In automotive applications problems related to management and diagnostics play an important role to improve engine performance and to reduce fuel consumption and pollutant emissions. In the design of control systems the use of theoretical models for the simulation of engine behaviour proved to be very useful, and it is apparent from the literature. However, since automotive engines have become very complex plants, their modelling requires a comprehensive description of the behaviour of many processes and components. Combustion process has a strong influence on performance and emissions, but its theoretical description can be hardly combined with the requirements of control-oriented models (especially as regards “real-time” applications). Two simplified theoretical models are proposed in the paper, based on a thermodynamic and a simplified approach respectively. In the first case a single-zone method was followed with the introduction of an apparent heat release rate (HRR) described as a superposition of two Wiebe functions. Coefficients of these burning functions are estimated by means of Learning Machines (LM), i.e. Support Vector Machines (SVM), trained from experimental data and then embedded in a Simulink® block. In order to make calculation time shorter, a simpler and faster model based on the application of SVM was defined to describe combustion process. Starting from experimental data, the proposed SVM was trained and implemented in a Simulink® block to evaluate exhaust gas temperature and bmep directly from engine operating parameters. Both blocks were defined to be easily embedded in engine simulation models for control-oriented applications. Results were promising for both models, showing very short computation time. A comparison of theoretical outputs with experimental data is reported in the paper, together with an application of both calculation procedures to a comprehensive model of a modern automotive turbocharged Diesel engine.

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