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A procedure has been developed to build system level predictive models that incorporate physical laws as well as information derived from experimental data. In particular a soot model was developed, trained and tested using experimental data. It was seen that the model could fit available experimental data given sufficient training time. Future accuracy on data points not encountered during training was estimated and seen to be good. The approach relies on the physical phenomena predicted by an existing system level phenomenological soot model coupled with ‘weights’ which use experimental data to adjust the predicted physical sub-model parameters to fit the data. This approach has developed from attempts at incorporating physical phenomena into neural networks for predicting emissions. Model training uses neural network training concepts.

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.

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].

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.

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.

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 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.

Diesel exhaust particulate trap system is one of the most effective means to control diesel particulate emissions from diesel vehicles. In this paper, a recently developed diesel exhaust particulate trap system was described and experimentally studied. This system employed a wall-flow ceramic foam filter, which was made of silicon carbide or chromium oxide. And this system was equipped with a microwave heater for the purpose of filter regeneration. Engine dynamometer testing, vehicle bench testing and on-road evaluation of this system were conducted. The experiments studied on the filtration efficiency of this system, the effectiveness of filter regeneration, the power penalty of the vehicle, the ability of noise suppression of this system, and the durability of this particulate trap system. The experimental results showed that this diesel particulate trap system was effective, reliable, and durable.

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.

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.

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.

As emissions regulations are becoming increasingly stringent worldwide, multiple exhaust aftertreatment devices are considered in order to minimize diesel engine tailpipe emissions. This paper presents the development of an integrated model of an advanced diesel aftertreatment system that consists of a diesel oxidation catalyst (DOC), a Lean NOx Trap (LNT), a diesel particulate filter (DPF), and connecting exhaust pipes. All component models are incorporated into a unified diesel exhaust aftertreatment simulation tool with uniform I/O in MATLAB/Simulink. The platform and approach to incorporate all component models into a single system model are described. The developed integrated diesel aftertreatment system model has been employed to simulate tailpipe emissions of a light-duty vehicle over the US Federal Test Procedure (FTP) emission certification cycle.

Many efforts have been made to apply the diesel exhaust particulate after-treatment system in practical use during the past ten years. In this paper, a diesel exhaust particulate after-treatment system with microwave energy as its external regeneration energy source is described, and the microwave regeneration characteristics of the diesel exhaust particulate after-treatment system developed are studied. The experimental results show that the regeneration efficiency can reach up to 80% with a wide regeneration window and a suitable regeneration duration. The effect of air supply on filter regeneration is also observed. It is proven that the microwave regeneration technique employed in the system is simple, effective and reliable.

Neural networks have been used for engine computations in the recent past. One reason for using neural networks is to capture the accuracy of multi-dimensional CFD calculations or experimental data while saving computational time, so that system simulations can be performed within a reasonable time frame. This paper describes three methods to improve upon neural network predictions. Improvement is demonstrated for in-cylinder pressure predictions in particular. The first method incorporates a physical combustion model within the transfer function of the neural network, so that the network predictions incorporate physical relationships as well as mathematical models to fit the data. The second method shows how partitioning the data into different regimes based on different physical processes, and training different networks for different regimes, improves the accuracy of predictions.

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.

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 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.

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.

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.

Using a clutch to disconnect and shut-off the engine when engine power is not required, the single-motor strong hybrid has the potential for significant fuel economy improvement with reduced costs and less system complexity. However, it is a challenge for the single-motor strong hybrid to maintain acceptable drivability at engine start since it requires diverting motor torque through a slipping clutch to start the engine. In this study, dynamic simulations of the hybrid transmission driveline with hydraulic and motor controls have been employed to assess the feasibility of the single-motor strong hybrid, to address drivability issues specific to this hybrid architecture at engine start, and to develop control methods to manage driveline disturbances to an acceptable level.