Brian C. Kaul

Novel Characterization of GDI Engine Exhaust for Gasoline and Mid- Level Gasoline-Alcohol Blends

Gasoline direct injection (GDI) engines can offer improved fuel economy and higher performance over their port fuelinjected (PFI) counterparts, and are now appearing in increasingly more U.S. and European vehicles. Small displacement, turbocharged GDI engines are replacing large displacement engines, particularly in light-duty trucks and sport utility vehicles, in order for manufacturers to meet more stringent fuel economy standards. GDI engines typically emit the most particulate matter (PM) during periods of rich operation such as start-up and acceleration, and emissions of air toxics are also more likely during this condition. A 2.0 L GDI engine was operated at lambda of 0.91 at typical loads for acceleration (2600 rpm, 8 bar BMEP) on three different fuels; an 87 anti-knock index (AKI) gasoline (E0), 30% ethanol blended with the 87 AKI fuel (E30), and 48% isobutanol blended with the 87 AKI fuel. E30 was chosen to maximize octane enhancement while minimizing ethanol-blend level and iBu48 was chosen to match the same fuel oxygen level as E30. Particle size and number, organic carbon and elemental carbon (OC/EC), soot HC speciation, and aldehydes and ketones were all analyzed during the experiment. A new method for soot HC speciation is introduced using a direct, thermal desorption/pyrolysis inlet for the gas chromatograph (GC). Results showed high levels of aromatic compounds were present in the PM, including downstream of the catalyst, and the aldehydes were dominated by the alcohol blending.

Neural Network Controller Development and Implementation for Spark Ignition Engines With High EGR Levels

Past research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10% -25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (see Dudek and Sain, 1989). However, under high EGR levels, the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance preventing commercial engines to operate with high EGR levels. A neural network (NN)-based output feedback controller is developed to reduce cyclic variation in the heat release under high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate control loop was designed for controlling EGR levels. The stability analysis of the closed-loop system is given and the boundedness of the control input is demonstrated by relaxing separation principle, persistency of excitation condition, certainty equivalence principle, and linear in the unknown parameter assumptions. Online training is used for the adaptive NN and no offline training phase is needed. This online learning feature and model-free approach is used to demonstrate the applicability of the controller on a different engine with minimal effort. Simulation results demonstrate that the cyclic dispersion is reduced significantly using the proposed controller when implemented on an engine model that has been validated experimentally. For a single cylinder research engine fitted with a modern four-valve head (Ricardo engine), experimental results at 15% EGR indicate that cyclic dispersion was reduced 33% by the controller, an improvement of fuel efficiency by 2%, and a 90% drop in NOx from stoichiometric operation without EGR was observed. Moreover, unburned hydrocarbons (uHC) drop by 6% due to NN control as compared to the uncontrolled scenario due to the drop in cyclic dispersion. Similar performance was observed with the controller on a different engine.

Invited Review: A review of deterministic effects in cyclic variability of internal combustion engines

We review developments in the understanding of cycle–to–cycle variability in internal combustion engines, with a focus on spark-ignited and premixed combustion conditions. Much of the research on cyclic variability has focused on stochastic aspects, that is, features that can be modeled as inherently random with no short–term predictability. In some cases, models of this type appear to work very well at describing experimental observations, but the lack of predictability limits control options. Also, even when the statistical properties of the stochastic variations are known, it can be very difficult to discern their underlying physical causes and thus mitigate them. Some recent studies have demonstrated that under some conditions, cyclic combustion variations can have a relatively high degree of low–dimensional deterministic structure, which implies some degree of predictability and potential for real–time control. These deterministic effects are typically more pronounced near critical stability limits (e.g. near tipping points associated with ignition or flame propagation) such during highly dilute fueling or near the onset of homogeneous charge compression ignition. We review recent progress in experimental and analytical characterization of cyclic variability where low–dimensional, deterministic effects have been observed. We describe some theories about the sources of these dynamical features and discuss prospects for interactive control and improved engine designs. Taken as a whole, the research summarized here implies that the deterministic component of cyclic variability will become a pivotal issue (and potential opportunity) as engine manufacturers strive to meet aggressive emissions and fuel economy regulations in the coming decades.

A Method for Predicting Performance Improvements with Effective Cycle-To-Cycle Control of Highly Dilute Spark Ignition Engine Combustion

Abstract Dilute spark ignition engine combustion offers a promising method of improving fuel efficiency and reducing engine-out emissions and yet is not currently feasible owing to high levels of cyclic variability under highly dilute homogeneous operation. The deterministic nature of the undesirable cycle-to-cycle variations in combustion heat release implies that appropriate control schemes should yield significant reductions in cyclic variability, making it possible to use higher levels of charge dilution in production engines. A novel analysis approach is used to predict the improvement in fuel conversion efficiency that could be expected with effective control. Additionally, this analysis gives some insight into the effect of spark timing on the dynamics and controllability of the system.

Neural Network Control of Spark Ignition Engines with High EGR Levels

Research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10% to 25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (Dudek and Sain, 1989). However under high EGR levels the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance. A suite of neural network (NN)-based output feedback controllers with and without reinforcement learning is developed to control the SI engine at high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate control loop was designed for controlling EGR levels. The neural network controllers consists of three NN: a) ANN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input; and c) a third NN for generating actual control input. For reinforcement learning, an additional NN is used as the critic. The stability analysis of the closed loop system is given and the boundedness of all signals is ensured without separation principle. Online training is used for the adaptive NN and no offline training phase is needed. Experimental results obtained by testing the controller on a research engine indicate an 80% drop of NOx from stoichiometric levels using 10% EGR. Moreover, unburned hydrocarbons drop by 25% due to NN control as compared to the uncontrolled scenario.

