CAREER: A New Model Order Reduction Framework for the Control of Advanced Propulsion and Energy Storage Systems for Electrified Vehicles

这个教师早期职业发展（CAREER）项目将研究一种新的框架，该框架系统地将基于物理模型的准确性和保真度转换为适用于汽车推进系统控制设计的低阶模型。随着制造商努力提高燃油经济性、排放、安全性和驾驶员体验，汽车推进技术变得相当复杂。这种复杂性要求采用先进的控制和估计算法。该项目将提供必要的工具，以控制工程师谁需要采用高保真，基于物理的模型，虽然大大降低了复杂性。该研究将直接应用于高性能电动汽车锂离子电池组的热不平衡估计，以及小型增压发动机的喘振动态估计和控制，这是当前和未来汽车动力系统的两项关键技术。汽车行业将通过提供见解和促进技术转让来参与研究。研究，教育和推广将通过俄亥俄州州立大学的Buckeye Current电动摩托车团队进行整合，灵感来自电子移动，电子赛车和可持续交通。整合还将通过丰富现有课程，创建基于网络的教学工具和学生实习计划，并通过与K-8教学工程（TEK 8）计划合作，吸引来自代表性不足群体的年轻人才。基于模型的控制设计的最新技术几乎完全依赖于基于经验方法和物理系统近似的低保真度模型，这最终导致精度的损失。本研究介绍了一个变革的框架，分析生成面向控制的模型，从非线性偏微分方程（PDE）形式的热，流体和化学系统的守恒定律。基于投影的模型降阶（莫尔）将被应用于生成降阶模型（ROM），其参数通过分析关系直接从物理常数导出，从而显著减少校准的需要。投影将在所得到的ROM中强制执行特定的属性“先验”，即稳定性，可观测性和可控性。将对高性能电动汽车锂离子电池组的热不平衡估计以及小型增压发动机的喘振动态估计和控制进行案例研究。

This Faculty Early Career Development (CAREER) project will investigate a novel framework that systematically transfers the accuracy and fidelity of physics-based models into low-order models suitable for control design of automotive propulsion systems. Automotive propulsion technologies have become considerably complex as manufacturers strive to improve fuel economy, emissions, safety and the driver experience. Such complexity requires the adoption of advanced control and estimation algorithms. The project will provide the necessary tools to control engineers who need to employ high-fidelity, physics-based models, albeit with considerably reduced complexity. The research will be directly applied to the estimation of thermal imbalances in Li-ion battery packs for high-performance electric vehicles, and to the estimation and control of surge dynamics in downsized boosted engines, two key technologies for current and future automotive powertrains. The automotive industry will participate to the research by providing insights and facilitating technology transfer. Research, education and outreach will be integrated through the Buckeye Current electric motorcycle team at The Ohio State University, inspired by e-mobility, e-racing and sustainable transportation. Integration will be also pursued through the enrichment of existing courses, creation of web-based teaching tools and student internship programs, and by collaborating with the Teaching Engineering to K-8 (TEK8) programs to attract young talents from underrepresented groups.The state of the art for model-based control design relies almost exclusively on low-fidelity models based on empirical approaches and on approximations of the physical system, which ultimately leads a loss of accuracy. This research introduces a transformative framework that analytically generates control-oriented models from the conservation laws for thermal, fluid and chemical systems in nonlinear Partial Differential Equations (PDEs) form. A projection-based Model Order Reduction (MOR) will be applied to generate Reduced Order Models (ROM) whose parameters are directly derived from physical constants through analytical relations, dramatically reducing the need for calibration. The projection will enforce specific properties "a priori" in the resulting ROM, namely stability, observability and controllability. Case studies will be conducted on the estimation of thermal imbalances in Li-ion battery packs for high-performance electric vehicles, and the estimation and control of surge dynamics in downsized boosted engines.