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Vehicle thermal protection is an important aspect of the overall vehicle development process. It involves optimizing the exhaust system routing and designing heat shields to protect various components that are in near proximity to the exhaust system. Reduced time to market necessitates an efficient process for thermal protection development. A robust procedure that utilizes state of the art CFD simulation techniques proactively during the design phase is described. Simulation allows for early detection of thermal issues and development of countermeasures several months before prototype vehicles are built. Physical testing is only used to verify the thermal protection package rather than to develop heat shields. The new procedure reduces the number of physical tests and results in a robust, efficient methodology.

Variable Valve Actuation (VVA) technology provides high potential in achieving high performance, low fuel consumption and pollutant reduction. However, more degrees of freedom impose a big challenge for engine characterization and calibration. In this study, a simulation based approach and optimization framework is proposed to optimize the setpoints of multiple independent control variables. Since solving an optimization problem typically requires hundreds of function evaluations, a direct use of the high-fidelity simulation tool leads to the unbearably long computational time. Hence, the Artificial Neural Networks (ANN) are trained with high-fidelity simulation results and used as surrogate models, representing engine's response to different control variable combinations with greatly reduced computational time. To demonstrate the proposed methodology, the cam-phasing strategy at Wide Open Throttle (WOT) is optimized for a dual-independent Variable Valve Timing (VVT) engine.

Cam-phasing is increasingly considered as a feasible Variable Valve Timing (VVT) technology for production engines. Additional independent control variables in a dual-independent VVT engine increase the complexity of the system, and achieving its full benefit depends critically on devising an optimum control strategy. A traditional approach relying on hardware experiments to generate set-point maps for all independent control variables leads to an exponential increase in the number of required tests and prohibitive cost. Instead, this work formulates the task of defining actuator set-points as an optimization problem. In our previous study, an optimization framework was developed and demonstrated with the objective of maximizing torque at full load. This study extends the technique and uses the optimization framework to minimize fuel consumption of a VVT engine at part load.

Component bench fatigue testing is a cost-effective way to evaluate the durability of exhaust catalytic converters. A successful bench fatigue test depends on the development of a test specification. The test specification should represent the actual customer duty cycle that the component is exposed to. Based on the concept of equivalent fatigue damage, a systematic approach is presented to obtain the test specification from the acquired road load data. A method based on damage analysis is proposed to determine the effective notch factor, and an empirical relationship is presented to account for the thermal effect on the test specification. The principles and procedures of multiple block testing and constant amplitude testing are also presented.

Vehicle evaluations and model calculations were conducted on a vacuum-insulated catalytic converter (VICC). This converter uses vacuum and a eutectic PCM (phase-change material) to prolong the temperature cool-down time and hence, may keep the converter above catalyst light-off between starts. Tailpipe emissions from a 1992 Tier 0 5.2L van were evaluated after 3hr, 12hr, and 24hr soak periods. After a 12hr soak the HC emissions were reduced by about 55% over the baseline HC emissions; after a 24hr soak the device did not exhibit any benefit in light-off compared to a conventional converter. Cool-down characteristics of this VICC indicated that the catalyst mid-bed temperature was about 180°C after 24hrs. Model calculations of the temperature warm-up were conducted on a VICC converter. Different warm-up profiles within the converter were predicted depending on the initial temperature of the device.

A design is robust when the performance targets have been achieved and the effects of variation have been minimized without eliminating the causes of the variation such as manufacturing tolerances, material properties, environmental temperature, humidity, operational wear etc. In recent years several robust design concepts have been introduced in an effort to obtain optimum designs and minimize the variation in the product characteristics [1,2]. In this study, a probabilistic design analysis was performed on a catalytic converter substrate in order to determine the required manufacturing tolerance that results in a robust design. Variation in circularity (roundness) and the ultimate shear stress of the substrate material were considered. The required manufacturing tolerance for a robust design with 1,2 and 3 sigma quality levels was determined. The same manufacturing tolerance for a reliability based design with reliability levels of 85%, 90% and 95% was also determined and compared.

A turbocharged version of the 2.4L engine has been developed by the Chrysler Group of DaimlerChrysler Corporation. This new engine is derived from the proven 2.4L 4-cylinder, with significant changes to achieve a durable, high performance package for the PT Cruiser vehicle. The package includes an integrated turbocharger / exhaust manifold, oil squirters for piston cooling, and numerous other upgrades to satisfy the demanding performance, emissions, and durability requirements unique to this powertrain. The purpose of this paper is to describe the mechanical changes to the base engine, the unique turbocharger configuration, and the new parts necessary to accommodate the higher output.

The emerging Variable Valve Timing (VVT) technology complicates the estimation of air flow rate because both intake and exhaust valve timings significantly affect engine's gas exchange and air flow rate. In this paper, we propose to use Artificial Neural Networks (ANN) to model the air flow rate through a 2.4 liter VVT engine with independent intake and exhaust camshaft phasers. The procedure for selecting the network architecture and size is combined with the appropriate training methodology to maximize accuracy and prevent overfitting. After completing the ANN training based on a large set of dynamometer test data, the multi-layer feedforward network demonstrates the ability to represent air flow rate accurately over a wide range of operating conditions. The ANN model is implemented in a vehicle with the same 2.4 L engine using a Rapid Prototype Controller.

An accurate air flow rate model is critical for high-quality air-fuel ratio control in Spark-Ignition engines using a Three-Way-Catalyst. Emerging Variable Valve Timing technology complicates cylinder air charge estimation by increasing the number of independent variables. In our previous study (SAE 2004-01-3054), an Artificial Neural Network (ANN) has been used successfully to represent the air flow rate as a function of four independent variables: intake camshaft position, exhaust camshaft position, engine speed and intake manifold pressure. However, in more general terms the air flow rate also depends on ambient temperature and pressure, the latter being largely a function of altitude. With arbitrary cam phasing combinations, the ambient pressure effects in particular can be very complex. In this study, we propose using a separate neural network to compensate the effects of altitude on the air flow rate.