Determination of combustion parameters using engine crankshaft speed

Taglialatela, F.; Lavorgna, Mario; Mancaruso, Ezio; Vaglieco, Bianca Maria

doi:10.1016/j.ymssp.2012.12.009

Cited by 46 publications

(27 citation statements)

References 5 publications

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“…However, the measurement of the in-cylinder pressure is typically obtained with intrusive sensors that require a special mounting process and engine structure modification. Also the in-cylinder pressure transducer has a high cost for mass production for diesel engines [10]. So the response signals discussed in this paper do not include the in-cylinder pressure signal.…”

Section: Response Signal For Combustion Eventmentioning

confidence: 99%

“…Taglialatela et al [10] utilized the multilayer perceptron neural network to model the relationship between the crankshaft speed and parameters extracted from the in-cylinder pressure, including peak pressure value and peak pressure angular location, instead of the pressure waveform. With the trained neural network, the peak pressure amplitude can be estimated with minimum error of 2.31 bar and maximum error of 6.97 bar which are 4.1% and 8.0%, respectively, in relative percentage scale.…”

Section: Neuralmentioning

confidence: 99%

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Review of Sensing Methodologies for Estimation of Combustion Metrics

Jia

Naber

Blough

2016

Journal of Combustion

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For reduction of engine-out emissions and improvement of fuel economy, closed-loop control of the combustion process has been explored and documented by many researchers. In the closed-loop control, the engine control parameters are optimized according to the estimated instantaneous combustion metrics provided by the combustion sensing process. Combustion sensing process is primarily composed of two aspects: combustion response signal acquisition and response signal processing. As a number of different signals have been employed as the response signal and the signal processing techniques can be different, this paper did a review work concerning the two aspects: combustion response signals and signal processing techniques. In-cylinder pressure signal was not investigated as one of the response signals in this paper since it has been studied and documented in many publications and also due to its high cost and inconvenience in the application.

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Section: Response Signal For Combustion Eventmentioning

confidence: 99%

Section: Neuralmentioning

confidence: 99%

Review of Sensing Methodologies for Estimation of Combustion Metrics

Jia

Naber

Blough

2016

Journal of Combustion

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“…within the range 2.3% -11.2% and θmax within -0.4° to 4.4°. Finally an ANN was used in [18 ] driven by crank speed and acceleration from a single cylinder turbocharged gasoline engine with speeds between 1000 to 2000 rpm to produce Pmax predictions within 4% -8%.…”

Section: Introductionmentioning

confidence: 99%

Engine cylinder pressure reconstruction using crank kinematics and recurrently-trained neural networks

Bennett

Dunne

Trimby

et al. 2017

Mechanical Systems and Signal Processing

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(2017) Engine cylinder pressure reconstruction using crank kinematics and recurrently-trained neural networks. Mechanical Systems and Signal Processing, This version is available from Sussex Research Online: http://sro.sussex.ac.uk/63580/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. ABSTRACTA recurrent non-linear autoregressive with exogenous input (NARX) neural network is proposed, and a suitable fully-recurrent training methodology is adapted and tuned, for reconstructing cylinder pressure in multi-cylinder IC engines using measured crank kinematics. This type of indirect sensing is important for cost effective closed-loop combustion control and for On-Board Diagnostics. The challenge addressed is to accurately predict cylinder pressure traces within the cycle under generalisation conditions: i.e. using data not previously seen by the network during training. This involves direct construction and calibration of a suitable inverse crank dynamic model, which owing to singular behaviour at top-dead-centre (TDC), has proved difficult via physical model construction, calibration, and inversion. The NARX architecture is specialised and adapted to cylinder pressure reconstruction, using a fully-recurrent training methodology which is needed because the alternatives are too slow and unreliable for practical network training on production engines. The fully-recurrent Robust Adaptive Gradient Descent (RAGD) algorithm, is tuned initially using synthesised crank kinematics, and then tested on real engine data to assess the reconstruction capability. Real data is obtained from a 1.125 litre, 3-cylinder, in-line, direct injection spark ignition (DISI) engine involving synchronised measurements of crank kinematics and cylinder pressure across a range of steady-state speed and load conditions. The paper shows that a RAGD-trained NARX network using both crank velocity and crank acceleration as input information, provides fast and robust training. By using the optimum epoch identified ...

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“…Earlier studies have shown the potential of neural networks in internal-combustion engine diagnostics. Different types of neural network models have been used to model the relationship between the engine crankshaft speed and parameters derived from in-cylinder pressure [6,7]. The crankshaft speed coupled with the Fourier transforms of the vibration signal was also used in [8] as an input for network simulation of in-cylinder pressure.…”

Section: Introductionmentioning

confidence: 99%

Application of RBF neural networks for real-time pressure prediction in a Diesel engine

Bizon¹,

Continillo

Mancaruso

et al. 2015

IJET

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This study aims at building efficient and robust artificial neural networks (ANN) in order to reconstruct the in-cylinder pressure of a Diesel engine starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. In this view, the ANN is meant to be efficient in terms of response time. In addition, robustness is sought in order to provide flexibility in terms of operation parameters. The neural network considered here is based on radial basis functions (RBF). The network is trained using measurements from a single cylinder Diesel engine operating under varying conditions. Training data are composed of time series from the accelerometer and corresponding measured in-cylinder pressure signals. The network parameters, including the spread parameter of the radial basis functions and the number of neurons, are used to optimize the network quality. The RBF network is then validated. The results show good correspondence between the measured and the simulated pressure signal. The accuracy of the simulated pressure signals is analyzed in terms of mean square error and in terms of a number of parameters, such as pressure peak and mass burned fraction (MBF), and their angular locations. Robustness is sought with respect to changes in the engine parameters and in the nature of the fuel. The encouraging results indicate that the reconstruction model based on RBF neural networks can be incorporated in the design of fuel-independent real-time control of Diesel engines.

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Determination of combustion parameters using engine crankshaft speed

Cited by 46 publications

References 5 publications

Review of Sensing Methodologies for Estimation of Combustion Metrics

Review of Sensing Methodologies for Estimation of Combustion Metrics

Engine cylinder pressure reconstruction using crank kinematics and recurrently-trained neural networks

Application of RBF neural networks for real-time pressure prediction in a Diesel engine

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