HiL-based ECU-Calibration of SI Engine with Advanced Camshaft Variability

Winsel, Thomas; Ayeb, Mohamed; Wilhelm, Christian; Theuerkauf, H. J.; Pischinger, Stefan; Woermann, R. J.; Schernus, Christof

doi:10.4271/2006-01-0613

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…This transformation of the cylinder representation is generally achieved by the introduction of a surrogate engine-block model that replicates the cycle-averaged behaviour of the cylinders [8,9]. Due to the continuous-flow characteristic of the cylinders and due to the neglected spatial variation of the variables, lumped-parameter MVEMs are not capable of capturing wave propagation phenomena in engine manifolds, which might influence the engine's performance, as reported in, e.g., [9,10]. To better match the engine's performance simulated with MVEMs, it is common to introduce additional inputs that consider non-modelled, unsteady effects, which are used to predict more accurate mean flows [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…The fluxes of all the adjoined transfer components can be seen as source or sink terms in the balance equations. It is commonly assumed that the transfer components do not feature inertial effects [7,10,11,15,16]. The state variables of the adjoined storage components are used as the input variables to evaluate the fluxes through the transfer elements, either out of algebraic correlations, given input maps or surrogate models, or as presented in this paper by solving the TMB equation.…”

Section: Introductionmentioning

confidence: 99%

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Transient Momentum Balance—A Method for Improving the Performance of Mean-Value Engine Plant Models

Katrašnik

2013

Energies

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Mean-value engine models (MVEMs) are frequently applied in system-level simulations of vehicle powertrains. In particular, MVEMs are a common choice in engine simulators, where real-time execution is mandatory. In the case of real-time applications with prescribed, fixed sampling times, the use of explicit integration schemes is almost mandatory. Thus the stability of MVEMs is one of the main limitations when it comes to optimizing their performance. It is limited either by the minimum size of the gas volume elements or by the maximum integration time step. An innovative approach that addresses both constraints arises from the fact that the mass flow through the transfer elements of the MVEM is not modelled considering the quasi-steady assumption, but instead the mass-flow is calculated using a single transient momentum balance (TMB) equation. The proposed approach closely resembles phenomena in the physical model, since it considers both the flow-field history and the inertial effects arising from the time variation of the mass flow. It is shown in this paper that a consideration of the TMB equation improves the stability and/or the computational speed of the MVEMs, whereas it also makes it possible to capture physical phenomena in a more physically plausible manner. Keywords

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transient Momentum Balance—A Method for Improving the Performance of Mean-Value Engine Plant Models

Katrašnik

2013

Energies

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“…[10] and [11], 0-D models can comply with realtime constraints and provide a higher level of detail compared to mean-value engine models, e.g. [12] and [13]. The latter models reach their limits when future control functions need to be taken into account, which incorporate CA resolved events such as cylinder pressure based control functions, electronic control unit controlled fuel injections, and variable valve timing.…”

Section: Introductionmentioning

confidence: 99%

“…[12] and [13]. The latter models reach their limits when future control functions need to be taken into account, which incorporate CA resolved events such as cylinder pressure based control functions, electronic control unit controlled fuel injections, and variable valve timing.…”

mentioning

confidence: 99%

On the Convergence, Stability, and Computational Speed of Numerical Schemes for 0-D IC Engine Cylinder Modelling

Katrašnik¹,

Schuemie²,

Wurzenberger³

2013

SV-JME

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The steadily increasing demands on vehicle fuel economy, emissions, and performance are a major driving force in developing and optimising future powertrains. In order to meet customers' and legislative requirements it is of particular importance not only to simulate and optimise different powertrain elements, but also to develop simulation models capable of modeling the performance of the powertrain system as a whole. The latter is of particular importance for developing and testing components in a virtual environment, as well as for accompanying the conventional development steps from design phases in the office to the application and validation phases on the test bed. Test bed validation, development and calibration of components in the hardware-in-the-loop (HiL) environment and modelbased engine controlling require real-time capable simulation models. Moreover, computational speed is also of considerable importance when simulating complete emission test cycles where engine and vehicle dynamics are simulated on time scales of 10 3 s.Recently, 0-dimensional (0-D) crank-angle (CA) resolved engine simulation models, e.g. [1] to [4], have emerged as a promising approach for real-time engine simulations of internal combustion engines (ICE) and hybrid powertrain of heavy-duty engines [5]. Unlike higher resolution 1-D, e.g.[6] to [9], and 3-D models, e.g.[10] and [11], 0-D models can comply with realtime constraints and provide a higher level of detail compared to mean-value engine models, e.g. [12] and [13]. The latter models reach their limits when future control functions need to be taken into account, which incorporate CA resolved events such as cylinder pressure based control functions, electronic control unit controlled fuel injections, and variable valve timing. Mean-value engine models are not able to model these phenomena predictably since they rely on experimental data that are used to tune or train the model as presented in [14] for camshaft variability.Besides computational speed, the convergence and stability of numerical solution algorithms are the prerequisites for the successful application of 0-D CA resolved engine simulation models. This topic has not yet been systematically analysed in the literature and therefore the presented paper aims to provide a study of the convergence, stability, and computational speed of different explicit and implicit integration schemes.The cylinder model represents the crucial element to attaining high computational speed and accuracy of results, since the time variation of the thermodynamic variables is most pronounced in the cylinder due to piston kinematics and combustion. It is common practice to validate the accuracy of simulation models by comparing numerical and experimental results. This indeed gives an indication of the consistency On the Convergence, Stability, and Computational Speed of Numerical Schemes for 0-D IC Engine Cylinder Modelling

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Towards a Powerful Hardware-in-the-Loop System for Virtual Calibration of an Off-Road Diesel Engine

Riccio

Monzani²,

Landi³

2022

Energies

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A common challenge among internal combustion engine (ICE) manufacturers is shortening the development time while facing requirements and specifications that are becoming more complex and border in scope. Virtual simulation and calibration are effective instruments in the face of these demands. This article presents the development of zero-dimensional (0D)—real-time engine and exhaust after-treatment system (EAS) models and their deployment on a Virtual test bench (VTB). The models are created using a series of measurements acquired in a real test bench, carefully performed in view of ensuring the highest reliability of the models themselves. A zero-dimensional approach was chosen to guarantee that models could be run in real-time and interfaced to the real engine Electronic Control Unit (ECU). Being physically based models, they react to changes in the ECU calibration parameters. Once the models are validated, they are then integrated into a Simulink® based architecture with all the Inputs/Outputs connections to the ECU. This Simulink® model is then deployed on a Hardware in the Loop (HiL) machine for ECU testing and calibration. The results for engine and EAS performance and emissions align with both steady-state and transient measurements. Finally, two different applications of the HiL system are presented to explain the opportunities and advantages of this tool integrated within the standard engine development. Examples cited refer to altitude calibration activities and soot loading investigation on vehicle duty cycles. The cases described in this work are part of the actual development of one of the latest engines developed by Kohler Engines: the KDI 1903 TCR Stage V. The application of this methodology reveals a great potential for engine development and may become an essential tool for calibration engineers.

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HiL-based ECU-Calibration of SI Engine with Advanced Camshaft Variability

Cited by 8 publications

References 2 publications

Transient Momentum Balance—A Method for Improving the Performance of Mean-Value Engine Plant Models

Transient Momentum Balance—A Method for Improving the Performance of Mean-Value Engine Plant Models

On the Convergence, Stability, and Computational Speed of Numerical Schemes for 0-D IC Engine Cylinder Modelling

Towards a Powerful Hardware-in-the-Loop System for Virtual Calibration of an Off-Road Diesel Engine

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