A Model-based estimator of engine cylinder pressure imbalance for combustion feedback control applications

Al‐Durra, Ahmed; Fiorentini, Lisa; Canova, Marcello; Yurkovich, Stephen

doi:10.1109/acc.2011.5991196

Cited by 13 publications

(17 citation statements)

References 17 publications

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“…For the closest calibration cycle, which corresponds to the minimum found from equation (11), the calibration peak pressure error ∈ is computed (as a percentage) i.e. ∈ = 100*abs(( ̂− )/ ) (12) where ̂ is the peak pressure predicted using the calibrated model (Equation (1)) with the 'closest' calibration kinematics, and is the corresponding measured (calibration data) peak pressure. If the magnitude of ∈ exceeds a chosen criterion ∆ i.e.…”

Section: Data Acquisition Systemmentioning

confidence: 99%

“…Before showing cylinder pressure results under transient engine operating conditions, a convergence study was undertaken using measured steady-state engine data to establish a suitable number of terms to include in Chebychev expansions in equation (1) convergence study of the 'calibration peak pressure error' given by Equation (12). The discrete time interval chosen was 0.1ms.…”

Section: Testing the Model Using Measured Engine Datamentioning

confidence: 99%

“…Observer was used with diesel engine crank data [12], producing within 2% and with ±2 . The use of a physically-based torque model in [13], applied to a 2 l 4-cylinder diesel engine reported obtaining within 2.3 to 11.3% and between −0.4 and 4.4 .…”

Section: Introductionmentioning

confidence: 99%

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A crank-kinematics-based engine cylinder pressure reconstruction model

Dunne

Bennett

2019

International Journal of Engine Research

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A new inverse model is proposed for reconstructing steady-state and transient engine cylinder pressure using measured crank kinematics. An adaptive nonlinear timedependent relationship is assumed between windowed-subsections of cylinder pressure and measured crank kinematics in a time-domain format (rather than in crank-angledomain). This relationship comprises a linear sum of four separate nonlinear functions of crank jerk, acceleration, velocity, and crank angle. Each of these four nonlinear functions is obtained at each time instant by fitting separate m-term Chebychev polynomial expansions, where the total 4m instantaneous expansion coefficients are found using a standard (over-determined) linear least-square solution method. A convergence check on the calibration accuracy shows this initially improves as more Chebychev polynomial terms are used, but with further increase, the over-determined system becomes singular. Optimal accuracy Chebychev expansions are found to be of degree m=4, using 90 or more cycles of engine data to fit the model. To confirm the model accuracy in predictive mode, a defined measure is used, namely the 'calibration peak pressure error'. This measure allows effective a priori exclusion of occasionally unacceptable predictions. The method is tested using varying speed data taken from a 3-cylinder DISI engine fitted with cylinder pressure sensors, and a high resolution shaft encoder. Using appropriately-filtered crank kinematics (plus the 'calibration peak pressure error'), the model produces fast and accurate predictions for previously unseen data. Peak pressure predictions are consistently within 6.5% of target, whereas locations of peak pressure are consistently within ± 2.7˚ CA. The computational efficiency makes it very suitable for real-time implementation. main-section pages (double-spaced) 33 references 15 FiguresNo Appendices

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Section: Data Acquisition Systemmentioning

confidence: 99%

Section: Testing the Model Using Measured Engine Datamentioning

confidence: 99%

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A crank-kinematics-based engine cylinder pressure reconstruction model

Dunne

Bennett

2019

International Journal of Engine Research

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“…Therefore, the intake gas properties may need to be estimated for real applications on production engines. In [24] and [25], Al-Durra et al and Stockar et al have employed a sliding mode observer and a model-order reduction method to predict the in-cylinder pressure. Although these methods can predict the pressure at IVC accurately, they disregard heat transfer and are more complex which makes these methods more challenging for use in CA50 controller design.…”

Section: Introductionmentioning

confidence: 99%

Cylinder-specific model-based control of combustion phasing for multiple-cylinder diesel engines operating with high dilution and boost levels

Sui

Hall

Kapadia

2018

International Journal of Engine Research

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Accurate control of combustion phasing is indispensable for diesel engines due to the strong impact of combustion timing on efficiency. In this work, a non-linear combustion phasing model is developed and integrated with a cylinder-specific model of intake gas. The combustion phasing model uses a knock integral model, a burn duration model and a Wiebe function to predict CA50 (the crank angle at which 50% of the mass of fuel has burned). Meanwhile, the intake gas property model predicts the EGR fraction and the incylinder pressure and temperature at intake valve closing (IVC) for different cylinders. As such, cylinder-to-cylinder variation of the pressure and temperature at intake valves closing is also considered in this model. This combined model is simplified for controller design and validated. Based on these models, two combustion phasing control strategies are explored. The first is an adaptive controller that is designed for closed-loop control and the second is a feedforward model-based control strategy for open-loop control. These two control approaches were tested in simulations for all six cylinders and the results demonstrate that the CA50 can reach steady state conditions within 10 cycles. In addition, the steady state errors are less than ±0.1 crank angle degree (CAD) with the adaptive control approach, and less than ±1.3 CAD with feedforward model-based control. The impact of errors on the control algorithms is also discussed in the paper.

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“…An ANN fed with crank speed (at 1800 rpm), motored-engine pressures, and spark advance, was tested in [15] using a heat-release model for cylinder pressure, to give results of 5 -10% accuracy on Pmax. Crank speed for a four-cylinder Diesel engine was also used in [16] to exploit sliding-mode observer predictions of Pmax within 2% and θmax within ± 2°. A physical torque model by contrast was used in [17] and te sted on a 2-litre 4…”

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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A Model-based estimator of engine cylinder pressure imbalance for combustion feedback control applications

Cited by 13 publications

References 17 publications

A crank-kinematics-based engine cylinder pressure reconstruction model

A crank-kinematics-based engine cylinder pressure reconstruction model

Cylinder-specific model-based control of combustion phasing for multiple-cylinder diesel engines operating with high dilution and boost levels

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

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