He Zhuoyao, Martín Gómez David, de la Escalera Hueso Arturo, Flores Peña Pablo, Lu Xingcai, Armingol Moreno José María
Intelligent Systems Laboratory (LSI), Universidad Carlos III de Madrid, Av. Universidad 30, Leganés, 28911 Madrid, Spain.
Key Laboratory for Power machinery and Engineering of M.O.E., Shanghai Jiao Tong University, Dongchuan Road No. 800, Shanghai 200240, China.
Sensors (Basel). 2023 Jul 15;23(14):6429. doi: 10.3390/s23146429.
Unmanned aerial vehicles (UAVs) have drawin increasing attention in recent years, and they are widely applied. Nevertheless, they are generally limited by poor flight endurance because of the limited energy density of their batteries. A robust power supply is indispensable for advanced UAVs; thus hybrid power might be a promising solution. State of charge (SOC) estimation is essential for the power systems of UAVs. The limitations of accurate SOC estimation can be partly ascribed to the inaccuracy of open circuit voltage (OCV), which is obtained through specific forms of identification. Considering the actual operation of a battery under hybrid conditions, this paper proposes a novel method, "fast OCV", for obtaining the OCVs of batteries. It is proven that fast OCV offers great advantages, related to its simplicity, duration and cost, over traditional ways of obtaining OCV. Moreover, fast-OCV also shows better accuracy in SOC estimation than traditional OCV. Furthermore, this paper also proposes a new method, "batch mode", for talking-data sampling for battery-parameter identification with the limited-memory recursive least-square algorithm. Compared with traditional the "single mode", it presents good de-noising effect by making use of all the sampled battery's terminal current and voltage data.
近年来，无人机（UAVs）越来越受到关注，并得到了广泛应用。然而，由于其电池能量密度有限，它们通常受到飞行续航能力差的限制。强大的电源对于先进的无人机来说是必不可少的；因此，混合动力可能是一个有前途的解决方案。荷电状态（SOC）估计对于无人机的动力系统至关重要。准确的SOC估计的局限性部分可归因于通过特定识别形式获得的开路电压（OCV）的不准确性。考虑到电池在混合条件下的实际运行情况，本文提出了一种获取电池OCV的新方法——“快速OCV”。事实证明，与传统的获取OCV的方法相比，快速OCV在简单性、持续时间和成本方面具有很大优势。此外，快速OCV在SOC估计方面也比传统OCV具有更高的准确性。此外，本文还提出了一种新方法——“批处理模式”，用于使用有限记忆递归最小二乘算法进行电池参数识别的数据采样。与传统的“单模式”相比，它通过利用所有采样的电池端电流和电压数据呈现出良好的去噪效果。
