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Model-based analysis of in-cylinder pressure sensor signals has been a key component for internal combustion engine research, diagnostics and controller development during the past decades. This analysis is often based on simple thermodynamic models of the in-cylinder processes. In order for the analysis to give accurate results, the models need to be sufficiently calibrated. This paper investigates the use of the extended Kalman filter and the particle filter for the purpose of online estimation of top-dead-center offset, a convective heat-transfer coefficient and cylinder-wall temperature in a Gatowski heat-release model. Simulation results show that the filters are consistent in estimating the true parameters, that the assumed model uncertainty and heat-release noise density works as filter tuning parameters. The filters were found to be sensitive to errors on pressure-sensor offset and the cylinder compression ratio.

Fumigation was studied in a 12 L six-cylinder heavy-duty engine. Port-injected ethanol was ignited with a small amount of diesel injected into the cylinder. The setup left much freedom for influencing the combustion process, and the aim of this study was to find operation modes that result in a combustion resembling that of a homogeneous charge compression ignition (HCCI) engine with high efficiency and low NOx emissions. Igniting the ethanol-air mixture using direct-injected diesel has attractive properties compared to traditional HCCI operation where the ethanol is ignited by pressure alone. No preheating of the mixture is required, and the amount of diesel injected can be used to control the heat release rate. The two fuel injection systems provide a larger flexibility in extending the HCCI operating range to low and high loads. It was shown that cylinder-to-cylinder variations present a challenge for this type of combustion.

The Homogeneous Charge Compression Ignition (HCCI) principle holds promise to increase efficiency and to reduce emissions from internal combustion engines. As HCCI combustion lacks direct ignition timing control and auto-ignition depends on the operating condition, control of auto-ignition is necessary. Since auto-ignition of a homogeneous mixture is very sensitive to operating conditions, a fast combustion phasing control is necessary for reliable operation. To this purpose, HCCI modeling and model-based control with experimental validation were studied. A six-cylinder heavy-duty HCCI engine was controlled on a cycle-to-cycle basis in real time by applying in-cylinder pressure feedback. A low-complexity physical model was developed, aiming at describing the major thermodynamic and chemical interactions in the course of an engine stroke. The model shows the importance of thermal interaction between the combustion and the cylinder walls.

A known problem of the HCCI engine is its lack of direct control and its requirements of feedback control. Today there exists several different means to control an HCCI engine, such as dual fuels, variable valve actuation, inlet temperature and compression ratio. Independent of actuation method a sensor is needed. In this paper we perform closed-loop control based on two different sensors, pressure and ion current sensor. Results showing that they give similar control performance within their operating range are presented. Also a comparison of two methods of designing HCCI timing controller, manual tuning and model based design is presented. A PID controller is used as an example of a manually tuned controller. A Linear Quadratic Gaussian controller exemplifies model based controller design. The models used in the design were estimated using system identification methods. The system used in this paper performs control on cycle-to-cycle basis. This leads to fast and robust control.

As the number of actuators and sensors increases in modern combustion engines, the task of optimizing engine performance becomes increasingly complex. Efficient information processing techniques are therefore important, both for off-line calibration of engine maps, and on-line adjustments based on sensor data. In-cylinder pressure sensors are slowly spreading from laboratory use to production engines, thus making data with high temporal resolution of the combustion process available. The standard way of using the cylinder pressure data for control and diagnostics is to focus on a few important physical features extracted from the pressure trace, such as the combustion phasing CA50, the indicated mean effective pressure IMEP, and the ignition delay. These features give important information on the combustion process, but much information is lost as the information from the high-resolution pressure trace is condensed into a few key parameters.

Autoignition of a homogeneous mixture is very sensitive to operating conditions, therefore fast control is necessary for reliable operation. There exists several means to control the combustion phasing of an Homogeneous Charge Compression Ignition (HCCI) engine, but most of the presented controlled HCCI result has been performed with single-input single-output controllers. In order to fully operate an HCCI engine several output variables need to be controlled simultaneously, for example, load, combustion phasing, cylinder pressure and emissions. As these output variables have an effect on each other, the controller should be of a structure which includes the cross-couplings between the output variables. A Model Predictive Control (MPC) controller is proposed as a solution to the problem of load-torque control with simultaneous minimization of the fuel consumption and emissions, while satisfying the constraints on cylinder pressure.

Partially Premixed Combustion (PPC) has shown to be a promising advanced combustion mode for future engines in terms of efficiency and emission levels. The combustion timing should be suitably phased to realize high efficiency. However, a simple constant model based predictive controller is not sufficient for controlling the combustion during transient operation. This article proposed one learning based model predictive control (LBMPC) approach to achieve controllability and feasibility. A learning model was developed to capture combustion variation. Since PPC engines could have unacceptably high pressure-rise rates at different operation points, triple injection is applied as a solvent, with the use of two pilot fuel injections. The LBMPC controller utilizes the main injection timing to manage the combustion timing. The cylinder pressure is used as the combustion feedback. The method is validated in a multi-cylinder heavy-duty PPC engine for transient control.

A new generation of strategies for vehicle and engine control systems has become necessary because of increasing requirements for accuracy, ride, comfort, safety, complexity, and emission levels. In contrast with earlier systems, new control systems are based on dynamic physical models and the principles of advanced nonlinear control. With contributions from leading scientists in the field, this book presents an overview of research in this rapidly-expanding area. New approaches to solving theoretical problems, as well as numerous systems and control research issues, are covered.