The emerging trend of vehicle electrification is transforming the transportation industry by replacing traditional mechanical and hydraulic components with higher performing, more reliable, and more efficient electrical components. However, the introduction of a complex electrical network onboard mobile systems poses significant challenges for control design. A notable challenge is the coordination of multi-domain and multi-timescale system dynamics. This article seeks to address this challenge through the design and validation of a model predictive controller for a hybrid unmanned aerial vehicle powertrain. A multi-domain extension of the graph-based modeling framework is formulated and used to model the multi-physics behavior of the air vehicle. An extensive model validation procedure is performed and the validated graph model is used to develop two control strategies: one baseline and one predictive controller. To coordinate multi-timescale system dynamics, the predictive controller leverages a hierarchical control architecture to plan a battery state of charge bound. The control strategies are experimentally validated and show that the advanced controller yields improvements in performance and reliability metrics while reducing fuel consumption by $\sim 10\text{\%}$.

车辆电气化的新兴趋势正在改变交通运输行业，用更高性能、更可靠和更高效的电气元件取代传统的机械和液压元件。然而，在车载移动系统中引入复杂的电气网络对控制设计提出了重大挑战。一个显着的挑战是多域和多时间尺度系统动力学的协调。本文旨在通过设计和验证混合无人机动力系统的模型预测控制器来解决这一挑战。制定了基于图的建模框架的多域扩展，并用于对飞行器的多物理场行为进行建模。执行广泛的模型验证程序，并使用经过验证的图形模型来开发两种控制策略：一种基线和一种预测控制器。为了协调多时间尺度系统动态，预测控制器利用分层控制架构来规划电池充电状态。控制策略经过实验验证，表明先进的控制器可提高性能和可靠性指标，同时将油耗降低$～10\text{\%}$.