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Precise prediction of combustion parameters such as peak firing pressure (PFP) or crank angle of 50% burned mass fraction (MFB50) is essential for optimal engine control. These quantities are commonly determined from in-cylinder pressure sensor signals and are crucial to reach high efficiencies and low emissions. Highly accurate in-cylinder pressure sensors are only applied to test rig engines due to their high cost, limited durability and special installation conditions. Therefore, alternative approaches which employ virtual sensing based on signals from non-intrusive sensors retrieved from common knock sensors are of great interest. This paper presents a comprehensive comparison of selected approaches from literature, as well as adjusted or further developed methods to determine engine combustion parameters based on knock sensor signals. All methods are evaluated on three different engines and two different sensor positions.

To increase reliability and the maintenance interval of an internal combustion engine while operating it with the lowest possible emissions, spark plug wear must be reduced. In this context, information about the spark plug center and the ground electrode temperature is key. Several measurement devices have been developed that measure the temperature of spark plug electrodes. The great challenge is to measure the temperature of the center electrode; on the one hand, the measurement device must be insulated and capable of withstanding the high voltage of the ignition system, and on the other hand, the device should not influence the ignition system. All previously studied devices presented in this paper have in common that major reconstruction of the ignition system and/or spark plugs whose design is very different from the standard engine spark plug were necessary.

Virtual sensing refers to the processing of desired physical data based on measured values. Virtual sensors can be applied not only to obtain physical quantities which cannot be measured or can only be measured at an unreasonable expense but also to reduce the number of physical sensors and thus lower costs. In the field of spark ignited internal combustion engines, the virtual sensing approach may be used to predict the cylinder pressure signal (or characteristic pressure values) based on the acceleration signal of a knock sensor. This paper presents a method for obtaining the cylinder pressure signal in the high-pressure phase of an internal combustion engine based on the measured acceleration signal of a knock sensor. The approach employs a partial differential equation to represent the physical transfer function between the measured signal and the desired pressure. A procedure to fit the modeling constants is described using the example of a large gas engine.

Lean burn combustion concepts with high mean effective pressures are being pursued for large gas engines in order to meet future stringent emission limits while maintaining high engine efficiencies. Since severe boundary conditions for the ignition process are encountered with these combustion concepts, the processes of spark ignition and flame initiation are important topics of applied research, which aims to avoid misfiring and to keep cycle-to-cycle combustion variability within reasonable limits. This paper focuses on the fundamental investigation of early flame kernel development using different ignition system settings. The investigations are carried out on a rapid compression-expansion machine in which the spark ignition process can be observed under engine-like pressure and excess air ratio conditions while low flow velocities are maintained.

In the research and development of internal combustion engines, there are several drivers for developing an accurate online lube oil consumption (LOC) measurement system. Lube oil consumption is considered to be a root cause of hydrocarbon and particle emissions and lubricating oil autoignition. It also negatively influences the life cycle cost for engine operators. Highly accurate measurement of lube oil consumption must be possible before it can be reduced - or rather optimized - to levels stakeholders will require in the future. State-of-the-art methods such as gravimetric and volumetric measurements are not fully satisfactory for several reasons. Generally, offline LOC measurement is no longer suitable for fast and accurate measuring cycles, oil condition monitoring and wear monitoring. At present, tracer methods are considered to be the most promising approach. However, current tracer methods have their downsides as well.

Experimental investigations on engine test beds represent a significant cost in engine development. To reduce development time and related costs, it is necessary to check the quality of measurements automatically whenever possible directly on the test bed to allow early detection of faults. A fault diagnosis system should provide information about the presence, cause and magnitude of an inconsistency in measurement. The main challenge in developing such a system is to detect the fault quickly and reliably. However, only faults that have actually occurred should be detected because the user will only adopt a system that provides accurate results. This paper presents a methodology for automated fault diagnosis at engine test beds, starting with an explanation of the general procedure. Next, the methods applied for fault detection are introduced.

Optimization of engine warm-up behavior has traditionally made use of experimental investigations. However, thermal engine models are a more cost-effective alternative and allow evaluation of the fuel saving potential of thermal management measures in different driving cycles. To simulate the thermal behavior of engines in general and engine warm-up in particular, knowledge of heat distribution throughout all engine components is essential. To this end, gas-side heat transfer inside the combustion chamber and in the exhaust port must be modeled as accurately as possible. Up to now, map-based models have been used to simulate heat transfer and fuel consumption; these two values are calculated as a function of engine speed and load. To extend the scope of these models, it is increasingly desirable to calculate gas-side heat transfer and fuel consumption as a function of engine operating parameters in order to evaluate different ECU databases.

Hydrogen is frequently cited as a future energy carrier. Hydrogen allows a further optimization of internal combustion engines, especially with direct injection. In order to assess various concepts, detailed thermodynamic analyses were carried out. Effects, which can be neglected with conventional fuels (e.g. losses due to injection during compression stroke) are considered. These basics as well as several results from test bed investigations are described within this article. Wall heat losses were found to have a major influence on overall efficiency and are thus investigated in detail, based on local surface temperature measurement. Finally, concepts that allow an increase in engine efficiency and lowest NOx emissions are demonstrated.