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With the advent of advanced diesel after-treatment technologies, sophisticated sensors are becoming a critical cost challenge to OEMs. This paper describes an approach for replacing the engine out NOx sensor with an artificial neural network (ANN) based virtual sensor. The technique centers around inferring NOx concentration from readily available engine operating parameters, eliminating the need for physical sensing and the cost associated with it. A multi-layer perceptron network was trained to estimate NOx concentration from engine speed, load, exhaust gas recirculation, and air-fuel ratio information. This supervised learning was conducted with measured engine data. The network was validated against measured data that was excluded from the training data set. The paper details application of this technique to both a heavy duty and light duty diesel engine. Results show good agreement between predictions and measured data under the steady state conditions studied.

The increasingly stringent limits on pollutant emissions from internal combustion engine-powered vehicles require the optimization of advanced combustion systems by means of virtual development and simulation tools. Among the gaseous emissions from spark-ignition engines, the unburned hydrocarbon (HC) emissions are the most challenging species to simulate because of the complexity of the multiple physical and chemical mechanisms that contribute to their emission. These mechanisms are mainly three-dimensional (3D) resulting from multi-phase physics - e.g., fuel injection, oil-film layer, etc. - and are difficult to predict even in complex 3D computational fluid-dynamic (CFD) simulations. Phenomenological models describing the relationships between the physical-chemical phenomena are of great interest for the modeling and simplification of such complex mechanisms.

Fuel consumption and NOx emissions of gasoline engines at part load can be significantly reduced by Controlled Auto-Ignition combustion concepts. However, the range of Gasoline Controlled Auto-Ignition (GCAI) operation is still limited by lacking combustion stability at low load and by high pressure-rise rates toward higher loads. Previous investigations indicate that the auto-ignition process is particularly determined by the thermodynamic state of the charge and by stratification effects of residual gas, temperature, and air-fuel ratio. However, little experimental data exist on the direct influence of mixture stratification on local ignition and heat-release rate (HRR) in direct-injection (DI) GCAI engines, because it is challenging to measure all the relevant charge and combustion parameters quasi-simultaneously with sufficient spatial/temporal resolution and precision.

Future Diesel engines must meet extended requirements regarding air-fuel ratio, exhaust gas recirculation (EGR) capability, and tailored exhaust gas temperatures in the complete engine map to comply with the future pollutant emission standards. In this respect, parallel turbines combined with two separate exhaust manifolds have the potential to increase the exhaust gas temperature upstream of the exhaust aftertreatment system and reduce the catalyst light-off time. Furthermore, variable exhaust valve (EV) lifts enable new control strategies of the boosting system without additional actuators. Therefore, hardware robustness can be improved. This article focuses on the parallel-sequential boosting concept (PSBC) for a high-performance four-cylinder Diesel engine with separated exhaust manifolds combined with EV deactivation. One EV per cylinder is connected to one of the separated exhaust manifolds and, thus, connected to one of the turbines.

Pre-ignition in a boosted spark-ignition engine can be triggered by several mechanisms, including oil-fuel droplets, deposits, overheated engine components and gas-phase autoignition of the fuel-air mixture. A high pre-ignition resistance of the fuel used mitigates the risk of engine damage, since pre-ignition can evolve into super-knock. This paper presents the pre-ignition propensities of 11 RON 89-100+ gasoline fuel blends in a single-cylinder research engine. Albeit the addition of two high-octane components (methanol and reformate) to a toluene primary reference fuel improved the pre-ignition resistance, one high-RON fuel experienced runaway pre-ignition at relatively low boost pressure levels. A comparison of RON 96 blends showed that the fuel composition can affect pre-ignition resistance at constant RON.

During a thermal regeneration of a Diesel particulate filter (DPF) the temperature inside the DPF may raise above critical thresholds in an uncontrolled way (thermal shock). Especially driving conditions with a comparable low exhaust gas mass flow and high oxygen content like idle speed may create a thermal shock. This paper presents a concept for an ECU software structure to prevent the DPF from reaching improper temperatures and the methodology in order to calibrate this ECU structure. The concept deals in general with a closed-loop control of the exhaust gas air-fuel-ratio during the critical engine operation phases. Those critical operation phases are identified at the engine test bench during “Drop-to-Idle” and “Drop-to-Overrun” experiments. The experiments show that those phases are critical having on the one hand a low exhaust gas mass flow and on the other hand a high oxygen percentage in the exhaust gas.