Please use this identifier to cite or link to this item: https://doi.org/10.1109/TAES.2019.2958161
Title: Battery Management System With State-of-Charge and Opportunistic State-of-Health for a Miniaturized Satellite
Authors: Aung, Htet
Soon, Jing Jun 
Goh, Shu Ting 
Lew, Jia Min
Low, Kay-Soon
Keywords: Science & Technology
Technology
Engineering, Aerospace
Engineering, Electrical & Electronic
Telecommunications
Engineering
Batteries
State of charge
Estimation
Satellites
Kalman filters
Integrated circuit modeling
Mathematical model
Battery management system (BMS)
equivalent circuit model
lithium-ion batteries
nanosatellite
state-of-charge (SOC)
state-of-health (SOH)
unscented Kalman filter (UKF)
EXTENDED KALMAN FILTER
LITHIUM-ION BATTERIES
POWER-SYSTEM
POLYMER BATTERY
NANO-SATELLITE
DESIGN
CUBESAT
TECHNOLOGY
ESTIMATOR
Issue Date: 1-Aug-2020
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation: Aung, 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
Abstract: The 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.
Source Title: IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
URI: https://scholarbank.nus.edu.sg/handle/10635/231043
ISSN: 0018-9251
1557-9603
DOI: 10.1109/TAES.2019.2958161
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