Machine Learning-Based Modeling and Predictive Control of Combustion
Phasing and Load in a Dual-Fuel Low-Temperature Combustion
Engine 03-17-04-0030
This also appears in
SAE International Journal of Engines-V133-3EJ
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 (NOx), 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.
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 R2 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.
Citation: Punasiya, M. and Sarangi, A., "Machine Learning-Based Modeling and Predictive Control of Combustion Phasing and Load in a Dual-Fuel Low-Temperature Combustion Engine," SAE Int. J. Engines 17(4):2024, https://doi.org/10.4271/03-17-04-0030. Download Citation
Author(s):
Mohit Punasiya, Asish Kumar Sarangi
Affiliated:
Indian Institute of Technology Bombay, Department of Energy Science and
Engineering, India
Pages: 21
ISSN:
1946-3936
e-ISSN:
1946-3944
Related Topics:
Diesel / compression ignition engines
Dual fuel engines
HCCI engines
Low temperature combustion (LTC)
Combustion and combustion processes
Engines
Fuel injection
Fuel economy
Machine learning
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