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

这个学院早期职业发展（Career）项目将研究先进燃烧策略的动力学和控制，这些策略有可能将燃料柔性柴油发动机的效率提高20%。在现代车辆中使用替代燃料通常会导致某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的结合有可能解决这一问题，并为交通运输提供高效、清洁的能源。虽然这种策略的好处已经在高度监控的实验室环境中得到了证明，但要在量产车辆中实现这些好处，还需要对多缸发动机的控制进行重大改进。该项目将为复杂的发动机系统创建估计和控制方法。该项目还将为代表性不足的学生提供在交通能源研究这一关键领域工作的机会。由于在恶劣的发动机环境下传感器测量的低可用性，系统的高度非线性和内部耦合行为，以及显著的循环和缸间变化，高级内燃机的控制尤其具有挑战性。为了应对这些挑战，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。随着对动力学的理解的提高，研究团队将创建非线性估计技术，以捕获无法测量的关键变量，这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，该技术可以为具有约束和耦合输入的复杂系统提供最佳性能。虽然可靠的线性控制技术已经存在了几十年，但高度非线性应用（如先进发动机）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些系统将不仅为内燃机应用，而且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有价值的见解。

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.