Control Design Based on FMI: a Diesel Engine Control Case Study

Nylén, Anders; Henningsson, Maria; Cervin, Anton; Tunestål, Per

doi:10.1016/j.ifacol.2016.08.035

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…For the exchange of interfaces between the simulation subsystems, the input and output quantities were exchanged at specific time intervals. Simulations between the plant model and the hardware environment can interact through the function mock-up interface (FMI) standard of Simulink [16][17][18][19]. The FMI standard provides a means of developing a model-based system and is used to design functions driven by electronic devices inside the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Diesel Mean Value Engine Modeling Based on Thermodynamic Cycle Simulation Using Artificial Neural Network

Park

2019

Energies

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This study aims to construct a reduced thermodynamic cycle model with high accuracy and high model execution speed based on artificial neural network training for real-time numerical analysis. This paper proposes a method of constructing a fast average-value model by combining a 1D plant model and exhaust gas recirculation (EGR) control logic. The combustion model of the detailed model uses a direct-injection diesel multi-pulse (DI-pulse) method similar to diesel combustion characteristics. The DI-pulse combustion method divides the volume of the cylinder into three zones, predicting combustion- and emission-related variables, and each combustion step comprises different correction variables. This detailed model is estimated to be within 5% of the reference engine test results. To reduce the analysis time while maintaining the accuracy of engine performance prediction, the cylinder volumetric efficiency and the exhaust gas temperature were predicted using an artificial neural network. Owing to the lack of input variables in the training of artificial neural networks, it was not possible to predict the 0.6–0.7 range for volumetric efficiency and the 1000–1200 K range for exhaust gas temperature. This is because the mean value model changes the fuel injection method from the common rail fuel injection mode to the single injection mode in the model reduction process and changes the in-cylinder combustion according to the injection timing of the fuel amount injected. In addition, the mean value model combined with EGR logic, i.e., the single-input single-output (SISO) coupled mean value model, verifies the accuracy and responsiveness of the EGR control logic model through a step-transient process. By comparing the engine performance results of the SISO coupled mean value model with those of the mean value model, it is observed that the SISO coupled mean value model achieves the desired target EGR rate within 10 s. The EGR rate is predicted to be similar to the response of volumetric efficiency. This process intuitively predicted the main performance parameters of the engine model through artificial neural networks.

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Section: Introductionmentioning

confidence: 99%

Diesel Mean Value Engine Modeling Based on Thermodynamic Cycle Simulation Using Artificial Neural Network

Park

2019

Energies

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“…The FMI standard has become an international standard and is being recognized by more and more simulation tools. Many scholars have also carried out research on FMI standards: Pannu [1] studied how to use the FMI standard to aggregate different models into a single model to solve in the same solver and achieved good results; Anders [2] presented a use case for an FMI-based workflow for engine control design.…”

Section: Introductionmentioning

confidence: 99%

Error Control in Co-Simulation based FMI

Qiang¹,

Li²

2018

dtcse

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Simulation based on multidisciplinary systems plays a pivotal role in the aerospace field. And it uses co-simulation as its primary approach. The functional model interface (FMI) standard makes it possible to easily implement by defining a series of standard interfaces. But, additional errors caused by co-simulation yet cannot be avoided in FMI. In order to compensate for this shortcoming, and to make the FMI standard be better applied. After an introduction of the FMI standard, this paper proposes two methods of error control: variable macro-step size and dynamic selection of extrapolation functions, and apply it to the FMI standard. A specific case is studied to demonstrate the effectiveness of the proposed control method.

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