Near Optimal Output-Feedback Control of Nonlinear Discrete-time Systems in Nonstrict Feedback Form with Application to Engines

A novel reinforcement-learning based output-adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to track a desired trajectory for a class of complex nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The controller includes an observer for estimating states and the outputs, critic, and two action NNs for generating virtual, and actual control inputs. The critic approximates certain strategic utility function and the action NNs are used to minimize both the strategic utility function and their outputs. All NN weights adapt online towards minimization of a performance index, utilizing gradient-descent based rule. A Lyapunov function proves the uniformly ultimate boundedness (UUB) of the closed-loop tracking error, weight, and observer estimation. Separation principle and certainty equivalence principles are relaxed; persistency of excitation condition and linear in the unknown parameter assumption is not needed. The performance of this controller is evaluated on a spark ignition (SI) engine operating with high exhaust gas recirculation (EGR) levels and experimental results are demonstrated.

An assessment of thermodynamic merits for current and potential future engine operating strategies

This work compares the fundamental thermodynamic underpinnings (i.e. working fluid properties and heat release profile) of various combustion strategies with engine measurements. The approach employs a model that separately tracks the impacts on efficiency due to differences in rate of heat addition, volume change, mass addition, and molecular weight change for a given combination of working fluid, heat release profile, and engine geometry. Comparative analysis between the measured and modeled efficiencies illustrates fundamental sources of efficiency reductions or opportunities inherent to various combustion regimes. Engine operating regimes chosen for analysis include stoichiometric spark-ignited combustion and lean compression-ignited combustion including homogeneous charge compression ignition, spark-assisted homogeneous charge compression ignition, and conventional diesel combustion. Within each combustion regime, the effects of engine load, combustion duration, combustion phasing, compression ratio, and charge dilution are explored. Model findings illustrate that even in the absence of losses such as heat transfer or incomplete combustion, the maximum possible thermal efficiency inherent to each operating strategy varies to a significant degree. Additionally, the experimentally measured losses are observed to be unique within a given operating strategy. The findings highlight the fact that to create a roadmap for future directions in internal combustion engine technologies, it is important to not only compare the absolute real-world efficiency of a given combustion strategy but also to examine the measured efficiency in context of what is thermodynamically possible with the working fluid and boundary conditions prescribed by a strategy.

Impact of Delayed Spark Restrike on the Dynamics of Cyclic Variability in Dilute SI Combustion

Spark-ignition (SI) engines can derive substantial efficiency gains from operation at high dilution levels. Additionally, the use of exhaust gas recirculation (EGR) for charge dilution also maintains compatibility with three-way catalysts by allowing stoichiometric operation. However, running high dilution levels increases the occurrence of misfires and partial burns, which induce higher levels of cyclic-variability in engine operation. This variability has been shown to have both stochastic and deterministic components. Factors such as in-cylinder turbulence and mixing-variations can be classified as stochastic; while, charge composition is the major source of the deterministic component through its non-linear effect on ignition and flame propagation characteristics. The use of these deterministic components has been previously explored to construct next-cycle control approaches that would allow stable operation near the edge of stability. Building on that work, this paper aims to understand the effect of spark strategies, specifically the use of a second spark (restrike) after the main spark, on engine operation at high dilution levels that were achieved using both excess air (i.e. lean combustion) and EGR.

Characterizing dilute combustion instabilities in a multi-cylinder spark-ignited engine using symbolic analysis.

Spark-ignited internal combustion engines have evolved considerably in recent years in response to increasingly stringent regulations for emissions and fuel economy. One new advanced engine strategy ustilizes high levels of exhaust gas recirculation (EGR) to reduce combustion temperatures, thereby increasing thermodynamic efficiency and reducing nitrogen oxide emissions. While this strategy can be highly effective, it also poses major control and design challenges due to the large combustion oscillations that develop at sufficiently high EGR levels. Previous research has documented that combustion instabilities can propagate between successive engine cycles in individual cylinders via self-generated feedback of reactive species and thermal energy in the retained residual exhaust gases. In this work, we use symbolic analysis to characterize multi-cylinder combustion oscillations in an experimental engine operating with external EGR. At low levels of EGR, intra-cylinder oscillations are clearly visible and appear to be associated with brief, intermittent coupling among cylinders. As EGR is increased further, a point is reached where all four cylinders lock almost completely in phase and alternate simultaneously between two distinct bi-stable combustion states. From a practical perspective, it is important to understand the causes of this phenomenon and develop diagnostics that might be applied to ameliorate its effects. We demonstrate here that two approaches for symbolizing the engine combustion measurements can provide useful probes for characterizing these instabilities.

Neuro Emission Controller for Minimising Cyclic Dispersion in Spark Ignition Engines with EGR Levels

Past literature shows that nitrous oxide (NO x ) emission can be reduced by operating a spark ignition (SI) engine at the stoichiometric condition with high exhaust gas recirculation (EGR) levels. However, an engine, whose dynamics are typically unknown, will exhibit instability due to cyclic dispersion in heat release. A suite of novel neural network (NN) control schemes is developed to reduce the cyclic dispersion in heat release by using fuel as the control input. A separate control loop is designed for controlling EGR levels. The first NN scheme uses the total fuel and air as state variables for feedback control whereas a heat release-based output feedback scheme is developed next to relax the need for state variable measurements. The stability analysis of the closed loop system is demonstrated for both the schemes. No offline training phase is needed since online NN learning is utilised. Simulation and experimental results demonstrate that with control, the cyclic dispersion is reduced by 30%, NO x by 80% from stiochiometric levels and unburned hydrocarbons by 28% from the uncontrolled scenario.