Health and safety enhancement of lithium-ion batteries for fast-charging

dc.contributor.advisorLin, Xianke
dc.contributor.advisorLang, Haoxiang
dc.contributor.authorOjo, Olaoluwa [Joseph]
dc.date.accessioned2021-10-15T16:36:27Z
dc.date.accessioned2022-03-29T16:46:45Z
dc.date.available2021-10-15T16:36:27Z
dc.date.available2022-03-29T16:46:45Z
dc.date.issued2021-08-01
dc.degree.disciplineMechanical Engineering
dc.degree.levelMaster of Applied Science (MASc)
dc.descriptionIn reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of University of Ontario Institute of Technology’s products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.en
dc.description.abstractWith the ever-increasing demand for battery electric vehicles, the expectation of safety, range, and fast charging has become a major focus for industry players and researchers. The research presented herein sought to improve the safety of Lithium-ion (Li-ion) batteries and the health of the cells while fast charging. The work presented has introduced recurrent neural network models and other innovative processes for the estimation of the core temperature in Li-ion cells, and the detection of thermal and voltage faults that could cause thermal runaway. A novel health-conscious fast charging (HCFC) strategy for Li-ion battery packs validated through experimentation was also developed to minimize the charge time while maximizing the longevity and useable capacity of the battery pack. Results from this research verified the reliability of recurrent neural networks for battery signal estimation and fault detection while also demonstrating the performance of the HCFC strategy compared to the standard approach.en
dc.description.sponsorshipUniversity of Ontario Institute of Technologyen
dc.identifier.urihttps://hdl.handle.net/10155/1375
dc.language.isoenen
dc.subjectLithium-ionen
dc.subjectFault detectionen
dc.subjectFast-chargingen
dc.subjectNeural networksen
dc.subjectLithium platingen
dc.titleHealth and safety enhancement of lithium-ion batteries for fast-chargingen
dc.typeThesisen
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorUniversity of Ontario Institute of Technology
thesis.degree.nameMaster of Applied Science (MASc)

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