E3S Web Conf.
Volume 197, 202075th National ATI Congress – #7 Clean Energy for all (ATI 2020)
|Number of page(s)||12|
|Section||Internal Combustion Engines|
|Published online||22 October 2020|
Engine Valvetrain Lift Prediction Using a Physic-based Model for The Electronic Control Unit Calibration
Università degli Studi di Napoli Federico II Corso Umberto I 40 80138 Napoli, Italy
2 Teoresi S.p.A. via F. Imparato 198, 80146 Napoli, Italy
* Corresponding author: email@example.com
The electronic control has an increasingly important role in the evolution of the internal combustion engine (ICE) and the vehicle. Research in the automotive sector, in this historical period, is dictated by three main guidelines: reducing polluting emissions and fuel consumption while maintaining high performance. The Electronic Control Unit (ECU) has made it possible, complicating the engine both in terms of architecture and in terms of strategies, controlling, through simplified functions, physical phenomena in an ever more precise way. The ECU functions are experimentally calibrated, reducing the error between the quantity estimated by the function and the experimental quantity over the entire operating range of the engine, developing extensive experimental campaigns. The calibration process of the ECU functions is one of the longest and most expensive processes in the development of a new vehicle. Some lines of research have been explored to reduce the experimental tests to be carried out on the test bench. The use of neural networks (NN) has proven to be effective, leading to a reduction in experimental tests from 40 to 60%. Another methodology consists in the use of 1D/0D Thermo-fluid dynamic models of the ICE. These models are used as virtual test benches and through them it is possible to carry out the experimental campaigns necessary for the calibration of the control unit functions. At the real test bench, only the few experimental tests necessary for the validation of the model must be carried out. One of the simplifications that is usually made in the 1D/0D ICE models consists in assigning a single intake and exhaust valve lift, without taking into account the effect of the engine speed on the valve lift in early intake valve closure (EIVC) mode for engines equipped with VVA. This phenomenon has a not negligible effect on engine performance, especially at high engine speeds. In the case of engine models equipped with VVA, the valve lift cannot be imposed, since it is unique for each closing angle at each engine speed. Indeed, in order to assign the correct valve lift for a given engine speed and EIVC, numerous experimental tests should be carried out, making vain the beneficial effects of the method. In this work, the authors propose the use of a 0D/1D CFD model of the entire electro-hydraulic valvetrain VVA module, coupled with 1D lumped mass for reproducing the linear displacements of the intake valve, and for simulating the interactions between flow and mechanical systems of the solenoid hydro-mechanical valve. Thus, model simulations allow to predict the valve lift in all the necessary conditions in the experimental campaigns for the calibration of the control unit functions. Starting from geometric valvetrain data, the model has been validated with a parametric analysis of some variables on which there was greater uncertainty, by comparing the valve lift obtained by the model with the experimental ones in certain engine speeds. Subsequently, the authors have obtained the valve lifts in conditions not used for model validation, comparing them with their respective experimental lifts. The model has proven to be sensitive to the effect of the variation of the engine speed, reproducing the valve lift with a low error. In this way it is possible to reduce the experimental effort aimed to the calibration process considering that the virtual experimental campaign has proven to be reliable.
Key words: Thermo-fluid dynamic analysis / Experimental tests / 1D engine model calibration / ECU base calibration
© The Authors, published by EDP Sciences, 2020
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