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The need for improved emissions control in lean exhaust to meet tightening, world-wide NOx emissions standards has led to the development of selective catalytic reduction of NOx with ammonia as a major technology for emissions control. Current systems are being designed to use a solution of urea (32.5 wt %) dissolved in water or Diesel Exhaust Fluid (DEF) as the ammonia source. While DEF or AdBlue® is widely used as a source of ammonia, it has a number of issues at low temperatures, including freezing below −12 °C, solid deposit formation in the exhaust, and difficulties in dosing at exhaust temperatures below 200 °C. Additionally creating a uniform ammonia concentration can be problematic, complicating exhaust packaging and usually requiring a discrete mixer.

Close-coupled or manifold catalysts have been extensively employed to reduce emissions during cold start by achieving quick catalyst light-off. These catalysts must have good thermal durability, high intrinsic light-off activity and high HC/CO/NOx conversions at high temperature and flow conditions. A number of studies have been dedicated to engine control, manifold design and converter optimization to reduce cold start emissions. The current paper focuses on the effect of catalyst design parameters and their performance response to different engine operating conditions. Key design parameters such as catalyst formulation (CeO2 vs. non CeO2), precious metal loading and composition (Pd vs. Pd/Rh), washcoat loading, catalyst thermal mass, substrate properties and key application (in use) parameters such as catalyst aging, exhaust A/F ratio, A/F ratio modulation, exhaust temperature, temperature rise rate and exhaust flow rate were studied on engine dynamometers in a systematic manner.

In this paper, we review existing software safety standards, guidelines, and other software safety documents. Common software safety elements from these documents are identified. We then describe an adaptable software safety process for automotive safety-critical systems based on these common elements. The process specifies high-level requirements and recommended methods for satisfying the requirements. In addition, we describe how the proposed process may be integrated into a proposed system safety process, and how it may be integrated with an existing software development process.

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.

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.

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.

Robust Engineering techniques developed by Taguchi have traditionally applied to the optimization of engineering designs. Robust Engineering methods also may be applied to software testing of ECU algorithms. The net result is an approach capable of improving the software algorithm in one of two ways. First the approach can identify the range of areas which prove problematic to the software such that a robust solution may be developed. Conversely, the approach can be used as a general strategy to verify that the software is robust over the range of inputs tested. The robust engineering methods applied to software testing utilize orthogonal array experiments to test software over a range of inputs. The actual software trials are best performed in the simulation environment and also via automated test hardware in the loop configurations in realtime. This paper outlines a process for applying Robust Engineering methods to software testing.

This paper presents the development of a transmission closed loop pressure control system. The objective of this system is to improve transmission pressure control accuracy by employing closed-loop technology. The control system design includes both feed forward and feedback control. The feed forward control algorithm continuously learns solenoid P-I characteristics. The closed loop feedback control has a conventional PID control with multi-level gain selections for each control channel, as well as different operating points. To further improve the system performance, Robust Optimization is carried out to determine the optimal set of control parameters and controller hardware design factors. The optimized design is verified via an L18 experiment on spin dynamometer. The design is also tested on vehicle.

Electro-hydraulic actuation has been used widely in automatic transmission designs. With greater demand for premium shift quality of automatic transmissions, higher pressure control accuracy of the transmission electro-hydraulic control system has become one of the main factors for meeting this growing demand. This demand has been the driving force for the development of closed loop pressure controls technology. This paper presents the further research done based upon a previously developed closed loop system. The focus for this research is on the system requirements, such as solenoid driver selection and system latency handling. Both spin-stand and test vehicle setups are discussed in detail. Test results for various configurations are given.

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.

Embedded controllers and digital signal processors are increasingly being used in automotive safety critical control systems. Controller integrity is a significant concern in these systems. Over the past decade, several techniques have been published about controller safety and integrity verification. These techniques include: single processor with watchdog, dual processors, dual core processor, and asymmetric processor (intelligent watchdog). Each of these techniques have benefits, however, many new non-distributed safety-critical systems are applying the asymmetric processor technique to help verify controller integrity. This paper discusses an overview of five controller integrity techniques, and then provides a detailed discussion of an asymmetric processor approach. This paper presents two different options within the asymmetric processor approach.

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.

As automotive electronic control systems continue to increase in usage and complexity, the challenges for developing automotive diagnostics also increase. Reduced development cycle times, the increased significance of diagnostics for safety critical systems, and the integration of vehicle systems across multiple control systems all add to the tasks of developing diagnostics for the automobiles of today and tomorrow. Addressing automotive diagnostics now requires the Tier 1 supplier to utilize a formal diagnostic development methodology. There are also opportunities for Tier 1 suppliers to add value by developing vehicle-level supervisory diagnostic strategies, in addition to subsystem and system-level diagnostic strategies. There is also a prospect to provide strategies and tools to enhance service at the vehicle level. This paper proposes an approach for Tier 1 suppliers to address diagnostic and service issues at the component, system, and vehicle level.

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.

Regulatory and market pressures continue to challenge the automotive industry to develop technologies focused on reducing exhaust emissions and improving fuel economy. This paper introduces a practical model, which evaluates the economic value of various technologies based on their ability to reduce fuel consumption, improve emissions or provide consumer benefits such as improved performance. By evaluating the individual elements of economic value as viewed by the OEM manufacturer, while keeping the end consumer in mind, technology selection decisions can be made. These elements include annual fuel usage, vehicle performance, mass reduction and emissions, among others. The following technologies are discussed and evaluated: gasoline direct injection, variable valvetrain technologies, common-rail diesel and hybrid vehicles.

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.

In this paper the effects of rear brake imprecision on vehicle braking performance and yaw dynamics are investigated for a vehicle with individually controlled brake actuators. The effects of side to side brake force imbalance on vehicle yaw rate and path deviation during straight line braking and in braking in turn maneuvers are examined through analysis, simulations and vehicle testing. These effects are compared to the influences of disturbances encountered during normal driving such as side winds and bank angles of the road. The loss of brake efficiency due to imprecision in generating actuating force is evaluated for different types of vehicles and different levels of vehicle deceleration. Requirements regarding path deviation during straight line braking and braking efficiency on low friction surfaces were found to lead to the most stringent specifications for actuator accuracy in realizing the desired braking forces.

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.

With the requirements for reducing the emissions and improving the fuel economy, the automotive companies are developing hybrid, 42 V and fuel cell vehicles. Power electronics is an enabling technology for the development of environmental friendly vehicles, and to implement the various vehicle electrical architectures to obtain the best performance. In this paper, the requirements of the power semiconductor devices and the criteria for selecting the power devices for various types of low emission vehicles are presented. A comparative study of the most commonly used power devices is presented. A brief review of the future power devices that would enhance the performance of the automotive power conversion systems is also presented.

Computer simulation was used as a complement to crash and injury field data analysis and physical sled and barrier tests to investigate and predict the spinal injuries of a rear impact in an IndyCar. The model was expected to relate the spinal loads to the observed injuries, thereby predicting the probability and location of spinal fractures. The final goal is to help reduce the fracture risk by optimizing the seat and restraint system design and the driver's position using computer modeling and sled testing. MADYMO Full FE Human Body Model (HBM) was selected for use because of its full spinal structural details and its compatibility with the vehicle and restraint system models. However, the IndyCar application imposed unique challenges to the HBM. First, the driver position in a race car is very different from that in a typical passenger car.