The need for precise control of complex air handling systems on modern engines has driven research into model-based methods. While model-based control can provide improved performance over prior map-based methods, they require the creation of an accurate model. Physics-based models can be precise, but can also be computationally expensive and require extensive calibration. To address this limitation, this work explores the integration of data-driven models into an overall physics-based framework and applies this approach to the gas exchange processes of a diesel engine with a variable geometry turbocharger and exhaust gas recirculation. One of the most complex parts of this gas exchange loop is the turbocharger. Data-driven methods are used to capture the turbocharger performance and are also applied to the intake manifold, while the simpler features are captured with more traditional physics-based models. This combined modeling approach is able to capture the temperature and pressure dynamics with varying error levels depending on measurement availability and the inter-dependency of the submodels, with the turbocharger neural network model achieving a Normalized Mean Square Error (NMSE) of 5e-5 and the overall engine model achieving a NMSE of 4.5e-3. The work illustrates that the integration of data-driven models can improve overall model accuracy and may be able to reduce the number of sensors needed on the system. The contributions of this work are the development and demonstration of a neural network based turbocharger model and intake air path model, the development of empirical equation-based models for the rest of the engine components along the air path and the demonstration of the integration and interaction of these two types of model to adequately characterize engine operation for control applications.

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This paper analyzes the feasibility of applying model predictive control strategies for mitigation of the auto-ignition phenomenon, which affects the performance of spark-ignition internal combustion engines. The first part of this paper shows the implementation and experimental validation of a two-dimensional model, based on thermodynamic equations, to simulate operating conditions in engines fueled with natural gas. Over this validated model, several control strategies are studied in order to evaluate, through simulation analysis, which of these offer the best handling capacity of the auto-ignition phenomenon. In order to achieve this goal, multivariate control strategies are implemented for a simultaneous manipulation of the fuel/air ratio, the crank angle at ignition, and the inlet pressure. The controlled variable in this research is the temperature at the ignition point. This temperature is obtained through an estimation based on pressure in the combustion chamber at that point, which is located toward the end zone of the compression stroke. If the ignition temperature of the fuel–air mixture is reached during the compression process, then auto-ignition takes place. Proposed control strategies consist of maintaining the temperature in the ignition point below the fuel–air mixture auto-ignition temperature. The results show that auto-ignition is difficult to avoid using a single input–single output (SISO) strategy. However, a multiple input–single output (MISO) approach avoids the influence of the phenomenon without a significant impact over the engine's performance. Among the developed strategies, an approach based on model predictive control and feedforward control strategy shows the best performance, measured through the integral absolute error (IAE) index. These results open the possibility of new ways for improving the control capacity of auto-ignition phenomenon in engines compared to currently available feedback control systems.

<div class="section abstract"><div class="htmlview paragraph">The knock resistance of fuels has been historically measured using the ASTM RON and MON methods. However, significant discrepancies between the fuel octane number and knock-limited performance in modern spark-ignited (SI) engines have been well-documented. Differences between the operating conditions of the Cooperative Fuels Research (CFR) engine during RON rating and those attained in modern SI engines have been highlighted in the literature. While octane ratings are performed for each fuel on the CFR engine at the lambda that provides the highest knockmeter reading, modern SI engines are generally operated at stoichiometry and knock intensity is based on the high frequency cylinder pressure oscillations associated with knocking combustion. In the present work, an instrumented CFR engine was used to analyze lambda effects on both the conventional knockmeter RON rating method and cylinder pressure transducer based knock intensity. It was found that both knock intensity metrics reduced significantly at stoichiometry for highly iso-paraffinic fuels (such as primary reference fuels), compared to test fuels composed of higher ethanol or aromatic content. This behavior had a significant impact on how the octane ratings of the fuels were perceived at stoichiometric conditions.</div></div>

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