Date of Award

8-2017

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Automotive Engineering

Committee Member

Dr. Robert Prucka, Committee Chair

Committee Member

Dr. Zoran Filipi, Committee Co-Chair

Committee Member

Dr. Mark Hoffman

Committee Member

Dr. Simona Onori

Abstract

Low-displacement turbocharged spark-ignition engines have become the dominant choice of auto makers in the effort to meet the increasingly stringent emission regulations and fuel efficiency targets. Low-Pressure cooled Exhaust Gas Recirculation introduces important efficiency benefits and complements the shortcomings of highly boosted engines. The main drawback of these configurations is the long air-path which may cause over-dilution limitations during transient operation. The pulsating exhaust environment and the low available pressure differential to drive the recirculation impose additional challenges with respect to feed-forward EGR estimation accuracy. For these reasons, these systems are currently implemented through calibration with less-than-optimum EGR dilution in order to ensure stable operation under all conditions. However, this technique introduces efficiency penalties. Aiming to exploit the full potential of this technology, the goal is to address these challenges and allow operation with near-optimum EGR dilution. This study is focused on three major areas regarding the implementation of Low-Pressure EGR systems:

 Combustion effects, benefits and constraints

 System optimization and transient operation

 Estimation and adaptation

Results from system optimization show that fuel efficiency benefits range from 2% – 3% over drive cycles through pumping and heat loss reduction, and up to 16% or

more at higher loads through knock mitigation and fuel enrichment elimination. Soot emissions are also significantly reduced with cooled EGR. Regarding the transient challenges, a methodology that correlates experimental data with simulation results is developed to identify over-dilution limitations related to the engine’s dilution tolerance. Different strategies are proposed to mitigate these issues, including a Neural Network-actuated VVT that controls the internal residual and increases the over-dilution tolerance by 3% of absolute EGR. Physics-based estimation algorithms are also developed, including an exhaust pressure/temperature model which is validated through real-time transient experiments and eliminates the need for exhaust sensors. Furthermore, the installation of an intake oxygen sensor is investigated and an adaptation algorithm based on an Extended Kalman Filter is created. This algorithm delivers short-term and long-term corrections to feed-forward EGR models achieving a final estimation error of less than 1%. The combination of the proposed methodologies, strategies and algorithms allows the implementation of near-optimum EGR dilution and translates to fuel efficiency benefits ranging from 1% at low-load up to 10% at high-load operation over the current state-of-the-art.

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