excess of several hundred thousand datapoints is not trivial and has not been discussed in the literature. The papers which have attempted to cover these topics are generally vague and overly mathematically eloquent but not easily understandable by a practicing engineer.

The Time Variant Discrete Fourier Transform was implemented as a real-time order tracking method using developed software and commercially available hardware. The time variant discrete Fourier transform (TVDFT) with the application of the orthogonality compensation matrix allows multiple tachometers to be tracked with close and/or crossing orders to be separated in real-time. Signal generators were used to create controlled experimental data sets to simulate tachometers and response channels. Computation timing was evaluated for the data collection procedure and each of the data processing steps to determine how each part of the process affects overall performance. Many difficulties are associated with a real-time data collection and analysis tool and it becomes apparent that an understanding of each component in the system is required to determine where time consuming computation is located.

Key fobs, also known as remote keys or remote transmitters, have become a common piece of equipment in today's vehicle, being ubiquitous in every market segment. Once limited to remote locking and unlocking operations, today's key fobs can be used to control many comfort and security features beyond locking and unlocking, such as alarm system operation, vehicle locate, approach lighting, memory seat recall, and remote starting systems. Key fobs are designed to be easy to use as well as easy to carry and transport in personal containers, such as purses, pockets, wallets, and the like. Accordingly, as with other personal effects, key fobs and other portable remote devices can be lost or misplaced or can be otherwise difficult to find. Even with careful tracking of a remote device, children and pets, among other factors, can make location difficult. Moreover, multiple remote devices are often distributed with each vehicle.

The usage of multi-body dynamics tools for the prediction of vehicle system/sub-system loads, has significantly reduced the need to measure vehicle loads at proving grounds. The success of these tools is limited by the quality of the digital representations being used to simulate the physical test roads. The development of these digital roads is not a trivial task due to the large quantity of data and processing required. In the end, the files must be manageable in size, have a globally common format, and be simulation-friendly. The authors present a methodology for the development of high quality 3-dimensional (3-D) digital proving ground profiles. These profiles will be used in conjunction with a multi-body dynamics software package (ADAMS) and the FTire™ model. The authors present a case study below.

As an innovative electric vehicle with some new approaches to energy usage and vehicle performance balance, the Chevy Volt required a special relationship between the OEM and tire supplier community. This paper details this relationship and how advanced tools and technology were leveraged between OEM and supplier to achieve tire component and overall vehicle performance results.

The imperative to get to the market faster with new and better products, has determined all automotive OEM to rethink their product development cycle, and, as a result, many hardware based processes were replaced and/or augmented with virtual, software based ones. However, the virtualization itself does not guaranties better and faster products. In the area of vehicle dynamics, we concentrate on improving the multi-body model development process, facilitating comprehensive virtual testing, and verifying the robustness of the design. The authors present a highly flexible and efficient environment that encourages, enforces, and facilitates model sharing, reusing of components, and parallelization of vehicle dynamics simulations, developed on top of an existing commercial off-the-shelf engineering software application.

This paper presents Telematics Battery Monitoring (TBM). TBM is a multi-faceted approach of collecting and analyzing electric power and vehicle data used to ultimately determine battery state of charge (SOC) and battery state of health (SOH) in both pre- and post-sale environments. Traditional methods of battery SOC analysis include labor intensive processes such as going out to the site of individual vehicle(s), gaining access to the vehicle battery, and then after the vehicle electrical system obtains its quiescent current level, performing a battery voltage check. This time-consuming manual method can practically only cover a small percentage of the vehicle population. In using the vehicle communication capabilities of Telematics, electric power and vehicle data are downloaded, compiled, and post-processed using decision-making software tools.

Recent trends in the automotive industry show growing demands for the introduction of new in-vehicle features (e.g., smart-phone integration, adaptive cruise control, etc.) at increasing rates and with reduced time-to-market. New technological developments (e.g., in-vehicle Ethernet, multi-core technologies, AUTOSAR standardized software architectures, smart video and radar sensors, etc.) provide opportunities as well as challenges to automotive designers for introducing and implementing new features at lower costs, and with increased safety and security. As a result, the design of Electrical/Electronic (E/E) architectures is becoming increasingly challenging as several hardware resources are needed. In our earlier work, we have provided top-level definitions for three relevant metrics that can be used to evaluate E/E architecture alternatives in the early stages of the design process: flexibility, scalability and expandability.

Digital human models (DHM) are now widely used to assess worker tasks as part of manufacturing simulation. With current DHM software, the simulation engineer or ergonomist usually makes a manual estimate of the likely worker standing location with respect to the work task. In a small number of cases, the worker standing location is determined through physical testing with one or a few workers. Motion capture technology is sometimes used to aid in quantitative analysis of the resulting posture. Previous research has demonstrated the sensitivity of work task assessment using DHM to the accuracy of the posture prediction. This paper expands on that work by demonstrating the need for a method and model to accurately predict worker standing location. The effect of standing location on work task posture and the resulting assessment is documented through three case studies using the Siemens Jack DHM software.

This paper deals with the development of a Hood Slam Process Automator (PA) to automate the pre-processing tasks of the virtual slam assessment with non-linear Nastran Transient Sol. 129 on all types of hoods. The slam analysis generally consumes a lot of analyst's time for building the slam models, typically six hours and is very tedious and has the potential for errors. The Hood Slam PA will automatically create and perform slam analysis pre-processing tasks within HyperMesh software such as creating latch striker interface, creating seals and bumpers with CBUSH1D elements, assigning transient slam speed to the hood and will finally generate the Nastran non-linear transient (Sol.129) hood slam analysis input files. The ready to run analysis input files will be submitted to the Nastran solver and the analysis results will then be post processed using HyperView software.

For many years, the computer aided math model has been the foundation for lowering cost and reducing time to market for many manufacturing industries. The automotive industry applies a variety of tools and methods to evaluate the expected vehicle performance to a forever expanding set of requirements. These mathematical predictions of performance are then repeated for both a set of design cycles and a multitude of vehicles in the product portfolio. This paper presents a CAE perspective of the unique problems of the large scale virtual vehicle engineering factory and a set of solutions. Different strategies to create the various complex math models required are explored. These strategies include using COTS FEA pre-processers, producing FEA models internal to the CAD tools, as well as custom built tools, macros and process automation tools.

Given the fast changing market demands, the growing complexity of features, the shorter time to market, and the design/development constraints, the need for efficient and effective verification and validation methods are becoming critical for vehicle manufacturers and suppliers. One such example is fault-tree analysis. While fault-tree analysis is an important hazard analysis/verification activity, the current process of translating design details (e.g., system level and software level) is manual. Current experience indicates that fault tree analysis involves both creative deductive thinking and more mechanical steps, which typically involve instantiating gates and events in fault trees following fixed patterns. Specifically for software fault tree analysis, a number of the development steps typically involve instantiating fixed patterns of gates and events based upon the structure of the code. In this work, we investigate a methodology to translate software programs to fault trees.

As part of standardizing the global portfolio, General Motors (GM) created an electrical architecture that will support the GM global product feature set. Introduced in 2009, this common electrical architecture is already being applied to multiple platforms in GM's regional engineering centers. The electrical architecture will be updated regularly to address the needs of new features in the automotive market and to take advantage of the latest technology advancements. The functional requirements of these new features result in technology challenges. In addition, many new features may result in challenges to the vehicle electrical architecture or the vehicle development process. The challenges have been evaluated so that needs and initiatives can be better understood.