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

全球每年生产约2亿台内燃机(ICE)，用于能源、交通和服务行业。此外，冰占美国总能源消耗的22%以上，在城市地区产生的二氧化碳温室气体排放量最大。先进低温燃烧模式的双燃料天然气(NG)发动机代表了最先进的内燃机技术，与传统发动机相比，具有最高的燃料转换效率和25%的二氧化碳排放。然而，由于这些发动机的高度非线性和不确定的动态行为，使用现有的控制技术在很大的工作范围内实现这些发动机的稳健和高效性能是不可能的。本研究旨在开发用于非线性系统动态建模和控制的基础工具，并将其应用于高效、低排放的先进冰。该项目将通过三个主要影响领域产生广泛的社会效益：第一，通过推动研究非线性控制系统以及混合和反应性流动，包括燃烧系统；第二，通过直接惠及发电、汽车、机车、海洋、石油和天然气钻探、建筑、公用事业和制造业中常用的内燃机；第三，通过在工业现场、地方社区和科博会举办教育和外联活动。该项目是密歇根理工大学、佐治亚大学和行业合作伙伴康明斯公司的共同努力。该项目旨在开发一套创新的面向控制的建模和随机预测控制设计工具，以应对先进的双燃料天然气发动机以及广泛的其他非线性和随机动态系统的控制挑战。该项目的成果包括六个主要部分：(I)描述先进燃烧状态下双燃料天然气发动机的动力学特性，(Ii)建立第一个基于物理的先进双燃料天然气发动机面向控制的模型，(Iii)通过机器学习和经典多元方法的强大融合，开发用于推导模型的新的分析工具，(Iv)提供解决方案，以填补第一原理模型和用于估计准确模型的数据驱动方法之间的空白，(V)弥合参数变化系统和随机控制之间的差距，以及(Vi)构造、测试、对双燃料天然气发动机的燃烧控制器进行了验证。这六项理论、建模和实验贡献的成果将是在发动机试验台上演示的用于非线性和随机工业系统的通用动态建模和预测控制设计工具。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.