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An advanced multi-layer material model has been developed to simulate the complex behavior in case-carburized gears where hardness dependent strength and elastic-plastic behavior is characterized. Also, an advanced fatigue model has been calibrated to material fatigue tests over a wide range of conditions and implemented in FEMFAT software for root bending fatigue life prediction in differential gears. An FEA model of a differential is setup to simulate the rolling contact and transient stresses occurring within the differential gears. Gear root bending fatigue life is predicted using the calculated stresses and the FEMFAT fatigue model. A specialized rig test is set up and used to measure the fatigue life of the differential over a range of load conditions. Root bending fatigue life predictions are shown to correlate very well with the measured fatigue life in the rig test.

Increasing powertrain complexity and the growing number of vehicle variants are putting a strain on current calibration development processes. This is particularly challenging for vehicle drivability calibration, which is traditionally completed late in the development cycle, only after mature vehicle hardware is available. Model-based calibration enables a shift in development tasks from the real world to the virtual world, allowing for increased system robustness while reducing development costs and time. A unique approach for drivability calibration was developed by incorporating drivability analysis software with online optimization software into a virtual engine test cell environment. Real-time, physics-based engine and vehicle simulation models were coupled with real engine controller hardware and software to execute automated drivability calibration within this environment.

A broad range of diesel, kerosene, and gasoline-like fuels has been tested in a single-cylinder diesel engine optimized for advanced combustion performance. These fuels were selected in order to better understand the effects of ignition quality, volatility, and molecular composition on engine-out emissions, performance, and noise levels. Low-level biofuel blends, both biodiesel (FAME) and ethanol, were included in the fuel set in order to test for short-term advantages or disadvantages. The diesel engine optimized in Part 1 of this study included cumulative engine hardware enhancements that are likely to be used to meet Euro 6 emissions limits and beyond, in part by operating under conditions of Homogeneous Charge Compression Ignition (HCCI), at least over some portions of the speed and load map.

Two single-cylinder diesel engines were optimised for advanced combustion performance by means of practical and cumulative hardware enhancements that are likely to be used to meet Euro 5 and 6 emissions limits and beyond. These enhancements included high fuel injection pressures, high EGR levels and charge cooling, increased swirl, and a fixed combustion phasing, providing low engine-out emissions of NOx and PM with engine efficiencies equivalent to today's diesel engines. These combustion conditions approach those of Homogeneous Charge Compression Ignition (HCCI), especially at the lower part-load operating points. Four fuels exhibiting a range of ignition quality, volatility, and aromatics contents were used to evaluate the performance of these hardware enhancements on engine-out emissions, performance, and noise levels.

The OBD II and EOBD legislation have significantly increased the number of system components that have to be monitored in order to avoid emissions degradation. Consequently, the algorithm design and the related calibration effort is becoming more and more challenging. Because of decreasing OBD thresholds, the monitoring strategy accuracy, which is tightly related with the components tolerances and the calibration quality, has to be improved. A model-based offline simulation of the monitoring strategies allows consideration of component and sensor tolerances as well as a first calibration optimization in the early development phase. AVL applied and improved a methodology that takes into account this information, which would require a big effort using testbed or vehicle measurements. In many cases a component influence analysis is possible before hardware is available for testbed measurements.