An HCCI Control Oriented Model that Includes Combustion Efficiency

Ebrahimi, Khashayar; Aliramezani, Masoud; Koch, Charles Robert

doi:10.1016/j.ifacol.2016.08.049

Cited by 17 publications

(6 citation statements)

References 20 publications

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“…where A valve is the valve opening area. The term C in (23) was set to consider a negative or a large value of the pressure difference in some conditions. C was described as…”

Section: Blowdown Process (Evo;eob) When the Exhaust Valvesmentioning

confidence: 99%

“…The term multiplied by C d L iv ð ÞA iv makes the influence of the intake valve opening area small in such conditions. By utilizing (23), (24), and (25), DP EVO2 was obtained by substituting ( 24) and ( 25) in (23), and then P EVO2 was estimated…”

Section: Blowdown Process (Evo;eob) When the Exhaust Valvesmentioning

confidence: 99%

“…Many researches have been conducted on the control models of HCCI engines with the NVO strategy 19,22,23 and the rebreathing strategy. 21,24,25 However, most of them focused on modeling the combustion process, and in-cylinder pressure during the gas exchange process has often been assumed to be constant.…”

Section: Introductionmentioning

confidence: 99%

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Control-oriented modeling of the gas exchange process in rebreathing homogeneous charge compression ignition engines

Takeshita

Yamasaki

Muto

et al. 2021

International Journal of Engine Research

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The purpose of this study is to develop a model for the gas exchange process in a rebreathing homogeneous charge compression ignition (HCCI) engine. HCCI engines are attracting significant attention due to their low emissions and high efficiency. To design the control system of an HCCI engine, it is necessary to develop a control-oriented engine model. The developed model lowers its computational load by combining two types of models. The model consists of a discrete model for the exhaust process (the first half of the gas exchange process) and a continuous model with a variable calculation step size for the rebreathing process (the latter half of the gas exchange process). Also, the constructed model maintained its prediction accuracy, as the pressure pulsation in the exhaust port was modeled, and an unsteady flow equation was used. It was confirmed that the model developed for the gas exchange process calculated in about half time of one cycle and reproduced the results of 1D engine simulation software with a maximum error of about 10% in the in-cylinder pressure, temperature, and trapped mass.

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“…where A valve is the valve opening area. The term C in (23) was set to consider a negative or a large value of the pressure difference in some conditions. C was described as…”

Section: Blowdown Process (Evo;eob) When the Exhaust Valvesmentioning

confidence: 99%

Section: Blowdown Process (Evo;eob) When the Exhaust Valvesmentioning

confidence: 99%

See 1 more Smart Citation

Control-oriented modeling of the gas exchange process in rebreathing homogeneous charge compression ignition engines

Takeshita

Yamasaki

Muto

et al. 2021

International Journal of Engine Research

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show abstract

“…5 There are various barriers to this technology's commercial success, including a limited working load range, inability to alter ignition timing, pounding, and higher unburned hydrocarbon and carbon monoxide emissions. 6,7 As a result of early fuel injection timings, combustion rate being uncontrollable, knocking happens more frequently in HCCI engine than conventional diesel engine. 8,9 To address these challenges, scientists have come up with innovative ideas that could be categorized widely into strategies for thermal and fuel management traditional diesel engines include steps like intake charge preheating, 10 supercharging 11 dilution of charge using Exhaust Gas Recirculation (EGR), 12 compression ratio is varied, 13 direct infusion of water, 14 and residual gas trapping.…”

Section: Introductionmentioning

confidence: 99%

Effects of multiple fuel injection schedules and LPG energy share on combustion stability and output characteristics of dual-fuel HCCI engine

Saiteja

Hadhi

Kiran

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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Homogeneous Charge Compression Ignition (HCCI) engine is a prominent solution to satisfy the stringent NOx emission norms. The current study investigates the influence of fuel injection strategies and LPG energy share on dual fuel powered HCCI engine at 1.8 bar and 3.6 bar BMEP at constant speed (2700 rpm) and injection pressure (50 MPa). Over the study D + LPO used as primary fuel and LPG as secondary fuel. The fuel injection strategies alike single, double, as well multi-pulse modes are studied at injection timings of 90obTDC aimed at single-pulse, 120°& 90°bTDC for double-pulse, 120°, 90°, & 30°bTDC for multi-pulse modes. All single, double, and multi-pulse injection modes at 0% LPG energy share obtains greater combustion rate and advanced combustion phasing. Further increase in LPG energy share, the rate of combustion is lessened and retarded toward TDC, consequently it enhanced the performance and emission characteristics of an HCCI engine. The optimum LPG energy share for single-pulse, double-pulse, and multi-pulse injections modes are 46.80%, 63.25%, and 78.06% respectively for 1.8 bar BMEP. For 3.6 bar BMEP optimum LPG energy shares are found to be 41.79%, 58.55%, and 71.76% for single, double, and multi-pulse injection respectively.

