{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1939"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1939","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Physics-informed gated recurrent unit neural networks model for surface temperature estimation of Lithium-ion batteries","abstract":"Thermal management is vital for optimizing Lithium-Ion Battery (LIB) performance. Accurately measuring individual cell temperatures using physical sensors poses significant cost and complexity challenges particularly in packs containing hundreds or thousands of cells. State-of-the-art estimation techniques struggle with nonlinear LIB characteristics under varying operating conditions. This thesis introduces a novel surface temperature estimation method that integrates a gated recurrent unit (GRU) network with physics-informed neural networks (PINNs). The GRU processes sequential data voltage, current, and ambient temperature capturing dynamic battery behavior, while the physics-informed layers embed critical physical parameters, including electrical, thermal, and heat generation models, during training. Validated experimentally on a 14-series connected cell module under varying C-rates and ambient temperatures. The model achieves mean absolute errors of 1.32°C, 1.33°C, and 1.91°C for 1C, 1.5C, and 2C rates at 0°C, 10°C, and 25°C. Results demonstrate superior robustness and adaptability compared to traditional machine learning models like LSTM and FNN.","abstract_html":"Thermal management is vital for optimizing Lithium-Ion Battery (LIB) performance. Accurately measuring individual cell temperatures using physical sensors poses significant cost and complexity challenges particularly in packs containing hundreds or thousands of cells. State-of-the-art estimation techniques struggle with nonlinear LIB characteristics under varying operating conditions. This thesis introduces a novel surface temperature estimation method that integrates a gated recurrent unit (GRU) network with physics-informed neural networks (PINNs). The GRU processes sequential data voltage, current, and ambient temperature capturing dynamic battery behavior, while the physics-informed layers embed critical physical parameters, including electrical, thermal, and heat generation models, during training. Validated experimentally on a 14-series connected cell module under varying C-rates and ambient temperatures. The model achieves mean absolute errors of 1.32°C, 1.33°C, and 1.91°C for 1C, 1.5C, and 2C rates at 0°C, 10°C, and 25°C. Results demonstrate superior robustness and adaptability compared to traditional machine learning models like LSTM and FNN.","abstract_has_math":false,"creators":["Sharma, Mohit"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Williamson, Sheldon"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-03-01","date_published":"2025-03-01","updated_at":"2026-07-24T05:35:34Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1939","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Williamson, Sheldon"]},{"key":"dc:creator","label":"Author","values":["Sharma, Mohit"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-04-29T19:01:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-04-29T19:01:00Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-03-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"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":["https://hdl.handle.net/10155/1939"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Thermal management is vital for optimizing Lithium-Ion Battery (LIB) performance. Accurately measuring individual cell temperatures using physical sensors poses significant cost and complexity challenges particularly in packs containing hundreds or thousands of cells. State-of-the-art estimation techniques struggle with nonlinear LIB characteristics under varying operating conditions. This thesis introduces a novel surface temperature estimation method that integrates a gated recurrent unit (GRU) network with physics-informed neural networks (PINNs). The GRU processes sequential data voltage, current, and ambient temperature capturing dynamic battery behavior, while the physics-informed layers embed critical physical parameters, including electrical, thermal, and heat generation models, during training. Validated experimentally on a 14-series connected cell module under varying C-rates and ambient temperatures. The model achieves mean absolute errors of 1.32°C, 1.33°C, and 1.91°C for 1C, 1.5C, and 2C rates at 0°C, 10°C, and 25°C. Results demonstrate superior robustness and adaptability compared to traditional machine learning models like LSTM and FNN."]},{"key":"dc:title","label":"Title","values":["Physics-informed gated recurrent unit neural networks model for surface temperature estimation of Lithium-ion batteries"]}]}],"canonical_facts":{"dc:contributor.advisor":["Williamson, Sheldon"],"dc:creator":["Sharma, Mohit"],"dc:date.accessioned":["2025-04-29T19:01:00Z"],"dc:date.available":["2025-04-29T19:01:00Z"],"dc:date.issued":["2025-03-01"],"dc:description.abstract":["Thermal management is vital for optimizing Lithium-Ion Battery (LIB) performance. Accurately measuring individual cell temperatures using physical sensors poses significant cost and complexity challenges particularly in packs containing hundreds or thousands of cells. State-of-the-art estimation techniques struggle with nonlinear LIB characteristics under varying operating conditions. This thesis introduces a novel surface temperature estimation method that integrates a gated recurrent unit (GRU) network with physics-informed neural networks (PINNs). The GRU processes sequential data voltage, current, and ambient temperature capturing dynamic battery behavior, while the physics-informed layers embed critical physical parameters, including electrical, thermal, and heat generation models, during training. Validated experimentally on a 14-series connected cell module under varying C-rates and ambient temperatures. The model achieves mean absolute errors of 1.32°C, 1.33°C, and 1.91°C for 1C, 1.5C, and 2C rates at 0°C, 10°C, and 25°C. Results demonstrate superior robustness and adaptability compared to traditional machine learning models like LSTM and FNN."],"dc:identifier.uri":["https://hdl.handle.net/10155/1939"],"dc:language.iso":["en"],"dc:title":["Physics-informed gated recurrent unit neural networks model for surface temperature estimation of Lithium-ion batteries"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:34Z"}