This paper develops a hierarchical control framework to manage both the electrical and thermal domains of an automotive electric vehicle (EV). Batteries, electric machines, and power electronics all have desired thermal operating ranges, with operation outside these limits leading to reduced component performance and lifespan. Previous studies present various component- and high-level energy management algorithms that seek to maintain desired temperatures. However, the literature contains limited efforts to develop comprehensive control approaches that coordinate the electrothermal dynamics within the vehicle, ensuring that electrical systems do not generate more thermal energy than can be managed within temperature constraints. To address this gap, this paper presents a hierarchical control framework that governs electrical and thermal states across multiple timescales while meeting operational requirements, such as tracking a desired vehicle velocity and cabin temperature. To develop this framework, a network of communicating model predictive controllers coordinates the system dynamics, with significant reduction in computational complexity over a centralized control approach. A graph-based model of the candidate EV powertrain is developed and then decomposed to generate models used in each controller of the hierarchical framework. Through the case study of this paper, it is demonstrated that the hierarchical controller can make important trade-offs between tracking desired operational references and maintaining temperatures within constraints.
Hierarchical predictive control of Wastewater Treatment Plants
