论文标题
具有模型预测控制的功率和能源系统的工厂和控制器优化
Plant and Controller Optimization for Power and Energy Systems with Model Predictive Control
论文作者
论文摘要
本文探讨了电气迁移率的植物特征和控制器参数的优化。汽车和飞机等移动运输系统的电气化具有提高关键性能指标(例如效率和成本)的能力。但是,新组件中电气和热动力学之间的强双向耦合会引起整合挑战,增加组件降解并降低性能。减少这些问题需要新颖的植物设计和控制策略。电气迁移率文献提供了有关植物和控制器优化的先前研究,称为控制共同设计(CCD)。这些研究中的一个空隙是缺乏模型预测控制(MPC),该模型被公认为在CCD框架内管理电气系统的多域动力学。本文通过三项贡献解决了这一点。首先,开发了适用于植物优化和MPC的热机电混合动力汽车(HEV)模型。其次,针对此多域系统执行同时的植物和控制器优化。第三,使用候选HEV模型将MPC集成在CCD框架中。结果表明,同时优化植物和MPC参数可以将物理组件大小降低60%以上,关键性能度量误差以上超过50%。
This article explores the optimization of plant characteristics and controller parameters for electrified mobility. Electrification of mobile transportation systems, such as automobiles and aircraft, presents the ability to improve key performance metrics such as efficiency and cost. However, the strong bidirectional coupling between electrical and thermal dynamics within new components creates integration challenges, increasing component degradation and reducing performance. Diminishing these issues requires novel plant designs and control strategies. The electrified mobility literature provides prior studies on plant and controller optimization, known as control co-design (CCD). A void within these studies is the lack of model predictive control (MPC), recognized to manage multi-domain dynamics for electrified systems, within CCD frameworks. This article addresses this through three contributions. First, a thermo-electro-mechanical hybrid electric vehicle (HEV) model is developed that is suitable for both plant optimization and MPC. Second, simultaneous plant and controller optimization is performed for this multi-domain system. Third, MPC is integrated within a CCD framework using the candidate HEV model. Results indicate that optimizing both the plant and MPC parameters simultaneously can reduce physical component sizes by over 60% and key performance metric errors by over 50%.