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Spark-ignited engines equipped by a three-way catalyst require a precise control of the air fuel ratio fed to the combustion chamber. A stoichiometric mixture is necessary for the proper working of the catalyst in order to meet the legislation requirement. A critical part of the air fuel ratio control is the feed-forward compensation of the fuel dynamics. Conventional strategies are based on a simplified model of the wall-wetting phenomena whose parameters are stored in off-line computed look-up tables. Unfortunately, errors in the parameters calibration over the whole engine map deteriorate the control performances in terms of emissions. In this paper an automatic procedure for a rapid and efficient identification of the wall-wetting parameters is presented. The whole procedure has been experimentally tested on a vehicle by using a test bench.

In automotive industry the Electronic Throttle Body (ETB) plays a crucial role in drive-by-wire operations since it controls the incoming air into the engine and so the produced torque. This implies the performances of the vehicle in terms of traction, emissions, idle speed regime, cold starting management, thermal transient and smoother movement during tip/in tip/out, strongly depends on the precise control of this device [17]. Despite its apparent simplicity, the behavior of the ETB is affected by many nonlinearities and uncertain parameters which can dramatically alter its dynamics. In order to cope the unwanted nonlinear phenomenons (stick-slip motion, hysteresis, hunting, impact, caos), sophisticated model based control strategies and compensators are proposed in the literature. A time consuming identification parameters of the throttle is fundamental for these approaches and it is the main drawback for their application.

A key point in three-way catalytic converter modeling problems is the definition of a possible chemical scheme able to represent the catalyzed process inside the converter, especially during transients. The lack of precise kinetic measurements during the transient thermal phase makes hard the choice of the kinetic expressions and, overall, of the chemical parameter values. To solve this problem here we propose the use of neural networks (NN) to model the reaction kinetics since a NN structure can provide enough degrees of freedom to capture all the significant features of the real system. Since the NN is embedded into the overall TWC dynamics, it cannot be trained through one of the standard method and some difficulties arise when dealing with the parameter tuning of this model, that are circumvented using a genetic algorithm (GA).