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“…The N O x components of diesel engine exhaust gas typically contain approximately 1 / 7 – 1 / 3 N O 2 / NO fraction 8 while NO 2 /NO ratio increases after the diesel oxidation catalyst (DOC) to approximately one. 9 Different methods have been used by the automotive industry to reduce diesel engine N O x emission, including exhaust gas recirculation (EGR), 10 low-temperature combustion (LTC) 11,12 and most effectively, urea-based selective catalytic reduction (SCR). 13–15 Although all these methods can help reducing the engine tailpipe N O x emissions, it has become more difficult to keep pace with increasingly stringent emission regulations by only using the conventional N O x reduction approaches.…”

Section: Introductionmentioning

confidence: 99%

A correlation-based model order reduction approach for a diesel engine NO_xand brake mean effective pressure dynamic model using machine learning

Norouzi

Aliramezani

Koch

2020

International Journal of Engine Research

Self Cite

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A correlation-based model order reduction algorithm is developed using support vector machine to model [Formula: see text] emission and break mean effective pressure of a medium-duty diesel engine. The support vector machine–based model order reduction algorithm is used to reduce the number of features of a 34-feature full-order model by evaluating the regression performance of the support vector machine–based model. Then, the support vector machine–based model order reduction algorithm is used to reduce the number of features of the full-order model. Two models for [Formula: see text] emission and break mean effective pressure are developed via model order reduction, one complex model with high accuracy, called high-order model, and the other with an acceptable accuracy and a simple structure, called low-order model. The high-order model has 29 features for [Formula: see text] and 20 features for break mean effective pressure, while the low-order model has nine features for [Formula: see text] and six features for break mean effective pressure. Then, the steady-state low-order model and high-order model are implemented in a nonlinear control-oriented model. To verify the accuracy of nonlinear control-oriented model, a fast response electrochemical [Formula: see text] sensor is used to experimentally study the engine transient [Formula: see text] emissions. The high-order model and low-order model support vector machine models of [Formula: see text] and break mean effective pressure are compared to a conventional artificial neural network with one hidden layer. The results illustrate that the developed support vector machine model has shorter training times (5–14 times faster) and higher accuracy especially for test data compared to the artificial neural network model. A control-oriented model is then developed to predict the dynamic behavior of the system. Finally, the performance of the low-order model and high-order model is evaluated for different rising and falling input transients at four different engine speeds. The transient test results validate the high accuracy of the high-order model and the acceptable accuracy of low-order model for both [Formula: see text] and break mean effective pressure. The high-order model is proposed as an accurate virtual plant while the low-order model is suitable for model-based controller design.

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An HCCI Control Oriented Model that Includes Combustion Efficiency

Cited by 17 publications

References 20 publications

Control-oriented modeling of the gas exchange process in rebreathing homogeneous charge compression ignition engines

Control-oriented modeling of the gas exchange process in rebreathing homogeneous charge compression ignition engines

Effects of multiple fuel injection schedules and LPG energy share on combustion stability and output characteristics of dual-fuel HCCI engine

A correlation-based model order reduction approach for a diesel engine NO_xand brake mean effective pressure dynamic model using machine learning

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An HCCI Control Oriented Model that Includes Combustion Efficiency

Cited by 17 publications

References 20 publications

Control-oriented modeling of the gas exchange process in rebreathing homogeneous charge compression ignition engines

Control-oriented modeling of the gas exchange process in rebreathing homogeneous charge compression ignition engines

Effects of multiple fuel injection schedules and LPG energy share on combustion stability and output characteristics of dual-fuel HCCI engine

A correlation-based model order reduction approach for a diesel engine NOxand brake mean effective pressure dynamic model using machine learning

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A correlation-based model order reduction approach for a diesel engine NO_xand brake mean effective pressure dynamic model using machine learning