Mahdi Shahbakhti

Characterizing the cyclic variability of ignition timing in a homogeneous charge compression ignition engine fuelled with n-heptane/iso-octane blend fuels

Abstract The cyclic variations of homogeneous charge compression ignition (HCCI) ignition timing is studied for a range of charge properties by varying the equivalence ratio, intake temperature, intake pressure, exhaust gas recirculation (EGR) rate, engine speed, and coolant temperature. Characterization of cyclic variations of ignition timing in HCCI at over 430 operating points on two single-cylinder engines for five different blends of primary reference fuel (PRF), (iso-octane and n-heptane) is performed. Three distinct patterns of cyclic variation for the start of combustion (SOC), combustion peak pressure (Pmax), and indicated mean effective pressure (i.m.e.p.) are observed. These patterns are normal cyclic variations, periodic cyclic variations, and cyclic variations with weak/misfired ignitions. Results also show that the position of SOC plays an important role in cyclic variations of HCCI combustion with less variation observed when SOC occurs immediately after top dead centre (TDC). Higher levels of cyclic variations are observed in the main (second) stage of HCCI combustion compared with that of the first stage for the PRF fuels studied. The sensitivity of SOC to different charge properties varies. Cyclic variation of SOC increases with an increase in the EGR rate, but it decreases with an increase in equivalence ratio, intake temperature, and coolant temperature.

A skeletal kinetic mechanism for PRF combustion in HCCI engines

Abstract A single zone thermodynamic model, coupled to a kinetic mechanism, is developed and is capable of predicting the ignition timing of Primary Reference Fuels (PRFs) in a Homogeneous Charge Compression Ignition (HCCI) engine. A new combination of kinetic mechanisms is used, which includes 120 reactions and 58 species for both ignition and high temperature reactions. The model is validated using a step by step methodology. The validation compares ignition delays predicted by the model with published measurements from a rapid compression machine, shock tube as well as the cylinder pressure histories taken from two different experimental HCCI engines for various operating conditions. The model is able to qualitatively predict the effect of different parameters such as gas temperature, gas pressure, equivalence ratio and octane number on the HCCI ignition delay.

Physics Based Control Oriented Model for HCCI Combustion Timing

Incorporating homogeneous charge compression ignition (HCCI) into combustion engines for better fuel economy and lower emission requires understanding the dynamics influencing the combustion timing in HCCI engines. A control oriented model to dynamically predict cycle-to-cycle combustion timing of a HCCI engine is developed. The model is designed to work with parameters that are easy to measure and to have low computation time with sufficient accuracy for control applications. The model is a full-cycle model and consists of a residual gas model, a modified knock integral model, fuel burn rate model, and thermodynamic models. In addition, semi-empirical correlations are used to predict the gas exchange process, generated work and completeness of combustion. The developed model incorporates the thermal coupling dynamics caused by the residual gases from one cycle to the next cycle. The model is parameterized by over 5700 simulations from a detailed thermokinetic model and experimental data obtained from a single-cylinder engine. Cross-validation of the model with both steady-state and transient HCCI experiments for four different primary reference fuel blends is detailed. With seven model inputs, the combustion timing of over 150 different HCCI points is predicted to within an average error of less than 1.5 deg of crank angle. A narrow window of combustion timing is found to provide stable and efficient HCCI operation.

Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive Control

Model-based control of building energy offers an attractive way to minimize energy consumption in buildings. Model-based controllers require mathematical models that can accurately predict the behavior of the system. For buildings, specifically, these models are difficult to obtain due to highly time varying, and nonlinear nature of building dynamics. Also, model-based controllers often need information of all states, while not all the states of a building model are measurable. In addition, it is challenging to accurately estimate building model parameters (e.g. convective heat transfer coefficient of varying outside air). In this paper, we propose a modeling framework for “on-line estimation ” of states and unknown parameters of buildings, leading to the Parameter-Adaptive Building (PAB) model. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques are used to design the PAB model which simultaneously tunes the parameters of the model and provides an estimate for all states of the model. The proposed PAB model is tested against experimental data collected from Lakeshore Center building at Michigan Tech University. Our results indicate that the new framework can accurately predict states and parameters of the building thermal model.

Experimental study of exhaust temperature variation in a homogeneous charge compression ignition engine

Abstract Homogeneous charge compression ignition (HCCI) engines have low nitrogen oxide and particulate matter engine-out emissions but have higher unburned hydrocarbon and carbon monoxide emissions than the conventional spark ignition (SI) and diesel engines do. Only for sufficiently high exhaust gas temperatures can an exhaust after-treatment be used; thus a low exhaust gas temperature in certain operating conditions can limit the operating range in HCCI engines. The influences of the engine conditions on the exhaust gas temperature in a single-cylinder experimental engine are investigated at 340 steady state operating points. The variation in the exhaust gas temperature is also studied under transient conditions and during mode switching between SI and HCCI combustion. For the conditions tested, a significant number of data have an exhaust gas temperature below 300°C which is below the light-off temperature of typical catalytic converters on the market. Three different categories of engine variables are recognized and classified by how the exhaust temperature is affected by changing that variable. The first category is defined as the primary variables (e.g. the intake pressure and the fuel octane number) for which the location of ignition timing is the dominant factor in influencing the exhaust temperature. The other groups include compounding variables such as the engine speed and opposing variables such as the intake temperature, the coolant temperature, and the equivalence ratio. In addition, experimental results show that the exhaust temperature for HCCI engines is not strongly dependent on the engine load, unlike that for SI engines where the engine load is a main factor in determining the exhaust temperature.

