Date of Award

2016

Document Type

Open Access Master's Thesis

Degree Name

Master of Science in Mechanical Engineering (MS)

Administrative Home Department

Department of Mechanical Engineering-Engineering Mechanics

Advisor 1

Jeffrey D. Naber

Committee Member 1

Jeffrey B. Burl

Committee Member 2

Scott A. Miers

Committee Member 3

Mahdi Shahbakhti

Abstract

An accurate estimation of cycle by cycle in-cylinder mass and the composition of the combustion chamber charge is required for engine control strategies to meet stringent pollution emission and fuel consumption regulations. Estimation of fresh charge and residual gas masses is beneficial in terms of fuel efficiency, tailpipe emissions, engine performance, for engine control strategies. Air-flow meter, which is mounted in the intake air circuit, can be utilized in a closed-loop strategy to control air charge. However, air flow meter has a response delay; moreover dynamics of intake manifold and pipes must be taken into consideration to improve the estimation of air charge and accurate feedback in transients. As an alternative to air flow meter, in-cylinder pressure sensors can be utilized to directly measure cylinder pressure, based on which, the amount of air charge can be estimated without the requirement to model the dynamics of the manifold.

In this work, an air charge estimation algorithm is proposed, which uses cylinder pressure trace data at specific cycle events, and by applying thermodynamics and heat transfer relationships, estimates individual cylinder air charge for each cycle in different test conditions. A residual gas estimator, which can be applied online, is also incorporated in the algorithm to estimate residual gas mass for each cycle. Estimator output is validated and calibrated based on experimental setup air charge, which is calculated from the amount of injected fuel in each cylinder and individual wide-band sensor data.

Uncertainty propagation analysis is performed to investigate the uncertainty in estimated air charge based on the uncertainties in measured and model parameters. This analysis reveals the information about the parameters with major contribution to the uncertainty in estimated air charge.

Available for download on Wednesday, August 01, 2018

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