A Comprehensive Review of Research Hotspots on Battery Management Systems for UAVs

被引:9
|
作者
Jiao, Shiqin [1 ]
Zhang, Guiyang [1 ]
Zhou, Mei [2 ]
Li, Guoqi [1 ]
机构
[1] Beihang Univ, Sch Reliabil & Syst Engn, Sci & Technol Reliabil & Environm Engn Lab, Beijing 100191, Peoples R China
[2] China Agr Univ, Dept Appl Phys, Beijing 100083, Peoples R China
关键词
Battery management systems; UAVs; charging and discharging control; battery state estimation; fault diagnosis; battery safety; LITHIUM-ION BATTERY; UNMANNED AERIAL VEHICLES; ENERGY MANAGEMENT; HEALTH MANAGEMENT; ELECTRIC VEHICLES; CHARGE ESTIMATION; KALMAN FILTER; STATE; OPTIMIZATION; CONTROLLER;
D O I
10.1109/ACCESS.2023.3301989
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Battery-powered unmanned aerial vehicles (UAVs), also known as drones, have emerged as the primary choice in the UAV market. The UAV Battery Management System (BMS) performs critical functions such as charging and discharging control, state detection, fault diagnosis and warning, data recording and analysis, etc., making it an essential component of UAVs. However, with the rapid advancements in battery-related materials and electrochemistry, new types of batteries are constantly emerging. Furthermore, the rise of big data has expanded the possibilities for information processing. This necessitates the development of BMS to keep pace with ongoing research efforts, adjusting and enhancing the design, analysis, and calculation methods of existing systems to meet the increasingly diverse requirements for UAV power battery performance. Despite the growing importance of BMS, research in this area has primarily focused on electric vehicles, leaving UAVs relatively understudied. To address this research gap, this paper offers a comprehensive background overview and investigates recent hotspots in the field of UAV BMS. A total of nine research hotspots have been identified and classified into three main categories. The first category focuses on battery charging and discharging, involving studies on charging control strategies, battery equalization strategies, and hybrid battery energy management strategies. The second category revolves around battery state estimation, with a primary emphasis on estimating crucial parameters such as State of Charge (SOC), State of Health (SOH), Remaining Useful Life (RUL), and other state parameters. The third category addresses system components and safety-related issues, including research on data storage and transmission within the BMS, data security considerations, fault diagnosis techniques, and other safety topics. Furthermore, the paper proposes potential future trends and areas for further research exploration.
引用
收藏
页码:84636 / 84650
页数:15
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