Title: Data-driven Modeling and Predictive Control of Maximum Pressure Rise Rate in RCCI Engines
Reactivity controlled compression ignition (RCCI)
is a promising low temperature combustion (LTC) regime
that offers lower nitrogen oxides (NOx), soot and particulate
matter (PM) emissions along with higher combustion efficiency
compared to conventional diesel engines. It is critical to control
maximum pressure rise rate (MPRR) in RCCI engines in order
to safely and efficiently operate at varying engine loads. In
this paper, a data-driven modeling (DDM) approach using
support vector machines (SVM) is adapted to develop a linear
parameter-varying (LPV) representation of MPRR for RCCI
combustion. This LPV representation is then used in the design
of a model predictive controller (MPC) to control crank angle
of 50% of fuel mass fraction burn (CA50) and indicated mean
effective pressure (IMEP) while limiting the MPRR. The results
show that the LPV-MPC control strategy can track CA50
and IMEP with mean tracking errors of 0.9 CAD and 4.7
kPa, respectively, while limiting the MPRR to the maximum
allowable value of 5.8 bar/CAD. more »« less
This study presents a data-driven identi cation method based on Kernelized Canonical
Correlation Analysis (KCCA) approach to generate a state-space Linear Parameter-Varying (LPV) dynamic representation for the RCCI engine combustion. An LPV model is used to estimate RCCI combustion phasing (CA50) and indicated mean e ective pressure (IMEP) based on fuel injection timing and quantity. The proposed data-driven method does not require prior knowledge of the plant model states and adjusts number of states to increase the accuracy of the identi ed state-space model. The results demonstrate that the proposed data-driven KCCA-LPV approach provides a dependable technique to establish a fast and reasonably accurate RCCI combustion model. The established model is then incorporated in a design of a constrained MIMO Model Predictive Controller (MPC) to track desired crank angle for 50% fuel burnt and IMEP at various engine conditions. The controller performance results demonstrate that the established data-driven constrained MPC combustion controller can follow desired
CA50 and IMEP with less than 1.5 CAD and 37 kPa error, respectively.
Khoshbakht Irdmousa, Behrouz; Rizvi, Syed Z; Mohammadpour Velni, Javad; Naber, Jeffrey; Shahbakhti, Mahdi(
, Proceedings of the ... American Control Conference)
Reactivity controlled compression ignition (RCCI) engines center on a combustion strategy with higher thermal efficiency, lower particulate matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) engines. However, real time optimal control of RCCI engines is challenging during transient operation due to the need for high fidelity combustion models. Development of a simple, yet accurate control-oriented RCCI model from physical laws is time consuming and often requires substantial calibrations. To overcome these challenges, data-driven models can be developed. In this paper, a data-driven linear parameter varying (LPV) model for an RCCI engine is developed. An LPV state space model is identified to predict RCCI combustion phasing as a function of multiple RCCI control variables. The results show that the proposed method provides a fast and reliable route to identify an RCCI engine model. The developed model is then used for the design of a model predictive controller (MPC) to control crank angle for 50% fuel burnt (CA50) for varying engine conditions. The experimental results show that the designed MPC with the data-driven LPV model can track desired CA50 with less than 1 crank angle degree (CAD) error against changes in engine load.
Khoshbakht Irdmousa, Behrouz; Rizvi, Syed Z; Mohammadpour Velni, Javad; Naber, Jeffrey; Shahbakhti, Mahdi(
, Proceedings of the ... American Control Conference)
Reactivity controlled compression ignition (RCCI)
engines center on a combustion strategy with higher thermal
efficiency, lower particulate matter (PM), and lower oxides
of nitrogen (NOx) emissions compared to conventional diesel
combustion (CDC) engines. However, real time optimal control
of RCCI engines is challenging during transient operation due
to the need for high fidelity combustion models. Development
of a simple, yet accurate control-oriented RCCI model from
physical laws is time consuming and often requires substantial
calibrations. To overcome these challenges, data-driven models
can be developed. In this paper, a data-driven linear parametervarying
(LPV) model for an RCCI engine is developed. An LPV
state space model is identified to predict RCCI combustion
phasing as a function of multiple RCCI control variables. The
results show that the proposed method provides a fast and
reliable route to identify an RCCI engine model. The developed
model is then used for the design of a model predictive
controller (MPC) to control crank angle for 50% fuel burnt
(CA50) for varying engine conditions. The experimental results
show that the designed MPC with the data-driven LPV model
can track desired CA50 with less than 1 crank angle degree
(CAD) error against changes in engine load.
