期刊
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
卷 56, 期 4, 页码 2978-2989出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAES.2019.2958161
关键词
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)
资金
- Nanyang Technological University
- Addvalue Technologies Pte. Ltd.
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.
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