In automotive applications problems related to management and diagnostics play an important role to improve engine perfonnance 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 perfonnance 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 thennodynamic and a simplified approach respective)y. 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.
A Learning-Machine Based Method for the Simulation of Combustion Process in Automotive I.C. Engines
ANGUITA, DAVIDE;
2003-01-01
Abstract
In automotive applications problems related to management and diagnostics play an important role to improve engine perfonnance 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 perfonnance 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 thennodynamic and a simplified approach respective)y. 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.
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