GOALI/Collaborative Research: Control-Oriented Modeling and Predictive Control of High Efficiency Low-emission Natural Gas Engines

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About 200 million internal combustion engines (ICEs) are produced in the world every year and used in energy, transport and service sectors. Furthermore, ICEs account for over 22% of the U.S. total energy consumption and produce the largest portion of CO2 greenhouse gas emissions in urban areas. Dual fuel natural gas (NG) engines in advanced low temperature combustion regimes represent the state-of-the-art ICE technology with some of the highest reported fuel conversion efficiencies and 25% lower CO2 emissions compared to conventional engines. However, achieving a robust and high-efficiency performance of these engines on a broad operational range using existing control technologies is not possible due to their highly nonlinear and uncertain dynamic behavior. This research aims at developing fundamental tools for dynamic modeling and control of nonlinear systems and applying them to high-efficiency low-emission advanced ICEs. The project will provide wide-ranging societal benefits through three major impact areas: first, by advancing research in nonlinear control systems, and mixing and reactive flow including combustion systems; second, by providing direct benefits for control of combustion engines, commonly used in power generation, automotive, locomotive, marine, oil and gas drilling, construction, utilities and manufacturing industries; and third, through educational and outreach activities delivered at industry sites, local communities and science fairs. This project is a collaborative effort between Michigan Technological University, University of Georgia, and the industry partner, Cummins Inc. The project intends to develop a suite of innovative control-oriented modeling and stochastic predictive control design tools to address control challenges for advanced dual fuel natural gas engines, as well as a broad range of other nonlinear and stochastic dynamic systems. The outcomes of this project result in six main components that include: (i) characterizing the dynamics of dual fuel NG engines in advanced combustion regimes, (ii) building the first physics-based control-oriented model for advanced dual fuel NG engines, (iii) developing new analytical tools for deriving models through the powerful fusion of machine learning and classical multivariate methods, (iv) providing solutions to fill the gaps between first-principles models and data-driven methods for estimating an accurate model, (v) bridging the gaps between parameter-varying systems and stochastic controls, and (vi) constructing, testing, and validating the combustion controllers for dual fuel NG engines. The outcomes from these six theoretical, modeling and experimental contributions will be generic dynamic modeling and predictive control design tools for nonlinear and stochastic industrial systems that are demonstrated on engine test-beds.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.