{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101474"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101474","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Extremum seeking control of battery powered vapor compression systems for vehicles","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Sharma, Sunny"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Alleyne, Andrew G."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-27T16:17:21Z","date_published":"2018-09-27T16:17:21Z","updated_at":"2026-07-22T22:24:40Z","subjects":["Control Systems","Dynamics","Optimization","Refrigeration","HVAC","Extremum Seeking Control","Vehicles"],"languages":["en"],"rights":["Copyright 2018 Sunny Sharma"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101474","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Alleyne, Andrew G."]},{"key":"dc:creator","label":"Author","values":["Sharma, Sunny"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-27T16:17:21Z","2018-06-05","2018-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Control Systems","Dynamics","Optimization","Refrigeration","HVAC","Extremum Seeking Control","Vehicles"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Sunny Sharma"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101474"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-09-27 without embargo terms","The student, Sunny Sharma, accepted the attached license on 2018-05-31 at 15:33.","The student, Sunny Sharma, submitted this Thesis for approval on 2018-05-31 at 15:57.","This Thesis was approved for publication on 2018-06-05 at 10:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12601 on 2018-09-27 at 10:44:20","Made available in DSpace on 2018-09-27T16:17:21Z (GMT). No. of bitstreams: 3 SHARMA-THESIS-2018.pdf: 10362817 bytes, checksum: 1d039ddef120d6b0fb9e2811eb7ba8c8 (MD5) SHARMA-THESIS-2018.docx: 15566523 bytes, checksum: 82ee5f2a481c0b7b8ef96780e5928ba4 (MD5) LICENSE.txt: 4209 bytes, checksum: b91991eddd4f16b536a2927271956735 (MD5) Previous issue date: 2018-06-05"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Extremum seeking control of battery powered vapor compression systems for vehicles"]}]}],"canonical_facts":{"dc:contributor":["Alleyne, Andrew G."],"dc:creator":["Sharma, Sunny"],"dc:date":["2018-09-27T16:17:21Z","2018-06-05","2018-08"],"dc:description":["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.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-09-27 without embargo terms","The student, Sunny Sharma, accepted the attached license on 2018-05-31 at 15:33.","The student, Sunny Sharma, submitted this Thesis for approval on 2018-05-31 at 15:57.","This Thesis was approved for publication on 2018-06-05 at 10:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12601 on 2018-09-27 at 10:44:20","Made available in DSpace on 2018-09-27T16:17:21Z (GMT). No. of bitstreams: 3 SHARMA-THESIS-2018.pdf: 10362817 bytes, checksum: 1d039ddef120d6b0fb9e2811eb7ba8c8 (MD5) SHARMA-THESIS-2018.docx: 15566523 bytes, checksum: 82ee5f2a481c0b7b8ef96780e5928ba4 (MD5) LICENSE.txt: 4209 bytes, checksum: b91991eddd4f16b536a2927271956735 (MD5) Previous issue date: 2018-06-05"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/101474"],"dc:language":["en"],"dc:rights":["Copyright 2018 Sunny Sharma"],"dc:subject":["Control Systems","Dynamics","Optimization","Refrigeration","HVAC","Extremum Seeking Control","Vehicles"],"dc:title":["Extremum seeking control of battery powered vapor compression systems for vehicles"],"dc:type":["text"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:40Z"}