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The auto industry has had decades of experience with designing safe vehicles. The introduction of highly integrated features brings new challenges that require innovative adaptations of existing safety methodologies and perhaps even some completely new concepts. In this paper, we describe some of the new challenges that will be faced by all OEMs and suppliers. We also describe a set of generic top-level potential hazards that can be used as a starting point for the Preliminary Hazard Analysis (PHA) of a vehicle software-intensive motion control system. Based on our experience with the safety analysis of a system of this kind, we describe some general categories of hazard causes that are considered for software-intensive systems and can be used systematically in developing the PHA.

Development and integration of the cooling system for an automotive vehicle requires a balancing act between several performance and styling objectives. The cooling system needs to provide sufficient air for heat rejection with minimal impact on the aerodynamic drag, styling requirements and other criteria. An optimization of various design parameters is needed to develop a design to meet these objectives in a short amount of time. Increase in the accuracy of the numerical predictions and reduction in the turn-around time has made it possible for Computational Fluid Dynamics (CFD) to be used early in the design phase of the vehicle development. This study shows application of the CFD for robust design of the engine cooling system.

Model-based design and development is emerging in the automotive industry, largely revealing its popularity in chassis control systems [1]. Although it is an efficient and accepted design tool for chassis systems, proper processes and strategies need to be in place to ensure the integrity and correctness of the production software. This paper describes software testing strategies for complex chassis control systems in a model-based environment. In detail, it highlights various testing methods for different phases, such as unit testing and integration testing. It will also address issues and challenges that were faced with each method and propose possible solutions.

In an endeavor to improve upon historically subjective and hardware-based steering tuning development, a team was formed to find an optimal and objective solution using Design For Six Sigma (DFSS). The goal was to determine the best valve assembly design within a hydraulic power-steering assist system to yield improved steering effort and feel robustness for all vehicle models in a future truck program. The methodology utilized was not only multifaceted with several Design of Experiments (DOEs), but also took advantage of a CAE-based approach leveraging modeling capabilities in ADAMS for simulating full-vehicle, On-Center Handling behavior. The team investigated thirteen control factors to determine which minimized a realistic, compounded noise strategy while also considering the ideal steering effort function (SEF) desired by the customer. In the end, it was found that response-dependent variability dominated the physics of our valve assembly design concept.

Simultaneously obtaining a linear temperature control curve along with the correct temperature stratification at module outlets is one of the most difficult tasks in developing an automotive HVAC module. Traditionally, Computational Fluid Dynamics (CFD) development of temperature control linearity has been accomplished by iteratively adjusting the location, size and orientation of baffles which redirect warm and cold airstreams. This approach demands considerable interaction from the engineer in building the computational mesh, defining boundary and operating conditions and post processing the simulation results. The present study was conducted to investigate the optimization of HVAC temperature regulation curves using the multi-objective optimization code modeFrontier (1, 3) in conjunction with CFD code, Fluent (2). An auxiliary HVAC module was selected for the present study.

This paper presents a comprehensive Matlab/Simulink-based framework that affords a rapid, systematic, and efficient engine control system development process including automated code generation. The proposed framework hinges on three essential pillars: 1 ) an accurate model for the target engine, 2) a toolset for systematic control design, and 3) a modular system architecture that enhances feature reusability and rapid algorithm deployment. The proposed framework promotes systematic model-based algorithm development and validation in virtual reality. Within this context, the framework affords integration and evaluation of the entire control system at an early development stage, seamless transitions across inherently incompatible product development stages, and rapid code generation for production target hardware.

The fierce competition and shifting consumer demands require automotive companies to be more efficient in all aspects of vehicle development and specifically in the area of embedded engine control system development. In order to reduce development cost, shorten time-to-market, and meet more stringent emission regulations without sacrificing quality, the increasingly complex control algorithms must be transportable and reusable. Within an efficient development process it is necessary that the algorithms can be seamlessly moved throughout different development stages and that they can be easily reused for different applications. In this paper, we propose a flexible engine control architecture that greatly boosts development efficiency.

Managing (minimizing and optimizing) the total mass of a vehicle is recognized as a critical task during the development of a new vehicle, as well as throughout its production lifecycle. This paper summarizes a literature review of, and investigation into, the strategies, methods and best practices for achieving low total mass in new vehicle programs, and/or mass reductions in existing production vehicle programs. Empirical and quantitative data and examples from the automotive manufacturers and suppliers are also provided in support of the material presented.

This study involves an application of Principal Component Analysis (PCA) conducted in support of a Design for Six Sigma (DFSS) project. Primary focus of the project is to optimize seat parameters that influence Low Speed Rear Impact (LSRI) whiplash performance. During the DFSS study, the project team identified a need to rank order critical design factors statistically and establish their contribution to LSRI performance. It is also required to develop a transfer function for the LSRI rating in terms of test response parameters that can be used for optimization. This statistical approach resulted in a reliable transfer function that can applied across all seat designs and enabled us to separate vital few parameters from several many.

By adapting vehicle control systems to the skill level of the driver, the overall vehicle active safety provided to the driver can be further enhanced for the existing active vehicle controls, such as ABS, Traction Control, Vehicle Stability Enhancement Systems. As a follow-up to the feasibility study in [1], this paper provides some recent results on data-driven driving skill characterization. In particular, the paper presents an enhancement of discriminant features, the comparison of three different learning algorithms for recognizer design, and the performance enhancement with decision fusion. The paper concludes with the discussions of the experimental results and some of the future work.