Khoshbakht Irdmousa, Behrouz; Rizvi, Syed Z; Mohammadpour Velni, Javad; Naber, Jeffrey; Shahbakhti, Mahdi(
, Proceedings of the American Control Conference)
Reactivity controlled compression ignition (RCCI)
engines center on a combustion strategy with higher thermal
efficiency, lower particulate matter (PM), and lower oxides
of nitrogen (NOx) emissions compared to conventional diesel
combustion (CDC) engines. However, real time optimal control
of RCCI engines is challenging during transient operation due
to the need for high fidelity combustion models. Development
of a simple, yet accurate control-oriented RCCI model from
physical laws is time consuming and often requires substantial
calibrations. To overcome these challenges, data-driven models
can be developed. In this paper, a data-driven linear parametervarying
(LPV) model for an RCCI engine is developed. An LPV
state space model is identified to predict RCCI combustion
phasing as a function of multiple RCCI control variables. The
results show that the proposed method provides a fast and
reliable route to identify an RCCI engine model. The developed
model is then used for the design of a model predictive
controller (MPC) to control crank angle for 50% fuel burnt
(CA50) for varying engine conditions. The experimental results
show that the designed MPC with the data-driven LPV model
can track desired CA50 with less than 1 crank angle degree
(CAD) error against changes in engine load.
fuel blend consisting of 10% S8 by mass (a Fischer-Tropsch synthetic kerosene), and 90% ULSD (Ultra Low Sulfur Diesel) was investigated for their combustion characteristics and impact on emissions during RCCI (Reactivity Controlled Compression Ignition) combustion in a single cylinder experimental engine utilizing a 65% by mass n-butanol port fuel injection (PFI). RCCI is a dual fuel combustion strategy achieved with the introduction of a PFI fuel of the low-reactive n-butanol, and a direct injection (DI) of a high-reactivity blend (FT-BLEND) into an experimental diesel engine. The combustion analysis and emissions testing were conducted at 1500 RPM at an engine load of 5 bar IMEP (Indicated Mean Effective Pressure), and CA50 of 9° ATDC (After Top Dead Center); CDC (Conventional Diesel Combustion) and RCCI with 65Bu-35ULSD were utilized as the baseline for AHRR (Apparent Heat Release Rate), ringing and emissions comparisons. It was found during a preliminary investigation with a Constant Volume Combustion Chamber (CVCC) that the introduction of 10% by mass S8 into a mixture with 90% ULSD by mass only increased Derived Cetane Number (DCN) by 0.8, yet it was found to have a significant effect on the combustion characteristics of the fuel blend.
