Reduction of the experimental effort in engine calibration by using neural networks and 1D engine simulation

Nola, Francesco de; Giardiello, Giovanni; Gimelli, Alfredo; Molteni, Andrea; Muccillo, Massimiliano; Picariello, Roberto; Tornese, Diego

doi:10.1016/j.egypro.2018.08.087

Cited by 13 publications

(13 citation statements)

References 10 publications

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“…The choice of the values of the calibration parameters is part of a much longer process named engine base calibration process [1][2][3][4][5][6][7][8], and is carried out with suitably developed software [1,4], which require an intense experimental campaign as described in [2][3]. In fact, at each single operating point, the software calibrates the functions, by comparing the experimental quantity and the quantity estimated by the function, reducing the error by changing the calibration parameters.…”

Section: Wherementioning

confidence: 99%

“…The purpose of carrying out the experimental campaigns is to provide the calibration software all the quantities necessary, collected in datasheets, to compare them with the quantities estimated by the ECU functions in certain operating conditions. In the past the authors have used two different approaches to try to decrease the experimental tests: the use of neural networks (NN) to extend the experimental campaigns has proven effective, leading to a reduction from 40 to 60% of the experimental tests without affecting the calibration performance [2][3]. The second approach is the use of 1D simulation models of the engine to be used as a virtual test bench in order to obtain all the experimental quantities necessary for the calibration of the functions [2,5].…”

Section: Wherementioning

confidence: 99%

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Engine Valvetrain Lift Prediction Using a Physic-based Model for The Electronic Control Unit Calibration

Giardiello

Gimelli

Nola³

2020

E3S Web Conf.

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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.

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Section: Wherementioning

confidence: 99%

Section: Wherementioning

confidence: 99%

Engine Valvetrain Lift Prediction Using a Physic-based Model for The Electronic Control Unit Calibration

Giardiello

Gimelli

Nola³

2020

E3S Web Conf.

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“…Objective of the optimization problem has been set the minimization of the error between numerical and experimental torque values in the 14 engine operating conditions considered during the experimental activity, as formalized in eq. (1). A genetic optimization algorithm has been used to solve the optimization problem [14,15].…”

Section: Figure 10 Vector Optimization Proceduresmentioning

confidence: 99%

“…Purpose of this study is the set up and calibration of a 1-D thermo-fluid dynamic engine model since the early stage of development of the engine, when limited experimental data are available. This way, preliminary analyses or ECU calibration [1][2][3] could be carried out. The same engine model can be progressively updated and refined as the experimental investigation is performed.…”

Section: Introductionmentioning

confidence: 99%

Early Stage Calibration of a Formula SAE Engine 1-D Fluid Dynamic Model with Limited Experimental Data

et al. 2020

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This work addresses the early stage calibration of a Formula SAE engine 1-D fluid dynamic model starting from limited experimental data. The availability of an engine model since the early stages of the development of a new Formula SAE vehicle allows to carry out preliminary analyses or ECU calibration. A few experimental tests have been executed at wide open throttle and variable engine speed. Then, a 1D thermo-fluid dynamic engine model has been developed starting from the geometry data of the engine. A vector optimization problem has been then solved to calibrate the engine model. In particular, the error minimization between numerical and experimental values of the torque in different engine operating conditions has been set as objective of the optimization process. Finally, starting from the results of the proposed calibration methodology, a decision-making criterion allowed the identification of a single optimal solution within the Pareto optimal front together with the related values for the set of calibration parameters. The results highlight how the proposed calibration procedure could be usefully adopted to set an early stage engine model which could be properly adopted to preliminarily detect the effects of geometric changes or control parameters variations on the main engine performance.

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“…Some authors [22] proposed two effective methodologies to overcome some critical issues concerning the base calibration of engine control parameters. Specifically, neural networks and 1-D CFD simulation were alternatively adopted to reliably calibrate specific ECU functions starting from a reduced number of experimental data.…”

Section: State Of the Artmentioning

confidence: 99%

Virtual Calibration Method for Diesel Engine by Software in The Loop Techniques

Cameretti

Landolfi²,

Tesone³

et al. 2019

IJAME

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The calibration of the engine control unit is increased for the development of the whole automotive system. The aim is to calibrate the electronic engine control to match the decreasing emission requirements and increasing fuel economy demands. The reduction of the number of tests on vehicles represents one of the most important requirements for increasing efficiency of the engine calibration process. However, the definition of the design of experiment is not straightforward because the data is not known beforehand, so it is difficult to process and analyse this data to achieve a globally valid model. To reduce time effort and costs the virtual calibration can be a valid solution. This procedure is called software in the loop (SIL) calibration able to develop a process to systematically identify the optimal balance of engine performance, emissions and fuel economy. In this work, a virtual calibration methodology is presented by using a two-stage model to get minimum exhaust emissions of a diesel engine. The data used are from a GT-Power model of a 3L supercharged diesel engine. The model is able to calculate the engine emissions for different engine parameters (such as the start of injection, EGR fraction and rail pressure) and from optimisation process, new injection start maps that reduce pollutant emissions are created.

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Reduction of the experimental effort in engine calibration by using neural networks and 1D engine simulation

Cited by 13 publications

References 10 publications

Engine Valvetrain Lift Prediction Using a Physic-based Model for The Electronic Control Unit Calibration

Engine Valvetrain Lift Prediction Using a Physic-based Model for The Electronic Control Unit Calibration

Early Stage Calibration of a Formula SAE Engine 1-D Fluid Dynamic Model with Limited Experimental Data

Virtual Calibration Method for Diesel Engine by Software in The Loop Techniques

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