Study on control-oriented emission predictions of PPCI diesel engine with two-stage fuel injection

Abstract

Model predictive control (MPC) is an effective technology used to improve the control accuracy of low temperature combustion engines. The main purpose of this study is to establish a control-oriented emission prediction model based on fuel injection parameters, which provides the basis for the development of MPC controller for PPCI engines. In this study, the combustion model and backpropagation (BP) neural network emission prediction model of a partially premixed compression ignition (PPCI) diesel engine were established, through which the real-time combustion and emissions can be realized by inputting two-stage fuel injection parameters. The Wiebe function linearization method was proposed for the combustion model to achieve high-precision reconstruction of combustion at different fuel injection parameters. The nitrogen oxide (NOx) and particulate matter (PM) emissions were predicted using the combustion characteristics calculated by the combustion model. The results show that the established combustion model can accurately reconstruct the heat release rate and cylinder pressure of PPCI combustion, and the maximum error between the calculated and test values of the combustion parameters is less than 9%. The three-layer BP neural network can achieve high-precision predictions of NOx and PM emissions, with a prediction accuracy greater than 95%. Under the 300th continuous transient seconds, the cumulative prediction error of NOx was 2%, and the cumulative prediction error of PM was 4.9%, indicating high accuracy. This provides the basis for developing MPC controllers for PPCI engines.