Evaluation of HCCI Engine Potentials in Comparison to Advanced Gasoline and Diesel Engines

Amann, Manfred; Buckingham, J; Kono, Naoki

doi:10.4271/2006-01-3249

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…Recently Amann et al [23] evaluated the HCCI engine performances in comparison with modern advanced gasoline and diesel engines by calibrating HCCI operation factors, and found that on a break-specific basis the HCCI combustion emitted higher levels of CO and UHC emissions than the other combustion concepts. In general, it is widely accepted that CO emissions are controlled primarily by the fuel/air equivalence ratio and the reaction from CO to CO2 is sensitive to the bulk gas temperature.…”

Section: Emission Characteristics Of Hcci-dimentioning

confidence: 99%

An experimental study of HCCI-DI combustion and emissions in a diesel engine with dual fuel

Lü

et al. 2008

International Journal of Thermal Sciences

121

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Section: Emission Characteristics Of Hcci-dimentioning

confidence: 99%

An experimental study of HCCI-DI combustion and emissions in a diesel engine with dual fuel

Lü

et al. 2008

International Journal of Thermal Sciences

121

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“…Homogeneous charge compression ignition (HCCI) is considered to be a promising alternative combustion technology to meet the future stringent emission regulations, because of its low NO x and particulate matter emissions as well as the good efficiency. In some regards, HCCI combustion incorporates the advantages of both spark ignition (SI) engines and direct injection compression ignition (DICI) engines, so that HCCI has been under widespread investigation in recent years. – …”

Section: Introductionmentioning

confidence: 99%

Evaluation of SCCI Potentials in Comparison to HCCI and Conventional DICI Combustion Using n-Heptane

Lü

et al. 2008

Energy Fuels

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This work investigates the potential of stratified charge compression ignition (SCCI) to extend the high-load limits of homogeneous charge compression ignition (HCCI) engine. Engine experiments were conducted on a single-cylinder diesel engine equipped with an electrical port fuel injection system. When the n-heptane fuel amount of port injection and in-cylinder direct injection was calibrated, partial fuel stratification was created and three different combustion modes as SCCI, HCCI, and direct injection compression ignition (DICI) were achieved. The premixed ratio (r p) was used to indicate the partial fuel stratification, and the effects of premixed ratio on SCCI combustion were examined. It was found that SCCI could achieve a staged combustion event of reduced pressure-rise rates, thus offering good potential to expand the operating range of the HCCI engine. Also, the engine performance and emission characteristics of SCCI were evaluated, focusing on the comparison with the HCCI and conventional DICI combustion. The results show that, with suitable partial fuel stratification, SCCI has the capability to achieve lower CO and HC emissions in comparison to HCCI and keep the NO x emissions on a low level within 50 ppm while maintaining good efficiency equivalent to DICI.

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“…Design of Experiments (DoE) [10][11][12] has been widely used as the baseline method. Major applications include catalyst system optimization [13], optimization of variable valve trains for performance and emissions [14][15][16][17], implementation of dynamic model-based engine calibrations [18,19], and optimization of fuel consumption in a spark ignition engine with dual-continuously controlled camshaft phasing with respect to valve timing [20].…”

Section: Detc2007-34718mentioning

confidence: 99%

A Learning Algorithm for Optimal Internal Combustion Engine Calibration in Real Time

Malikopoulos

Papalambros

Assanis

2007

Volume 6: 33rd Design Automation Conference, Parts a and B

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Advanced internal combustion engine technologies have increased the number of accessible variables of an engine and our ability to control them. The optimal values of these variables are designated during engine calibration by means of a static correlation between the controllable variables and the corresponding steady-state engine operating points. While the engine is running, these correlations are being interpolated to provide values of the controllable variables for each operating point. These values are controlled by the electronic control unit to achieve desirable engine performance, for example in fuel economy, pollutant emissions, and engine acceleration. The state-of-the-art engine calibration cannot guarantee continuously optimal engine operation for the entire operating domain, especially in transient cases encountered in driving styles of different drivers. This paper presents the theoretical basis and algorithmic implementation for allowing the engine to learn the optimal set values of accessible variables in real time while running a vehicle. Through this new approach, the engine progressively perceives the driver’s driving style and eventually learns to operate in a manner that optimizes specified performance indices. The effectiveness of the approach is demonstrated through simulation of a spark ignition engine, which learns to optimize fuel economy with respect to spark ignition timing, while it is running a vehicle.

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Evaluation of HCCI Engine Potentials in Comparison to Advanced Gasoline and Diesel Engines

Cited by 7 publications

References 6 publications

An experimental study of HCCI-DI combustion and emissions in a diesel engine with dual fuel

An experimental study of HCCI-DI combustion and emissions in a diesel engine with dual fuel

Evaluation of SCCI Potentials in Comparison to HCCI and Conventional DICI Combustion Using n-Heptane

A Learning Algorithm for Optimal Internal Combustion Engine Calibration in Real Time

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