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The paper describes a new strategy for real-time sensor diagnostics that is based on the statistical correlation of various sensor signal pairs. During normal fault-free operation there is a certain correlation between the sensor signals which is lost in the event of a fault. The proposed algorithm quantifies the correlation between sensor signal pairs using real-time scalar metrics based on the Mahalanobis-distance concept. During normal operation all metrics follow a similar pattern, however in the event of a fault; metrics involving the faulty sensor would increase in proportion to the magnitude of the fault. Thus, by monitoring this pattern and using a suitable fault-signature table it is possible to isolate the faulty sensor in real-time. Preliminary simulation results suggest that the strategy can mitigate the false-alarms experienced by most model-based diagnostic algorithms due to an intrinsic ability to distinguish nonlinear vehicle behavior from actual sensor faults.

A new optimal control strategy for dealing with braking actuator failures in a vehicle equipped with a brake-by-wire and steer-by- wire system is described. The main objective of the control algorithm during the failure mode is to redistribute the control tasks to the functioning actuators, so that the vehicle performance remains as close as possible to the desired performance in spite of a failure. The desired motion of the vehicle in the yaw plane is determined using driver steering and braking inputs along with vehicle speed. For the purpose of synthesizing the control algorithm, a non-linear vehicle model is developed, which describes the vehicle dynamics in the yaw plane in both linear and non-linear ranges of handling. A control allocation algorithm determines the control inputs that minimize the difference between the desired and actual vehicle motions, while satisfying all actuator constraints.

The laboratory test method commonly known as “random vibration” is almost always used for Squeak & Rattle testing in today's automotive applications due to its obvious advantages: the convenience in simulating the real road input, the relatively low cost, and efficiency in obtaining the desired test results. Typically, Loudness N10 is used to evaluate the Squeak & Rattle (S&R) performance. However, due to the nature of random distribution of the excitation input, the repeatability of the loudness N10 measurements may vary significantly. This variation imposes a significant challenge when one is searching for a fine design improvement solution in minimizing S&R noise, such as a six-sigma study. This study intends to investigate (1) the range of the variations of random vibration control method as an excitation input with a given PSD, (2) the possibility of using an alternate control method (“time-history replication”) to produce the vibration of a given PSD for a S&R evaluation.

This paper reviews the current strategies for physical prototyping of Magnesium instrument panel (I/P) structures. Bottlenecks in the traditional physical prototype based product development process are discussed. As demand for fast-to-market and cost-reduction mounts, virtual prototyping becomes increasingly important in meeting the timing and performance goals. A virtual prototyping methodology is presented in this paper to enable high performance Magnesium I/P structures in Safety, NVH, and initial part quality aspects. Examples of Finite Element Analysis (FEA) results and correlations are included.

Execution of a software safety program is an accepted best practice to help verify that potential software hazards are identified and their associated risks are mitigated. Successful execution of a software safety program involves selecting and applying effective analysis methods and tasks that are appropriate for the specific needs of the development project and that satisfy software safety program requirements. This paper describes the effective application of a set of software safety methods and tasks that satisfy software safety program requirements for many applications. A key element of this approach is a tightly coupled fault tree analysis and failure modes and effects analysis. The approach has been successfully applied to several automotive embedded control systems with positive results.

A steering wheel is an indispensable component in an automobile. Although the steering wheel was invented about one hundred years ago and its structure has since become more and more complex with numerous innovations, documented analysis on steering wheel performance is very limited. Today, a steering wheel is not only a wheel that controls where your car goes; it also plays an important role in a vehicle occupant protection system. Therefore, many requirements have to be met before a steering wheel goes into production. With the development of computational mechanics and increasing computer capability, it has become much easier to evaluate the steering wheel performance in a totally different way. Instead of running prototype tests, steering wheel designs can be modeled virtually in various scenarios using finite element analysis, thus facilitating the development cycle.

This paper will address the electronic development in the wireless industry and compare it to the electronic development in the automotive industry. The wireless industry is characterized by rapid, dramatic high tech changes with a less than two-year cycle time and an equivalent life cycle. The automotive electronics industry is working toward reducing the typical 2 to 3 year development cycle down 1 to 2 years but with a life cycle of 10 years or more. In addition to realizing the electronic development benefits seen in the wireless industry, the automotive industry places significantly more emphasis on the quality and reliability aspects of their designs as many of them are targeted toward, or interface with, safety critical applications. One of the lessons learned from the wireless industry is the development process; where the hardware selection process can be accomplished in a virtual environment in conjunction with concurrent software development.

The perceived interior noise has been one of the major driving factors in the design of automotive interior assemblies. Buzz, Squeak and Rattle (BSR) issues are one of the major contributors toward the perceived quality in a vehicle. Traditionally BSR issues have been identified and rectified through extensive hardware testing. In order to reduce the product development cycle and minimize the number of costly hardware builds, however, one must rely on engineering analysis and simulation upfront in the design cycle. In this paper, an analytical and experimental study to identify potential BSR locations in a cockpit assembly is presented. The analytical investigation utilizes a novel and practical methodology, implemented in the software tool Nhance.BSR, for identification and ranking of potential BSR issues. The emphasis here is to evaluate the software for the BSR predictions and the identification of modeling issues, rather than to evaluate the cockpit design itself for BSR issues.

