Chaos theory-based time series analysis of in-cylinder pressure and its application in combustion control of SI engines

Abstract

Combustion control is a significant topic for achieving high efficiency and low emissions of internal combustion engines. Recently, in-cylinder pressure sensor-based closed-loop control strategies have become the preferred solution. However, their practical applications in automotive industries are limited due to the intensive acquisition of pressure series for a whole cycle and subsequent calculation of combustion indicators. This paper proposes a method for in-cylinder pressure information extraction and combustion phase estimation of spark ignition (SI) engines based on pressure measurements at several points coordinated by the crank angle. First, nonlinear dynamics analysis is introduced to analyze the system of in-cylinder pressure evolution, which is proved to be a deterministic nonlinear dynamic system with chaotic characteristics. Then, a 3-dimensional system state variable is determined to replace the pressure series during combustion. Second, with the determined system state variable, the in-cylinder pressure series during combustion and the combustion phase are learned and estimated by a machine learning method, namely, extreme learning machine (ELM). As a result, only pressure measurements at 3 points and ELM estimation models are required, instead of intensive data acquisition and calculation. The experimental validations carried out on a gasoline engine test bench have proved that the reconstruction and estimation results are accurate and that the method can perform well in real-time combustion control.