Study on the closed-loop combustion control for a diesel engine by using a dynamic-target online prediction model

Abstract

In-cylinder pressure-based closed-loop combustion control is a critical technology used to address the issue of cylinder-to-cylinder inhomogeneity throughout the life cycle of a modern diesel engine. Nevertheless, closed-loop combustion control technology (CLCC), which requires a reference or set-point that the control attempts to achieve, causes an increase in the operating maps and the complexity of the calibration effort. In this study, a reference governor-based combustion control (RGCC) is developed in order to reduce the cylinder-to-cylinder combustion dispersions and the calibration effort associated with enabling of the CLCC simultaneously. The RGCC consists of a dynamic-target online prediction model and an indicated mean effective pressure (IMEP) control strategy. The dynamic-target prediction model is designed based on the IMEP of the engine real-time tests; it uses a modified Kalman filter algorithm, which provides the set-point value for the IMEP control strategy. The effectiveness of the proposed RGCC is experimentally validated on a commercial diesel engine test bench under steady-state and transient-state conditions. The experimental results show that the RGCC displayed far better transient tracking performance than the closed-loop IMEP control based on MAP. Furthermore, the controller can effectively reduce cylinder-to-cylinder IMEP variations under steady-state and transient-state conditions.