A Model-Based Computer Aided Calibration Methodology Enhancing Accuracy, Time and Experimental Effort Savings Through Regression Techniques and Neural Networks

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

doi:10.4271/2017-24-0054

Cited by 13 publications

(6 citation statements)

References 26 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%

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%

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 identification criterion, expressing the minimum distance from the axes origin, is formalized in eq. (2). The optimized values of the calibration parameters have been reported in TABLE 5.…”

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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“…To achieve these goals, the automotive industries developed complex engines configuration adopting innovative solutions, such as Variable Valve Actuation [2], Exhaust Gas Recirculation [3], Gasoline Direct Injection, turbocharging and powertrain hybridization, that allow advanced management strategies. The efficient operation of such complex engines depends on the accuracy of the ECU calibration [4,5]: the increased number of engine control parameters that have to be adjusted and optimized led to an exponential growth of the experimental effort required for the base calibration process. The authors have already adopted an effective methodology based on a 0D -1D CFD engine modeling [6,7] to reduce the experimental activity within the base calibration process through the simulation of the engine thermo-fluid dynamic behavior [8][9][10]: a reduced number of experimental data, measured at the test bench, are used to calibrate a 0D-1D CFD engine model and a friction experimental relationship.…”

Section: Introductionmentioning

confidence: 99%

Crank Mechanism Friction Modeling for Control-Oriented Applications

Brancati

Muccillo

Tufano

2020

Mechanisms and Machine Science

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Purpose of the study is to improve the base calibration process of engine Electronic Control Unit (ECU) through the use of 0D-1D CFD engine models coupled to a physics-based model of the engine frictions. Once reliably calibrated, the overall engine model can be used to reduce the experimental effort of the base calibration process. In this paper a specific methodology based on the vector optimization approach is also proposed in order to reliably calibrate the numerical friction model. This methodology was applied to an engine crank mechanism model adopting specific friction sub models, generated using a multibody approach. The vector optimization problem was solved using experimental engine friction data measured in 13 operating conditions: the comparison between numerical results and experimental data highlights how the proposed modelling approach can be effectively adopted to simulate the friction losses, since the average error is about 5%. Therefore, coupling reliably calibrated friction models to 0D-1D CFD engine models could be useful to improve the estimation of the main engine quantities to be used in the base calibration process.

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A Model-Based Computer Aided Calibration Methodology Enhancing Accuracy, Time and Experimental Effort Savings Through Regression Techniques and Neural Networks

Cited by 13 publications

References 26 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

Crank Mechanism Friction Modeling for Control-Oriented Applications

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