{"id":{"repo_id":"umn","oai_identifier":"oai:conservancy.umn.edu:11299/209178"},"canonical_url":"https://search.dev.ndltd.org/etd/umn/oai:conservancy.umn.edu:11299/209178","repository":{"repo_id":"umn","name":"University of Minnesota","base_url":"https://conservancy.umn.edu/server/oai/request"},"display":{"title":"Advanced Modeling and Control Strategies for Charging Electric Vehicle Batteries","abstract":"The research in this master's thesis presents an advanced modeling and control strategy for charging electric vehicle (EV) batteries. The purpose of modeling the battery incorporating the optimal control mechanism is developing a fast-charging system for EVs. The thesis starts with a literature survey to find out the latest EV battery model within an appropriate format of interest. Then, on the selected battery model, it applies the state-dependent Riccati equation (SDRE) technique to develop a closed-loop optimal control strategy. For the purpose of optimization, the battery model aims to track a reference trajectory with a performance index which is minimizing the quadratic error between a reference and an actual trajectory. To harness the unified benefits of optimal and intelligent control systems, the thesis also sheds light upon fuzzy logic by generating a reference trajectory with it. Finally, to determine the correctness of the modeling, MATLAB simulations for a lithium-ion (li-ion) battery have been carried out and they display a satisfactory tracking performance.","abstract_html":"The research in this master's thesis presents an advanced modeling and control strategy for charging electric vehicle (EV) batteries. The purpose of modeling the battery incorporating the optimal control mechanism is developing a fast-charging system for EVs. The thesis starts with a literature survey to find out the latest EV battery model within an appropriate format of interest. Then, on the selected battery model, it applies the state-dependent Riccati equation (SDRE) technique to develop a closed-loop optimal control strategy. For the purpose of optimization, the battery model aims to track a reference trajectory with a performance index which is minimizing the quadratic error between a reference and an actual trajectory. To harness the unified benefits of optimal and intelligent control systems, the thesis also sheds light upon fuzzy logic by generating a reference trajectory with it. Finally, to determine the correctness of the modeling, MATLAB simulations for a lithium-ion (li-ion) battery have been carried out and they display a satisfactory tracking performance.","abstract_has_math":false,"creators":["Pasha Khan, Murtaza Kamal"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-10","date_published":"2019-10","updated_at":"2026-07-24T05:20:01Z","subjects":["Charging","closed loop optimal control","Intelligent Control System","Nonlinear EV battery model","state dependent Riccati Equation (SDRE)"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11299/209178","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Pasha Khan, Murtaza Kamal"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-12-16T20:12:21Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2019-12-16T20:12:21Z"]},{"key":"dc:date.issued","label":"Date","values":["2019-10"]},{"key":"dc:type","label":"Dc Type","values":["Thesis or Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Charging","closed loop optimal control","Intelligent Control System","Nonlinear EV battery model","state dependent Riccati Equation (SDRE)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11299/209178"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["University of Minnesota M.S.E.E. thesis. October 2019. Major: Electrical Engineering. Advisor: Desineni Subbaram Naidu. 1 computer file (PDF); iv, 72 pages."]},{"key":"dc:description.abstract","label":"Abstract","values":["The research in this master's thesis presents an advanced modeling and control strategy for charging electric vehicle (EV) batteries. The purpose of modeling the battery incorporating the optimal control mechanism is developing a fast-charging system for EVs. The thesis starts with a literature survey to find out the latest EV battery model within an appropriate format of interest. Then, on the selected battery model, it applies the state-dependent Riccati equation (SDRE) technique to develop a closed-loop optimal control strategy. For the purpose of optimization, the battery model aims to track a reference trajectory with a performance index which is minimizing the quadratic error between a reference and an actual trajectory. To harness the unified benefits of optimal and intelligent control systems, the thesis also sheds light upon fuzzy logic by generating a reference trajectory with it. Finally, to determine the correctness of the modeling, MATLAB simulations for a lithium-ion (li-ion) battery have been carried out and they display a satisfactory tracking performance."]},{"key":"dc:title","label":"Title","values":["Advanced Modeling and Control Strategies for Charging Electric Vehicle Batteries"]}]}],"canonical_facts":{"dc:creator":["Pasha Khan, Murtaza Kamal"],"dc:date.accessioned":["2019-12-16T20:12:21Z"],"dc:date.available":["2019-12-16T20:12:21Z"],"dc:date.issued":["2019-10"],"dc:description":["University of Minnesota M.S.E.E. thesis. October 2019. Major: Electrical Engineering. Advisor: Desineni Subbaram Naidu. 1 computer file (PDF); iv, 72 pages."],"dc:description.abstract":["The research in this master's thesis presents an advanced modeling and control strategy for charging electric vehicle (EV) batteries. The purpose of modeling the battery incorporating the optimal control mechanism is developing a fast-charging system for EVs. The thesis starts with a literature survey to find out the latest EV battery model within an appropriate format of interest. Then, on the selected battery model, it applies the state-dependent Riccati equation (SDRE) technique to develop a closed-loop optimal control strategy. For the purpose of optimization, the battery model aims to track a reference trajectory with a performance index which is minimizing the quadratic error between a reference and an actual trajectory. To harness the unified benefits of optimal and intelligent control systems, the thesis also sheds light upon fuzzy logic by generating a reference trajectory with it. Finally, to determine the correctness of the modeling, MATLAB simulations for a lithium-ion (li-ion) battery have been carried out and they display a satisfactory tracking performance."],"dc:identifier.uri":["http://hdl.handle.net/11299/209178"],"dc:language.iso":["en"],"dc:subject":["Charging","closed loop optimal control","Intelligent Control System","Nonlinear EV battery model","state dependent Riccati Equation (SDRE)"],"dc:title":["Advanced Modeling and Control Strategies for Charging Electric Vehicle Batteries"],"dc:type":["Thesis or Dissertation"]},"updated_at":"2026-07-24T05:20:01Z"}