混合动力无人机的电池-SOC估计使用快速OCV曲线与无臭卡尔曼波器
Zhuoyao He1,2, David Martín Gómez1, Arturo de la Escalera Hueso1
1Intelligent Systems Laboratory (LSI), Universidad Carlos III de Madrid, Av. Universidad 30, Leganés, 28911 Madrid, Spain.
这项研究引入了"快速OCV",用于在无人飞行器 (UAV) 中准确估计电池充电状态 (SOC). 这种方法提高了SOC的准确性,并比传统的开通电路电压 (OCV) 技术提供了优势.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
背景情况:
- 无人驾驶飞行器 (UAV) 需要强大的动力系统来延长飞行耐久性.
- 有限的电池能量密度限制了无人机的运行时间.
- 精确的电荷状态 (SOC) 估计对于无人机电源管理至关重要.
研究的目的:
- 提出一种新的"快速OCV"方法,用于改进电池中的开通电路电压 (OCV) 测定.
- 开发一个"批量模式"数据采样技术,用于增强电池参数识别.
- 为了提高在混合动力条件下的无人机SOC估计的准确性.
主要方法:
- "快速OCV"方法是为了在操作条件下高效地获得电池开放电路电压 (OCV) 而开发的.
- 为电池参数识别实施了"批量模式"数据采样策略.
- 用有限内存的递归最小平方算法用于参数识别.
主要成果:
- 与传统的OCV获取相比",快速OCV"方法在简单性,持续时间和成本方面表现出显著的优势.
- "快速OCV"的结果是比传统的OCV方法更准确的SOC估计.
- 与"单一模式"相比",批量模式"采样在电池终端电流和电压数据上表现出更好的降噪效应.
结论:
- 拟议的"快速OCV"方法是无人机中电池SOC估计的实用和准确方法.
- "批量模式"数据采样提高了电池参数识别的可靠性.
- 这些进步有助于为先进的无人机应用提供更强大,更有效的动力系统.
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