Jacek Czarnigowski

Chaotic Combustion in Spark Ignition Engines

Abstract We analyse the combustion process in a spark ignition engine using the experimental data of an internal pressure during the combustion process and show that the system can be driven to chaotic behaviour. Our conclusion is based on the observation of unperiodicity in the time series, suitable stroboscopic maps and a complex structure of a reconstructed strange attractor. This analysis can explain that in some circumstances the level of noise in spark ignition engines increases considerably due to nonlinear dynamics of a combustion process.

NONPERIODIC OSCILLATIONS OF PRESSURE IN A SPARK IGNITION COMBUSTION ENGINE

We report our results on nonperiodic experimental time series of pressure in a spark ignition engine. The experiments were performed for a low rotational velocity of a crankshaft and a relatively large spark advance angle. We show that the combustion process has many chaotic features. Surprisingly, the reconstructed attractor has a characteristic butterfly shape similar to a chaotic attractor of Lorentz type. The suitable recurrence plot shows that the dynamics of the combustion is a nonlinear multidimensional process mediated by stochastic noise.

A neural network model-based observer for idle speed control of ignition in SI engine

The paper presents an algorithm of idle speed stabilization in the spark ignition automotive engine by means of spark advance control. The algorithm is based on a well-known approach of a model-based adaptive control and uses artificial neural networks. The control algorithm is based on a neural network model observer of the additional effective torque. The additional load is estimated as difference between effective torque, estimated by the neural network observer, and brake torque, estimated on the basis of a linear quadratic model. In that case the additional load is understood as the sum of the alternator brake torque (additional load form electric car equipments) and the momentary and/or permanent changes of the engines characteristics. On the basis of estimated values of the additional load, the required value of angular acceleration is determined to make the engine return to the specified speed. This acceleration is achieved by adjusting the spark advance. The required value of spark advance is estimated by means of a neural network model converse to that of the effective torque. The algorithm was experimentally compared with PID and adaptive algorithms in the same test bed. The tests were conducted under sudden change of external load. The proposed algorithm proved to be more effective in terms of control error.

A Numerical Study of a Simple Stochastic/ Deterministic Model of Cycle-to-Cycle Combustion Fluctuations in Spark Ignition Engines

We examine a simple, fuel-air model of combustion in a spark ignition (SI) engine with indirect injection. In our two-fluid model, variations of fuel mass burned in cycle sequences appear due to stochastic fluctuations of a fuel feed amount. We have shown that a small amplitude of these fluctuations affects considerably the stability of a combustion process strongly depending on the quality of the air-fuel mixture. The largest influence was found in the limit of a lean combustion. The possible effect of nonlinearities in the combustion process has been also discussed.

Adaptive Control of the Idle Speed

Idle speed control of an spark ignition automotive engine based on adaptive techniques has been presented. In the paper the ignition advance control was activated to stabilise the idle speed. The adaptation of the spark advance angle requires defining an adaptive coefficient, which is a compromise between operation speed and estimation accuracy. The adaptive coefficient design was evaluated through engine testing, and the performance was compared with an up-to-date tuned PID controller. The success of the adaptive controller was demonstrated in engine testing. The controller tracks not only the set point speed but also shows robustness to the load torque disturbances.Copyright