实时估计电池SOC和自主充电策略,用于带有扩展卡尔曼波器的动态储能充电机器人
Yanfei Zhou1, Xiaojiao Liang2, Wenjie Li2
1State Grid Shandong Electric Power Company Dongying Power Supply Company, Dongying, 257000, China. Zhou_86_Yanfei@163.com.
Scientific reports
|February 9, 2026
概括
本研究介绍了一种使用扩展卡尔曼选的实时算法,用于机器人电池充电状态估计. 该方法增强了电池管理,提高了预测准确度,并在动态环境中延长了电池寿命.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 电气工程 电气工程
- 电池技术 电池技术
背景情况:
- 移动机器人开发需要先进的电池检测和电源管理.
- 传统的方法与动态环境和协作式多电池系统管理扎.
- 精确的电池健康状况监测对于机器人的运行效率至关重要.
研究的目的:
- 为移动机器人电池提出一个实时充电状态估计算法.
- 增强在动态环境中的适应能力,并协同管理电池健康.
- 为了提高电池预测准确度和延长使用寿命.
主要方法:
- 开发了一个基于扩展卡尔曼过的实时充电状态估计算法.
- 模拟机器人电池组信号传输和用于充电状态的电容指数.
- 应用最小平方法与遗忘因子,以适应电压/电流曲线和处理动态变化.
主要成果:
- 实现了电池充电状态的高预测准确度,范围从97.34%到98.75%.
- 与其他算法相比,在充电测试中显示出更高的充电保留率 (12.68%-30.04%更高).
- 在充电策略下展示了更好的电池耐用性 (14.62%-28.98%更高).
结论:
- 拟议的扩展卡尔曼过算法有效地实时估计机器人电池充电状态.
- 该方法通过提高预测准确性和充电保留来增强电池管理.
- 这种方法通过优化充电计划和电池耐用性,有助于延长机器人的使用寿命.
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