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Adjusting the Residual Gas Fraction (RGF) by means of Variable Valve Actuation (VVA) is a strong candidate for controlling the ignition timing in Homogeneous Charge Compression Ignition (HCCI) engines. However, at high levels of residual gas fraction, insufficient mixing can lead to the presence of considerable temperature and composition variations. This paper extends previous modeling efforts to include the effect of RGF distribution on the onset of ignition and the rate of combustion using a multi-dimensional fluid mechanics code (KIVA-3V) sequentially with a multi-zone code with detailed chemical kinetics. KIVA-3V is used to simulate the gas exchange processes, while the multi-zone code computes the combustion event. It is shown that under certain conditions the effect of composition stratification is significant and cannot be captured by a single-zone model or a multi-zone model using only temperature zones.

Variable geometry turbines (VGT) are of particular interest to advanced diesel powertrains for future conventional trucks, since they can dramatically improve system transient response to sudden changes in speed and load, characteristic of automotive applications. VGT systems are also viewed as the key enabler for the application of the EGR system for reduction of heavy-duty diesel emissions. This paper applies an artificial neural network methodology to VGT modeling in order to enable representation of the VGT characteristics for any blade (nozzle) position. Following validation of the ANN model of the baseline, fixed geometry turbine, the VGT model is integrated with the diesel engine system. The latter is linked to the driveline and the vehicle dynamics module to form a complete, high-fidelity vehicle simulation.

The present study introduces a modeling approach for investigating the effects of valve events and gas exchange processes in the framework of a full-cycle HCCI engine simulation. A multi-dimensional fluid mechanics code, KIVA-3V, is used to simulate exhaust, intake and compression up to a transition point, before which chemical reactions become important. The results are then used to initialize the zones of a multi-zone, thermo-kinetic code, which computes the combustion event and part of the expansion. After the description and the validation of the model against experimental data, the application of the method is illustrated in the context of variable valve actuation. It has been shown that early exhaust valve closing, accompanied by late intake valve opening, has the potential to provide effective control of HCCI combustion.

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.

Despite remarkable progress made over the past 30 years, automobiles continue to be a major source of hydrocarbon emissions. The objective of this study is to evaluate whether variable exhaust valve opening (EVO) and exhaust valve closing (EVC) can be used to reduce hydrocarbon emissions. An automotive gasoline engine was tested with different EVO and EVC timings under steady-state and start-up conditions. The first strategy that was evaluated uses early EVO with standard EVC. Although exhaust gas temperature is increased and catalyst light-off time is reduced, the rapid drop in cylinder temperature increases cylinder-out hydrocarbons to such a degree that a net increase in hydrocarbon emissions results. The second strategy that was evaluated uses early EVO with early EVC. Early EVO reduces catalyst light-off time by increasing exhaust gas temperature and early EVC keeps the hydrocarbon-rich exhaust gas from the piston crevice from leaving the cylinder.

Wall wetting of injected fuel onto the intake manifold and cylinder wall causes unpredictable transient behavior of air-fuel mixing which results in a significant emission of unburned hydrocarbon (HC) emission during cold start operation. Heated exhaust gas oxygen (HEGO) sensors cannot measure the air-fuel ratio (A/F) of exhaust gas during cold start condition. Precise and fast estimation of air/fuel ratio of the exhaust gas is required to elucidate the wall wetting phenomena and subsequent HC formation. Refined A/F estimation can enable the control of fuel injection minimizing HC emissions during cold start conditions so that HC emissions can be minimized. A new estimator for A/F of the exhaust gas has been developed. The A/F estimator described in this study utilizes measured exhaust gas temperature and general engine parameters such as engine speed, airflow, coolant temperature, etc.