Department of IT Convergence Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Korea.
Cultural Contents Technology Institute, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Korea.
Sensors (Basel). 2022 Jun 12;22(12):4444. doi: 10.3390/s22124444.
An uninterruptible power supply (UPS) is a device that can continuously supply power for a certain period when a power outage occurs. UPS devices are used by national institutions, hospitals, and servers, and are located in numerous public places that require continuous power. However, maintaining such devices in good condition requires periodic maintenance at specific time points. Efficient monitoring can currently be achieved using a battery management system (BMS). However, most BMSs are administrator-centered. If the administrator is not careful, it becomes difficult to accurately grasp the data trend of each battery cell, which in turn can lead to a leakage or heat explosion of the cell. In this study, a deep-learning-based intelligent model that can predict battery life, known as the state of health (SoH), is investigated for the efficient operation of a BMS applied to a lithium-based UPS device.
不间断电源 (UPS) 是一种在停电时可以持续供电一定时间的设备。UPS 设备被国家机构、医院和服务器使用,并位于许多需要持续供电的公共场所。然而,要保持这些设备的良好状态,需要在特定时间点进行定期维护。目前,可以使用电池管理系统 (BMS) 实现高效监控。然而,大多数 BMS 是以管理员为中心的。如果管理员不小心,就很难准确掌握每个电池单元的数据趋势,这反过来又可能导致电池泄漏或热爆炸。在这项研究中,研究了一种基于深度学习的智能模型,即健康状态 (SoH),用于高效运行应用于基于锂离子的 UPS 设备的 BMS。