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dc.contributor.advisorFang, Huazhen
dc.contributor.authorTian, Ning
dc.date.accessioned2022-03-19T17:24:00Z
dc.date.available2022-03-19T17:24:00Z
dc.date.issued2020-12-31
dc.date.submitted2020
dc.identifier.otherhttp://dissertations.umi.com/ku:17512
dc.identifier.urihttp://hdl.handle.net/1808/32634
dc.description.abstractLithium-ion batteries (LiBs) are a revolutionary technology for energy storage. They have become a dominant power source for consumer electronics and are rapidly penetrating into the sectors of electrified transportation and renewable energies, due to the high energy/power density, long cycle life and low memory effect. With continuously falling prices, they will become more popular in foreseeable future. LiBs demonstrate complex dynamic behaviors and are vulnerable to a number of operating problems including overcharging, overdischarging and thermal runaway. Hence, battery management systems (BMSs) are needed in practice to extract full potential from them and ensure their operational safety. Recent years have witnessed a growing amount of research on BMSs, which usually involves topics such as dynamic modeling, parameter identification, state estimation, cell balancing, optimal charging, thermal management, and fault detection. A common challenge for them is computational efficiency since BMSs typically run on embedded systems with limited computing and memory capabilities. Inspired by the challenge, this dissertation aims to address a series of problems towards advancing BMSs with low computational complexity but still high performance. Specifically, the efforts will focus on novel battery modeling and parameter identification (Chapters 2 and 3), highly efficient optimal charging control (Chapter 4) and spatio-temporal temperature estimation of LiB packs (Chapter 5). The developed new LiB models and algorithms can hopefully find use in future LiB systems to improve their performance, while offering insights into some key challenges in the field of BMSs. The research will also entail the development of some fundamental technical approaches concerning parameter identification, model predictive control and state estimation, which have a prospect of being applied to dynamic systems in various other problem domains.
dc.format.extent168 pages
dc.language.isoen
dc.publisherUniversity of Kansas
dc.rightsCopyright held by the author.
dc.subjectMechanical engineering
dc.subjectbattery management system
dc.subjectbattery modeling
dc.subjectbattery optimal charging
dc.subjectbattery thermal management
dc.subjectlithium-ion battery
dc.subjectparameter identification
dc.titleA Study of Computationally Efficient Advanced Battery Management: Modeling, Identification, Estimation and Control
dc.typeDissertation
dc.contributor.cmtememberLuchies, Carl W.
dc.contributor.cmtememberWilson, Sara E.
dc.contributor.cmtememberLi, Jian
dc.contributor.cmtememberNguyen, Trung V.
dc.thesis.degreeDisciplineMechanical Engineering
dc.thesis.degreeLevelPh.D.
dc.identifier.orcid


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