Goal-Oriented Data-Driven Control for a Holistic Thermal Management System of an Electric Vehicle

This work presents a goal-oriented data-driven control using Bayesian Optimization (BO) to train an MPC in a cascade control scheme. Unlike most works focusing on control parameters, this work focuses on system matrices. The control training scheme is used to develop a controller for a complicated holistic thermal management system (TMS) that provides cooling and lubrication for the electric and mechanical components in one circuit. The control goal is to reduce the total energy consumption instead of reference tracking. Compared to a basic controller, the goal-oriented trained controller reduces the energy cost by up to 1.8% in simulation studies. Thanks to the reduction of the operating temperature, the thermal lifetime of one EM is extended by 5.31 times and that of the other EM by 5.38 times. The TMS can be theoretically analysed so that the systems matrices can be restricted in certain search spaces during BO to avoid contradiction to the physics of the real system. In contrary to the literature, the restriction neither speeds up BO nor achieves a better control goal. Therefore, goal-oriented control with restrictions based on prior knowledge does not guarantee better control for similar systems. Note to Practitioners—A holistic TMS is a complex system to be identified or controlled. Goal-oriented data-driven control makes it possible to design a high-performance controller for it and complex systems alike with less complication. Important to practitioners, the resulting controller reduces energy consumption and extend thermal lifetime of the EM at the same time. Simulation results also show that restricting the search space of BO based on theoretical analysis does not guarantee a controller with better control. It’s, therefore, unnecessary for practitioners to spend extensive effort to analyse the system and provide such restrictions.