University of Illinois Urbana-Champaign

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

Garrow, Sarah Grace

Content Files
GARROW-THESIS-2018.pdf

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https://hdl.handle.net/2142/101473

Description

Title
Dynamic modeling and control of transcritical vapor compression system for battery electric vehicle thermal management
Author(s)
Garrow, Sarah Grace
Issue Date
2018-06-11
Director of Research (if dissertation) or Advisor (if thesis)
Alleyne, Andrew G.
Department of Study
Mechanical Sci & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2018-09-27T16:17:21Z
Keyword(s)
  • transcritical vapor compression system
  • CO2 refrigerant
  • battery electric vehicle
  • thermal management
  • model predictive control
  • dynamic modeling
Abstract
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.
Graduation Semester
2018-08
Type of Resource
text
Permalink
http://hdl.handle.net/2142/101473
Copyright and License Information
Copyright 2018 Sarah Garrow

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Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois
Dissertations and Theses - Mechanical Science and Engineering