Development of an Improved Residuals Estimation Model for Dual Independent Cam Phasing Spark-Ignition Engines

Kale, Vaibhav; Yeliana, Yeliana; Worm, Jeremy; Naber, Jeffrey

doi:10.4271/2013-01-0312

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…In a recent study, Kale et al [16] developed an RGF model in the dual cam phasing condition that distinguishes the valve overlap duration considering the location of the TDC (Top dead center) and timing with the same intake/exhaust valve curtain area. Although this model has many coefficients (13) that must be adjusted, it provided relatively accurate results.…”

Section: Zero-dimensional Residual Gas Fraction Modelsmentioning

confidence: 99%

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Prediction Modeling and Analysis of Knocking Combustion using an Improved 0D RGF Model and Supervised Deep Learning

et al. 2019

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The knock phenomenon is one of the major hindrances for enhancing the thermal efficiency in spark-ignited engines. Due to the stochastic behavior of knocking combustion, analytical cycle studies are required. However, there are many problems to be addressed with regard to the individual cycle analysis of in-cylinder pressure data. This study thus proposes novel, comprehensive and efficient methodologies for evaluating the knocking combustion in the internal combustion engine. The proposed methodologies include a filtering method for the in-cylinder pressure, the determination of the knock onset, and the calculation of the residual gas fraction. Consequently, a smart knock onset model with high accuracy could be developed using a supervised deep learning that was not available in the past. Moreover, an improved zero-dimensional (0D) estimation model for the residual gas fraction was developed to obtain better accuracy for closed system analysis. Finally, based on a cyclic analysis, a knock prediction model is suggested; the model uses 0D ignition delay correlation under various experimental conditions including aggressive cam phase shifting by a dual variable valve timing (VVT) system. Using the proposed analysis method, insight into stochastic knocking combustion can be obtained, and a faster combustion speed can lead to a higher knock intensity in a steady-state operation.

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Section: Zero-dimensional Residual Gas Fraction Modelsmentioning

confidence: 99%

“…11a,b show the results of the 0D RGF models proposed by Fox et al[15] and Kale et al[16], respectively. As previously described, validation data was provided by a 1D simulation.…”

mentioning

confidence: 99%

Prediction Modeling and Analysis of Knocking Combustion using an Improved 0D RGF Model and Supervised Deep Learning

et al. 2019

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“…Control-oriented residual gas calculation models have been developed using several different semi-empirical correlations. 6–12 While some of these models may not have originally been intended for real-time control, their computational complexity is of a level that they have now become feasible for implementation in modern engine controllers. These correlations utilize either standard engine sensors (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…The model prediction appears to be more accurate, but the fitting process is complicated which reduces the adaptive ability of the model and weakens the robustness. In 2013, Kale et al 10 modified the Fox RGF model by means of isolation and characterization of the gas exchange physical processes. For this improved model, the overlap factor (OF) considers the phasing of intake and exhaust valves with respect to the piston motion.…”

Section: Introductionmentioning

confidence: 99%

Control-oriented residual gas mass prediction for spark ignition engines

Wang

Prucka

et al. 2014

International Journal of Engine Research

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Trapped residual gas mass is an important physical factor that influences combustion phasing and variation, fuel consumption, and air mass prediction for fuel control. There are currently no mass-production sensors available to directly measure in-cylinder residual gas mass, so prediction models must be utilized for control. Residual gas content of the cylinder can be difficult to directly model for control purposes because it involves complex flows during gas exchange that are driven by fluctuating pressures in the intake and exhaust systems. Capturing these effects in an accurate manner generally requires high model complexity and computational effort outside of the capability of most production intent engine controllers. This paper presents a semi-physics-based control oriented residual gas mass (RGM) prediction method. The RGM model is based on Bernoulli’s principle and considers engine operating conditions, valve timing and geometry, and piston motion effects. Moreover, to more accurately estimate the burned gas back flow, this model captures gas wave dynamic effects in intake and exhaust manifold pressures. The model is described in detail and its prediction accuracy is compared to that of a high fidelity simulation that utilizes experimentally measured crank angle resolved intake, exhaust, and cylinder pressures as boundary conditions. The new model is incorporated into a rapid-prototype control system for real-time operation during transient and steady-state engine operation. The results show that the proposed RGM model provides real-time predictions within 1.9-2.3% RGF, creating relative estimation errors in the range of 10-24%, and is capable of running real-time for engine control.

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“…Several works have been done to estimate Residual Gas Fraction (RGF), among which are [48], [49], [50] and [33]. The RGF is defined as:…”

Section: Residual Mass Estimationmentioning

confidence: 99%

Air Charge Estimation for an Si Engine Using in-Cylinder Pressure Sensor

Yazdani¹

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Development of an Improved Residuals Estimation Model for Dual Independent Cam Phasing Spark-Ignition Engines

Cited by 7 publications

References 9 publications

Prediction Modeling and Analysis of Knocking Combustion using an Improved 0D RGF Model and Supervised Deep Learning

Prediction Modeling and Analysis of Knocking Combustion using an Improved 0D RGF Model and Supervised Deep Learning

Control-oriented residual gas mass prediction for spark ignition engines

Air Charge Estimation for an Si Engine Using in-Cylinder Pressure Sensor

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