In-Cylinder Pressure Estimation from Rotational Speed Measurements via Extended Kalman Filter

Quartullo, Renato; Garulli, Andrea; Giannitrapani, Antonio; Minamino, Ryota; Vichi, Giovanni

doi:10.3390/s23094326

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…In this way, considerable computational efforts are required to optimize the engine performance, thus leading to a dramatic increase in operating times and costs [6]. Therefore, the main efforts of automotive researchers have aimed to discover advanced technologies capable of effectively monitoring the engine parameters [7][8][9]. Machine learning (ML) approaches are increasingly proposed in many automotive applications such as virtual sensors [10,11], fault diagnosis systems [12], and performance optimizations [13] for real-time and low-cost hardware implementation and compact configuration [14].…”

Section: Introductionmentioning

confidence: 99%

NARX Technique to Predict Torque in Internal Combustion Engines

et al. 2023

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To carry out increasingly sophisticated checks, which comply with international regulations and stringent constraints, on-board computational systems are called upon to manipulate a growing number of variables, provided by an ever-increasing number of real and virtual sensors. The optimization phase of an ICE passes through the control of these numerous variables, which often exhibit rapidly changing trends over time. On the one hand, the amount of data to be processed, with narrow cyclical frequencies, entails ever more powerful computational equipment. On the other hand, computational strategies and techniques are required which allow actuation times that are useful for timely and optimized control. In the automotive industry, the ‘machine learning’ approach is becoming one the most used approaches to perform forecasting activities with reduced computational effort, due to both its cost-effectiveness and its simple and compact structure. In the present work, the nonlinear dynamic system we address is related to the torque estimation of an ICE through a nonlinear autoregressive with exogenous inputs (NARX) approach. Preliminary activities were performed to optimize the neural network in terms of neurons, hidden layers, and the number of input parameters to be assessed. A Shapley sensitivity analysis allowed quantification of the impact of each variable on the target prediction, and therefore, a reduction in the amount of data to be processed by the architecture. In all cases analyzed, the optimized structure was able to achieve average percentage errors on the target prediction that were always lower than a critical threshold of 10%. In particular, when the dataset was augmented or the analyzed cases merged, the architecture achieved average prediction errors of about 1%, highlighting its remarkable ability to reproduce the target with fidelity.

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Section: Introductionmentioning

confidence: 99%

NARX Technique to Predict Torque in Internal Combustion Engines

et al. 2023

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Control of Combustion Phasing Using Accelerometer-Based Non-Intrusive Sensing

Govind Raju,

Reisetter,

Miganakallu

et al. 2024

ASME Letters in Dynamic Systems and Control

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Measuring the combustion phasing of an engine using in-cylinder pressure sensors is well established. However, pressure sensors need to be directly exposed to the in-cylinder environment, requiring changes to the cylinder head. Several methods have been proposed for sensing combustion phasing non-intrusively by mounting an accelerometer on the engine block. This paper presents real-time control of combustion phasing in a compression-ignition engine using non-intrusive accelerometer-based sensing during a dynamic fuel switch. A systematic data-driven control framework capable of handling fuel switching in real-time is used. The control is designed based on the pressure data, and the real-time implementation is performed using the accelerometer signal. Results from combustion phasing tracking experiments performed on a compression-ignition engine are presented.

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In-Cylinder Pressure Estimation from Rotational Speed Measurements via Extended Kalman Filter

Cited by 2 publications

References 33 publications

NARX Technique to Predict Torque in Internal Combustion Engines

NARX Technique to Predict Torque in Internal Combustion Engines

Control of Combustion Phasing Using Accelerometer-Based Non-Intrusive Sensing

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