Dynamic modeling of combustion and gas exchange processes for controlled auto-ignition engines

Abstract

This paper is concerned with the development of a simple physical model of a gasoline engine cycle where the energy release is via Controlled Auto-Ignition. It uses simple thermodynamic concepts, and well-established gas exchange and heat transfer sub-models to predict the pressures and temperatures in the engine cycle. The combustion event itself is modelled in a semi-empirical fashion. The model is an important extension of existing single-zone models and it persists between multiple cycles, enabling the capture of the cycle-to-cycle exhaust gas coupling. The model was compared with data obtained at steady engine running conditions, and good agreement was found. Controlled Auto-Ignition was attained by diluting the mixture with exhaust gas trapped in the cylinder, as a result of an early Exhaust Valve Closing.

Smooth switching between 2-stroke and 4-stroke modes of HCCI operation

“…Oscillations in IMEP and the combustion phasing are reduced significantly. It is important to mention that the responses can be improved further by the appropriate tuning of the correction sequences through the weighting matrices Q y and Q u in the proposed cost function (4). Numerous simulation experiments with various reference signals in IMEP and MFB50 and different switching points showed consistent improvement in tracking the outputs.…”

“…Control inputs used to track desired reference signals in the two modes are different due to the differences in the gas exchange processes for the 2-stoke and the 4-stroke modes [1], [4], [5], [14], [15]. It is natural to assume that for each of the two modes we design and tune a mode-specific controller which regulates operation of the engine.…”

A control-oriented model for piston trajectory-based HCCI combustion

“…The heavy computational burden of the complex model with chemical kinetics is usually caused by the tedious calculation process, such as (15), (16) and (17). This burden is exacerbated as the number of species increases.…”

“…a temperature threshold or an integral of Arrhenius global reaction rate, to predict the start of combustion [14][15][16]. Meanwhile heat release is either assumed as instantaneous [15] or simulated via Wiebe function [16,17]. Even though the computational cost is decreased in these cases, these assumptions oversimplify the chemical kinetics, which reduces the accuracy of the prediction of combustion process and losses important information on emission production.…”

Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks

“…These models were used to predict combustion phasing [22][23][24][25], load [26], exhaust gas temperature [27], cyclic variability [28] and heat release [29] in HCCI engines. A second model group includes detailed thermo-kinetic models that were used to predict auto-ignition delay [30][31][32][33][34][35], combustion phasing [36,37], HC and CO emissions [38] and heat release [39,40]. The first group requires substantial experimental data, while the second group requires computational resources which are not available for real-time engine control.…”