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dc.contributor.advisorMukhopadhyay, Shayok
dc.contributor.advisorRehman, Habib-ur
dc.contributor.authorButt, Hafiz Muhammad Usman
dc.date.accessioned2019-05-21T09:22:19Z
dc.date.available2019-05-21T09:22:19Z
dc.date.issued2019-05
dc.identifier.other35.232-2019.06
dc.identifier.urihttp://hdl.handle.net/11073/16439
dc.descriptionA Master of Science thesis in Electrical Engineering by Hafiz Muhammad Usman Butt entitled, “Real Time Li-ion Battery Bank Parameters Estimation for Electric Vehicle Traction System”, submitted in May 2019. Thesis advisor is Dr. Shayok Mukhopadhyay and thesis co-advisor is Dr. Habibur Rehman. Soft and hard copy available.en_US
dc.description.abstractThis work focuses on accurate and efficient real-time estimation of Li-ion battery model parameters for electric vehicle (EV) traction systems. The contributions made by this thesis are: accurate estimation of Li-ion battery parameters using a two-stage adaptive optimization strategy, which minimizes the need of offline processing, and enables efficient real-time estimation of Li-ion battery model parameters for EV traction systems. In the first part of this thesis, a two-stage universal adaptive stabilizer (UAS) based optimization technique is proposed for estimation of Li-ion battery model parameters. The first stage utilizes a UAS based APE technique to acquire an initial estimate of battery parameters. The second stage utilizes one of the three different optimization techniques, i.e., fmincon, particle swarm optimization (PSO), and hybrid PSO to improve the accuracy of battery model parameters obtained by the APE. The parameters estimated by the APE help in reducing the search space interval required by the optimization technique, thus reducing the computation time for the optimization process. This thesis presents detailed comparison of experimental results using the proposed approach, and other well-known optimization techniques from the literature. In the second part of this thesis, a modification to the existing UAS based APE strategy is proposed. The existing UAS based APE strategy requires a small amount of prior offline experimentation and some post-processing to determine some of the battery parameters. However, the proposed modified APE strategy estimates all battery parameters in a single experimental run. Mathematical proofs, simulation and experimental results supporting the proposed modified APE strategy are also presented. In the third part of this thesis, the modified APE strategy is employed for real-time parameters estimation of a 400 V, 6.6 Ah Li-ion battery bank, which supplies power to a field-oriented control based EV drive system. Some of the distinct features of the modified APE strategy, such as simple real-time implementation, fast convergence, and minimal experimental effort, show the effectiveness of the modified APE strategy developed in this work for real-time Li-ion battery model parameters estimation of EV traction systems.en_US
dc.description.sponsorshipCollege of Engineeringen_US
dc.description.sponsorshipDepartment of Electrical Engineeringen_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesAmerican University of Sharjah Student Worken_US
dc.relation.ispartofseriesMaster of Science in Electrical Engineering (MSEE)en_US
dc.subjectAdaptive parameters estimationen_US
dc.subjectLi-ion batteryen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectUniversal adaptive stabilizeren_US
dc.titleReal Time Li-ion Battery Bank Parameters Estimation for Electric Vehicle Traction Systemen_US
dc.typeThesisen_US


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