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Fuel economy improvement and stringent emission regulations worldwide require advanced air charging and combustion technologies, such as low temperature combustion, PCCI or HCCI combustion. Furthermore, NOx aftertreatment systems, like Selective Catalyst Reduction (SCR) or lean NOx trap (LNT), are needed to reduce vehicle tailpipe emissions. The information on engine-out NOx emissions is essential for engine combustion optimization, for engine and aftertreatment system development, especially for those involving combustion optimization, system integration, control strategies, and for on-board diagnosis (OBD). A physical NOx sensor involves additional cost and requires on-board diagnostic algorithms to monitor the performance of the NOx sensor.

A cylinder model was developed using artificial neural networks (ANN). The cylinder model utilized the trained ANN models to predict engine parameters including cylinder pressures, cylinder temperatures, cylinder wall heat transfer, NOx and soot emissions. The ANN models were trained to approximate CFD simulation results of an engine. The ANN cylinder model was then applied to predict engine performance and emissions over the standard heavy-duty FTP transient cycle. The engine responses varying over the engine speed and torque range were simulated in the course of the transient test cycle. It was demonstrated that the ANN cylinder model is capable of simulating the characteristics of the engine operating under transient conditions reasonably well.

Artificial neural networks (ANN) have been recognized as universal approximators for nonlinear continuous functions and actively applied in engine research in recent years [1, 2, 3, 4, 5, 6, 7 and 8]. This paper describes the methodology and results of using the ANN to model a turbocharged DI diesel engine. The engine was simulated using the CFD code (KIVA-ERC) over a wide range of operating conditions, and numerical simulation results were used to train the ANN. An efficient data collection methodology using the Design of Experiments (DOE) techniques was developed to select the most characteristic engine operating conditions and hence the most informative data to train the ANN. This approach minimizes the time and cost of collecting training data from either computational or experimental resources. The trained ANN was then used to predict engine parameters such as cylinder pressure, cylinder temperature, NOx and soot emissions, and cylinder heat transfer.

A large proportion (about 33%) of the fuel energy is lost through exhaust gas in a gasoline engine. Electric turbo compounding (ETC) is a promising technology for gasoline engine exhaust energy recovery. In this paper, optimization of an ETC system for turbocharged gasoline engines is carried out. The ETC system has a turbo-generator that is in parallel with the turbocharger, the flow distribution between the turbocharger and the turbo-generator is controlled. The engine exhaust energy is recovered by the turbo-generator with fixed geometry turbine (FGT) or variable nozzle turbine (VNT). The design and control of the ETC system are optimized for best recovery of engine exhaust energy at engine full load and part load operating conditions. The system performance is studied by 1D simulation methods. The gasoline engine is modeled with the GT-POWER software and the turbochargers and turbo-generators are modeled with turbo through-flow models.

Stochastic pre-ignition (SPI) has been commonly observed in turbocharged spark-ignition direct-injection (SIDI) engines at low-speed and high-load conditions, which causes extremely high cylinder pressures that can damage an engine immediately or degrade the engine life. The compositions and properties of fuels and lubricants have shown a strong impact on SPI frequency. This study experimentally evaluated SPI behaviors on a 2.0-liter 4-cylinder turbocharged SIDI engine with China V market fuel and China fuel blended to US Tier II fuel specifications. China V market fuel showed significantly higher SPI frequency and severity than China blended US Tier II fuel, which was attributed to its lower volatility between 100 °C to 150 °C (or lower T60 to T90 in the distillation curve). Two different formulations of lubricant oils were also tested and their impact on SPI were compared.

This study employs experimental and computational methods to investigate the thermal state of the exhaust manifold of a multi-cylinder turbocharged diesel engine operating under steady-state conditions. The local skin temperatures and surface heat fluxes varied significantly throughout the external surface of the manifold. The augmentation of the local heat flux with increasing load and engine speed may be represented solely by the increase in the fuel mass flow rate. The results of the 1D simulation are in good agreement with the measurements of the exit gas temperatures, skin temperatures, and surface heat fluxes.

A one-dimensional engine model was developed for a 4.9-liter V-configuration 6-cylinder turbocharged direction-injection diesel engine. The engine model was first calibrated using the experimental data taken on dynamometer at eight steady-state engine operating conditions. Then the model was extensively validated with four transient dynamometer tests that were conducted mainly with step changes in the engine load, the EGR valve position, the intake throttle position, and/or the VGT vane position. It is shown that the developed model predicts the engine performance and gas dynamics with an error less than 3% in general, both at steady-state and transient engine operating conditions. The validated engine model is very useful in several future applications, such as engine development and optimization, and engine and aftertreatment system integration.