CAREER: Control of Advanced Fuel-Flexible Multi-Cylinder Engines

这一学院早期职业发展(CALEAR)项目将研究先进燃烧策略的动态和控制，这些策略有可能将燃料柔性柴油发动机的效率提高高达20%。在现代车辆中使用替代燃料通常会导致一些污染物的产生增加，以及效率的下降。然而，替代燃料与更先进的燃烧技术相结合，有可能解决这一问题，并为交通运输提供高效、清洁的动力。虽然这一策略的好处已经在高度监控的实验室环境中得到了证明，但在生产车辆实现这些好处之前，还需要在多缸发动机控制方面进行重大改进。该项目将为复杂的发动机系统创建评估和控制方法。该项目还将为未被充分代表的学生提供在这一关键的交通能源研究领域工作的机会。先进内燃机的控制尤其具有挑战性，因为在恶劣的发动机环境中，传感器测量的可用性很低，系统的高度非线性和内部耦合行为，以及循环和气缸之间的显著变化。为了迎接这些挑战，本研究将对多缸先进发动机系统的动力学进行研究和建模。在对动力学有了更好的了解后，研究团队将创建非线性估计技术，捕捉无法获得测量结果的关键变量，以及哪些变量是燃烧变化的主要驱动因素。该项目将最终研究非线性模型预测控制技术，该技术可以为这个具有受限和耦合输入的复杂系统提供最优性能。虽然可靠的线性控制技术已经存在了几十年，但针对诸如先进发动机等高度非线性应用的控制策略还没有很好地建立起来。将当前的非线性控制策略扩展到这类系统，不仅将为内燃机应用提供宝贵的见解，而且将为机器人和混合动力汽车等领域的其他复杂系统结构提供宝贵的见解。

This Faculty Early Career Development (CAREER) project will investigate the dynamics and control of advanced combustion strategies that have the potential to increase the efficiency of fuel-flexible diesel engines by up to 20 percent. The use of alternative fuels in modern vehicles typically results in higher production of some pollutants, as well as a drop in efficiency. However, the combination of alternative fuels along with more advanced combustion techniques has the potential to solve this problem, and provide efficient, clean power for transportation. While the benefits of this strategy have been demonstrated in highly monitored laboratory environments, significant improvements in the control of multi-cylinder engines are needed before these benefits can be realized in production vehicles. This project will create estimation and control methods for complex engine systems. The project will also provide opportunities for underrepresented students to work in this critical area of transportation energy research.The control of advanced combustion engines is particularly challenging due to the low availability of sensor measurements in the harsh engine environment, the highly nonlinear and internally coupled behavior of the system, and significant cycle-to-cycle and cylinder-to-cylinder variations. To meet these challenges, this research will study and model the dynamics of a multi-cylinder advanced engine system. With an improved understanding of the dynamics, the research team will then create nonlinear estimation techniques that capture key variables for which measurements are not available and which are primary drivers in combustion variations. This project will culminate in the investigation of nonlinear model predictive control techniques that can provide optimal performance to this complex system with its constrained and coupled inputs. While reliable linear control techniques have existed for decades, control strategies for highly nonlinear applications such as advanced engines are not well established. Expansion of the current nonlinear control strategies to such systems will provide valuable insight not only for internal combustion engine applications but other complex system structures in areas such as robotics and hybrid vehicles.