<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<sub>x</sub>), 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><sup>2</sup> 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>