This led to the change in injection timing necessary for maintaining 65Bu-35F-T BLEND RCCI at a CA50 of 5° ATDC (After Top Dead Center) to be shifted 3° closer to TDC, thus affecting the Ringing Intensity (RI), Pressure Rise Rate, and heat release of the blend all to decrease. CDC was conducted with a primary injection of 14ᵒ BTDC at a rail pressure of 800 bar, all RCCI testing was conducted with 65% PFI of n-butanol by mass and 35% DI, to prevent knock, with a rail pressure of 600 bar and a pilot injection of 60° BTDC for 0.35 ms. 65Bu-35ULSD RCCI was conducted with a primary injection at 6° BTDC with neat ULSD#2, the fuel 65Bu-35F-T BLEND in RCCI had a primary injection at 3° BTDC to maintain CA50 at 9° ATDC. 65Bu-35ULSD RCCI experienced a NOX and soot emissions decrease of 40.8% and 91.44% respectively in comparison to CDC. The fuel 65Bu-35F-T BLEND in RCCI exhibited an additional decrease of NOX and soot of 32.9 and 5.3%, in comparison to 65Bu-35ULSD RCCI for an overall decrease in emissions of 73.7% and 96.71% respectively. Ringing Intensity followed a similar trend with reductions in RI for 65Bu-35ULSD RCCI decreasing only by 6.2% whereas 65Bu-35F-T BLEND had a decrease in RI of 76.6%. Although emissions for both RCCI fuels experienced a decrease in NOX and soot in comparison to CDC, UHC and CO did increase as a result of RCCI. CO emissions for 65Bu-35ULSD RCCI and 65Bu-35F-T BLEND where increased from CDC by a factor of 5 and 4 respectively with UHC emissions rising from CDC by a factor of 3.4. The fuel 65Bu-35F-T BLEND had a higher combustion efficiency than 65Bu-35ULSD in RCCI at 91.2% due to lower CO emissions of the blend.
Basina, L. N., Irdmousa, B. K., Mohammadpour Velni, J., Borhan, H., Naber, J. D., and Shahbakhti, M. Data-driven Modeling and Predictive Control of Maximum Pressure Rise Rate in RCCI Engines. Retrieved from https://par.nsf.gov/biblio/10188474. IEEE Conference on Control Technology and Applications . Web. doi:10.1109/CCTA41146.2020.9206358.
Basina, L. N., Irdmousa, B. K., Mohammadpour Velni, J., Borhan, H., Naber, J. D., & Shahbakhti, M. Data-driven Modeling and Predictive Control of Maximum Pressure Rise Rate in RCCI Engines. IEEE Conference on Control Technology and Applications, (). Retrieved from https://par.nsf.gov/biblio/10188474. https://doi.org/10.1109/CCTA41146.2020.9206358
Basina, L. N., Irdmousa, B. K., Mohammadpour Velni, J., Borhan, H., Naber, J. D., and Shahbakhti, M.
"Data-driven Modeling and Predictive Control of Maximum Pressure Rise Rate in RCCI Engines". IEEE Conference on Control Technology and Applications (). Country unknown/Code not available. https://doi.org/10.1109/CCTA41146.2020.9206358.https://par.nsf.gov/biblio/10188474.
@article{osti_10188474,
place = {Country unknown/Code not available},
title = {Data-driven Modeling and Predictive Control of Maximum Pressure Rise Rate in RCCI Engines},
url = {https://par.nsf.gov/biblio/10188474},
DOI = {10.1109/CCTA41146.2020.9206358},
abstractNote = {Reactivity controlled compression ignition (RCCI) is a promising low temperature combustion (LTC) regime that offers lower nitrogen oxides (NOx), soot and particulate matter (PM) emissions along with higher combustion efficiency compared to conventional diesel engines. It is critical to control maximum pressure rise rate (MPRR) in RCCI engines in order to safely and efficiently operate at varying engine loads. In this paper, a data-driven modeling (DDM) approach using support vector machines (SVM) is adapted to develop a linear parameter-varying (LPV) representation of MPRR for RCCI combustion. This LPV representation is then used in the design of a model predictive controller (MPC) to control crank angle of 50% of fuel mass fraction burn (CA50) and indicated mean effective pressure (IMEP) while limiting the MPRR. The results show that the LPV-MPC control strategy can track CA50 and IMEP with mean tracking errors of 0.9 CAD and 4.7 kPa, respectively, while limiting the MPRR to the maximum allowable value of 5.8 bar/CAD.},
journal = {IEEE Conference on Control Technology and Applications},
author = {Basina, L. N. and Irdmousa, B. K. and Mohammadpour Velni, J. and Borhan, H. and Naber, J. D. and Shahbakhti, M.},
}
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