Experimental Modeling of Engines

Isermann, Rolf

doi:10.1007/978-3-642-39934-3_3

Cited by 5 publications

(13 citation statements)

References 32 publications

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“…As Figure 8 shows, AFR is indicated as air to fuel equivalence ratio, lambda ( λ ), which is the ratio of actual AFR to the stoichiometric value 1,2,36–38…”

Section: Methodsmentioning

confidence: 99%

Enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air–fuel ratio control system

Zhou

Zhang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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The self-adaptive and highly robust proportional-integral-like fuzzy knowledge–based controller has been developed to regulate air–fuel ratio for gasoline direct injection engines, in order to improve the transient response behaviour and reduce the effort to be spent on calibration of parameter settings. However, even though the proportional-integral-like fuzzy knowledge–based controller can automatically correct the initially calibrated proportional and integral parameters, a more appropriate selection of controller parameter settings will lead to better transient performance. Thus, this article proposes an enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm to automatically define the most optimal parameter settings. An alternative time-domain objective function is applied for the transient calibration programme without the need for prior selection of the search-domain. The real-time transient performance of the enhanced controller is investigated on the air–fuel ratio control system of a gasoline direct injection engine. The experimental results show that the enhanced proportional-integral-like fuzzy knowledge–based controller based on chaos-enhanced accelerated particle swarm optimization is able to damp out the oscillations with less settling time (up to 75% reduction) and less integral of absolute error (up to 64.07% reduction) compared with the conventional self-adaptive proportional-integral-like fuzzy knowledge–based controller. Repeatability tests indicate that the chaos-enhanced accelerated particle swarm optimization algorithm–based proportional-integral-like fuzzy knowledge–based controller is also able to reduce the mean value of objective function by up to 10.61% reduction and the standard deviation of the objective function by up to 28.29% reduction, compared with the conventional accelerated particle swarm optimization algorithm–based proportional-integral-like fuzzy knowledge–based controller.

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“…As Figure 8 shows, AFR is indicated as air to fuel equivalence ratio, lambda ( λ ), which is the ratio of actual AFR to the stoichiometric value 1,2,36–38…”

Section: Methodsmentioning

confidence: 99%

Enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air–fuel ratio control system

Zhou

Zhang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…In the air charge dynamic subsystem, the throttle position ( θ ) and engine speed ( ω ) will decide the air mass through the throttle ( m · th ) . Then the air mass flow m · a in the intake manifold volume is calculated based on m · th and the air mass flow at the cylinder inlet port ( m · o ) 1,2,31,32…”

Section: Afr Dynamicsmentioning

confidence: 99%

“…In equation (2), m · o is decided from the air intake speed-density principle and can be described by the intake manifold pressure and engine speed 1,2,31,32…”

Section: Afr Dynamicsmentioning

confidence: 99%

“…Because of the mechanical structure of the GDI engine, the wall wetting effect could be ignored. 2,32 Thus, this paper assumes that the mass of fuel from the injector is equal to that into the cylinder. Here, m · fc denotes the fuel mass entering the cylinder.…”

Section: Afr Dynamicsmentioning

confidence: 99%

“…λ is the feedback signal for AFR regulator in this study. It is the ratio of actual AFR to stoichiometry for a given mixture 1,2,32–36…”

Section: Afr Dynamicsmentioning

confidence: 99%

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Intelligent air/fuel ratio control strategy with a PI-like fuzzy knowledge–based controller for gasoline direct injection engines

Zhou

Zhang

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper proposes a new concept of PI-like fuzzy knowledge–based controller with self-tuning capability, high robustness and rapid development capability to regulate air/fuel ratio for gasoline direct injection engines. The proportional and integral terms of the PI-like fuzzy knowledge–based controller are nonlinear functions of the input signals and self-tuned in real time at operation points, which can reduce the effort spent on calibrating controller parameters. The PI-like fuzzy knowledge–based controller is implemented on a production gasoline direct injection engine and evaluated through case studies. The experimental results show that the proposed controller can regulate air/fuel ratio at stoichiometric value with less settling time and less oscillation compared with the conventional Proportional-Integral (PI) controller for transient operation.

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Adaptive calibration of Diesel engine injection for minimising fuel consumption with constrained NOx emissions in actual driving missions

Luján

Plá

Bares

et al. 2020

International Journal of Engine Research

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This article proposes a method for fuel minimisation of a Diesel engine with constrained [Formula: see text] emission in actual driving mission. Specifically, the methodology involves three developments: The first is a driving cycle prediction tool which is based on the space-variant transition probability matrix obtained from an actual vehicle speed dataset. Then, a vehicle and an engine model is developed to predict the engine performance depending on the calibration for the estimated driving cycle. Finally, a controller is proposed which adapts the start-of-injection calibration map to fulfil the [Formula: see text] emission constraint while minimising the fuel consumption. The calibration is adapted during a predefined time window based on the predicted engine performance on the estimated cycle and the difference between the actual and the constraint on engine [Formula: see text] emissions. The method assessment was done experimentally in the engine test set-up. The engine performace using the method is compared with the state-of-the-art static calibration method for different [Formula: see text] emission limits on real driving cycles. The online implementation of the method shows that the fuel consumption can be reduced by 3%–4% while staying within the emission limits, indicating that the estimation method is able to capture the main driving cycle characterstics.

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Experimental Modeling of Engines

Cited by 5 publications

References 32 publications

Enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air–fuel ratio control system

Enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air–fuel ratio control system

Intelligent air/fuel ratio control strategy with a PI-like fuzzy knowledge–based controller for gasoline direct injection engines

Adaptive calibration of Diesel engine injection for minimising fuel consumption with constrained NOx emissions in actual driving missions

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