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This paper describes the reduction of cyclic combustion variations in spark-ignited engines, especially under idle conditions in which the air-fuel mixture is lean of stoichiometry. Under such conditions, the combination of residual cylinder gas and parametric variations (such as variations in fuel preparation) gives rise to significant combustion instabilities that may lead to customer-perceived engine roughness and transient emissions spikes. Such combustion instabilities may preclude operation at air-fuel ratios that would otherwise be advantageous for fuel economy and emissions. This approach exploits the recognition that a component of the observed combustion instability results from a noise-driven, nonlinear deterministic mechanism that can be actively stabilized by small feedback control actions which result in little if any additional use of fuel.

It is well known that hydrogen addition to spark-ignited (SI) engines can reduce exhaust emissions and increase efficiency. Micro plasmatron fuel converters can be used for onboard generation of hydrogen-rich gas by partial oxidation of a wide range of fuels. These plasma-boosted microreformers are compact, rugged, and provide rapid response. With hydrogen supplement to the main fuel, SI engines can run very lean resulting in a large reduction in nitrogen oxides (NOx) emissions relative to stoichiometric combustion without a catalytic converter. This paper presents experimental results from a microplasmatron fuel converter operating under variable oxygen to carbon ratios. Tests have also been carried out to evaluate the effect of the addition of a microplasmatron fuel converter generated gas in a 1995 2.3-L four-cylinder SI production engine. The tests were performed with and without hydrogen-rich gas produced by the plasma boosted fuel converter with gasoline.

We describe a method for detecting and quantifying time irreversibility in experimental engine data. We apply this method to experimental heat-release measurements from four spark-ignited engines under leaning fueling conditions. We demonstrate that the observed behavior is inconsistent with a linear Gaussian random process and is more appropriately described as a noisy nonlinear dynamical process.

We present techniques of symbolic time-series analysis which are useful for analyzing temporal patterns in dynamic measurements of engine combustion variables. We focus primarily on techniques that characterize predictability and the occurrence of repeating temporal patterns. These methods can be applied to standard, cycle-resolved engine combustion measurements, such as IMEP and heat release. The techniques are especially useful in cases with high levels of measurement and/or dynamic noise. We illustrate their application to experimental data from a production V8 engine and a laboratory single-cylinder engine.

We propose a simple, physically oriented model that explains important characteristics of cyclic combustion variations in spark-ignited engines. A key model feature is the interaction between stochastic, small-scale fluctuations in engine parameters and nonlinear deterministic coupling between successive engine cycles. Prior-cycle effects are produced by residual cylinder gas which alters volume-average in-cylinder equivalence ratio and subsequent combustion efficiency. The model's simplicity allows rapid simulation of thousands of engine cycles, permitting in-depth statistical studies of cyclic variation patterns. Additional mechanisms for stochastic and prior-cycle effects can be added to evaluate their impact on overall engine performance. We find good agreement with our experimental data.