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A kinetic carbon monoxide (CO) emission model is developed to simulate engine out CO emissions for conventional diesel combustion. The model also incorporates physics governing CO emissions for low temperature combustion (LTC). The emission model will be used in an integrated system level model to simulate the operation and interaction of conventional and low temperature diesel combustion with aftertreatment devices. The Integrated System Model consists of component models for the diesel engine, engine-out emissions (such as NOx and Particulate Matter), and aftertreatment devices (such as DOC and DPF). The addition of CO emissions model will enhance the capability of the Integrated System Model to predict major emission species, especially for low temperature combustion. In this work a CO emission model is developed based on a two-step global kinetic mechanism [8].

In response to the emissions standards for diesel engines, it is essential to have separate aftertreatment devices for controlling the specific tailpipe emissions like HC, CO, NOx, and particulate matter. An advanced diesel exhaust aftertreatment system consists of channel-flow catalytic converters such as diesel oxidation catalyst (DOC), selective catalyst reduction (SCR) and wall-flow diesel particulate filters (DPF) each with discrete functions. Because of this multi-component aftertreatment system configuration, there are an increase in system complexity, development time and cost for doing experiments in order to evaluate various options and find the optimum aftertreatment system architecture. The objective of this work is the development and application of an integrated aftertreatment system model including DOC, SCR, DPF and all connecting pipes. The study includes the baseline system performance, i.e.

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.

An integrated system model containing sub-models for diesel engine, emissions, and aftertreatment devices has been developed. The objective is to study engine-device and device-device interactions. The emissions sub-models used are for NOx and PM (particulate matter) prediction. The aftertreatment sub-models used include a diesel oxidation catalyst (DOC) and a diesel particulate filter (DPF). Controllers have also been developed to allow for transient simulations, active DPF regeneration, and prevention/control of runaway DPF regenerations. The integrated system-level model has been used to simulate DPF regeneration via exhaust fuel injection ahead of the DOC. In addition, the controller model can use intake throttling to assist in active DPF regeneration if needed. Regeneration studies have been done for both steady engine load and with load transients. High to low engine load transients are of particular interest because they can lead to runaway DPF regeneration.

This paper describes the development of a mean value model for a turbocharged diesel engine. The objective is to develop a fast-running engine model with sufficient accuracy over a wide range of operating conditions for efficient evaluation of control algorithms and control strategies. The mean value engine model was derived from a detailed 1D engine model, using the Design of Experiments (DOE) and hybrid Radial Basis Functions (RBF) to approximate the simulation results of the detailed model for cylinder quantities (e.g., the engine volumetric efficiency, the indicated efficiency, and the energy fraction of the exhaust gas). Furthermore, the intake and exhaust systems (especially intake and exhaust manifolds) were completely simplified by lumping flow components together. In addition, to compare with hybrid RBF, neural networks were also used to approximate the simulation results of the detailed engine model.

An integrated system model containing sub-models for a multi-cylinder diesel engine, NOx and soot(PM) emissions, diesel oxidation catalyst (DOC) and diesel particulate filter (DPF) has been developed to simulate the engine and aftertreatment systems at transient engine operating conditions. The objective of this work is two-fold; ensure correct implementation of the integrated system level model and apply the integrated model to understand the fuel economy trade-off for various DPF regeneration strategies. The current study focuses on a 1.9L turbocharged diesel engine and its exhaust system. The engine model was built in GT-Power and validated against experimental data at full-load conditions. The DPF model is calibrated for the current engine application by matching the clean DPF pressure drop for different mass flow rates. Load, boost pressure, speed and EGR controllers are tuned and linked with the current engine model.

Diesel engines offer better fuel economy compared to their gasoline counterpart, but simultaneous control of NOx and particulates is very challenging. The blended 2-way SCR/DPF is recently emerging as a compact and cost-effective technology to reduce NOx and particulates from diesel exhaust using a single aftertreatment device. By coating SCR catalysts on and inside the walls of the conventional wall-flow filter, the 2-way SCR/DPF eliminates the volume and mass of the conventional SCR device. Compared with the conventional diesel aftertreatment system with a SCR and a DPF, the 2-way SCR/DPF technology offers the potential of significant cost saving and packaging flexibility. In this study, an engine dynamometer test cell was set up to repeatedly load and regenerate the SCR/DPF devices to mimic catalyst aging experienced during periodic high-temperature soot regenerations in the real world.

