Decentralized Model Predictive Control of a Multiple Evaporator HVAC System

Vapor compression cooling systems are the primary method used for refrigeration and air conditioning, and as such are a major component of household and commercial building energy consumption. Application of advanced control techniques to these systems is still a relatively unexplored area, and has the potential to significantly improve the energy efficiency of these systems, thereby decreasing their operating costs. This thesis explores a new method of decentralizing the capacity control of a multiple evaporator system in order to meet the separate temperature requirements of two cooling zones. The experimental system used for controller evaluation is a custom built small-scale water chiller with two evaporators; each evaporator services a separate body of water, referred to as a cooling zone. The two evaporators are connected to a single condenser and variable speed compressor, and feature variable water flow and electronic expansion valves. The control problem lies in development of a control architecture that will chill the water in the two tanks (referred to as cooling zones) to a desired temperature setpoint while minimizing the energy consumption of the system. A novel control architecture is developed that relies upon time scale separation of the various dynamics of the system; each evaporator is controlled independently with a model predictive control (MPC) based controller package, while the compressor reacts to system conditions to supply the total cooling required by the system as a whole. MPC’s inherent constraint-handling capability allows the local controllers to directly track an evaporator cooling setpoint while keeping superheat within a tight band, rather than the industrially standard approach of regulating superheat directly. The compressor responds to system conditions to track a pressure setpoint; in this configuration, pressure serves as the signal that informs the compressor of cooling demand changes. Finally, a global controller is developed that has knowledge of the energy consumption characteristics of the system. This global controller calculates the setpoints for the local controllers in pursuit of a global objective; namely, regulating the temperature of a cooling zone to a desired setpoint while minimizing energy usage

Cascaded Control for Improved Building HVAC Performance

As of 2011 buildings consumed 41% of all primary energy in the U.S. and can represent more than 70% of peak demand on the electrical grid. Usage by this sector has grown almost 50% since the 1980s and projections foresee an additional growth of 17% by 2035 due to increases in population, new home construction, and commercial development. Three-quarters of building energy is derived from fossil fuels making it a large contributor of the country’s CO2 and NOx output both of which greatly affect the environment and local air quality. Up to half of energy used by the building sector is related to Heating, Ventilation, and Air-Condition systems. Focusing on improving building HVAC control therefore has a large aggregate effect on US energy usage with economic and environmental benefits for end users. This dissertation develops cascaded loop architectures as a solution to common HVAC control issues. These systems display strong load-dependent nonlinearities and coupling behaviors that can lead to actuator hunting (sustained input oscillations) from standard PI controllers that waste energy and cost money. Cascaded loops offer a simple way to eliminate hunting and decouple complex HVAC systems with minimal a priori knowledge of system dynamics. As cascaded loops are easily implementable in building automation systems they can be readily and widely adopted in the field. An examination of the current state of PI control in HVAC and discussion of coordinated, optimal control strategies being developed for reduced energy usage are discussed in Chapter 1. The following two chapters outline the structure and benefits of the cascaded architecture and demonstrate the same using a series of simulation case studies. Implementation approaches and parameterizations of the architecture are explored in Chapter 4 with a derivation showing that the addition of an additional feedback path (i.e., inner loop control) provides more design freedom and ultimately allows for improved control. Finally, Chapter 5 details results from initial cascaded loop implementation at three campus buildings. Results showed improved control performance and an elimination of identified hunting behavior

Dynamic modeling and control of transcritical vapor compression system for battery electric vehicle thermal management

