Gray box dynamic modeling of vapor compression systems for control optimization

Liu, Haopeng

Gray box dynamic modeling of vapor compression systems for control optimization

dc.contributor.advisor	Cai, Jie
dc.contributor.author	Liu, Haopeng
dc.contributor.committeeMember	Merchan-Merchan, Wilson
dc.contributor.committeeMember	Moses, Paul
dc.contributor.committeeMember	Shabgard, Hamidreza
dc.date.accessioned	2022-06-27T18:50:51Z
dc.date.available	2022-06-27T18:50:51Z
dc.date.issued	2022-08
dc.date.manuscript	2022-06-01
dc.description.abstract	Buildings account for 75% of electricity use in the U.S. and more than 24% of building electrical energy is consumed by vapor compression equipment, including air-conditioners, refrigerators/freezers and heat pumps. Dynamic modeling of vapor compression systems (VCS) is particularly important for developing and validating optimal control strategies to maximize the system efficiency and reliability. However, existing modeling techniques are rarely used in control practices because of the significant model development effort and requirement of high computational resources. This dissertation presents an efficient and robust gray-box dynamic modeling approach for VCS to support control optimization. The presented methodology allows automated construction of data-driven VCS models with minimum training data and human inputs. The overall approach incorporates a multi-stage training procedure with separate estimation of the steady-state and dynamic model parameters along with a finite control volume scheme to achieve good model identifiability while ensuring adequate prediction accuracy. To improve model reliability, the modeling approach incorporates sensitivity analysis and de-correlating steps in a pre-conditioning procedure to avoid over-parameterization. The system-level training identifies the refrigerant charge that minimizes the steady-state simulation errors while the dynamic modeling stage transforms the established steady-state system model into a dynamic counterpart, in which the optimal thermal capacitances of the heat exchanger walls are identified to best reproduce system transient responses.	en_US
dc.identifier.uri	https://hdl.handle.net/11244/335879
dc.language	en_US	en_US
dc.subject	dynamic modeling, control, vapor compression system, system identification	en_US
dc.thesis.degree	Ph.D.	en_US
dc.title	Gray box dynamic modeling of vapor compression systems for control optimization	en_US
ou.group	Gallogly College of Engineering::School of Aerospace and Mechanical Engineering	en_US
shareok.orcid	0000-0002-1542-2965	en_US