Current Research

Random Fuel Adaptation of Spark-Ignition Internal Combustion Engines Using Model-Based Combustion Phasing Prediction

Principal investigator: 

Robert Prukca, Mechanical Engineering

Graduate student:

Baitao Xiao

Brief abstract: 

The objective of this research is to develop a control-oriented combustion model that regulates combustion phasing in a spark-ignition engine operating on a random mix of input fuels.  This research utilizes advanced engine simulations and experimental data to develop a robust physics-based feed-forward control algorithm for spark timing prediction that is sensitive to fuel type.  Proposed control models are incorporated into a rapid-prototype engine control system and experimentally evaluated under transient conditions in the dynamometer cell.

Impact: 

Random fuel adaptation of internal combustion engines is a technology that would facilitate a reduction in automotive biofuel processing, encourage locally-appropriate biofuel production, and allow new fuel formulations to enter the market with minimal infrastructure impediment.  The combination of these aspects could help make the production of biofuels cost-competitive with other transportation fuels, lessen dependence on foreign sources of energy, and reduce life-cycle greenhouse gas emissions from automotive transportation; all of which are pivotal societal issues.