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The most challenging part of the engine combustion development is the reduction of pollutants (e.g. CO, THC, NOx, soot, etc.) and CO2 emissions. In order to achieve this goal, new combustion techniques are required, which enable a clean and efficient combustion. For compression ignition engines, combustion rate shaping, which manipulates the injected fuel mass to control the in-cylinder pressure trace and the combustion rate itself, turned out to be a promising opportunity. One possibility to enable this technology is the usage of specially developed rate shaping injectors, which can control the injection rate continuously. A feasible solution with series injectors is the usage of multiple injections to control the injection rate and, therefore, the combustion rate. For the control of the combustion profile, a detailed injector model is required for predicting the amount of injected fuel. Simplified 0D models can easily predict single injection rates with low deviation.

Especially for internal combustion engine simulations, various combustion models rely on the laminar burning velocity. With respect to computational time needed for CFD, the calculation of laminar burning velocities using a detailed chemical mechanism can be replaced by incorporation of approximation formulas, based on rate-ratio asymptotics. This study revisits an existing analytical approximation formula [1]. It investigates applicable temperature, pressure, and equivalence ratio ranges with special focus on engine combustion conditions. The fuel chosen here is methane and mixtures are composed of methane and air. The model performance to calculate the laminar burning velocity are compared with calculated laminar burning velocities using existing state of the art detailed chemical mechanisms, the GRI Mech 3.0 [2], the ITV RWTH [3], and the Aramco mechanism [4].

Simultaneous reduction of engine pollutants (e.g., CO, THC, NOx, and soot) is one of the main challenges in the development of new combustion systems. Low-temperature combustion (LTC) concepts in compression ignition (CI) engines like premixed charged compression ignition (PCCI) make use of pre-injections to create a partly homogenous mixture. In the PCCI combustion regime, a direct correlation between injection and pollutant formation is no longer present because of long ignition delay times. In LTC combustion systems, the in-cylinder pressure sensor is normally used to help the combustion control. However, to allow the control of PCCI engines, new sensor concepts are investigated to obtain additional information about the PCCI combustion for advanced controller structures. In LTC combustion systems like gasoline-controlled autoignition (GCAI) concepts, the application of ion current sensors enables additional monitoring of the combustion process with real-time capability.

Premixed charged compression ignition (PCCI) is an advanced combustion strategy, which has the potential to achieve ultra-low nitrogen oxide and soot emissions at high thermal efficiencies. PCCI combustion is characterized by a complex nonlinear chemical-physical process, which indicates that a physical description involves significant development times and also high computation cost. This paper presents a method to use cylinder pressure data and engine operations parameters for prediction of PCCI engine emissions by unsupervised learning and nonlinear identification techniques. The proposed method first uses principal component analysis (PCA) to reduce the dimension of the cylinder-pressure data. Based on the PCA analysis, a multi-input multi-out model was developed for nitrogen oxide and soot emission prediction by multi-layer perceptron (MLP) neural network.

The laminar burning velocity is an important parameter in various combustion models for engine simulations. With respect to computational time for computational fluid dynamics (CFD) and full system engine simulations, the calculation of laminar burning velocities using a detailed chemical mechanism can be replaced by incorporation of approximation formulas, based on rate-ratio asymptotics. In the present study, a work flow is developed to analyze the engine efficiency performance of spark ignition engines with respect to the laminar burning velocity as a fundamental fuel property. Firstly, methane is used as a fuel to assess practicability of the approach. The procedure is subsequently adopted for market type gasoline surrogates, RON95 and RON100. Detailed chemistry calculations are carried out for the three target fuels using existing state of the art mechanisms, the Aramco [Zhou et al., Proc. Combust. Inst., pp. 403-411, 2017] and the ITV RWTH mechanism [Cai et al., Combust.

One of the main challenges in internal combustion engine design is the simultaneous reduction of all engine pollutants like carbon monoxide (CO), total unburned hydrocarbons (THC), nitrogen oxides (NOx), and soot. Low-temperature combustion (LTC) concepts for compression ignition (CI) engines, e.g., premixed charged compression ignition (PCCI), make use of pre-injections to create a partially homogenous mixture and achieve an emission reduction. However, they present challenges in the combustion control, with the usage of in-cylinder pressure sensors as feedback signal is insufficient to control heat release and pollutant emissions simultaneously. Thus, an additional sensor, such as an ion-current sensor, could provide further information on the combustion process and effectively enable clean and efficient PCCI operation.