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dc.contributor.advisorTrimboli, Michael S.
dc.contributor.authorRodríguez Marco, Albert
dc.contributor.committeememberPlett, Gregory L.
dc.contributor.committeememberHarrison, Willie K.
dc.contributor.committeememberSmith, Kandler
dc.contributor.committeememberCascaval, Radu
dc.date.accessioned2017-12-11T16:04:41Z
dc.date.available2017-12-11T16:04:41Z
dc.date.submitted2017-12
dc.descriptionIncludes bibliographical references.
dc.description.abstractAbstract Battery management systems (BMS) require computationally simple but highly accurate models of the battery cells they are monitoring and controlling. Historically, empirical equivalent-circuit models have been used, but increasingly researchers are focusing their attention on physics-based models due to their greater predictive capabilities. These models are of high intrinsic computational complexity and so must undergo some kind of order-reduction process to make their use by a BMS feasible: we favor methods based on a transfer-function approach of battery cell dynamics. Abstract In prior works, transfer functions have been found from full-order PDE models via two simplifying assumptions: (1) a linearization assumption—which is a fundamental necessity in order to make transfer functions—and (2) an assumption made out of expedience that decouples the electrolyte-potential and electrolyte-concentration PDEs in order to render an approach to solve for the transfer functions from the PDEs. This dissertation improves the fidelity of physics-based models by eliminating the need for the second assumption and, by linearizing nonlinear dynamics around different constant currents. Abstract Electrochemical transfer functions are infinite-order and cannot be expressed as a ratio of polynomials in the Laplace variable s. Thus, for practical use, these systems need to be approximated using reduced-order models that capture the most significant dynamics. This dissertation improves the generation of physics-based reduced-order models by introducing different realization algorithms, which produce a low-order model from the infinite-order electrochemical transfer functions. Abstract Physics-based reduced-order models are linear and describe cell dynamics if operated near the setpoint at which they have been generated. Hence, multiple physics-based reduced-order models need to be generated at different setpoints (i.e., state-of-charge, temperature and C-rate) in order to extend the cell operating range. This dissertation improves the implementation of physics-based reduced-order models by introducing different blending approaches that combine the pre-computed models generated (offline) at different setpoints in order to produce good electrochemical estimates (online) along the cell state-of-charge, temperature and C-rate range.
dc.identifierRodrxEDguezMarco_uccs_0892D_10320.pdf
dc.identifier.urihttps://hdl.handle.net/10976/166734
dc.languageEnglish
dc.publisherUniversity of Colorado Colorado Springs. Kraemer Family Library
dc.relation.ispartofDissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectPhysics-based model
dc.subjectLithium-ion cell model
dc.subjectReduced-order model
dc.titleImprovements to Fidelity, Generation and Implementation of Physics-Based Lithium-Ion Reduced-Order Models
dc.typeText
dcterms.cdm.subcollectionElectrical Engineering
thesis.degree.disciplineCollege of Engineering and Applied Science–Electrical Engineering
thesis.degree.grantorUniversity of Colorado Colorado Springs
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)


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