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Structural Diagnosability Analysis of Switched and Modular Battery Packs
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-3016-0131
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-0114-5186
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-7349-1937
Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Vehicular Systems.ORCID iD: 0000-0003-4965-1077
2024 (English)In: 2024 Prognostics and System Health Management Conference (PHM), Institute of Electrical and Electronics Engineers (IEEE), 2024, p. 362-369Conference paper, Published paper (Refereed)
Abstract [en]

Safety, reliability, and durability are targets of all engineering systems, including Li-ion batteries in electric vehicles. This paper focuses on sensor setup exploration for a battery-integrated modular multilevel converter (BI-MMC) that can be part of a solution to sustainable electrification of vehicles. BI-MMC contains switches to convert DC to AC to drive an electric machine. The various configurations of switches result in different operation modes, which in turn, pose great challenges for diagnostics. The study explores diverse sensor arrangements and system configurations for detecting and isolating faults in modular battery packs. Configurations involving a minimum of two modules integrated into the pack are essential to successfully isolate all faults. The findings indicate that the default sensor setup is insufficient for achieving complete fault isolability. Additionally, the investigation also demonstrates that current sensors in the submodules do not contribute significantly to fault isolability. Further, the results on switch positions show that the system configuration has a significant impact on fault isolability. A combination of appropriate sensor data and system configuration is important in achieving optimal diagnosability, which is a paramount objective in ensuring system safety.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024. p. 362-369
Series
2024 Prognostics and System Health Management Conference (PHM), ISSN 2166-563X, E-ISSN 2166-5656
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:liu:diva-207716DOI: 10.1109/phm61473.2024.00070ISBN: 9798350360585 (electronic)ISBN: 9798350360592 (print)OAI: oai:DiVA.org:liu-207716DiVA, id: diva2:1898749
Conference
Prognostics and System Health Management Conference (PHM), Stockholm, Sweden, 28-31 May, 2024.
Funder
Swedish Energy AgencyAvailable from: 2024-09-18 Created: 2024-09-18 Last updated: 2024-09-18

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Hashemniya, FatemehBalachandran, ArvindFrisk, ErikKrysander, Mattias

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