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

职业生涯：用于控制电动汽车先进推进和储能系统的新模型降阶框架

• 批准号: 1554063
• 负责人: Marcello Canova
• 金额: $ 50万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-02-01 至 2022-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1554063&HistoricalAwards=false
• 关键词: CAREER; New; Model; Order; Reduction

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.

这个学院早期职业发展（Career）项目将研究一个新的框架，系统地将基于物理的模型的准确性和保真度转化为适合于汽车推进系统控制设计的低阶模型。随着汽车制造商努力提高燃油经济性、排放、安全性和驾驶体验，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来控制需要使用高保真度、基于物理的模型的工程师，尽管大大降低了复杂性。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，以及小型化增压发动机的浪涌动力学估计和控制，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立大学的七叶树电流电动摩托车团队进行整合，该团队的灵感来自电动交通、电动赛车和可持续交通。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，以及与教学工程到K-8 （TEK8）计划合作，以吸引代表性不足的群体的年轻人才。目前基于模型的控制设计几乎完全依赖于基于经验方法和物理系统近似值的低保真度模型，这最终会导致精度的损失。本研究引入了一个变革性的框架，可以从非线性偏微分方程（PDEs）形式的热、流体和化学系统的守恒定律中解析生成面向控制的模型。基于投影的模型降阶（MOR）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中导出，大大减少了校准的需要。投影将在生成的ROM中“先验地”强制执行特定属性，即稳定性、可观察性和可控性。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化增压发动机的喘振动力学估计和控制进行案例研究。