Turbocharged Diesel Engine Modeling for Nonlinear Engine Control and State Estimation

Kao, Minghui; Moskwa, John J.

doi:10.1115/1.2798519

Cited by 231 publications

(117 citation statements)

References 14 publications

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“…The structure of this simulation model approximately follows that described in [9]. A simpler model, developed specifically for HIL applications, is presented in [10].…”

Section: Architecture Of Simulation and Hil Implementation 31 Simulamentioning

confidence: 99%

Integration of a mean-torque diesel engine model into a hardware-in-the-loop shipboard network simulation using lambda tuning

Roscoe¹,

Elders²,

Hill

et al. 2011

IET Electr. Syst. Transp.

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This version is available at https://strathprints.strath.ac.uk/34266/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output.This paper is a postprint of a paper submitted to and accepted for publication in IET Electric Systems in Transportation (EST) and is subject to IET copyright. AbstractThis paper describes the creation of a hardware-in-the-loop (HIL) environment for use in evaluating network architecture, control concepts and equipment for use within marine electrical systems. The environment allows a scaled hardware network to be connected to a simulation of a multi-megawatt marine diesel prime-mover, coupled via a synchronous generator. This allows All-Electric marine scenarios to be investigated without large-scale hardware trials. The method of closing the loop between simulation and hardware is described, with particular reference to the control of the laboratory synchronous machine which represents the simulated generator(s). The fidelity of the HIL simulation is progressively improved in this paper. Firstly a faster and more powerful field drive is implemented to improve voltage tracking. Secondly the phase tracking is improved by using two nested PIDA (proportional integral derivative acceleration) controllers for torque control, tuned using lambda-tuning. The HIL environment is tested using a scenario involving a large constant-power load step. This both provides a very severe test of the HIL environment, and also reveals the potentially adverse effects of constant-power loads within marine power systems.

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“…The structure of this simulation model approximately follows that described in [9]. A simpler model, developed specifically for HIL applications, is presented in [10].…”

Section: Architecture Of Simulation and Hil Implementation 31 Simulamentioning

confidence: 99%

Integration of a mean-torque diesel engine model into a hardware-in-the-loop shipboard network simulation using lambda tuning

Roscoe¹,

Elders²,

Hill

et al. 2011

IET Electr. Syst. Transp.

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“…This tradeoff can be avoided by employing and manipulating EGR and VGT actuators in order to achieve the control objective, which is to supply an amount of air and a fraction of appropriate EGR for given operating conditions. The emissions can be reduced by increasing the intake manifold EGR-fraction, and the smoke can be reduced by increasing the air/fuel ratio (Kao and Moskwa, 1995). These are main reasons why diesel engines are solutions for technical applications due to their low fuel consumption and durability.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear control for a diesel engine: A CLF-based approach

Kuzmych

Aïtouche

Bosche

2014

International Journal of Applied Mathematics and Computer Science

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In this paper, we propose a control Lyapunov function based on a nonlinear controller for a turbocharged diesel engine. A model-based approach is used which predicts the experimentally observed engine performance for a biodiesel. The basic idea is to develop an inverse optimal control and to employ a Lyapunov function in order to achieve good performances. The obtained controller gain guarantees the global convergence of the system and regulates the flows for the variable geometry turbocharger as well as exhaust gas recirculation systems in order to minimize the NOx emission and the smoke of a biodiesel engine. Simulation of the control performances based on professional software and experimental results show the effectiveness of this approach.

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“…Diesel engines can be modeled in two different ways: The models of knowledge quasi-static (Winterbonne, 1984), draining-replenishment (Kao, 1995), semi mixed (Ouenou-Gamo, 2001;Younes, 1993), bond graph (Hassenfolder, 1993), and the models of representation by transfer functions (Younes, 1993), neural networks (Ouladssine, 2004). Seen our optimization objective, the model of knowledge will be adopted in this work.…”

Section: Engine Dynamic Modelingmentioning

confidence: 99%