Control Oriented Modeling of Combustion Phasing for an HCCI Engine

A promising method for reducing emissions and fuel consumption of internal combustion engines is the Homogeneous charge compression ignition (HCCI) engine. Control of ignition timing must be realized before the potential benefits of HCCI combustion can be implemented in production engines. A model suitable for real time implementation is developed and this model is able to predict ignition timing with an average error of less than 2 crank angle degrees. A modified knock- integral model (MKIM), with correlations for gas exchange process and fuel heat release, is used to predict HCCI combustion timing (CA50, crank angle where 50% of the fuel mass is burnt). The MKIM model is parameterized using a thermokinetic simulation model. Experimental data from a single cylinder engine at several HCCI operation conditions and three fuel blends is used to validate the model.

Selecting building predictive control based on model uncertainty

Model uncertainty limits the utilization of Model Predictive Controllers (MPC) to minimize building energy consumption. We propose a new Robust Model Predictive Control (RMPC) structure to make a building controller robust to model uncertainty. The results from RMPC are compared with those from a nominal MPC and a common building Rule Based Control (RBC). The results are then used to develop a methodology for selecting a controller type (i.e. RMPC, MPC, and RBC) as a function of building model uncertainty. RMPC is found to be the desirable controller for the cases with an intermediate level (30%-67%) of model uncertainty, while MPC is preferred for the cases with a low level (0-30%) of model uncertainty. A common RBC is found to outperform MPC or RMPC if the model uncertainty goes beyond a certain threshold (e.g. 67%).

HCCI Engine Combustion Phasing Prediction Using a Symbolic-Statistics Approach

Temporal dynamics of cyclic variation in a homogeneous charge compression ignition (HCCI) engine near misfire is analyzed using chaotic theory methods. The analysis of variation in consecutive cycles of CA50 (crank angle of 50% mass fraction fuel burnt) for an n-heptane fueled engine is performed for a test point near the misfire condition. The return map of the time series of CA50 cycle values reveals the deterministic and random portions of dynamics near misfire occurring in an HCCI engine. A symbol-statistic approach is also used to find the occurrence of possible probabilities of the data points under the same operating conditions. These techniques are then used to predict CA50 one cycle ahead. Simulated data points in phase space have similar dynamical structure to the experimental measurements.

Modeling of combustion phasing of a reactivity-controlled compression ignition engine for control applications:

Reactivity-controlled compression ignition (RCCI) is a promising combustion strategy to achieve near-zero NOx and soot emissions and diesel-like efficiencies. Model-based control of RCCI combustion phasing requires a computationally efficient combustion model that encompasses factors such as injection timings, fuel blend composition, and reactivity. In this work, physics-based models are developed to predict the onset of auto-ignition in RCCI and to estimate the burn duration based on an approximation of the spontaneous ignition front speed. A mean value control-oriented model of RCCI is then developed by combining the auto-ignition model, the burn duration model, and a Wiebe function to predict combustion phasing. The control-oriented model is parameterized and validated using simulation data from an experimentally validated, detailed computational fluid dynamics combustion model developed using the KIVA-3V code. The validation results show that the control-oriented model can predict the start of combustion, burn duration, and crank angle of 50% burnt fuel with an average error of less than 2 crank angle degrees. Thus, the control-oriented model demonstrates sufficient accuracy in predicting RCCI combustion phasing for control applications. The control-oriented model is an integral part of designing a model-based controller, which in the case of RCCI is of paramount importance due to various attributes concerning combustion, particularly for transient engine operation.

Early model-based verification of automotive control system implementation

Controller Software Verification (CSV) is the critical process used to avoid mismatch between a designed and implemented controller. Common CSV practice in the automotive industry is to test a controller after its software is fully implemented. In this paper, an early model-based CSV methodology is proposed to reduce the development time and improve the robustness of automotive controllers. The application of the proposed methodology is demonstrated on a “Cold Start Emission” control problem in passenger cars. A non-linear model-based controller is designed to reduce cold start hydrocarbon emissions from a mid-size modern passenger car. The controller robustness is analyzed by testing the controller against the major steps occurring during the software implementation process of a controller. The main focus is on the imprecision from sampling, quantization and fixed-precision arithmetic. The results from the robustness analysis are used to specify requirements for the controller implementation for passing current North American ULEV emission standard.