Model-based design is a collection of practices in which a system model is at the center of the development process, from requirements definition and system design to implementation and testing. This approach provides a number of benefits such as reducing development time and cost, improving product quality, and generating a more reliable final product through the use of computer models for system verification and testing. Model-based design is particularly useful in automotive control applications where ease of calibration and reliability are critical parameters. A novel application of the model-based design approach is demonstrated by The Ohio State University (OSU) student team as part of the Challenge X advanced vehicle development competition. In 2008, the team participated in the final year of the competition with a highly refined hybrid-electric vehicle (HEV) that uses a through-the-road parallel architecture.

This paper demonstrates the benefit of using simulation and robust optimization for the problem of balancing vehicle noise, vibration, and ride performance over road impacts. The psychophysics associated with perception of vehicle performance on an impact is complex because the occupants encounter both tactile and audible stimuli. Tactile impact vibration has multiple dimensions, such as impact hardness and memory shake. Audible impact sound also affects occupant perception of the vehicle quality. This paper uses multiple approaches to produce the similar, robust, optimized tuning strategies for impact performance. A Design for Six Sigma (DFSS) project was established to help identify a balanced, optimized solution. The CAE simulations were combined with software tools such as iSIGHT and internally developed Kriging software to identify response surfaces and find optimal tuning.

In this article, the authors illustrate the benefits of axiomatic design (AD) for robust optimization and how to integrate axiomatic design into a total robust design process. Similar to traditional robust design, the purpose of axiomatic design is to improve the probability of a design in meeting its functional targets at early concept generation stage. However, axiomatic design is not a standalone method or tool and it needs to be integrated with other tools to be effective in a total robust development process. A total robust development process includes: system design, parameter design, tolerance design, and tolerance specifications [1]. The authors developed a step-by-step procedure for axiomatic design practices in industrial applications for consistent and efficient deliverables. The authors also integrated axiomatic design with the CAD/CAE/statistical/visualization tools and methods to enhance the efficiency of a total robust development process.

A DFSS study identified a new mechanism for clamp load loss in aluminum threads due to thermal cycling. In bolted joints tightened to yield, the difference in thermal expansion between the aluminum and steel threads can result in a loss of clamp load with each thermal cycle. This clamp load loss is significantly greater than the loss that can be explained by creep alone. A math model was created and used to conduct a robust analysis. This analysis led to an understanding of the design factors necessary to reduce the cyclic clamp load loss in the aluminum threads. This understanding was then used to create optimized design solutions that satisfy constraints common to powertrain applications. Estimations of clamp load loss due to thermal cycling from the math model will be presented. The estimates of the model will be compared to observed physical test data. A robust analysis, including S/N and mean effect summary will be presented.

The market growth for electric-hybrid passenger vehicles has been very significant and is expected to reach nearly 25% of all vehicles sold in the US by 2015. Hybrid commercial vehicles are also being developed by several OEM's. This paper discusses the progress of Delphi Thermal Systems in developing an integrated electric compressor drive with high cooling capacity (9 kW+), sufficient for large hybrid SUV's and commercial vehicles such as Class 8 tractors with sleeper. An important driver for use of the electric compressor in the hybrid truck application is the reduction of engine idling time while maintaining comfort in the cab or sleeper. Design details of a compact 5 kW SPM motor, its inverter drive, and issues related to its integration into the compressor housing are described. Test results are given confirming excellent performance.

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.

Full-load operation of a small-displacement spark-ignition direct-injection (SIDI) engine was thoroughly investigated by means of computational analysis and engine measurements. The performance is affected by many different factors, which can be grouped as those pertaining to volumetric efficiency, to mixing and stratification, and to system issues, respectively. Volumetric efficiency is affected by flow losses, tuning and charge cooling. Charge cooling due to spray vaporization is often touted as the most significant benefit of direct-injection on full-load performance. However, if wall wetting occurs, this benefit may be completely negated or even reversed. The fuel-air mixing is strongly affected by the injection timing and characteristics at lower engine speeds, while at higher engine speeds the intake flow dominates the transport of fuel particles and resultant vapor distribution. A higher injector flow rate enhances mixing especially at higher engine speeds.

Signal delivery is the means of translating a physical parameter from a sensor measurement to the application software in the electronic controller. Signal delivery is also translating a digital word from the application software to an actuator response. In both cases, there are many transform functions along the path that will introduce noise, error, and non-linearity. This paper will discuss the importance of understanding the error and sensitivity to variation that signal delivery analysis provides. The analysis will direct design change to improve control system robustness as well as decisions for failure events.

With the revolutionary advances in computing power and software technology, the future trend of integrating design and CFD analysis software package to realize an automated design optimization has been explored in this study. The integrated process of UG, ICEMCFD, and FLUENT was accomplished using iSIGHT for vehicle Aero/Thermal applications. Process integration, CFD solution strategy, optimization algorithm and the practicality for real world problem of this process have been studied, and will be discussed in this paper. As an example of this application, the results of an underhood thermal design will be presented. The advantage of systematical and rapid design exploration is demonstrated by using this integrated process. It also shows the great potential of computer based design automation in vehicle Aero/Thermal development.

Analytical study of vehicle idle shake performance is standard NVH work within the vehicle development process. Robust design for idle shake performance takes variations into account besides nominal design based performance evaluation. In other words, in addition to the nominal design, Robust Design includes additional evaluations that may incorporate variation due to manufacturing, usage or the environment. This paper presents an example of how to obtain a robust design through performing Robust Optimization on idle shake performance with respect to powertrain mount rates and their tolerance variation. The paper describes a two-phase process that has been systematically implemented to analytically obtain a robust design. In the first phase, performance variation assessment is conducted. Then a Robust Optimization is performed to obtain a robust design.