Machine Learning-Based Modeling and Predictive Control of Combustion Phasing and Load in a Dual-Fuel Low-Temperature Combustion Engine

Abstract

<div>Reactivity-controlled compression ignition (RCCI) engine is an innovative dual-fuel strategy, which uses two fuels with different reactivity and physical properties to achieve low-temperature combustion, resulting in reduced emissions of oxides of nitrogen (NO_x), particulate matter, and improved fuel efficiency at part-load engine operating conditions compared to conventional diesel engines. However, RCCI operation at high loads poses challenges due to the premixed nature of RCCI combustion. Furthermore, precise controls of indicated mean effective pressure (IMEP) and CA50 combustion phasing (crank angle corresponding to 50% of cumulative heat release) are crucial for drivability, fuel conversion efficiency, and combustion stability of an RCCI engine. Real-time manipulation of fuel injection timing and premix ratio (PR) can maintain optimal combustion conditions to track the desired load and combustion phasing while keeping maximum pressure rise rate (MPRR) within acceptable limits.</div> <div>In this study, a model-based controller was developed to track CA50 and IMEP accurately while limiting MPRR below a specified threshold in an RCCI engine. The research workflow involved development of an imitative dynamic RCCI engine model using a data-driven approach, which provided reliable measured state feedback during closed-loop simulations. The model exhibited high prediction accuracy, with an <i>R</i>² score exceeding 0.91 for all the features of interest. A linear parameter-varying state space (LPV-SS) model based on least squares support vector machines (LS-SVM) was developed and integrated into the model predictive controller (MPC). The controller parameters were optimized using genetic algorithm and closed-loop simulations were performed to assess the MPC’s performance. The results demonstrated the controller’s effectiveness in tracking CA50 and IMEP, with mean average errors (MAE) of 0.89 crank angle degree (CAD) and 46 kPa and Mean absolute percentage error (MAPE) of 9.7% and 7.1%, respectively, while effectively limiting MPRR below of 10 bar/CAD. This comprehensive evaluation showcased the efficacy of the model-based control approach in tracking CA50 and IMEP while constraining MPRR in the dual-fuel engine.</div>