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.

A Soot-NOx Trap (SNT) is a combinatorial aftertreatment device intended to decrease both particulate and NOx emissions simultaneously. A system-level Soot-NOx Trap model was developed by adding Lean NOx Trap kinetics to a 1D Diesel Particulate Filter model. The hybrid model was validated against each parent model for the limiting cases, then exercised to investigate the interacting redox behavior. Modulations in temperature and exhaust air-fuel ratio were investigated for their ability to facilitate particulate oxidation and NOx reduction in the trap.

Over the last decade significant efforts have been made in the automotive industry to move into a math-based control development approach where much of the development could be done off-line using computer simulations. High-fidelity simulation of an engine and control system helps to shorten controller development time with reduced risk. This requires the integration of a detailed engine model with a representative controller model. This paper describes the development and validation of an integrated engine and controller model of a turbocharged diesel engine. The integrated model incorporates a detailed engine model in GT-Power and a comprehensive controller model in Simulink with functionalities like the production ECM. The focus of this study is a non-real time simulation and analysis of the control of EGR, turbocharger, and fueling with engine performance.

An integrated system model containing sub-models for a diesel engine, NOx and soot emissions, and a diesel particulate filter (DPF) has been used to simulate stead-state engine operating conditions. The simulation results have been used to investigate the effect of DPF loading and passive regeneration on engine performance and emissions. This work is the continuation of previous work done to create an overall diesel engine/exhaust system integrated model. As in the previous work, a diesel engine, exhaust system, engine soot emissions, and diesel particulate filter (DPF) sub-models have been integrated into an overall model using Matlab Simulink. For the current work new sub-models have been added for engine-out NOx emissions and an engine feedback controller. The integrated model is intended for use in simulating the interaction of the engine and exhaust aftertreatment components.

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 model was developed for a lean NOx trap (LNT) used in a diesel application. The accuracy of the LNT model was validated using data from flow reactor experiments and vehicle testing. It is demonstrated that the model agrees with the experiments reasonably well on both reactor and vehicle test data. The LNT model was then applied to simulate the NOx emissions at the trap outlet over the FTP cycle, and quantitatively evaluate the effect of inlet CO concentration, inlet H2 concentration, inlet gas temperature, and trap size on NOx conversion performance of the LNT. The LNT model was also integrated with an exhaust pipe model to investigate the impact of engine exhaust configuration on NOx conversion. The integration of the LNT model with the engine exhaust model is valuable in the assessment of engine exhaust configuration and NOx trap performance.

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.

An overall diesel engine and aftertreatment system model has been created that integrates diesel engine, exhaust system, engine emissions, and diesel particulate filter (DPF) models using MATLAB Simulink. The 1-D engine and exhaust system models were developed using WAVE. The engine emissions model combines a phenomenological soot model with artificial neural networks to predict engine out soot emissions. Experimental data from a light-duty diesel engine was used to calibrate both the engine and engine emissions models. The DPF model predicts the behavior of a clean and particulate-loaded catalyzed wall-flow filter. Experimental data was used to validate this sub-model individually. Several model integration issues were identified and addressed. These included time-step selection, continuous vs. limited triggering of sub-models, and code structuring for simulation speed. Required time-steps for different sub models varied by orders of magnitude.

In a parallel hybrid electric vehicle, higher fuel economy gains are typically achieved if significant electric drive (or engine-off) operation is possible, shifting the engine operating schedule so that it only runs at medium to high load for best efficiency. To enable efficient engine-off driving, a typical configuration will have a disconnect clutch between the engine and the rest of the driveline. In some configurations, when engine-on operation is requested the disconnect clutch is applied in conjunction with the traction motor/generator to crank the engine (i.e., a flying engine start). In this paper we describe the development of a control system for a flying engine start using an engine disconnect clutch. The clutch is located between the engine and electric motor, which is connected to the input of a multispeed transmission. We first describe an initial control algorithm evaluation using a driveline model.