Extremum seeking control of battery powered vapor compression systems for vehicles

This thesis investigates the real-time energy optimization of battery powered vapor compression systems (VCS) for vehicles. Battery powered VCS are critical for maintaining passenger comfort in engine-off situations, and are especially important to long-haul truck drivers who sleep inside their vehicle overnight. However, one drawback of battery powered vehicle VCS is their short lifespan which may not provide cooling through the whole night while the vehicle engine is turned off. One reason for short system lifespan is suboptimal input selection; the combination of inputs to the VCS often yields a power consumption higher than necessary to generate the required vehicle cooling. This thesis proposes the use of extremum seeking control (ESC), a class of real-time model-free optimization algorithms, to determine the optimal combination of system inputs that minimizes the VCS power consumption while meeting given objectives. In order to determine algorithm efficacy, we implemented three different ESC algorithms (perturbation-ESC, least squares-ESC and recursive least squares-ESC) on a simulated and physical integrated VCS (the VCS in conjunction with the battery pack and vehicle cabin). Simulation and experimental results demonstrate significant increases in energy efficiency and battery life through the use of these algorithms, with least squares-ESC and recursive least squares-ESC being the most effective of the three.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2018

Author dc:creator

• Sharma, Sunny

Contributors dc:contributor

• Alleyne, Andrew G.

• Control Systems
• Dynamics
• Optimization
• Refrigeration
• HVAC
• Extremum Seeking Control
• Vehicles

Statement dc:rights

• Copyright 2018 Sunny Sharma

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/101474

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/101474