This paper proposes the concept of Generalized Diagnostic Component (GDC) and presents a modular fault diagnostic strategy for safety critical automotive systems. The diagnostic strategy makes full use of hierarchical techniques, integrates the generalized diagnostic design into all-purpose vehicle diagnoses based on reconfiguration of the GDCs, and inherits the model-based diagnostic algorithms developed for Steering/Braking-By-Wire systems. The GDC-based approach simplifies the design and integration of diagnostics in complex dynamical control systems, and has been successfully implemented in an eight degrees of freedom NAVDyn (Non-Linear Analysis of Vehicle Dynamics) simulation model using Matlab Simulink. The simulation results are provided in this paper to testify that the diagnostic strategy and implementation are feasible, efficient and dependable.

This paper reviews the state of the art on analytical design of cockpit modules in two most crucial performance categories: safety and comfort. On safety, applications of finite element analysis (FEA) for achieving robust designs that meet FMVSS 201, 208 and 214 requirements and score top frontal and side NCAP star-ratings are presented. On comfort, focus is placed on Noise, Vibration and Harshness (NVH) performance. Cutting-edge analytical tools for Buzz, Squeak and Rattle (BSR) avoidance and passenger compartment noise reduction are demonstrated. Most of the analytical results shown in this paper are based on the development work of a real-life application program. Correlations between the analytical results and physical test results are included. Examples of Computational Fluid Dynamics (CFD) analysis for climate control are also included. At the end, the road map toward 100 percent virtual prototyping and validation is presented.

Computer modeling and simulation have become one of the primary methods for development and design of automobile occupant protection systems (OPS). To ensure the accuracy and reliability of a math-based OPS design, the correlation quality assessment of mathematical models is essential for program success. In a typical industrial approach, correlation quality is assessed by comparing chart characteristics and scored based on an engineer's modeling experience and judgment. However, due to the complexity of the OPS models and their responses, a systematic approach is needed for accuracy and consistency. In this paper, a correlation grading methodology for the OPS models is presented. The grading system evaluates a wide spectrum of a computer model's performances, including kinematics, dynamic responses, and dummy injury measurements. Statistical analysis is utilized to compare the time histories of the tested and simulated dynamic responses.

The interaction between the deploying airbag and the Out-Of-Position (OOP) occupants remains a challenge in occupant protection system simulations. The integration of Computational Fluid Dynamics (CFD) analysis into Finite Element (FE) airbag model is a helpful and important tool to address this challenge. Three major commercial crash simulation software packages widely used in the automotive safety industry, LS-DYNA, MADYMO and PAM-CRASH are in the process of implementing different approaches for airbag CFD simulation. In this study, an attempt was made to evaluate and compare the CFD integrated airbag models in these software packages. Specially designed tests were conducted to study and capture the pressure distribution inside a flat airbag and the test results were used for the evaluation. Strengths and limitations of each software package are discussed in this paper.

This paper presents a methodology to predict component and system reliability and durability. The methodology is illustrated with an electronic diesel fuel injector case study that integrates customer usage data, component failure distribution, system failure criteria, manufacturing variation, and variation in customer severity. Extension to the vehicle system level enables correlation between component and system requirements. Further, this analysis provides the basis to establish a knowledge-based test option for a success test validation program to demonstrate reliability.

A seat belt usability study was conducted to investigate factors associated with seat belt comfort and convenience related to shoulder belt contact pressure, shoulder belt fit, and seat belt upper anchorage location. Two major objectives were addressed in this study: (1) Determine the shift in the contact pressure while changing the seat back angle and seat belt attachment points / B-pillar location by utilizing a body pressure measurement system; (2) Identify how seat belt contact pressure and fit affect users' subjective feeling of comfort. Results from the statistical analysis shows that the seat belt contact pressure increases when the D-ring moves away from the driver in the fore-aft direction (X-axis) whereas height adjustment of the D-ring (Z-axis) is not statistically significant in terms of pressure distribution.

An integrated optimization system has been developed to combine optimization algorithms with Finite Element Analysis for airbag design. A number of industry standard software packages are employed to work in coherence to complete the optimization procedure automatically with minimal user intervention. The system can be easily tailored to fit multiple performance requirements and various design constraints for different airbag systems. Compared with the commonly used Design of Experiment (DOE) method, time and computer resources requirements are greatly curtailed. The integrated optimization system was successfully used in single-chamber and dual-chamber airbag optimizations. The results proved the effectiveness of the system and demonstrated its capability in product design.

Brake performance can be divided into two distinct classes: base brake performance and controlled brake performance. A base brake event can be described as a normal or typical stop in which the driver maintains the vehicle in its intended direction at a controlled deceleration level that does not closely approach wheel lock. All other braking events where additional intervention may be necessary, such as wheel brake pressure control to prevent lock-up, application of a wheel brake to transfer torque across an open differential, or application of an induced torque to one or two selected wheels to correct an under- or oversteering condition, may be classified as controlled brake performance. Statistics from the field indicate the majority of braking events stem from base brake applications and as such can be classified as the single most important function.