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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.

In this paper, a control-oriented soot model was developed for real-time soot prediction and combustion condition optimization in a gasoline Partially Premixed Combustion (PPC) Engine. PPC is a promising combustion concept that achieves high efficiency, low soot and NOx emissions simultaneously. However, soot emissions were found to be significantly increased with high EGR and pilot injection, therefore a predictive soot model is needed for PPC engine control. The sensitivity of soot emissions to injection events and late-cycle heat release was investigated on a multi-cylinder heavy duty gasoline PPC engine, which indicated main impact factors during soot formation and oxidation processes. The Hiroyasu empirical model was modified according to the sensitivity results, which indicated main influences during soot formation and oxidation processes. By introducing additional compensation factors, this model can be used to predict soot emissions under pilot injection.

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.

Partially Premixed Combustion (PPC) is a promising combustion concept with high thermodynamic efficiency and low emission level, and also with minimal modification of standard engine hardware. To use PPC in a production oriented engine, the optimal intake charge conditions for PPC should be included in the analysis. The experiments in this paper investigated and confirmed that the optimal intake conditions of net indicated efficiency for PPC are EGR between 50% and 55% as possible and the lambda close to 1.4. Heat-transfer energy and exhaust gas waste-energy contribute to the majority of the energy loss in the engine. The low EGR region has high heat-transfer and low exhaust gas enthalpy-waste, while the high EGR region has low heat-transfer and high exhaust gas waste-enthalpy. The optimal EGR condition is around 50% where the smallest energy loss is found as a trade-off between heat transfer and exhaust-gas enthalpy-waste.

In low-temperature combustion concepts such as partially premixed combustion, the ignition delay should be large enough in order to ensure sufficient fuel and air mixing before the start of combustion. It is also necessary that the combustion timing is sufficiently well phased for high thermal efficiency. Since the ignition delay and combustion timing are intimately coupled, the decoupling of these two quantities gives rise to an interesting multiple input, multiple output control problem where the control of the air system and the fuel injection system have to be combined. In a multi-cylinder engine this problem becomes underdetermined or uncontrollable with more outputs than inputs. This article investigates model-based cycle-to-cycle cylinder-individual closed-loop control of the ignition delay and the combustion phasing in a multi-cylinder heavy-duty DI engine running on a gasoline fuel mixture.

The combustion timing in internal combustion engines affects the fuel consumption, in-cylinder peak pressure, engine noise and emission levels. The combination of an in-cylinder pressure sensor together with a direct injection fuel system lends itself well for cycle-to-cycle control of the combustion timing. This paper presents a method of controlling the combustion timing by the use of a cycle-to-cycle injection-timing algorithm. At each cycle the currently estimated heat-release rate is used to predict the in-cylinder pressure change due to a combustion-timing shift. The prediction is then used to obtain a cycle-to-cycle model that relates combustion timing to gross indicated mean effective pressure, max pressure and max pressure derivative. Then the injection timing that controls the combustion timing is decided by solving an optimization problem involving the model obtained.

Partially Premixed Combustion (PPC) is a promising advanced combustion mode for future engines. In order to investigate the sensitivity of PPC to exhaust gas recirculation (EGR) rate, intake gas temperature, intake gas pressure, and injection timing, these parameters were swept individually at three different loads in a single cylinder diesel engine with gasoline-like fuel. A factor of sensitivity was defined to indicate the combustion's controllability and sensitivity to inlet gas parameters and injection timings. Through analysis of experimental results, a control window of inlet gas parameters and injection timings is obtained at different loads in PPC mode from 5 bar to 10 bar IMEPg load at 1200 rpm. To further study the PPC controllability with injection timing, main injection timing was adjusted to sustain steady combustion phasing subject to perturbation of inlet gas state.

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.

This study investigates mode switching from spark ignited operation with early intake valve closing to residual gas enhanced HCCI using negative valve overlap on a port-fuel injected light-duty diesel engine. A mode switch is demonstrated at 3.5 bar IMEPnet and 1500 rpm. Valve timings and fuel amount have to be selected carefully prior to the mode switch. During mode transition, IMEPnet deviates by up to 0.5 bar from the set point. The time required to return to the set point as well as the transient behavior of the engine load varies depending on which control structure that is used. Both a model-based controller and a PI control approach were implemented and evaluated in experiments. The controllers were active in HCCI mode. The model-based controller achieved a smoother transition and while using it, the transition could be accomplished within three engine cycles.

This paper offers a two-zone NOx model suitable for vehicle on-board, on-line implementation. Similar NOx modeling attempts have previously been undertaken. The hereby suggested method does however offer clear and important benefits over the previously methods, utilizing a significantly different method to handle temperature calculations within the (two) different zones avoiding iterative computation. The new method significantly improves calculation speed and, most important of all, reduces implementation complexity while still maintaining reasonable accuracy and the physical interpretation of earlier suggested methods. The equations commonly used to compute NOx emissions is also rewritten in order to suit a two-zone NOx model. An algorithm which can be used to compute NOx emissions is presented and the intended contribution of the paper is a NOx model, implementation feasible for an embedded system, e.g. embedded processor or embedded electronic hardware (FPGA).

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.

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.

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.

Measurement of ion current signal from HCCI combustion was performed. The aim of the work was to investigate if a measurable ion current signal exists and if it is possible to obtain useful information about the combustion process. Furthermore, influence of mixture quality in terms of air/fuel ratio and EGR on the ion current signal was studied. A conventional spark plug was used as ionization sensor. A DC voltage (85 Volt) was applied across the electrode gap. By measuring the current through the gap the state of the gas can be probed. A comparison between measured pressure and ion current signal was performed, and dynamic models were estimated by using system identification methods. The study shows that an ion current signal can be obtained from HCCI combustion and that the signal level is very sensitive to the fuel/air equivalence ratio.

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.