Narmax model identification of a variable geometry turbocharged diesel engine

Zito, Gianluca; Landau, Ioan Doré

doi:10.1109/acc.2005.1470094

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…However, the identification of the engine's model parameters is time consuming and the nonlinear controller remains complex. Studies are currently in progress [25], [26] in order to improve the online identification of the engine and turbocharger dynamic behavior.…”

Section: Control Strategiesmentioning

confidence: 99%

“…is the Mach number; it is the ratio of the blade's velocity to the velocity of the sound at the entry of the compressor (23) The parameter in (21) is given by the equation (24) is the heat capacity of the air at constant pressure and is the compression ratio of the compressor (25) The power consumed by the compressor is expressed by (26) is the compressor isentropic efficiency expressed by (27) where are calculated using the following equation: (28) are constants identified from the experimental maps of the compressor.…”

Section: Variable Geometry Turbo-compressormentioning

confidence: 99%

See 1 more Smart Citation

Optimal Control of a Variable Geometry Turbocharged Diesel Engine Using Neural Networks: Applications on the ETC Test Cycle

Omran

Younès

Champoussin

2009

IEEE Trans. Contr. Syst. Technol.

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Modern diesel engines are typically equipped with variable geometry turbo-compressor, exhaust gas recirculation (EGR) system, common rail injection system, and post-treatment devices in order to increase their power while respecting the emissions standards. Consequently, the control of diesel engines has become a difficult task involving five to ten control variables that interact with each other and that are highly nonlinear. Actually, the control schemes of the engines are all based on static lookup tables identified on test-benches; the values of the control variables are interpolated using these tables and then, they are corrected, online, by using the control techniques in order to obtain better engine's response under dynamic conditions. In this paper, we are interested in developing a mathematical optimization process that search for the optimal control schemes of the diesel engines under static and dynamic conditions. First, we suggest modeling a turbocharged diesel engine and its opacity using the mean value model which requires limited experiments; the model's simulations are in excellent agreement with the experimental data. Then the created model is integrated in a dynamic optimization process based on the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. The optimization results show the reduction of the opacity while enhancing the engine's effective power. Finally, we proposed a practical control technique based on the neural networks in order to apply these control schemes online to the engine. The neural controller is integrated into the engine's simulations and is used to control the engine in real time on the European transient cycle (ETC). The results confirm the validity of the neural controller.Index Terms-Diesel engines, dynamic optimization process, mean value model, optimal control and neural networks.

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Section: Control Strategiesmentioning

confidence: 99%

Section: Variable Geometry Turbo-compressormentioning

confidence: 99%

Optimal Control of a Variable Geometry Turbocharged Diesel Engine Using Neural Networks: Applications on the ETC Test Cycle

Omran

Younès

Champoussin

2009

IEEE Trans. Contr. Syst. Technol.

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“…In the classic approach, such methods as the nonlinear autoregressive moving average with exogenous inputs (NARMAX), 16 the subspace method 14 and the linear parameter-varying (LPV) 15 method are used to identify a dynamic model for a diesel engine. The NARMAX method can predict a model for whole engine operation; on the other hand, the subspace method can model the engine around a specific operating point.…”

Section: Diesel Engine Modellingmentioning

confidence: 99%

More than one decade with development of common-rail diesel engine management systems: a literature review on modelling, control, estimation and calibration

Nikzadfar

Shamekhi

2014

Proceedings of the Institution of Mechanical Engineers, Part D:

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Diesel engines are becoming more popular owing to their low fuel consumption and low emissions. The performance and emissions of diesel engines are strictly influenced by the injection pattern and the induced air quality. Utilization of diesel engines with mechatronic systems together with the advent of common-rail injection systems, which promotes the flexibility of injection control, has further increased diesel engine favourability and decreased the level of relevant emissions and noise. Development of mechatronic systems is a hierarchical procedure; in particular, in the case of common-rail diesel engine control, the extent and diversity of involved topics lead to a complex multi-stage development procedure. Many research studies have been carried out, thereby providing much literature in this field, which addresses different aspects of the development procedure for diesel engine management systems. Investigation of the existing literature will help researchers in the field to obtain a better understanding of the whole development procedure of diesel engine management systems and to make them familiar with new trends, state-of-the-art technologies and research fields. In this article, the control strategy development of common-rail diesel engine management systems is considered using the existing literature, i.e. the related literature was collected and categorized into the five main categories of modelling, sensitivity analysis, controller design, estimation and calibration. A brief description of every category followed by the relevant literature is given in the paper, and the common methods as well as the state-of-the-art techniques are discussed in each section. Finally, the strengths and weaknesses of the existing methods and the potential for further advancement are mentioned for each stage of the development of the engine management system, and a road map for the development of a common-rail diesel engine management system is presented.

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“…The models also called grey-box modeling. The non-parametric models are built by a priori chosen structure and the input-output behaviors of the diesel engine [5][6][7][8][9]. This paper chooses non-parametrical modeling method.…”

Section: Introductionmentioning

confidence: 99%

The Hardware-In-The-Loop simulation system of the diesel generator set based on the NARMAX model

Ai¹,

Zheng

et al. 2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

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The control of the speed of the diesel engine is the key to guarantee the AC output quality of the DGS (Diesel Generator Set). Because of the heavy noise and pollution of the physical DGS, it is a good way to build the mathematical model for research on the control of the diesel engine output speed. But the theoretical models often were not accurate and their control algorithms had poor usability due to nonlinearity and soft parameters of the diesel engine. To solve these problems, a Hardware-in-the-Loop (HIL) simulation system of the DGS has been built in this paper. In the HIL system, the power unit (includes diesel engine, generator and load tank) is replaced by the mathematic model, and the other devices are same as ones in the physical DGS. In order to ensure the accuracy of the model, this paper proposes modeling method for the power unit of the DGS. Thanks to the NARMAX (Nonlinear Auto-Regressive Moving Average with eXogenous inputs) model's good ability to describe the nonlinear system, the measurement data of the power unit are used to training NARMAX model and a high accuracy model of the power unit has been obtained. The experiments on the HIL system show the model can reproduce the static and dynamic characteristic of the physical DGS power unit to meet the research needs of the electronic governor.

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Narmax model identification of a variable geometry turbocharged diesel engine

Cited by 14 publications

References 19 publications

Optimal Control of a Variable Geometry Turbocharged Diesel Engine Using Neural Networks: Applications on the ETC Test Cycle

Optimal Control of a Variable Geometry Turbocharged Diesel Engine Using Neural Networks: Applications on the ETC Test Cycle

More than one decade with development of common-rail diesel engine management systems: a literature review on modelling, control, estimation and calibration

The Hardware-In-The-Loop simulation system of the diesel generator set based on the NARMAX model

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