Electrification is an increasing trend among vehicle systems such as aircrafts, heavy machinery, and civilian transportation. Battery electric vehicles (BEVs) are one such development that use a battery pack to generate electrical energy used to propel the vehicle and power its auxiliaries. However, the battery pack also generates thermal energy as a byproduct which affects the electrical performance of the battery pack. The inherent coupling between electrical and thermal performance creates a challenge in design and control of these complex systems. Furthermore, phase-out of common refrigerants drives interest in CO2 refrigerant, an environmentally friendly and safe alternative. However, these vapor compression systems operate transcritical, thus requiring novel control techniques. This thesis develops a framework for architecture and control design of BEV subsystems. The foundation of this process is the development of multi-domain models. Models for the transcritical vapor compression system and the vehicle cabin are derived from a first principles analysis. A model for a battery pack is derived from an equivalent circuit electrical model and a conservation of energy thermal model. All of the models capture dynamic, nonlinear behaviors important for control development and understanding of coupling between variables. Additionally, the models are scalable and able to be parameterized in order to represent many variations of system architectures. An air-cooled cabin and air-cooled battery pack configuration is demonstrated in open-loop and closed-loop simulations. For closed loop simulation, a model predictive controller (MPC) is compared to baseline decentralized, proportional-integral controllers. The model predictive control makes control decisions based on the minimization of a cost function that weights the regulation of specific variables (such as temperature of the battery pack and cabin) and power consumption of the actuators. It will be shown that the MPC, in the face of disturbances, is able to maintain outputs within their bounds while consuming less energy than baseline controllers

Learning-Based Controller Design with Application to a Chiller Process

In this thesis, we present and study a few approaches for constructing controllers for uncertain systems, using a combination of classical control theory and modern machine learning methods. The thesis can be divided into two subtopics. The first, which is the focus of the first two papers, is dual control. The second, which is the focus of the third and last paper, is multiple-input multiple-output (MIMO) control of a chiller process. In dual control, the goal is to construct controllers for uncertain systems that in expectation minimize some cost over a certain time horizon. To achieve this, the controller must take into account the dual goals of accumulating more information about the process, by applying some probing input, and using the available information for controlling the system. This is referred to as the exploration-exploitation trade-off. Although optimal dual controllers in theory can be computed by solving a functional equation, this is usually intractable in practice, with only some simple special cases as exceptions. Therefore, it is interesting to examine methods for approximating optimal dual control. In the first paper, we take the approach of approximating the value function, which is the solution of the functional equation that can be used to deduce the optimal control, by using artificial neural networks. In the second paper, neural networks are used to represent and estimate hyperstates, which contain information about the conditional probability distributions of the system uncertainties. The optimal dual controller is a function of the hyperstate, and hence it should be useful to have a representation of this quantity when constructing an approximately optimal dual controller. The hyperstate transition model is used in combination with a reinforcement learning algorithm for constructing a dual controller from stochastic simulations of a system model that includes models of the system uncertainties. In the third paper, we suggest a simple reinforcement learning method that can be used to construct a decoupling matrix that allows MIMO control of a chiller process. Compared to the commonly used single-input single-output (SISO) structures, these controllers can decrease the variations in some system signals. This makes it possible to run the system at operating points closer to some constraints, which in turn can enable more energy-efficient operation

Parameter Estimation of Dynamic Air-conditioning Component Models Using Limited Sensor Data

This thesis presents an approach for identifying critical model parameters in dynamic air-conditioning systems using limited sensor information. The expansion valve model and the compressor model parameters play a crucial role in the system model's accuracy. In the past, these parameters have been estimated using a mass flow meter; however, this is an expensive devise and at times, impractical. In response to these constraints, a novel method to estimate the unknown parameters of the expansion valve model and the compressor model is developed. A gray box model obtained by augmenting the expansion valve model, the evaporator model, and the compressor model is used. Two numerical search algorithms, nonlinear least squares and Simplex search, are used to estimate the parameters of the expansion valve model and the compressor model. This parameter estimation is done by minimizing the error between the model output and the experimental systems output. Results demonstrate that the nonlinear least squares algorithm was more robust for this estimation problem than the Simplex search algorithm. In this thesis, two types of expansion valves, the Electronic Expansion Valve and the Thermostatic Expansion Valve, are considered. The Electronic Expansion Valve model is a static model due to its dynamics being much faster than the systems dynamics; the Thermostatic expansion valve model, however, is a dynamic one. The parameter estimation algorithm developed is validated on two different experimental systems to confirm the practicality of its approach. Knowing the model parameters accurately can lead to a better model for control and fault detection applications. In addition to parameter estimation, this thesis also provides and validates a simple usable mathematical model for the Thermostatic expansion valve