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The wall-wetting dynamics represent a very important subsystem of the air/fuel path of an SI engine. The precise feedforward control of the air/fuel ratio requires a valid model of the wall-wetting dynamics over the whole operating region of the engine. A global wall-wetting model has been developed for a production SPFI gasoline engine. This model is capable of describing the wall-wetting dynamics not only in a fixed operating point, but also for radical changes of the operating point. Its structure specifically allows for model-based compensator design and on-line parameter identification. Earlier, related publications discussed linear model structures. Those models described the dynamics around a fixed operating point only. This paper shows how one global model for the whole operating range can be constructed from a linear model and its parameter range.

Modern engine management systems increasingly rely on on-line identification schemes. These are used either for self-tuning regulators or the rapid parametrization of controllers. In this paper the on-line parameter identification of the wall-wetting dynamics is studied in detail. The identification is performed by exciting the fuel path dynamics of the engine at a constant operating point. The amount of fuel injected serves as input and the air-to-fuel ratio, which is measured with a linear oxygen sensor, as output. In order to gain precise information about the amount of fuel in the cylinder, a new measurement concept is used. For one, the placement of the lambda sensor close to the exhaust valve minimizes the effects of gas mixing on the measurements. Additionally, by an appropriate collection of the data, the sensor dynamics are bypassed. This is also illustrated by a measurement with a very fast NOx sensor.

In modern engine control applications, there is a distinct trend towards model-based control schemes. There are various reasons for this trend: Physical models allow deeper insights compared to heuristic functions, controllers can be designed faster and more accurately, and the possibility of obtaining an automated application scheme for the final engine to be controlled is a significant advantage. Another reason is that if physical effects can be separated, higher order models can be applied for different subsystems. This is in contrast to heuristic functions where the determination of the various maps poses large problems and is thus only feasible for low order models. One of the most important parts of an engine management system is the air-to-fuel control. The catalytic converter requires the mean air-to-fuel ratio to be very accurate in order to reach its optimal conversion rate. Disturbances from the active carbon filter and other additional devices have to be compensated.