{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-2257"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-2257","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Dynamic reconfigurable battery systems via graph-based deep reinforcement learning","abstract":"Existing large-scale batteries, such as those used in electric vehicles, electric planes, and electric boats/ferries, are built from hundreds or thousands of battery cells connected in fixed series-parallel configurations. These rigid topologies limit efficient power management, making it difficult to respond to dynamic cell imbalance and thereby reducing overall battery operating time. To address these limitations, reconfigurable battery designs have been proposed where the interconnection topology among cells can be adjusted in real time to reflect evolving cell dynamics and uncertainties in the operating environment. In this thesis, we model reconfigurable batteries as dynamic graphs and investigate graph-based deep reinforcement learning approaches for adaptively optimizing cell-to-cell topology under practical operational constraints. We evaluate the proposed methods by using the open-source battery simulation platform PyBaMM, measuring performance in terms of voltage balancing and State of Charge (SOC) uniformity. Overall, this work establishes a foundation and offers insights for the development of future intelligent reconfigurable battery systems.","abstract_html":"Existing large-scale batteries, such as those used in electric vehicles, electric planes, and electric boats/ferries, are built from hundreds or thousands of battery cells connected in fixed series-parallel configurations. These rigid topologies limit efficient power management, making it difficult to respond to dynamic cell imbalance and thereby reducing overall battery operating time. To address these limitations, reconfigurable battery designs have been proposed where the interconnection topology among cells can be adjusted in real time to reflect evolving cell dynamics and uncertainties in the operating environment. In this thesis, we model reconfigurable batteries as dynamic graphs and investigate graph-based deep reinforcement learning approaches for adaptively optimizing cell-to-cell topology under practical operational constraints. We evaluate the proposed methods by using the open-source battery simulation platform PyBaMM, measuring performance in terms of voltage balancing and State of Charge (SOC) uniformity. Overall, this work establishes a foundation and offers insights for the development of future intelligent reconfigurable battery systems.","abstract_has_math":false,"creators":["Hasan, Mehedi"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Wu, Dalei","Liang, Yu; Yuan, Yukun","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2027,"date_issued":"2027-05-31T07:00:00Z","date_published":"2027-05-31T07:00:00Z","updated_at":"2026-07-24T05:47:28Z","subjects":["Deep learning (Machine learning)","Electric batteries","Reinforcement learning"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/1071","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wu, Dalei","Liang, Yu; Yuan, Yukun","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Hasan, Mehedi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2026-05-01T07:00:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2027-05-31T07:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Masters theses","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Deep learning (Machine learning)","Electric batteries","Reinforcement learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/1071"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Computer Science and Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."]},{"key":"dc:description.abstract","label":"Abstract","values":["Existing large-scale batteries, such as those used in electric vehicles, electric planes, and electric boats/ferries, are built from hundreds or thousands of battery cells connected in fixed series-parallel configurations. These rigid topologies limit efficient power management, making it difficult to respond to dynamic cell imbalance and thereby reducing overall battery operating time. To address these limitations, reconfigurable battery designs have been proposed where the interconnection topology among cells can be adjusted in real time to reflect evolving cell dynamics and uncertainties in the operating environment. In this thesis, we model reconfigurable batteries as dynamic graphs and investigate graph-based deep reinforcement learning approaches for adaptively optimizing cell-to-cell topology under practical operational constraints. We evaluate the proposed methods by using the open-source battery simulation platform PyBaMM, measuring performance in terms of voltage balancing and State of Charge (SOC) uniformity. Overall, this work establishes a foundation and offers insights for the development of future intelligent reconfigurable battery systems."]},{"key":"dc:title","label":"Title","values":["Dynamic reconfigurable battery systems via graph-based deep reinforcement learning"]}]}],"canonical_facts":{"dc:contributor":["Wu, Dalei","Liang, Yu; Yuan, Yukun","College of Engineering and Computer Science"],"dc:creator":["Hasan, Mehedi"],"dc:date":["2026-05-01T07:00:00Z"],"dc:date.available":["2027-05-31T07:00:00Z"],"dc:description":["Dept. of Computer Science and Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."],"dc:description.abstract":["Existing large-scale batteries, such as those used in electric vehicles, electric planes, and electric boats/ferries, are built from hundreds or thousands of battery cells connected in fixed series-parallel configurations. These rigid topologies limit efficient power management, making it difficult to respond to dynamic cell imbalance and thereby reducing overall battery operating time. To address these limitations, reconfigurable battery designs have been proposed where the interconnection topology among cells can be adjusted in real time to reflect evolving cell dynamics and uncertainties in the operating environment. In this thesis, we model reconfigurable batteries as dynamic graphs and investigate graph-based deep reinforcement learning approaches for adaptively optimizing cell-to-cell topology under practical operational constraints. We evaluate the proposed methods by using the open-source battery simulation platform PyBaMM, measuring performance in terms of voltage balancing and State of Charge (SOC) uniformity. Overall, this work establishes a foundation and offers insights for the development of future intelligent reconfigurable battery systems."],"dc:identifier":["https://scholar.utc.edu/theses/1071"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Deep learning (Machine learning)","Electric batteries","Reinforcement learning"],"dc:title":["Dynamic reconfigurable battery systems via graph-based deep reinforcement learning"],"dc:type":["Masters theses","Text"]},"updated_at":"2026-07-24T05:47:28Z"}