Nonlinear Systems and Circuits in Internal Combustion Engines - Modeling and Control

Today, cars include approximately one-third of their value in electric and electronic components, and the mobility paradigm is being transformed toward "the smart driving" concept, with the aim of enhancing the driver experience, improving the safety, supporting the connectivity and automated driving, but also lowering environmental impact. The engine is only a part of this complex structure of the vehicle. However, internal combustion engine remains the main source of energy; its function is not different from that of the first prototypes, which is to convert the chemical energy contained in the fuel in mechanical power. This process involves many complex thermo-fluid dynamic phenomena affected by nonlinear dynamics: intake air motion, air-fuel mixture dosage, combustion process itself, knock and misfire occurrence, particulate particle formation, just to cite few of them. The challenge during all these decades has been to optimize the combustion process in terms of engine efficiency and pollutant emissions reduction, also to comply with the more and more strict governmental rules. The challenge is still open. With this work, the authors want to refocus the attention of academic and industrial automotive experts on nonlinear processes in internal combustion engine, analyzing specific nonlinear conditions, providing original modeling description and effective control solutions able to compensate these nonlinear dynamics. Chapter 1 is aimed at describing the use of Artificial Neural Networks and Expert Systems in engine applications. Artificial intelligence techniques allow to solve highly nonlinear problems offering an alternative and effective way to deal with complex dynamic systems. Air-fuel ratio (AFR) modeling and control is a typical highly nonlinear problem where a huge number of interconnected parameters needs to be considered and controlled (amount of fuel injected, residual gas fraction, wall wetting are some of the parameters that have to be processed). In this context, we propose a neural network and fuzzy logic approach for AFR modeling and control. In Chap. 2, advanced non-interfering diagnostics based on optical spectroscopy are presented. Optical diagnostics allow to take a look in what really happens in thecylinder in terms of flame propagation, gas turbulences, and pollutant formation. In other words, most of the phenomena occurring during the combustion process. The evaluation of these nonlinear phenomena is the key point to design effective control solutions able to optimize engine combustion in terms of engine power, efficiency, and emissions. Nowadays, great attention is paid to the impact of particulate matter (PM) emitted from vehicles on the environment and, in turn, to the negative effects that it has on human health. Pollutant particles are classified according their diameters in micron (PM10, PM2.5, etc.); smaller the particles are, more dangerous for human health they are as they penetrate more easily the cell membranes. The chemical nature of the emitted particles as well as the number and size depends on engine type and its operating conditions. In Chap. 3, the authors deal with the particulate emission reduction problem, suggesting a real-time approach to model the number and size of emitted particles. The parameter widely considered as the most important for diagnosis of the combustion process in internal combustion engines is the cylinder pressure. This signal represents, in fact, the most direct signal available for engine control. However, in-cylinder pressure direct measure involves an intrusive approach to the cylinder using expensive sensors and a special mounting process. For this reason, several alternative methods for combustion diagnosis have been suggested in literature. In Chap. 4, we p