Experimental Validation of Graph-Based Hierarchical Control for Thermal Management

Abstract

This paper proposes and experimentally validates a hierarchical control framework for fluid flow systems performing thermal management in mobile energy platforms. A graph-based modeling approach derived from the conservation of mass and energy inherently captures coupling within and between physical domains. Hydrodynamic and thermodynamic graph-based models are experimentally validated on a thermal-fluid testbed. A scalable hierarchical control framework using the graph-based models with model predictive control (MPC) is proposed to manage the multidomain and multi-timescale dynamics of thermal management systems. The proposed hierarchical control framework is compared to decentralized and centralized benchmark controllers and found to maintain temperature bounds better while using less electrical energy for actuation.

Plant and Controller Optimization for Power and Energy Systems With Model Predictive Control

“…This section reviews the formulation of the centralized MPC problem [27,53]. This formulation is used to control the HEV powertrain energy dynamics within the design optimization process.…”

Nonlinear Hierarchical MPC With Application to Aircraft Fuel Thermal Management Systems

“…However, this article primarily considers hierarchical MPC approaches where MPC is used at multiple levels of the hierarchy, making coordination between MPC controllers critical. Hierarchical control with multiple levels of MPC controllers has been investigated for applications such as coordination of electricity generators [12], energy management in electric or hybrid vehicles [18], and thermal management [27], in particular for aircraft [6], [7]. It should be noted that the vast majority of existing works in theoretical hierarchical MPC formulations are limited to linear system models [7], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18] to provide guarantees such as recursive feasibility [10], [12], [13] and stability [11], [13], [14], [15] and, thus, are not directly applicable to this work considering nonlinear system models.…”

“…The work in [15] differs slightly from this paradigm in that the upper level sends to the lower level a piecewise constant reference signal that is part of the lower level control input, augmented with the output of a stabilizing linear controller. In [6] and [7], the upper level MPC computes reference state trajectories to be tracked by a lower level MPC. The reference tracking mechanism is changed to waypoint tracking in [10], where the optimal state computed by the upper level for its next time step is used by the lower level MPC as a terminal state constraint.…”

“…It should be noted though that El Chamie and Lin [22] considered a time-varying mass flow rate of fuel sent to the engine, whereas this work considers a constant flow rate. Pangborn et al [6] used linear hierarchical MPC for coordinated control of several aircraft subsystems including the FTMS. The authors show that the hierarchical controller achieves far fewer constraint violations compared to PI controllers while needing less control effort.…”

Aircraft fuel thermal management system and flight thermal endurance

“…It is estimated that the avionic system of fourth generation aircraft such as the Lockheed Martin F22 can generate as much as 100 kW of thermal energy (Dooley et al 2013;Behbahani et al 2016;Mao et al 2019), and this number is expected to increase for the future generations of aircraft (Roberts and Decker 2014). Furthermore, the development of compact avionic systems along with advances in directed energy weapons and radars are expected to add to this thermal stress (Pangborn et al 2018). Freeman et al (2014) outlined the challenges of thermal management for new generation, so-called more-electric, hybrid-electric and all-electric aircraft, illustrating that a state-of-the-art non-superconducting electric motor alone would generate 55 kW of heat based on a very conservative efficiency of 95%.…”