{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1517"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1517","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Deep transfer-learning based lithium-ion battery fault diagnosis","abstract":"Fault detection in lithium-ion batteries (LiB) is paramount to ensuring the long life and proper functioning of the batteries. To that end, this thesis proposes a combined fault diagnosis framework that leverages voltage charging curves and voltage charging curve fault residuals to accurately detect multiple faults within a LIB during partial and full charging regimes. This framework removes the need for parameter tuning and is also adaptable to varying battery chemistries and performs well with a small amount of available data. The framework leverages voltage residuals generated via a randomly initialized or pre-trained LSTM (Long Short Term Memory) model. Experimental results show its ability to accurately detect the different types of faults utilizing full voltage charging curve residuals with an accuracy of 95%. The framework can also detect faults utilizing partial voltage charging curve residuals & a pre-trained LSTM model with an accuracy of 94%.","abstract_html":"Fault detection in lithium-ion batteries (LiB) is paramount to ensuring the long life and proper functioning of the batteries. To that end, this thesis proposes a combined fault diagnosis framework that leverages voltage charging curves and voltage charging curve fault residuals to accurately detect multiple faults within a LIB during partial and full charging regimes. This framework removes the need for parameter tuning and is also adaptable to varying battery chemistries and performs well with a small amount of available data. The framework leverages voltage residuals generated via a randomly initialized or pre-trained LSTM (Long Short Term Memory) model. Experimental results show its ability to accurately detect the different types of faults utilizing full voltage charging curve residuals with an accuracy of 95%. The framework can also detect faults utilizing partial voltage charging curve residuals & a pre-trained LSTM model with an accuracy of 94%.","abstract_has_math":false,"creators":["Nwauche, Chukwuemeka Nelson"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Automotive Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Lin, Xianke"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08-01","date_published":"2022-08-01","updated_at":"2026-07-24T05:35:18Z","subjects":["Lithium-ion batteries","Transfer learning","Convolutional neural networks","Electric vehicles","Battery fault diagnosis"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1517","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Lin, Xianke"]},{"key":"dc:creator","label":"Author","values":["Nwauche, Chukwuemeka Nelson"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-09-06T18:42:48Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-09-06T18:42:48Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-08-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Automotive 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":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Lithium-ion batteries","Transfer learning","Convolutional neural networks","Electric vehicles","Battery fault diagnosis"]}]},{"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/10155/1517"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Fault detection in lithium-ion batteries (LiB) is paramount to ensuring the long life and proper functioning of the batteries. To that end, this thesis proposes a combined fault diagnosis framework that leverages voltage charging curves and voltage charging curve fault residuals to accurately detect multiple faults within a LIB during partial and full charging regimes. This framework removes the need for parameter tuning and is also adaptable to varying battery chemistries and performs well with a small amount of available data. The framework leverages voltage residuals generated via a randomly initialized or pre-trained LSTM (Long Short Term Memory) model. Experimental results show its ability to accurately detect the different types of faults utilizing full voltage charging curve residuals with an accuracy of 95%. The framework can also detect faults utilizing partial voltage charging curve residuals & a pre-trained LSTM model with an accuracy of 94%."]},{"key":"dc:title","label":"Title","values":["Deep transfer-learning based lithium-ion battery fault diagnosis"]}]}],"canonical_facts":{"dc:contributor.advisor":["Lin, Xianke"],"dc:creator":["Nwauche, Chukwuemeka Nelson"],"dc:date.accessioned":["2022-09-06T18:42:48Z"],"dc:date.available":["2022-09-06T18:42:48Z"],"dc:date.issued":["2022-08-01"],"dc:description.abstract":["Fault detection in lithium-ion batteries (LiB) is paramount to ensuring the long life and proper functioning of the batteries. To that end, this thesis proposes a combined fault diagnosis framework that leverages voltage charging curves and voltage charging curve fault residuals to accurately detect multiple faults within a LIB during partial and full charging regimes. This framework removes the need for parameter tuning and is also adaptable to varying battery chemistries and performs well with a small amount of available data. The framework leverages voltage residuals generated via a randomly initialized or pre-trained LSTM (Long Short Term Memory) model. Experimental results show its ability to accurately detect the different types of faults utilizing full voltage charging curve residuals with an accuracy of 95%. The framework can also detect faults utilizing partial voltage charging curve residuals & a pre-trained LSTM model with an accuracy of 94%."],"dc:identifier.uri":["https://hdl.handle.net/10155/1517"],"dc:language.iso":["en"],"dc:subject":["Lithium-ion batteries","Transfer learning","Convolutional neural networks","Electric vehicles","Battery fault diagnosis"],"dc:title":["Deep transfer-learning based lithium-ion battery fault diagnosis"],"dc:type":["Thesis"],"thesis:degree_discipline":["Automotive Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:18Z"}