Please use this identifier to cite or link to this item: https://doi.org/10.1109/TAES.2019.2958161
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dc.titleBattery Management System With State-of-Charge and Opportunistic State-of-Health for a Miniaturized Satellite
dc.contributor.authorAung, Htet
dc.contributor.authorSoon, Jing Jun
dc.contributor.authorGoh, Shu Ting
dc.contributor.authorLew, Jia Min
dc.contributor.authorLow, Kay-Soon
dc.date.accessioned2022-09-12T03:48:57Z
dc.date.available2022-09-12T03:48:57Z
dc.date.issued2020-08-01
dc.identifier.citationAung, Htet, Soon, Jing Jun, Goh, Shu Ting, Lew, Jia Min, Low, Kay-Soon (2020-08-01). Battery Management System With State-of-Charge and Opportunistic State-of-Health for a Miniaturized Satellite. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 56 (4) : 2978-2989. ScholarBank@NUS Repository. https://doi.org/10.1109/TAES.2019.2958161
dc.identifier.issn0018-9251
dc.identifier.issn1557-9603
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/231043
dc.description.abstractThe lifespan of a satellite is primarily dependent on its battery performance. Thus, proper management and monitoring of the battery is important. Most miniaturized satellites of cubeSat and nanosatellite primarily rely on battery voltage readings for monitoring and seldom provide battery health status in a satellite. As the voltage readings can be affected by satellite operating conditions such as temperature and battery lifespan, it can give unreliable readings that might jeopardize satellite operation. The availability of the battery health status can prevent unexpected battery failures and provide useful insights into the planning of satellite operations. In this article, the battery management system of a satellite with state-of-charge (SOC) and state-of-health (SOH) monitoring is presented. For SOC estimation, a scaled unscented Kalman filter (UKF) is proposed. When compared to the existing UKF approach, it requires fewer sigma points and the positive weights used in the scaled unscented transform ensure the positive semidefiniteness of the covariance matrix leading to the improved numerical stability of the filter. Conversely, SOH monitoring is achieved by taking advantage of the opportunity arising from the satellite operations. The battery parameters are extracted without artificially injecting charge and discharge pulses. To validate the performance of the BMS, the experimental prelaunch tests and the actual in-flight data of the VELOX-II satellite is used as an example. From the results, the SOC estimation error is approximately 1% and the SOH estimation is consistent with the manufacturer's datasheet.
dc.language.isoen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.sourceElements
dc.subjectScience & Technology
dc.subjectTechnology
dc.subjectEngineering, Aerospace
dc.subjectEngineering, Electrical & Electronic
dc.subjectTelecommunications
dc.subjectEngineering
dc.subjectBatteries
dc.subjectState of charge
dc.subjectEstimation
dc.subjectSatellites
dc.subjectKalman filters
dc.subjectIntegrated circuit modeling
dc.subjectMathematical model
dc.subjectBattery management system (BMS)
dc.subjectequivalent circuit model
dc.subjectlithium-ion batteries
dc.subjectnanosatellite
dc.subjectstate-of-charge (SOC)
dc.subjectstate-of-health (SOH)
dc.subjectunscented Kalman filter (UKF)
dc.subjectEXTENDED KALMAN FILTER
dc.subjectLITHIUM-ION BATTERIES
dc.subjectPOWER-SYSTEM
dc.subjectPOLYMER BATTERY
dc.subjectNANO-SATELLITE
dc.subjectDESIGN
dc.subjectCUBESAT
dc.subjectTECHNOLOGY
dc.subjectESTIMATOR
dc.typeArticle
dc.date.updated2022-09-12T01:47:50Z
dc.contributor.departmentEPT OF ELECTRICAL & COMPUTER ENGG
dc.description.doi10.1109/TAES.2019.2958161
dc.description.sourcetitleIEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
dc.description.volume56
dc.description.issue4
dc.description.page2978-2989
dc.published.statePublished
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